From 23a1e801e8cfd93f739e81b6427427f51c3ba91d Mon Sep 17 00:00:00 2001
From: Cathy Wong <cathy.wong@huawei.com>
Date: Tue, 31 May 2022 19:45:38 -0400
Subject: [PATCH] [MD] Transforms Unification Feature - Updates models to use
unified dataset transforms
---
.jenkins/check/config/filter_linklint.txt | 3 +-
.jenkins/check/config/whitelizard.txt | 1 +
benchmark/ascend/bert/src/dataset.py | 4 +-
benchmark/ascend/resnet/src/dataset.py | 90 +++++++++----------
benchmark/ascend/resnet/src/dataset_infer.py | 62 ++++++-------
official/cv/Deepsort/modelarts/start_train.py | 2 +-
official/cv/Deepsort/src/deep/train.py | 4 +-
official/cv/alexnet/src/dataset.py | 6 +-
official/cv/brdnet/src/dataset.py | 4 +-
.../cv/cnn_direction_model/src/dataset.py | 4 +-
official/cv/crnn/src/dataset.py | 6 +-
official/cv/crnn_seq2seq_ocr/src/dataset.py | 9 +-
official/cv/cspdarknet53/src/dataset.py | 6 +-
official/cv/ctpn/src/dataset.py | 6 +-
official/cv/darknet53/src/dataset.py | 6 +-
official/cv/deeptext/src/dataset.py | 6 +-
.../densenet/src/datasets/classification.py | 6 +-
official/cv/depthnet/src/data_loader.py | 6 +-
official/cv/depthnet/train.py | 6 +-
official/cv/dncnn/eval.py | 4 +-
.../infer/data/preprocess/export_bin_file.py | 2 +-
official/cv/dncnn/src/dataset.py | 4 +-
official/cv/dpn/src/imagenet_dataset.py | 6 +-
official/cv/east/detect.py | 6 +-
official/cv/east/src/dataset.py | 4 +-
official/cv/efficientnet/src/dataset.py | 6 +-
official/cv/efficientnet/src/transform.py | 10 +--
official/cv/faster_rcnn/src/dataset.py | 4 +-
official/cv/fastscnn/eval.py | 8 +-
official/cv/fastscnn/modelarts/start_train.py | 8 +-
official/cv/fastscnn/src/dataloader.py | 4 +-
official/cv/fastscnn/train.py | 8 +-
official/cv/googlenet/src/dataset.py | 6 +-
official/cv/inceptionv3/src/dataset.py | 6 +-
official/cv/inceptionv4/src/dataset.py | 6 +-
official/cv/lenet/src/dataset.py | 6 +-
official/cv/maskrcnn/src/dataset.py | 4 +-
.../cv/maskrcnn_mobilenetv1/src/dataset.py | 4 +-
official/cv/mobilenetv1/src/dataset.py | 34 +++----
official/cv/mobilenetv2/src/dataset.py | 24 ++---
official/cv/mobilenetv3/src/dataset.py | 50 +++++------
official/cv/nasnet/src/dataset.py | 6 +-
official/cv/nima/src/dataset.py | 6 +-
official/cv/patchcore/preprocess.py | 22 ++---
official/cv/patchcore/src/dataset.py | 22 ++---
official/cv/posenet/src/dataset.py | 4 +-
official/cv/psenet/src/dataset.py | 14 +--
official/cv/pvnet/eval.py | 9 +-
official/cv/pvnet/src/dataset.py | 11 ++-
official/cv/pwcnet/src/flyingchairs.py | 20 ++---
official/cv/pwcnet/src/sintel.py | 20 ++---
official/cv/resnet/gpu_resnet_benchmark.py | 16 ++--
official/cv/resnet/src/dataset.py | 90 +++++++++----------
official/cv/resnet/src/dataset_infer.py | 62 ++++++-------
official/cv/resnet_thor/src/dataset.py | 6 +-
official/cv/resnext/src/dataset.py | 6 +-
official/cv/retinanet/src/dataset.py | 2 +-
official/cv/se_resnext50/src/dataset.py | 6 +-
.../cv/semantic_human_matting/src/dataset.py | 4 +-
official/cv/shufflenetv1/src/dataset.py | 6 +-
official/cv/shufflenetv2/src/dataset.py | 6 +-
official/cv/simclr/src/dataset.py | 11 ++-
official/cv/simple_pose/src/dataset.py | 4 +-
.../sphereface/src/datasets/classification.py | 6 +-
official/cv/squeezenet/src/dataset.py | 6 +-
official/cv/ssd/src/dataset.py | 10 +--
official/cv/ssim-ae/src/dataset.py | 2 +-
official/cv/tinydarknet/src/dataset.py | 6 +-
official/cv/unet/src/data_loader.py | 4 +-
official/cv/unet3d/src/dataset.py | 4 +-
official/cv/vgg16/src/dataset.py | 6 +-
official/cv/vit/src/dataset.py | 7 +-
official/cv/warpctc/src/dataset.py | 6 +-
official/cv/xception/src/dataset.py | 6 +-
.../cv/yolov3_darknet53/src/yolo_dataset.py | 6 +-
official/cv/yolov3_resnet18/src/dataset.py | 4 +-
official/cv/yolov4/src/yolo_dataset.py | 4 +-
official/cv/yolov5/src/transforms.py | 6 +-
official/cv/yolov5/src/yolo_dataset.py | 6 +-
official/nlp/bert/src/dataset.py | 4 +-
.../nlp/bert/src/finetune_data_preprocess.py | 4 +-
official/nlp/bert_thor/pretrain_eval.py | 4 +-
official/nlp/bert_thor/src/dataset.py | 4 +-
official/nlp/cpm/train.py | 4 +-
official/nlp/dgu/src/utils.py | 4 +-
official/nlp/duconv/src/dataset.py | 4 +-
official/nlp/emotect/src/dataset.py | 4 +-
official/nlp/ernie/src/dataset.py | 4 +-
official/nlp/fasttext/eval.py | 4 +-
.../nlp/gnmt_v2/src/dataset/load_dataset.py | 4 +-
official/nlp/gpt/src/dataset.py | 4 +-
official/nlp/gru/src/dataset.py | 4 +-
official/nlp/mass/src/dataset/load_dataset.py | 4 +-
official/nlp/pangu_alpha/src/dataset.py | 4 +-
.../prophetnet/src/dataset/load_dataset.py | 4 +-
official/nlp/tinybert/src/dataset.py | 4 +-
official/nlp/transformer/eval.py | 2 +-
official/nlp/transformer/src/dataset.py | 2 +-
research/audio/ctcmodel/src/dataset.py | 4 +-
.../audio/speech_transformer/src/dataset.py | 4 +-
research/cv/3dcnn/src/dataset.py | 4 +-
.../APDrawingGAN/src/data/aligned_dataset.py | 14 +--
.../cv/APDrawingGAN/src/data/base_dataset.py | 22 ++---
research/cv/AVA_cifar/src/datasets.py | 48 +++++-----
research/cv/AVA_hpa/src/datasets.py | 50 +++++------
.../cv/AlignedReID++/src/dataset_loader.py | 20 ++---
research/cv/AlignedReID/src/dataset.py | 4 +-
.../infer/sdk/postprocess/src/dataset.py | 4 +-
research/cv/AlphaPose/src/dataset.py | 4 +-
research/cv/AttGAN/src/data.py | 28 +++---
research/cv/AttentionCluster/make_dataset.py | 4 +-
research/cv/AutoSlim/src/dataset.py | 25 +++---
research/cv/CBAM/src/data.py | 2 +-
research/cv/CGAN/src/dataset.py | 4 +-
research/cv/CMT/src/dataset.py | 17 ++--
research/cv/CascadeRCNN/src/dataset.py | 4 +-
.../CycleGAN/src/dataset/cyclegan_dataset.py | 4 +-
research/cv/DBPN/src/dataset/dataset.py | 12 +--
research/cv/DDAG/eval.py | 12 +--
research/cv/DDAG/train.py | 40 ++++-----
research/cv/DDRNet/src/data/imagenet.py | 25 +++---
research/cv/DRNet/src/dataset.py | 19 ++--
research/cv/DeepID/src/dataset.py | 16 ++--
research/cv/EfficientDet_d0/src/dataset.py | 4 +-
research/cv/FDA-BNN/src/dataset.py | 58 ++++++------
research/cv/FaceAttribute/preprocess.py | 10 +--
research/cv/FaceAttribute/src/dataset_eval.py | 10 +--
.../cv/FaceAttribute/src/dataset_train.py | 10 +--
research/cv/FaceDetection/preprocess.py | 6 +-
.../cv/FaceDetection/src/data_preprocess.py | 10 +--
research/cv/FaceNet/src/LFWDataset.py | 9 +-
research/cv/FaceNet/src/data_loader.py | 15 ++--
.../data_loader_generate_triplets_online.py | 15 ++--
.../cv/FaceQualityAssessment/src/dataset.py | 4 +-
research/cv/FaceRecognition/eval.py | 9 +-
.../cv/FaceRecognition/src/dataset_factory.py | 8 +-
.../cv/FaceRecognitionForTracking/eval.py | 6 +-
.../FaceRecognitionForTracking/preprocess.py | 8 +-
.../FaceRecognitionForTracking/src/dataset.py | 4 +-
research/cv/GENet_Res50/src/dataset.py | 6 +-
research/cv/HRNetW48_cls/src/dataset.py | 6 +-
research/cv/HireMLP/src/dataset.py | 17 ++--
research/cv/HourNAS/src/dataset.py | 60 ++++++-------
research/cv/ICNet/Res50V1_PRE/src/dataset.py | 6 +-
research/cv/ICNet/eval.py | 8 +-
.../cv/ICNet/src/cityscapes_mindrecord.py | 11 +--
research/cv/ICNet/src/visualize.py | 8 +-
research/cv/ISyNet/src/dataset.py | 15 ++--
research/cv/ISyNet/src/transform.py | 8 +-
research/cv/ISyNet/utils/preprocess_310.py | 4 +-
research/cv/Inception-v2/src/dataset.py | 4 +-
research/cv/JDE/eval_detect.py | 4 +-
research/cv/JDE/train.py | 4 +-
research/cv/LightCNN/src/dataset.py | 24 ++---
research/cv/MGN/src/dataset.py | 2 +-
research/cv/MVD/eval.py | 12 +--
research/cv/MVD/train.py | 36 ++++----
research/cv/ManiDP/src/dataset.py | 56 ++++++------
.../cv/MaskedFaceRecognition/test_dataset.py | 6 +-
.../cv/MaskedFaceRecognition/train_dataset.py | 6 +-
research/cv/NFNet/src/data/imagenet.py | 13 ++-
research/cv/Neighbor2Neighbor/src/dataset.py | 4 +-
research/cv/PAMTRI/MultiTaskNet/preprocess.py | 4 +-
.../MultiTaskNet/src/dataset/dataset.py | 4 +-
.../MultiTaskNet/src/dataset/transforms.py | 8 +-
.../PAMTRI/PoseEstNet/src/dataset/dataset.py | 10 +--
research/cv/PAMTRI/PoseEstNet/trans.py | 10 +--
research/cv/PDarts/src/dataset.py | 4 +-
.../cv/Pix2Pix/src/dataset/pix2pix_dataset.py | 2 +-
research/cv/ReIDStrongBaseline/src/dataset.py | 2 +-
research/cv/RefineDet/src/dataset.py | 4 +-
research/cv/RefineNet/src/dataset.py | 2 +-
research/cv/ResNeSt50/src/datasets/autoaug.py | 8 +-
research/cv/ResNeSt50/src/datasets/dataset.py | 15 ++--
research/cv/SE-Net/src/dataset.py | 6 +-
research/cv/SE_ResNeXt50/src/dataset.py | 6 +-
research/cv/SPPNet/src/dataset.py | 4 +-
research/cv/STGAN/modelarts/dataset/celeba.py | 4 +-
research/cv/STGAN/src/dataset/celeba.py | 4 +-
research/cv/SiamFC/ModelArts/start_train.py | 8 +-
research/cv/SiamFC/train.py | 8 +-
research/cv/StarGAN/src/dataset.py | 20 ++---
research/cv/TCN/src/dataset.py | 6 +-
research/cv/TNT/src/data/imagenet.py | 13 ++-
research/cv/U-GAT-IT/src/dataset/dataset.py | 30 +++----
research/cv/UNet3+/src/dataset.py | 4 +-
research/cv/VehicleNet/src/dataset.py | 11 ++-
research/cv/ViG/src/data/imagenet.py | 11 ++-
research/cv/Yolact++/src/dataset.py | 4 +-
research/cv/advanced_east/src/dataset.py | 4 +-
research/cv/arcface/src/dataset.py | 6 +-
research/cv/augvit/src/c10_dataset.py | 8 +-
.../src/dataset/autoaugment/aug.py | 8 +-
.../src/dataset/autoaugment/ops/__init__.py | 16 ++--
.../src/dataset/autoaugment/ops/crop.py | 8 +-
.../src/dataset/autoaugment/ops/cutout.py | 2 +-
.../src/dataset/autoaugment/ops/ops_test.py | 4 +-
.../src/dataset/autoaugment/ops/transform.py | 15 ++--
.../cv/autoaugment/src/dataset/cifar10.py | 4 +-
.../autoaugment/src/dataset/svhn_dataset.py | 4 +-
research/cv/cait/src/data/imagenet.py | 21 +++--
research/cv/cct/src/data/cifar10.py | 20 ++---
research/cv/cct/src/data/imagenet.py | 23 +++--
research/cv/convnext/src/data/imagenet.py | 11 ++-
research/cv/dcgan/src/dataset.py | 6 +-
.../cv/delf/src/data_augmentation_parallel.py | 6 +-
research/cv/ecolite/src/transforms.py | 6 +-
research/cv/efficientnet-b0/src/dataset.py | 6 +-
research/cv/efficientnet-b1/src/dataset.py | 6 +-
research/cv/efficientnet-b2/src/dataset.py | 6 +-
research/cv/efficientnet-b3/src/dataset.py | 6 +-
.../src/data/imagenet_finetune.py | 6 +-
research/cv/eppmvsnet/src/blendedmvs.py | 16 ++--
research/cv/faster_rcnn_dcn/src/dataset.py | 4 +-
research/cv/fishnet99/src/dataset.py | 6 +-
research/cv/ghostnet/src/dataset.py | 6 +-
research/cv/ghostnet_quant/src/dataset.py | 19 ++--
research/cv/glore_res/src/autoaugment.py | 82 ++++++++---------
research/cv/glore_res/src/dataset.py | 6 +-
research/cv/glore_res/src/transform.py | 10 +--
research/cv/hardnet/src/dataset.py | 4 +-
research/cv/hed/src/dataset.py | 4 +-
research/cv/ibnnet/src/dataset.py | 6 +-
.../cv/inception_resnet_v2/src/dataset.py | 6 +-
research/cv/lresnet100e_ir/src/dataset.py | 4 +-
research/cv/mae/src/datasets/dataset.py | 13 ++-
research/cv/mae/src/datasets/imagenet.py | 2 +-
.../meta-baseline/src/data/mini_Imagenet.py | 16 ++--
research/cv/metric_learn/src/dataset.py | 6 +-
research/cv/mnasnet/src/dataset.py | 6 +-
.../cv/mobilenetV3_small_x1_0/src/dataset.py | 6 +-
research/cv/mobilenetv3_large/src/dataset.py | 6 +-
research/cv/nas-fpn/src/dataset.py | 4 +-
research/cv/nima_vgg16/src/MyDataset.py | 4 +-
research/cv/ntsnet/src/dataset.py | 4 +-
research/cv/ntsnet/src/dataset_gpu.py | 4 +-
research/cv/osnet/model_utils/transforms.py | 6 +-
research/cv/pcb_rpp/src/dataset.py | 4 +-
research/cv/pnasnet/src/dataset.py | 6 +-
research/cv/proxylessnas/src/dataset.py | 6 +-
research/cv/ras/src/dataset_test.py | 4 +-
research/cv/ras/src/dataset_train.py | 4 +-
research/cv/rcnn/eval.py | 2 +-
research/cv/relationnet/src/dataset.py | 10 +--
research/cv/renas/src/dataset.py | 58 ++++++------
research/cv/repvgg/src/data/imagenet.py | 11 ++-
research/cv/res2net/src/dataset.py | 6 +-
research/cv/res2net/src/dataset_infer.py | 6 +-
.../cv/res2net_faster_rcnn/src/dataset.py | 4 +-
.../cv/res2net_yolov3/src/yolo_dataset.py | 4 +-
research/cv/resnet3d/src/dataset.py | 4 +-
research/cv/resnet3d/src/pil_transforms.py | 32 +++----
.../resnet50_adv_pruning/src/pet_dataset.py | 20 ++---
research/cv/resnet50_bam/src/dataset.py | 6 +-
research/cv/resnetv2/src/dataset.py | 6 +-
research/cv/resnetv2_50_frn/src/dataset.py | 6 +-
research/cv/resnext152_64x4d/src/dataset.py | 6 +-
.../cv/retinanet_resnet101/src/dataset.py | 4 +-
.../cv/retinanet_resnet152/src/dataset.py | 4 +-
research/cv/rfcn/src/dataset.py | 2 +-
research/cv/simple_baselines/src/dataset.py | 4 +-
research/cv/single_path_nas/src/dataset.py | 6 +-
research/cv/sknet/src/dataset.py | 6 +-
research/cv/squeezenet/src/dataset.py | 6 +-
research/cv/squeezenet1_1/src/dataset.py | 6 +-
research/cv/ssc_resnet50/src/dataset.py | 32 +++----
research/cv/ssd_ghostnet/src/dataset.py | 4 +-
research/cv/ssd_inception_v2/src/dataset.py | 4 +-
research/cv/ssd_inceptionv2/src/dataset.py | 4 +-
research/cv/ssd_mobilenetV2/src/dataset.py | 4 +-
.../cv/ssd_mobilenetV2_FPNlite/src/dataset.py | 4 +-
research/cv/ssd_resnet34/src/dataset.py | 4 +-
research/cv/ssd_resnet50/src/dataset.py | 4 +-
research/cv/ssd_resnet_34/src/dataset.py | 4 +-
research/cv/stpm/src/dataset.py | 18 ++--
.../cv/swin_transformer/src/data/imagenet.py | 17 ++--
research/cv/textfusenet/src/dataset.py | 4 +-
research/cv/tinynet/src/dataset.py | 53 ++++++-----
research/cv/tracktor/src/dataset.py | 2 +-
research/cv/u2net/src/data_loader.py | 4 +-
research/cv/vgg19/src/dataset.py | 6 +-
research/cv/vit_base/src/dataset.py | 6 +-
research/cv/wave_mlp/src/dataset.py | 21 +++--
research/cv/wgan/src/dataset.py | 6 +-
research/cv/wideresnet/src/dataset.py | 6 +-
research/cv/yolov3_tiny/src/transforms.py | 6 +-
research/cv/yolov3_tiny/src/yolo_dataset.py | 4 +-
research/mm/wukong/src/dataset/dataset.py | 4 +-
research/nlp/DYR/src/dataset.py | 4 +-
research/nlp/albert/src/dataset.py | 4 +-
research/nlp/gpt2/src/dataset.py | 4 +-
research/nlp/hypertext/src/dataset.py | 4 +-
research/nlp/ktnet/src/dataset.py | 4 +-
.../src/reading_comprehension/dataLoader.py | 4 +-
.../nlp/seq2seq/src/dataset/load_dataset.py | 4 +-
research/recommend/mmoe/src/load_dataset.py | 2 +-
.../model_scaffolding/example/src/dataset.py | 6 +-
297 files changed, 1494 insertions(+), 1531 deletions(-)
diff --git a/.jenkins/check/config/filter_linklint.txt b/.jenkins/check/config/filter_linklint.txt
index bbd5911cd..e5a98919a 100644
--- a/.jenkins/check/config/filter_linklint.txt
+++ b/.jenkins/check/config/filter_linklint.txt
@@ -1,2 +1,3 @@
http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
-http://dl.yf.io/dla/models/imagenet/dla34-ba72cf86.pth
\ No newline at end of file
+http://dl.yf.io/dla/models/imagenet/dla34-ba72cf86.pth
+https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py_key
diff --git a/.jenkins/check/config/whitelizard.txt b/.jenkins/check/config/whitelizard.txt
index 5f42aed7c..a571f39f3 100644
--- a/.jenkins/check/config/whitelizard.txt
+++ b/.jenkins/check/config/whitelizard.txt
@@ -55,4 +55,5 @@ models/research/cvtmodel/resnet_ipl/src/resnet26t.py:__init__
models/research/cvtmodel/resnet_ipl/src/resnet101d.py:__init__
models/research/cvtmodel/resnet_ipl/src/resnetrs50.py:__init__
models/official/audio/lpcnet/ascend310_infer/src/main.cc:main
+models/official/nlp/bert/src/finetune_data_preprocess.py:process_msra
diff --git a/benchmark/ascend/bert/src/dataset.py b/benchmark/ascend/bert/src/dataset.py
index a611c1ef5..433ebe2cc 100644
--- a/benchmark/ascend/bert/src/dataset.py
+++ b/benchmark/ascend/bert/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,7 +20,7 @@ import math
import numpy as np
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
from mindspore import log as logger
diff --git a/benchmark/ascend/resnet/src/dataset.py b/benchmark/ascend/resnet/src/dataset.py
index 77809beb7..d96526cb3 100644
--- a/benchmark/ascend/resnet/src/dataset.py
+++ b/benchmark/ascend/resnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -49,18 +49,18 @@ def create_dataset1(dataset_path, do_train, batch_size=32, train_image_size=224,
trans = []
if do_train:
trans += [
- ds.vision.c_transforms.RandomCrop((32, 32), (4, 4, 4, 4)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5)
+ ds.vision.RandomCrop((32, 32), (4, 4, 4, 4)),
+ ds.vision.RandomHorizontalFlip(prob=0.5)
]
trans += [
- ds.vision.c_transforms.Resize((train_image_size, train_image_size)),
- ds.vision.c_transforms.Rescale(1.0 / 255.0, 0.0),
- ds.vision.c_transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Resize((train_image_size, train_image_size)),
+ ds.vision.Rescale(1.0 / 255.0, 0.0),
+ ds.vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=type_cast_op, input_columns="label",
num_parallel_workers=get_num_parallel_workers(8))
@@ -115,18 +115,18 @@ def create_dataset2(dataset_path, do_train, batch_size=32, train_image_size=224,
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5)
+ ds.vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(prob=0.5)
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(256),
- ds.vision.c_transforms.CenterCrop(eval_image_size)
+ ds.vision.Decode(),
+ ds.vision.Resize(256),
+ ds.vision.CenterCrop(eval_image_size)
]
- trans_norm = [ds.vision.c_transforms.Normalize(mean=mean, std=std), ds.vision.c_transforms.HWC2CHW()]
+ trans_norm = [ds.vision.Normalize(mean=mean, std=std), ds.vision.HWC2CHW()]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
if device_num == 1:
trans_work_num = 24
else:
@@ -187,21 +187,21 @@ def create_dataset_pynative(dataset_path, do_train, batch_size=32, train_image_s
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(prob=0.5),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(256),
- ds.vision.c_transforms.CenterCrop(eval_image_size),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(256),
+ ds.vision.CenterCrop(eval_image_size),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=4)
# only enable cache for eval
@@ -253,21 +253,21 @@ def create_dataset3(dataset_path, do_train, batch_size=32, train_image_size=224,
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(rank_id / (rank_id + 1)),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(rank_id / (rank_id + 1)),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(256),
- ds.vision.c_transforms.CenterCrop(eval_image_size),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(256),
+ ds.vision.CenterCrop(eval_image_size),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=get_num_parallel_workers(8))
# only enable cache for eval
@@ -321,21 +321,21 @@ def create_dataset4(dataset_path, do_train, batch_size=32, train_image_size=224,
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(prob=0.5),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(292),
- ds.vision.c_transforms.CenterCrop(eval_image_size),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(292),
+ ds.vision.CenterCrop(eval_image_size),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=get_num_parallel_workers(12))
# only enable cache for eval
if do_train:
diff --git a/benchmark/ascend/resnet/src/dataset_infer.py b/benchmark/ascend/resnet/src/dataset_infer.py
index 5d0a655e8..ce032b1db 100644
--- a/benchmark/ascend/resnet/src/dataset_infer.py
+++ b/benchmark/ascend/resnet/src/dataset_infer.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -130,21 +130,21 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(prob=0.5),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(256),
- ds.vision.c_transforms.CenterCrop(image_size),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(256),
+ ds.vision.CenterCrop(image_size),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
@@ -202,21 +202,21 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(rank_id / (rank_id + 1)),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(rank_id / (rank_id + 1)),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(256),
- ds.vision.c_transforms.CenterCrop(image_size),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(256),
+ ds.vision.CenterCrop(image_size),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
@@ -271,21 +271,21 @@ def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target=
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(prob=0.5),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(292),
- ds.vision.c_transforms.CenterCrop(256),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(292),
+ ds.vision.CenterCrop(256),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=12)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=12)
if do_train:
diff --git a/official/cv/Deepsort/modelarts/start_train.py b/official/cv/Deepsort/modelarts/start_train.py
index 879d8f232..bed103450 100644
--- a/official/cv/Deepsort/modelarts/start_train.py
+++ b/official/cv/Deepsort/modelarts/start_train.py
@@ -21,7 +21,7 @@ import numpy as np
import moxing as mox
import mindspore.nn as nn
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.common import set_seed
from mindspore.common import dtype as mstype
diff --git a/official/cv/Deepsort/src/deep/train.py b/official/cv/Deepsort/src/deep/train.py
index 3d5e0a227..9488a1f22 100644
--- a/official/cv/Deepsort/src/deep/train.py
+++ b/official/cv/Deepsort/src/deep/train.py
@@ -16,7 +16,7 @@ import argparse
import os
import ast
import numpy as np
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
import mindspore.dataset as ds
import mindspore.nn as nn
from mindspore import Tensor, context
@@ -24,7 +24,7 @@ from mindspore.communication.management import init, get_rank
from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor
from mindspore.train.model import Model
from mindspore.context import ParallelMode
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.transforms as C2
from mindspore.common import set_seed
import mindspore.common.dtype as mstype
from original_model import Net
diff --git a/official/cv/alexnet/src/dataset.py b/official/cv/alexnet/src/dataset.py
index 149ffdb05..6fcc88205 100644
--- a/official/cv/alexnet/src/dataset.py
+++ b/official/cv/alexnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ Produce the dataset
import os
from multiprocessing import cpu_count
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as CV
from mindspore.common import dtype as mstype
from mindspore.communication.management import get_rank, get_group_size
diff --git a/official/cv/brdnet/src/dataset.py b/official/cv/brdnet/src/dataset.py
index 9e870625f..dbdde6da5 100644
--- a/official/cv/brdnet/src/dataset.py
+++ b/official/cv/brdnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@ import glob
import numpy as np
import PIL.Image as Image
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.vision as CV
class BRDNetDataset:
""" BRDNetDataset.
diff --git a/official/cv/cnn_direction_model/src/dataset.py b/official/cv/cnn_direction_model/src/dataset.py
index ec1bbdefe..b91ec5985 100644
--- a/official/cv/cnn_direction_model/src/dataset.py
+++ b/official/cv/cnn_direction_model/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,7 +20,7 @@ import cv2
import numpy as np
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from src.dataset_utils import lucky, noise_blur, noise_speckle, noise_gamma, noise_gaussian, noise_salt_pepper, \
shift_color, enhance_brightness, enhance_sharpness, enhance_contrast, enhance_color, gaussian_blur, \
randcrop, resize, rdistort, rgeometry, rotate_about_center, whole_rdistort, warp_perspective, random_contrast, \
diff --git a/official/cv/crnn/src/dataset.py b/official/cv/crnn/src/dataset.py
index 1d07a34f9..2f07b734a 100644
--- a/official/cv/crnn/src/dataset.py
+++ b/official/cv/crnn/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ import numpy as np
from PIL import Image, ImageFile
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vc
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vc
from src.model_utils.config import config as config1
from src.ic03_dataset import IC03Dataset
from src.ic13_dataset import IC13Dataset
diff --git a/official/cv/crnn_seq2seq_ocr/src/dataset.py b/official/cv/crnn_seq2seq_ocr/src/dataset.py
index 40abc60fd..9d12f36c2 100644
--- a/official/cv/crnn_seq2seq_ocr/src/dataset.py
+++ b/official/cv/crnn_seq2seq_ocr/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,9 +19,8 @@ import numpy as np
from PIL import Image
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.vision.py_transforms as P
-import mindspore.dataset.transforms.c_transforms as ops
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as ops
import mindspore.common.dtype as mstype
from src.model_utils.config import config
@@ -36,7 +35,7 @@ class AugmentationOps():
self.min_area_ratio = min_area_ratio
self.aspect_ratio_range = aspect_ratio_range
self.img_tile_shape = img_tile_shape
- self.random_image_distortion_ops = P.RandomColorAdjust(brightness=brightness,
+ self.random_image_distortion_ops = C.RandomColorAdjust(brightness=brightness,
contrast=contrast,
saturation=saturation,
hue=hue)
diff --git a/official/cv/cspdarknet53/src/dataset.py b/official/cv/cspdarknet53/src/dataset.py
index 9025cffdd..e1c3c8e85 100644
--- a/official/cv/cspdarknet53/src/dataset.py
+++ b/official/cv/cspdarknet53/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as V_C
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as V_C
from PIL import Image, ImageFile
from .utils.sampler import DistributedSampler
diff --git a/official/cv/ctpn/src/dataset.py b/official/cv/ctpn/src/dataset.py
index a7936800e..dfdf942a4 100644
--- a/official/cv/ctpn/src/dataset.py
+++ b/official/cv/ctpn/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import numpy as np
from numpy import random
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as CC
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as CC
import mindspore.common.dtype as mstype
from src.model_utils.config import config
diff --git a/official/cv/darknet53/src/dataset.py b/official/cv/darknet53/src/dataset.py
index d5bf8dde2..8984090c9 100644
--- a/official/cv/darknet53/src/dataset.py
+++ b/official/cv/darknet53/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ create train or eval dataset.
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="GPU", distribute=False):
diff --git a/official/cv/deeptext/src/dataset.py b/official/cv/deeptext/src/dataset.py
index 7198f4d8d..7e598b96a 100644
--- a/official/cv/deeptext/src/dataset.py
+++ b/official/cv/deeptext/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -22,8 +22,8 @@ from numpy import random
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as CC
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as CC
import mindspore.common.dtype as mstype
from mindspore.mindrecord import FileWriter
from model_utils.config import config
diff --git a/official/cv/densenet/src/datasets/classification.py b/official/cv/densenet/src/datasets/classification.py
index 438689992..84444a6dc 100644
--- a/official/cv/densenet/src/datasets/classification.py
+++ b/official/cv/densenet/src/datasets/classification.py
@@ -1,4 +1,4 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,8 +21,8 @@ import os
from PIL import Image, ImageFile
from mindspore import dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as vision_C
-import mindspore.dataset.transforms.c_transforms as normal_C
+import mindspore.dataset.vision as vision_C
+import mindspore.dataset.transforms as normal_C
from src.datasets.sampler import DistributedSampler
ImageFile.LOAD_TRUNCATED_IMAGES = True
diff --git a/official/cv/depthnet/src/data_loader.py b/official/cv/depthnet/src/data_loader.py
index 41ae8f41f..f5a87c896 100644
--- a/official/cv/depthnet/src/data_loader.py
+++ b/official/cv/depthnet/src/data_loader.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@ import os
import numpy as np
from PIL import Image
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as CV
import mindspore.dataset as ds
from mindspore import dtype as mstype
diff --git a/official/cv/depthnet/train.py b/official/cv/depthnet/train.py
index 4308d7337..aaa809214 100644
--- a/official/cv/depthnet/train.py
+++ b/official/cv/depthnet/train.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import time
import mindspore.numpy as np
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as CV
import mindspore as ms
from mindspore import nn, Tensor, Model
from mindspore import dtype as mstype
diff --git a/official/cv/dncnn/eval.py b/official/cv/dncnn/eval.py
index 14683113f..dca93f8a5 100644
--- a/official/cv/dncnn/eval.py
+++ b/official/cv/dncnn/eval.py
@@ -1,5 +1,5 @@
#!/usr/bin/env python3
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -27,7 +27,7 @@ import mindspore
import mindspore.dataset as ds
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
from src.model import DnCNN
class DnCNN_eval_Dataset():
diff --git a/official/cv/dncnn/infer/data/preprocess/export_bin_file.py b/official/cv/dncnn/infer/data/preprocess/export_bin_file.py
index b4b5462ec..14c0cb596 100644
--- a/official/cv/dncnn/infer/data/preprocess/export_bin_file.py
+++ b/official/cv/dncnn/infer/data/preprocess/export_bin_file.py
@@ -23,7 +23,7 @@ import numpy as np
import cv2
import mindspore
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
def ResziePadding(img, fixed_side=256):
diff --git a/official/cv/dncnn/src/dataset.py b/official/cv/dncnn/src/dataset.py
index b8240c79e..69b6565c7 100644
--- a/official/cv/dncnn/src/dataset.py
+++ b/official/cv/dncnn/src/dataset.py
@@ -1,5 +1,5 @@
#!/usr/bin/env python3
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,7 +24,7 @@ import cv2
import PIL
import mindspore
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
def create_train_dataset(data_path, model_type, noise_level=25, batch_size=128):
# define dataset
diff --git a/official/cv/dpn/src/imagenet_dataset.py b/official/cv/dpn/src/imagenet_dataset.py
index 42ad7f9b9..cd73134f4 100644
--- a/official/cv/dpn/src/imagenet_dataset.py
+++ b/official/cv/dpn/src/imagenet_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,8 +20,8 @@ import cv2
from PIL import ImageFile
from mindspore.common import dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as V_C
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as V_C
ImageFile.LOAD_TRUNCATED_IMAGES = True
diff --git a/official/cv/east/detect.py b/official/cv/east/detect.py
index 48f825168..eb807cd62 100644
--- a/official/cv/east/detect.py
+++ b/official/cv/east/detect.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ import numpy as np
import mindspore.ops as P
from mindspore import Tensor
-import mindspore.dataset.vision.py_transforms as V
+import mindspore.dataset.vision as V
from src.dataset import get_rotate_mat
import lanms
@@ -44,7 +44,7 @@ def load_pil(img):
"""convert PIL Image to Tensor
"""
img = V.ToTensor()(img)
- img = V.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(img)
+ img = V.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), is_hwc=False)(img)
img = Tensor(img)
img = P.ExpandDims()(img, 0)
return img
diff --git a/official/cv/east/src/dataset.py b/official/cv/east/src/dataset.py
index 79c6ddfbc..2f0da3520 100644
--- a/official/cv/east/src/dataset.py
+++ b/official/cv/east/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ import cv2
from PIL import Image
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.vision as CV
from src.distributed_sampler import DistributedSampler
diff --git a/official/cv/efficientnet/src/dataset.py b/official/cv/efficientnet/src/dataset.py
index 76a67f149..7b8bd3108 100644
--- a/official/cv/efficientnet/src/dataset.py
+++ b/official/cv/efficientnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,8 +20,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
from mindspore.communication.management import get_group_size, get_rank
from mindspore.dataset.vision import Inter
diff --git a/official/cv/efficientnet/src/transform.py b/official/cv/efficientnet/src/transform.py
index c34a8fe94..39a1eebcb 100644
--- a/official/cv/efficientnet/src/transform.py
+++ b/official/cv/efficientnet/src/transform.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,7 +16,7 @@
random augment class
"""
import numpy as np
-import mindspore.dataset.vision.py_transforms as P
+import mindspore.dataset.vision as vision
from src import transform_utils
@@ -35,9 +35,9 @@ class RandAugment:
# assert the imgs object are pil_images
ret_imgs = []
ret_labels = []
- py_to_pil_op = P.ToPIL()
- to_tensor = P.ToTensor()
- normalize_op = P.Normalize(self.mean, self.std)
+ py_to_pil_op = vision.ToPIL()
+ to_tensor = vision.ToTensor()
+ normalize_op = vision.Normalize(self.mean, self.std, is_hwc=False)
rand_augment_ops = transform_utils.rand_augment_transform(self.config_str, self.hparams)
for i, image in enumerate(imgs):
img_pil = py_to_pil_op(image)
diff --git a/official/cv/faster_rcnn/src/dataset.py b/official/cv/faster_rcnn/src/dataset.py
index 64c955a4c..783f81f8c 100644
--- a/official/cv/faster_rcnn/src/dataset.py
+++ b/official/cv/faster_rcnn/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -552,7 +552,7 @@ def create_fasterrcnn_dataset(config, mindrecord_file, batch_size=2, device_num=
de.config.set_prefetch_size(8)
ds = de.MindDataset(mindrecord_file, columns_list=["image", "annotation"], num_shards=device_num, shard_id=rank_id,
num_parallel_workers=4, shuffle=is_training)
- decode = ms.dataset.vision.c_transforms.Decode()
+ decode = ms.dataset.vision.Decode()
ds = ds.map(input_columns=["image"], operations=decode)
compose_map_func = (lambda image, annotation: preprocess_fn(image, annotation, is_training, config=config))
diff --git a/official/cv/fastscnn/eval.py b/official/cv/fastscnn/eval.py
index 86b94b430..9e6993bc2 100644
--- a/official/cv/fastscnn/eval.py
+++ b/official/cv/fastscnn/eval.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -23,8 +23,8 @@ import mindspore.ops as ops
from mindspore.context import ParallelMode
from mindspore import load_checkpoint, load_param_into_net
from mindspore.communication.management import init, get_rank, get_group_size
-from mindspore.dataset.transforms.py_transforms import Compose
-from mindspore.dataset.vision.py_transforms import ToTensor, Normalize
+from mindspore.dataset.transforms.transforms import Compose
+from mindspore.dataset.vision import ToTensor, Normalize
from src.dataloader import create_CitySegmentation
from src.fast_scnn import FastSCNN
@@ -140,7 +140,7 @@ def validation():
# image transform
input_transform = Compose([
ToTensor(),
- Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+ Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], is_hwc=False),
])
if args.use_modelarts:
import moxing as mox
diff --git a/official/cv/fastscnn/modelarts/start_train.py b/official/cv/fastscnn/modelarts/start_train.py
index 665956f5e..8a17519e9 100644
--- a/official/cv/fastscnn/modelarts/start_train.py
+++ b/official/cv/fastscnn/modelarts/start_train.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -30,8 +30,8 @@ from mindspore.common.tensor import Tensor
from mindspore.context import ParallelMode
from mindspore import FixedLossScaleManager
from mindspore import load_checkpoint, load_param_into_net
-from mindspore.dataset.transforms.py_transforms import Compose
-from mindspore.dataset.vision.py_transforms import ToTensor, Normalize
+from mindspore.dataset.transforms.transforms import Compose
+from mindspore.dataset.vision import ToTensor, Normalize
from mindspore.communication.management import init, get_rank, get_group_size
from mindspore.train.callback import TimeMonitor, LossMonitor, CheckpointConfig, ModelCheckpoint
@@ -138,7 +138,7 @@ def train():
# image transform
input_transform = Compose([
ToTensor(),
- Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+ Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], is_hwc=False),
])
train_dataset, args.steps_per_epoch = create_CitySegmentation(args, data_path=args.dataset, \
diff --git a/official/cv/fastscnn/src/dataloader.py b/official/cv/fastscnn/src/dataloader.py
index bdaae9a50..b6a770a63 100644
--- a/official/cv/fastscnn/src/dataloader.py
+++ b/official/cv/fastscnn/src/dataloader.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ import numpy as np
from PIL import Image
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.vision as CV
from src.seg_data_base import SegmentationDataset
diff --git a/official/cv/fastscnn/train.py b/official/cv/fastscnn/train.py
index 8ba9af1a4..17b98e5e0 100644
--- a/official/cv/fastscnn/train.py
+++ b/official/cv/fastscnn/train.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -27,8 +27,8 @@ from mindspore.common.tensor import Tensor
from mindspore.context import ParallelMode
from mindspore import FixedLossScaleManager
from mindspore import load_checkpoint, load_param_into_net
-from mindspore.dataset.transforms.py_transforms import Compose
-from mindspore.dataset.vision.py_transforms import ToTensor, Normalize
+from mindspore.dataset.transforms.transforms import Compose
+from mindspore.dataset.vision import ToTensor, Normalize
from mindspore.communication.management import init, get_rank, get_group_size
from mindspore.train.callback import TimeMonitor, LossMonitor, CheckpointConfig, ModelCheckpoint
@@ -130,7 +130,7 @@ def train():
# image transform
input_transform = Compose([
ToTensor(),
- Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+ Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], is_hwc=False),
])
if args.use_modelarts:
diff --git a/official/cv/googlenet/src/dataset.py b/official/cv/googlenet/src/dataset.py
index e7a82f05d..bd6bb2b52 100644
--- a/official/cv/googlenet/src/dataset.py
+++ b/official/cv/googlenet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
def create_dataset_cifar10(data_home, repeat_num=1, training=True, cifar_cfg=None):
"""Data operations."""
diff --git a/official/cv/inceptionv3/src/dataset.py b/official/cv/inceptionv3/src/dataset.py
index e7fb6076d..f248974fb 100644
--- a/official/cv/inceptionv3/src/dataset.py
+++ b/official/cv/inceptionv3/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset_imagenet(dataset_path, do_train, cfg, repeat_num=1):
diff --git a/official/cv/inceptionv4/src/dataset.py b/official/cv/inceptionv4/src/dataset.py
index a27e4f150..3939cafba 100644
--- a/official/cv/inceptionv4/src/dataset.py
+++ b/official/cv/inceptionv4/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset_imagenet(dataset_path, do_train, cfg, repeat_num=1):
diff --git a/official/cv/lenet/src/dataset.py b/official/cv/lenet/src/dataset.py
index b3801105b..623ee7b73 100644
--- a/official/cv/lenet/src/dataset.py
+++ b/official/cv/lenet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@ Produce the dataset
"""
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as CV
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.vision as CV
+import mindspore.dataset.transforms as C
from mindspore.dataset.vision import Inter
from mindspore.common import dtype as mstype
diff --git a/official/cv/maskrcnn/src/dataset.py b/official/cv/maskrcnn/src/dataset.py
index bc05b98c7..e6178203f 100644
--- a/official/cv/maskrcnn/src/dataset.py
+++ b/official/cv/maskrcnn/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ import numpy as np
from numpy import random
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from .model_utils.config import config
diff --git a/official/cv/maskrcnn_mobilenetv1/src/dataset.py b/official/cv/maskrcnn_mobilenetv1/src/dataset.py
index 5acba3a77..19e6e1c30 100644
--- a/official/cv/maskrcnn_mobilenetv1/src/dataset.py
+++ b/official/cv/maskrcnn_mobilenetv1/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020-21 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -23,7 +23,7 @@ import numpy as np
from numpy import random
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from mindspore import context
diff --git a/official/cv/mobilenetv1/src/dataset.py b/official/cv/mobilenetv1/src/dataset.py
index 3d7c67150..3b62fd343 100644
--- a/official/cv/mobilenetv1/src/dataset.py
+++ b/official/cv/mobilenetv1/src/dataset.py
@@ -47,18 +47,18 @@ def create_dataset1(dataset_path, do_train, device_num=1, batch_size=32, target=
trans = []
if do_train:
trans += [
- ds.vision.c_transforms.RandomCrop((32, 32), (4, 4, 4, 4)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5)
+ ds.vision.RandomCrop((32, 32), (4, 4, 4, 4)),
+ ds.vision.RandomHorizontalFlip(prob=0.5)
]
trans += [
- ds.vision.c_transforms.Resize((224, 224)),
- ds.vision.c_transforms.Rescale(1.0 / 255.0, 0.0),
- ds.vision.c_transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Resize((224, 224)),
+ ds.vision.Rescale(1.0 / 255.0, 0.0),
+ ds.vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=THREAD_NUM)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=THREAD_NUM)
@@ -97,21 +97,21 @@ def create_dataset2(dataset_path, do_train, device_num=1, batch_size=32, target=
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(prob=0.5),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(256),
- ds.vision.c_transforms.CenterCrop(image_size),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(256),
+ ds.vision.CenterCrop(image_size),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=THREAD_NUM)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=THREAD_NUM)
diff --git a/official/cv/mobilenetv2/src/dataset.py b/official/cv/mobilenetv2/src/dataset.py
index 6149458d6..8d09a7e8c 100644
--- a/official/cv/mobilenetv2/src/dataset.py
+++ b/official/cv/mobilenetv2/src/dataset.py
@@ -52,24 +52,24 @@ def create_dataset(dataset_path, do_train, config, enable_cache=False, cache_ses
buffer_size = 1000
# define map operations
- decode_op = ds.vision.c_transforms.Decode()
- resize_crop_op = ds.vision.c_transforms.RandomCropDecodeResize(resize_height,
- scale=(0.08, 1.0), ratio=(0.75, 1.333))
- horizontal_flip_op = ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5)
-
- resize_op = ds.vision.c_transforms.Resize((256, 256))
- center_crop = ds.vision.c_transforms.CenterCrop(resize_width)
- rescale_op = ds.vision.c_transforms.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
- normalize_op = ds.vision.c_transforms.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
- std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
- change_swap_op = ds.vision.c_transforms.HWC2CHW()
+ decode_op = ds.vision.Decode()
+ resize_crop_op = ds.vision.RandomCropDecodeResize(resize_height,
+ scale=(0.08, 1.0), ratio=(0.75, 1.333))
+ horizontal_flip_op = ds.vision.RandomHorizontalFlip(prob=0.5)
+
+ resize_op = ds.vision.Resize((256, 256))
+ center_crop = ds.vision.CenterCrop(resize_width)
+ rescale_op = ds.vision.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
+ normalize_op = ds.vision.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
+ std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
+ change_swap_op = ds.vision.HWC2CHW()
if do_train:
trans = [resize_crop_op, horizontal_flip_op, rescale_op, normalize_op, change_swap_op]
else:
trans = [decode_op, resize_op, center_crop, normalize_op, change_swap_op]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=num_workers)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_workers)
diff --git a/official/cv/mobilenetv3/src/dataset.py b/official/cv/mobilenetv3/src/dataset.py
index 8061ca3eb..e29ab6767 100644
--- a/official/cv/mobilenetv3/src/dataset.py
+++ b/official/cv/mobilenetv3/src/dataset.py
@@ -49,24 +49,24 @@ def create_dataset(dataset_path, do_train, config, device_target, batch_size=32,
buffer_size = 1000
# define map operations
- decode_op = ds.vision.c_transforms.Decode()
- resize_crop_op = ds.vision.c_transforms.RandomCropDecodeResize(resize_height,
- scale=(0.08, 1.0), ratio=(0.75, 1.333))
- horizontal_flip_op = ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5)
-
- resize_op = ds.vision.c_transforms.Resize(256)
- center_crop = ds.vision.c_transforms.CenterCrop(resize_width)
- rescale_op = ds.vision.c_transforms.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
- normalize_op = ds.vision.c_transforms.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
- std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
- change_swap_op = ds.vision.c_transforms.HWC2CHW()
+ decode_op = ds.vision.Decode()
+ resize_crop_op = ds.vision.RandomCropDecodeResize(resize_height,
+ scale=(0.08, 1.0), ratio=(0.75, 1.333))
+ horizontal_flip_op = ds.vision.RandomHorizontalFlip(prob=0.5)
+
+ resize_op = ds.vision.Resize(256)
+ center_crop = ds.vision.CenterCrop(resize_width)
+ rescale_op = ds.vision.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
+ normalize_op = ds.vision.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
+ std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
+ change_swap_op = ds.vision.HWC2CHW()
if do_train:
trans = [resize_crop_op, horizontal_flip_op, rescale_op, normalize_op, change_swap_op]
else:
trans = [decode_op, resize_op, center_crop, normalize_op, change_swap_op]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
@@ -99,24 +99,24 @@ def create_dataset_cifar(dataset_path,
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCrop((32, 32), (4, 4, 4, 4)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5),
- ds.vision.c_transforms.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
- ds.vision.c_transforms.Resize((224, 224)),
- ds.vision.c_transforms.Rescale(1.0 / 255.0, 0.0),
- ds.vision.c_transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
- ds.vision.c_transforms.CutOut(112),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCrop((32, 32), (4, 4, 4, 4)),
+ ds.vision.RandomHorizontalFlip(prob=0.5),
+ ds.vision.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
+ ds.vision.Resize((224, 224)),
+ ds.vision.Rescale(1.0 / 255.0, 0.0),
+ ds.vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
+ ds.vision.CutOut(112),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Resize((224, 224)),
- ds.vision.c_transforms.Rescale(1.0 / 255.0, 0.0),
- ds.vision.c_transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Resize((224, 224)),
+ ds.vision.Rescale(1.0 / 255.0, 0.0),
+ ds.vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=type_cast_op,
input_columns="label",
diff --git a/official/cv/nasnet/src/dataset.py b/official/cv/nasnet/src/dataset.py
index efa36ab28..63b32b9f6 100644
--- a/official/cv/nasnet/src/dataset.py
+++ b/official/cv/nasnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ Data operations, will be used in train.py and eval.py
import mindspore
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, rank, group_size,
num_parallel_workers=8, batch_size=128,
diff --git a/official/cv/nima/src/dataset.py b/official/cv/nima/src/dataset.py
index be1be6b7f..35c480c19 100644
--- a/official/cv/nima/src/dataset.py
+++ b/official/cv/nima/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ import numpy as np
import mindspore
import mindspore.dataset as ds
from mindspore.dataset.vision import Inter
-from mindspore.dataset.vision import c_transforms as v_ct
-from mindspore.dataset.transforms import c_transforms as t_ct
+from mindspore.dataset.vision import transforms as v_ct
+from mindspore.dataset.transforms import transforms as t_ct
class Dataset:
diff --git a/official/cv/patchcore/preprocess.py b/official/cv/patchcore/preprocess.py
index a4f35c46f..03586d845 100644
--- a/official/cv/patchcore/preprocess.py
+++ b/official/cv/patchcore/preprocess.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,10 +20,10 @@ from pathlib import Path
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as vision
from mindspore.common import set_seed
-from mindspore.dataset.transforms.py_transforms import Compose
+from mindspore.dataset.transforms.transforms import Compose
from mindspore.dataset.vision import Inter
from src.config import _C as cfg
@@ -89,15 +89,15 @@ def createDataset(dataset_path, category):
std = cfg.std_dft
data_transforms = Compose([
- py_vision.Resize((256, 256), interpolation=Inter.ANTIALIAS),
- py_vision.CenterCrop(224),
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std)
+ vision.Resize((256, 256), interpolation=Inter.ANTIALIAS),
+ vision.CenterCrop(224),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False)
])
gt_transforms = Compose([
- py_vision.Resize((256, 256)),
- py_vision.CenterCrop(224),
- py_vision.ToTensor()
+ vision.Resize((256, 256)),
+ vision.CenterCrop(224),
+ vision.ToTensor()
])
train_json_path, test_json_path = createDatasetJson(dataset_path, category, data_transforms, gt_transforms)
diff --git a/official/cv/patchcore/src/dataset.py b/official/cv/patchcore/src/dataset.py
index c8c4b48a9..29a05d788 100644
--- a/official/cv/patchcore/src/dataset.py
+++ b/official/cv/patchcore/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,9 +20,9 @@ from pathlib import Path
import numpy as np
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.py_transforms as py_vision
-from mindspore.dataset.transforms.py_transforms import Compose
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as vision
+from mindspore.dataset.transforms.transforms import Compose
from mindspore.dataset.vision import Inter
from PIL import Image
@@ -137,15 +137,15 @@ def createDataset(dataset_path, category):
std = [0.229, 0.224, 0.225]
data_transforms = Compose([
- py_vision.Resize((256, 256), interpolation=Inter.ANTIALIAS),
- py_vision.CenterCrop(224),
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std)
+ vision.Resize((256, 256), interpolation=Inter.ANTIALIAS),
+ vision.CenterCrop(224),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False)
])
gt_transforms = Compose([
- py_vision.Resize((256, 256)),
- py_vision.CenterCrop(224),
- py_vision.ToTensor()
+ vision.Resize((256, 256)),
+ vision.CenterCrop(224),
+ vision.ToTensor()
])
train_json_path, test_json_path = createDatasetJson(dataset_path, category, data_transforms, gt_transforms)
diff --git a/official/cv/posenet/src/dataset.py b/official/cv/posenet/src/dataset.py
index c9454fadc..573d18992 100644
--- a/official/cv/posenet/src/dataset.py
+++ b/official/cv/posenet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@ import os
import numpy as np
from mindspore.mindrecord import FileWriter
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
class Dataset:
"""dataset read"""
diff --git a/official/cv/psenet/src/dataset.py b/official/cv/psenet/src/dataset.py
index 8c88120b7..e0e0f94e7 100644
--- a/official/cv/psenet/src/dataset.py
+++ b/official/cv/psenet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -23,7 +23,7 @@ import numpy as np
import Polygon as plg
import pyclipper
import mindspore.dataset as ds
-import mindspore.dataset.vision.py_transforms as py_transforms
+import mindspore.dataset.vision as vision
from src.model_utils.config import config
__all__ = ['train_dataset_creator', 'test_dataset_creator']
@@ -255,13 +255,13 @@ class TrainDataset:
if self.is_transform:
img = Image.fromarray(img)
img = img.convert('RGB')
- img = py_transforms.RandomColorAdjust(brightness=32.0 / 255, saturation=0.5)(img)
+ img = vision.RandomColorAdjust(brightness=32.0 / 255, saturation=0.5)(img)
else:
img = Image.fromarray(img)
img = img.convert('RGB')
- img = py_transforms.ToTensor()(img)
- img = py_transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(img)
+ img = vision.ToTensor()(img)
+ img = vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False)(img)
gt_text = gt_text.astype(np.float32)
gt_kernels = gt_kernels.astype(np.float32)
@@ -306,8 +306,8 @@ def IC15_TEST_Generator():
img_resized = Image.fromarray(img_resized)
img_resized = img_resized.convert('RGB')
- img_resized = py_transforms.ToTensor()(img_resized)
- img_resized = py_transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(img_resized)
+ img_resized = vision.ToTensor()(img_resized)
+ img_resized = vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False)(img_resized)
yield img, img_resized, img_name
diff --git a/official/cv/pvnet/eval.py b/official/cv/pvnet/eval.py
index b687eee4a..52c273dc3 100644
--- a/official/cv/pvnet/eval.py
+++ b/official/cv/pvnet/eval.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,7 @@ import time
import numpy as np
import mindspore
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.py_transforms as P
+import mindspore.dataset.transforms as C
from mindspore import context
from model_utils.config import config as cfg
@@ -82,9 +81,9 @@ def test(args):
pose = test_db[idx]['RT'].copy()
rgb = read_rgb_np(rgb_path)
- rgb = P.ToTensor()(rgb)
+ rgb = C.ToTensor()(rgb)
rgb = C.TypeCast(mindspore.dtype.float32)(rgb)
- rgb = P.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(rgb)
+ rgb = C.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False)(rgb)
rgb = np.expand_dims(rgb, axis=0)
rgb = mindspore.Tensor(rgb)
diff --git a/official/cv/pvnet/src/dataset.py b/official/cv/pvnet/src/dataset.py
index 4a3d59580..29ee73acd 100644
--- a/official/cv/pvnet/src/dataset.py
+++ b/official/cv/pvnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,9 +18,8 @@ import os
import cv2
import mindspore.common.dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as CV
-import mindspore.dataset.vision.py_transforms as P
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as CV
import numpy as np
from model_utils.config import config as cfg
@@ -242,9 +241,9 @@ def create_dataset(cls_list, batch_size=16, workers=16, devices=1, rank=0, multi
CV.RandomColorAdjust(
cfg.brightness, cfg.contrast,
cfg.saturation, cfg.hue),
- P.ToTensor(), # 0~255 HWC to 0~1 CHW
+ C.ToTensor(), # 0~255 HWC to 0~1 CHW
C.TypeCast(mstype.float32),
- P.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+ C.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), is_hwc=False),
])
mask_transforms = [
diff --git a/official/cv/pwcnet/src/flyingchairs.py b/official/cv/pwcnet/src/flyingchairs.py
index c1d68d031..fdcefc667 100644
--- a/official/cv/pwcnet/src/flyingchairs.py
+++ b/official/cv/pwcnet/src/flyingchairs.py
@@ -18,8 +18,8 @@ from glob import glob
import mindspore.dataset as de
import mindspore
-import mindspore.dataset.vision.py_transforms as CV
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.vision as V
+import mindspore.dataset.transforms as T
import src.common as common
import src.transforms as transforms
@@ -131,16 +131,16 @@ class FlyingChairs():
# photometric_augmentations
if augmentations:
self._photometric_transform = transforms.ConcatTransformSplitChainer([
- CV.ToPIL(),
- CV.RandomColorAdjust(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5),
- CV.ToTensor(),
+ V.ToPIL(),
+ V.RandomColorAdjust(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5),
+ V.ToTensor(),
transforms.RandomGamma(min_gamma=0.7, max_gamma=1.5, clip_image=True)
])
else:
self._photometric_transform = transforms.ConcatTransformSplitChainer([
- CV.ToPIL(),
- CV.ToTensor(),
+ V.ToPIL(),
+ V.ToTensor(),
])
def __getitem__(self, index):
@@ -176,9 +176,9 @@ def FlyingChairsTrain(dir_root, augmentations, dstype, batchsize, num_parallel_w
shuffle=True, num_shards=world_size, shard_id=local_rank)
# apply map operations on images
- de_dataset = de_dataset.map(input_columns="im1", operations=C.TypeCast(mindspore.float32))
- de_dataset = de_dataset.map(input_columns="im2", operations=C.TypeCast(mindspore.float32))
- de_dataset = de_dataset.map(input_columns="flo", operations=C.TypeCast(mindspore.float32))
+ de_dataset = de_dataset.map(input_columns="im1", operations=T.TypeCast(mindspore.float32))
+ de_dataset = de_dataset.map(input_columns="im2", operations=T.TypeCast(mindspore.float32))
+ de_dataset = de_dataset.map(input_columns="flo", operations=T.TypeCast(mindspore.float32))
de_dataset = de_dataset.batch(batchsize, drop_remainder=True)
return de_dataset, dataset_len
diff --git a/official/cv/pwcnet/src/sintel.py b/official/cv/pwcnet/src/sintel.py
index 0c7b6caee..cce038c86 100644
--- a/official/cv/pwcnet/src/sintel.py
+++ b/official/cv/pwcnet/src/sintel.py
@@ -19,8 +19,8 @@ import numpy as np
import mindspore.dataset as de
import mindspore
-import mindspore.dataset.vision.py_transforms as CV
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.vision as V
+import mindspore.dataset.transforms as T
import src.common as common
import src.transforms as transforms
@@ -129,16 +129,16 @@ class Sintel():
# photometric_augmentations
if augmentations:
self._photometric_transform = transforms.ConcatTransformSplitChainer([
- CV.ToPIL(),
- CV.RandomColorAdjust(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5),
- CV.ToTensor(),
+ V.ToPIL(),
+ V.RandomColorAdjust(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5),
+ V.ToTensor(),
transforms.RandomGamma(min_gamma=0.7, max_gamma=1.5, clip_image=True)
])
else:
self._photometric_transform = transforms.ConcatTransformSplitChainer([
- CV.ToPIL(),
- CV.ToTensor(),
+ V.ToPIL(),
+ V.ToTensor(),
])
self._size = len(self._image_list)
@@ -182,8 +182,8 @@ def SintelTraining(dir_root, augmentations, imgtype, dstype, batchsize, num_para
shuffle=True, num_shards=world_size, shard_id=local_rank)
# apply map operations on images
- de_dataset = de_dataset.map(input_columns="im1", operations=C.TypeCast(mindspore.float32))
- de_dataset = de_dataset.map(input_columns="im2", operations=C.TypeCast(mindspore.float32))
- de_dataset = de_dataset.map(input_columns="flo", operations=C.TypeCast(mindspore.float32))
+ de_dataset = de_dataset.map(input_columns="im1", operations=T.TypeCast(mindspore.float32))
+ de_dataset = de_dataset.map(input_columns="im2", operations=T.TypeCast(mindspore.float32))
+ de_dataset = de_dataset.map(input_columns="flo", operations=T.TypeCast(mindspore.float32))
de_dataset = de_dataset.batch(batchsize, drop_remainder=True)
return de_dataset, dataset_len
diff --git a/official/cv/resnet/gpu_resnet_benchmark.py b/official/cv/resnet/gpu_resnet_benchmark.py
index 01e3be045..094301e02 100644
--- a/official/cv/resnet/gpu_resnet_benchmark.py
+++ b/official/cv/resnet/gpu_resnet_benchmark.py
@@ -90,28 +90,28 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
# define map operations
- normalize_op = ds.vision.c_transforms.Normalize(mean=mean, std=std)
+ normalize_op = ds.vision.Normalize(mean=mean, std=std)
if dtype == "fp16":
if config.eval:
x_dtype = "float32"
else:
x_dtype = "float16"
- normalize_op = ds.vision.c_transforms.NormalizePad(mean=mean, std=std, dtype=x_dtype)
+ normalize_op = ds.vision.NormalizePad(mean=mean, std=std, dtype=x_dtype)
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5),
+ ds.vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(prob=0.5),
normalize_op,
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(256),
- ds.vision.c_transforms.CenterCrop(image_size),
+ ds.vision.Decode(),
+ ds.vision.Resize(256),
+ ds.vision.CenterCrop(image_size),
normalize_op,
]
if dtype == "fp32":
- trans.append(ds.vision.c_transforms.HWC2CHW())
+ trans.append(ds.vision.HWC2CHW())
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=map_num_parallel_worker)
# apply batch operations
data_set = data_set.batch(batch_size, drop_remainder=True, num_parallel_workers=batch_num_parallel_worker)
diff --git a/official/cv/resnet/src/dataset.py b/official/cv/resnet/src/dataset.py
index a13626f18..7cf8bc978 100644
--- a/official/cv/resnet/src/dataset.py
+++ b/official/cv/resnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -50,18 +50,18 @@ def create_dataset1(dataset_path, do_train, batch_size=32, train_image_size=224,
trans = []
if do_train:
trans += [
- ds.vision.c_transforms.RandomCrop((32, 32), (4, 4, 4, 4)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5)
+ ds.vision.RandomCrop((32, 32), (4, 4, 4, 4)),
+ ds.vision.RandomHorizontalFlip(prob=0.5)
]
trans += [
- ds.vision.c_transforms.Resize((train_image_size, train_image_size)),
- ds.vision.c_transforms.Rescale(1.0 / 255.0, 0.0),
- ds.vision.c_transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Resize((train_image_size, train_image_size)),
+ ds.vision.Rescale(1.0 / 255.0, 0.0),
+ ds.vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=type_cast_op, input_columns="label",
num_parallel_workers=get_num_parallel_workers(8))
@@ -117,18 +117,18 @@ def create_dataset2(dataset_path, do_train, batch_size=32, train_image_size=224,
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5)
+ ds.vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(prob=0.5)
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(256),
- ds.vision.c_transforms.CenterCrop(eval_image_size)
+ ds.vision.Decode(),
+ ds.vision.Resize(256),
+ ds.vision.CenterCrop(eval_image_size)
]
- trans_norm = [ds.vision.c_transforms.Normalize(mean=mean, std=std), ds.vision.c_transforms.HWC2CHW()]
+ trans_norm = [ds.vision.Normalize(mean=mean, std=std), ds.vision.HWC2CHW()]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
if device_num == 1:
trans_work_num = 24
else:
@@ -190,21 +190,21 @@ def create_dataset_pynative(dataset_path, do_train, batch_size=32, train_image_s
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(prob=0.5),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(256),
- ds.vision.c_transforms.CenterCrop(eval_image_size),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(256),
+ ds.vision.CenterCrop(eval_image_size),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=4)
# only enable cache for eval
@@ -257,21 +257,21 @@ def create_dataset3(dataset_path, do_train, batch_size=32, train_image_size=224,
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(rank_id / (rank_id + 1)),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(rank_id / (rank_id + 1)),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(256),
- ds.vision.c_transforms.CenterCrop(eval_image_size),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(256),
+ ds.vision.CenterCrop(eval_image_size),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=get_num_parallel_workers(8))
# only enable cache for eval
@@ -326,21 +326,21 @@ def create_dataset4(dataset_path, do_train, batch_size=32, train_image_size=224,
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(prob=0.5),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(292),
- ds.vision.c_transforms.CenterCrop(eval_image_size),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(292),
+ ds.vision.CenterCrop(eval_image_size),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=get_num_parallel_workers(12))
# only enable cache for eval
if do_train:
diff --git a/official/cv/resnet/src/dataset_infer.py b/official/cv/resnet/src/dataset_infer.py
index 5d0a655e8..ce032b1db 100644
--- a/official/cv/resnet/src/dataset_infer.py
+++ b/official/cv/resnet/src/dataset_infer.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -130,21 +130,21 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(prob=0.5),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(256),
- ds.vision.c_transforms.CenterCrop(image_size),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(256),
+ ds.vision.CenterCrop(image_size),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
@@ -202,21 +202,21 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(rank_id / (rank_id + 1)),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(rank_id / (rank_id + 1)),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(256),
- ds.vision.c_transforms.CenterCrop(image_size),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(256),
+ ds.vision.CenterCrop(image_size),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
@@ -271,21 +271,21 @@ def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target=
# define map operations
if do_train:
trans = [
- ds.vision.c_transforms.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ ds.vision.RandomHorizontalFlip(prob=0.5),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
else:
trans = [
- ds.vision.c_transforms.Decode(),
- ds.vision.c_transforms.Resize(292),
- ds.vision.c_transforms.CenterCrop(256),
- ds.vision.c_transforms.Normalize(mean=mean, std=std),
- ds.vision.c_transforms.HWC2CHW()
+ ds.vision.Decode(),
+ ds.vision.Resize(292),
+ ds.vision.CenterCrop(256),
+ ds.vision.Normalize(mean=mean, std=std),
+ ds.vision.HWC2CHW()
]
- type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = ds.transforms.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=12)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=12)
if do_train:
diff --git a/official/cv/resnet_thor/src/dataset.py b/official/cv/resnet_thor/src/dataset.py
index 443817150..ae1dbee0a 100644
--- a/official/cv/resnet_thor/src/dataset.py
+++ b/official/cv/resnet_thor/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ create train or eval dataset.
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
diff --git a/official/cv/resnext/src/dataset.py b/official/cv/resnext/src/dataset.py
index d7f5b4678..a3aba86c3 100644
--- a/official/cv/resnext/src/dataset.py
+++ b/official/cv/resnext/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
from PIL import Image, ImageFile
from mindspore.common import dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as V_C
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as V_C
from src.utils.sampler import DistributedSampler
ImageFile.LOAD_TRUNCATED_IMAGES = True
diff --git a/official/cv/retinanet/src/dataset.py b/official/cv/retinanet/src/dataset.py
index c5105e9c4..312c35d89 100644
--- a/official/cv/retinanet/src/dataset.py
+++ b/official/cv/retinanet/src/dataset.py
@@ -23,7 +23,7 @@ import xml.etree.ElementTree as et
import numpy as np
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from src.model_utils.config import config
from .box_utils import jaccard_numpy, retinanet_bboxes_encode
diff --git a/official/cv/se_resnext50/src/dataset.py b/official/cv/se_resnext50/src/dataset.py
index 4ce7b43c0..9fc23bc1d 100644
--- a/official/cv/se_resnext50/src/dataset.py
+++ b/official/cv/se_resnext50/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
from PIL import Image, ImageFile
from mindspore.common import dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as V_C
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as V_C
from src.utils.sampler import DistributedSampler
ImageFile.LOAD_TRUNCATED_IMAGES = True
diff --git a/official/cv/semantic_human_matting/src/dataset.py b/official/cv/semantic_human_matting/src/dataset.py
index 31e97d1fa..ac7202e9d 100644
--- a/official/cv/semantic_human_matting/src/dataset.py
+++ b/official/cv/semantic_human_matting/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ import cv2
import numpy as np
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
from mindspore import dtype as mstype
diff --git a/official/cv/shufflenetv1/src/dataset.py b/official/cv/shufflenetv1/src/dataset.py
index 48588b5ab..6656d3503 100644
--- a/official/cv/shufflenetv1/src/dataset.py
+++ b/official/cv/shufflenetv1/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,8 +16,8 @@
from src.model_utils.config import config
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, device_num=1, rank=0):
diff --git a/official/cv/shufflenetv2/src/dataset.py b/official/cv/shufflenetv2/src/dataset.py
index 0fbe28daa..96b8835a9 100644
--- a/official/cv/shufflenetv2/src/dataset.py
+++ b/official/cv/shufflenetv2/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import numpy as np
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
class toBGR():
def __call__(self, img):
diff --git a/official/cv/simclr/src/dataset.py b/official/cv/simclr/src/dataset.py
index 1b14f0a5e..ef9133359 100644
--- a/official/cv/simclr/src/dataset.py
+++ b/official/cv/simclr/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,9 +17,8 @@ create train or eval dataset.
"""
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.dataset.vision import Inter
import cv2
import numpy as np
@@ -70,8 +69,8 @@ def create_dataset(args, dataset_mode, repeat_num=1):
color_jitter = C.RandomColorAdjust(0.8 * scale, 0.8 * scale, 0.8 * scale, 0.2 * scale)
trans += [C2.RandomApply([color_jitter], prob=0.8)]
if args.use_color_gray:
- trans += [py_vision.ToPIL(),
- py_vision.RandomGrayscale(prob=0.2),
+ trans += [C.ToPIL(),
+ C.RandomGrayscale(prob=0.2),
np.array] # need to convert PIL image to a NumPy array to pass it to C++ operation
if args.use_blur:
trans += [C2.RandomApply([gaussian_blur], prob=0.8)]
diff --git a/official/cv/simple_pose/src/dataset.py b/official/cv/simple_pose/src/dataset.py
index 35b85ba24..9e8bbc741 100644
--- a/official/cv/simple_pose/src/dataset.py
+++ b/official/cv/simple_pose/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ import numpy as np
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as V_C
+import mindspore.dataset.vision as V_C
from src.utils.transform import fliplr_joints, get_affine_transform, affine_transform
diff --git a/official/cv/sphereface/src/datasets/classification.py b/official/cv/sphereface/src/datasets/classification.py
index c5bc2f984..3d9c04eab 100644
--- a/official/cv/sphereface/src/datasets/classification.py
+++ b/official/cv/sphereface/src/datasets/classification.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -22,8 +22,8 @@ import os
from PIL import Image, ImageFile
from mindspore import dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as vision_C
-import mindspore.dataset.transforms.c_transforms as normal_C
+import mindspore.dataset.vision as vision_C
+import mindspore.dataset.transforms as normal_C
from src.datasets.sampler import DistributedSampler
from src.model_utils.matlab_cp2tform import get_similarity_transform_for_cv2
import cv2
diff --git a/official/cv/squeezenet/src/dataset.py b/official/cv/squeezenet/src/dataset.py
index ac70267a3..407091415 100644
--- a/official/cv/squeezenet/src/dataset.py
+++ b/official/cv/squeezenet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ create train or eval dataset.
import multiprocessing
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
diff --git a/official/cv/ssd/src/dataset.py b/official/cv/ssd/src/dataset.py
index 11a1945e2..a79f605f5 100644
--- a/official/cv/ssd/src/dataset.py
+++ b/official/cv/ssd/src/dataset.py
@@ -398,12 +398,12 @@ def create_ssd_dataset(mindrecord_file, batch_size=32, device_num=1, rank=0,
num_parallel_workers = cores
ds = de.MindDataset(mindrecord_file, columns_list=["img_id", "image", "annotation"], num_shards=device_num,
shard_id=rank, num_parallel_workers=num_parallel_workers, shuffle=is_training)
- decode = de.vision.c_transforms.Decode()
+ decode = de.vision.Decode()
ds = ds.map(operations=decode, input_columns=["image"])
- change_swap_op = de.vision.c_transforms.HWC2CHW()
- normalize_op = de.vision.c_transforms.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
- std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
- color_adjust_op = de.vision.c_transforms.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
+ change_swap_op = de.vision.HWC2CHW()
+ normalize_op = de.vision.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
+ std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
+ color_adjust_op = de.vision.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
compose_map_func = (lambda img_id, image, annotation: preprocess_fn(img_id, image, annotation, is_training))
if is_training:
output_columns = ["image", "box", "label", "num_match"]
diff --git a/official/cv/ssim-ae/src/dataset.py b/official/cv/ssim-ae/src/dataset.py
index 54a415dc7..aa783b2b6 100644
--- a/official/cv/ssim-ae/src/dataset.py
+++ b/official/cv/ssim-ae/src/dataset.py
@@ -20,7 +20,7 @@ import numpy as np
import cv2
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as c_trans
+import mindspore.dataset.vision as c_trans
from model_utils.config import config as cfg
from src.utils import read_img, get_file_list
diff --git a/official/cv/tinydarknet/src/dataset.py b/official/cv/tinydarknet/src/dataset.py
index cadeb0834..48cf89368 100644
--- a/official/cv/tinydarknet/src/dataset.py
+++ b/official/cv/tinydarknet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,8 +20,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from mindspore.communication.management import init, get_rank
from src.model_utils.config import config as imagenet_cfg
diff --git a/official/cv/unet/src/data_loader.py b/official/cv/unet/src/data_loader.py
index 494c40643..16cd33e04 100644
--- a/official/cv/unet/src/data_loader.py
+++ b/official/cv/unet/src/data_loader.py
@@ -1,4 +1,4 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,7 +20,7 @@ import cv2
import numpy as np
from PIL import Image, ImageSequence
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as c_vision
+import mindspore.dataset.vision as c_vision
from mindspore.dataset.vision.utils import Inter
from mindspore.communication.management import get_rank, get_group_size
diff --git a/official/cv/unet3d/src/dataset.py b/official/cv/unet3d/src/dataset.py
index b3b828e3c..d2165808b 100644
--- a/official/cv/unet3d/src/dataset.py
+++ b/official/cv/unet3d/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@ import os
import glob
import numpy as np
import mindspore.dataset as ds
-from mindspore.dataset.transforms.py_transforms import Compose
+from mindspore.dataset.transforms.transforms import Compose
from src.model_utils.config import config
from src.transform import Dataset, ExpandChannel, LoadData, Orientation, ScaleIntensityRange, RandomCropSamples, OneHot
diff --git a/official/cv/vgg16/src/dataset.py b/official/cv/vgg16/src/dataset.py
index 6fb95f9ac..013205eb9 100644
--- a/official/cv/vgg16/src/dataset.py
+++ b/official/cv/vgg16/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
from PIL import Image, ImageFile
from mindspore.common import dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from src.utils.sampler import DistributedSampler
ImageFile.LOAD_TRUNCATED_IMAGES = True
diff --git a/official/cv/vit/src/dataset.py b/official/cv/vit/src/dataset.py
index 305faebc2..cd63da9b1 100644
--- a/official/cv/vit/src/dataset.py
+++ b/official/cv/vit/src/dataset.py
@@ -22,9 +22,8 @@ import numpy as np
import mindspore as ms
import mindspore.dataset.engine as de
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.py_transforms as P
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.dataset.vision.utils import Inter
from .autoaugment import ImageNetPolicy
@@ -107,7 +106,7 @@ def create_dataset(dataset_path,
]
if autoaugment:
trans += [
- P.ToPIL(),
+ C.ToPIL(),
ImageNetPolicy(),
ToNumpy(),
]
diff --git a/official/cv/warpctc/src/dataset.py b/official/cv/warpctc/src/dataset.py
index c58348805..08eb86590 100644
--- a/official/cv/warpctc/src/dataset.py
+++ b/official/cv/warpctc/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,8 +20,8 @@ import numpy as np
from PIL import Image
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as c
-import mindspore.dataset.vision.c_transforms as vc
+import mindspore.dataset.transforms as c
+import mindspore.dataset.vision as vc
from src.model_utils.config import config
diff --git a/official/cv/xception/src/dataset.py b/official/cv/xception/src/dataset.py
index adcc10f87..d5b76f8e1 100644
--- a/official/cv/xception/src/dataset.py
+++ b/official/cv/xception/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@ Data operations, will be used in train.py and eval.py
"""
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, batch_size=16, device_num=1, rank=0):
diff --git a/official/cv/yolov3_darknet53/src/yolo_dataset.py b/official/cv/yolov3_darknet53/src/yolo_dataset.py
index 684b1b6eb..82f06c7f4 100644
--- a/official/cv/yolov3_darknet53/src/yolo_dataset.py
+++ b/official/cv/yolov3_darknet53/src/yolo_dataset.py
@@ -156,7 +156,7 @@ def create_yolo_dataset(image_dir, anno_path, batch_size, device_num, rank,
yolo_dataset = COCOYoloDataset(root=image_dir, ann_file=anno_path, filter_crowd_anno=filter_crowd,
remove_images_without_annotations=remove_empty_anno, is_training=is_training)
- hwc_to_chw = ds.vision.c_transforms.HWC2CHW()
+ hwc_to_chw = ds.vision.HWC2CHW()
config.dataset_size = len(yolo_dataset)
cores = multiprocessing.cpu_count()
@@ -168,12 +168,12 @@ def create_yolo_dataset(image_dir, anno_path, batch_size, device_num, rank,
"gt_box1", "gt_box2", "gt_box3"]
if device_num != 8:
dataset = ds.GeneratorDataset(yolo_dataset, column_names=dataset_column_names, sampler=distributed_sampler)
- dataset = dataset.map(operations=ds.vision.c_transforms.Decode(), input_columns=["image"])
+ dataset = dataset.map(operations=ds.vision.Decode(), input_columns=["image"])
dataset = dataset.batch(batch_size, per_batch_map=multi_scale_trans, input_columns=dataset_column_names,
num_parallel_workers=min(32, num_parallel_workers), drop_remainder=True)
else:
dataset = ds.GeneratorDataset(yolo_dataset, column_names=dataset_column_names, sampler=distributed_sampler)
- dataset = dataset.map(operations=ds.vision.c_transforms.Decode(), input_columns=["image"])
+ dataset = dataset.map(operations=ds.vision.Decode(), input_columns=["image"])
dataset = dataset.batch(batch_size, per_batch_map=multi_scale_trans, input_columns=dataset_column_names,
num_parallel_workers=min(8, num_parallel_workers), drop_remainder=True)
else:
diff --git a/official/cv/yolov3_resnet18/src/dataset.py b/official/cv/yolov3_resnet18/src/dataset.py
index 4a2651957..e5d3f391d 100644
--- a/official/cv/yolov3_resnet18/src/dataset.py
+++ b/official/cv/yolov3_resnet18/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -22,7 +22,7 @@ import numpy as np
from PIL import Image
import mindspore.dataset as de
from mindspore.mindrecord import FileWriter
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from src.config import ConfigYOLOV3ResNet18
iter_cnt = 0
diff --git a/official/cv/yolov4/src/yolo_dataset.py b/official/cv/yolov4/src/yolo_dataset.py
index 1ddca7782..f01e55021 100644
--- a/official/cv/yolov4/src/yolo_dataset.py
+++ b/official/cv/yolov4/src/yolo_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ import numpy as np
from PIL import Image
from pycocotools.coco import COCO
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.vision as CV
from model_utils.config import config
from src.distributed_sampler import DistributedSampler
from src.transforms import reshape_fn, MultiScaleTrans
diff --git a/official/cv/yolov5/src/transforms.py b/official/cv/yolov5/src/transforms.py
index 928dbab2c..ac2352f24 100644
--- a/official/cv/yolov5/src/transforms.py
+++ b/official/cv/yolov5/src/transforms.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,7 +20,7 @@ import copy
import numpy as np
from PIL import Image
import cv2
-import mindspore.dataset as ds
+import mindspore.dataset.vision as vision
def _rand(a=0., b=1.):
@@ -524,7 +524,7 @@ class MultiScaleTrans:
def __call__(self, img, anno, input_size, mosaic_flag):
if mosaic_flag[0] == 0:
- img = ds.vision.py_transforms.Decode()(img)
+ img = vision.Decode(True)(img)
img, anno = preprocess_fn(img, anno, self.config, input_size, self.device_num)
return img, anno, np.array(img.shape[0:2])
diff --git a/official/cv/yolov5/src/yolo_dataset.py b/official/cv/yolov5/src/yolo_dataset.py
index f4c602c51..a0b8dbf11 100644
--- a/official/cv/yolov5/src/yolo_dataset.py
+++ b/official/cv/yolov5/src/yolo_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -240,7 +240,7 @@ def create_yolo_dataset(image_dir, anno_path, batch_size, device_num, rank,
remove_images_without_annotations=remove_empty_anno, is_training=is_training)
distributed_sampler = DistributedSampler(len(yolo_dataset), device_num, rank, shuffle=shuffle)
yolo_dataset.size = len(distributed_sampler)
- hwc_to_chw = ds.vision.c_transforms.HWC2CHW()
+ hwc_to_chw = ds.vision.HWC2CHW()
config.dataset_size = len(yolo_dataset)
cores = multiprocessing.cpu_count()
@@ -267,7 +267,7 @@ def create_yolo_dataset(image_dir, anno_path, batch_size, device_num, rank,
num_parallel_workers=min(4, num_parallel_workers), python_multiprocessing=False)
mean = [m * 255 for m in [0.485, 0.456, 0.406]]
std = [s * 255 for s in [0.229, 0.224, 0.225]]
- dataset = dataset.map([ds.vision.c_transforms.Normalize(mean, std), hwc_to_chw],
+ dataset = dataset.map([ds.vision.Normalize(mean, std), hwc_to_chw],
num_parallel_workers=min(4, num_parallel_workers))
def concatenate(images):
diff --git a/official/nlp/bert/src/dataset.py b/official/nlp/bert/src/dataset.py
index 2864d3e8c..ece989012 100644
--- a/official/nlp/bert/src/dataset.py
+++ b/official/nlp/bert/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,7 +20,7 @@ import math
import numpy as np
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
from mindspore import log as logger
diff --git a/official/nlp/bert/src/finetune_data_preprocess.py b/official/nlp/bert/src/finetune_data_preprocess.py
index 44cd375f7..3f9f682b5 100644
--- a/official/nlp/bert/src/finetune_data_preprocess.py
+++ b/official/nlp/bert/src/finetune_data_preprocess.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -25,7 +25,7 @@ from lxml import etree
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.text as text
-import mindspore.dataset.transforms.c_transforms as ops
+import mindspore.dataset.transforms as ops
from utils import convert_labels_to_index
diff --git a/official/nlp/bert_thor/pretrain_eval.py b/official/nlp/bert_thor/pretrain_eval.py
index 73c236905..a4f824d73 100644
--- a/official/nlp/bert_thor/pretrain_eval.py
+++ b/official/nlp/bert_thor/pretrain_eval.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,7 +24,7 @@ from src.evaluation_config import cfg, bert_net_cfg
import mindspore.common.dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
import mindspore.nn as nn
from mindspore import context
from mindspore.common.parameter import Parameter
diff --git a/official/nlp/bert_thor/src/dataset.py b/official/nlp/bert_thor/src/dataset.py
index 8e6dccec0..620b66807 100644
--- a/official/nlp/bert_thor/src/dataset.py
+++ b/official/nlp/bert_thor/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ Data operations, will be used in run_pretrain.py
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
from mindspore import log as logger
from .config import cfg
diff --git a/official/nlp/cpm/train.py b/official/nlp/cpm/train.py
index 8ce5edc27..ca9b4d249 100644
--- a/official/nlp/cpm/train.py
+++ b/official/nlp/cpm/train.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -29,7 +29,7 @@ from mindspore.train.callback import TimeMonitor, ModelCheckpoint, CheckpointCon
from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import mindspore.common.dtype as mstype
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
from mindspore.parallel import set_algo_parameters
from src.cpm_train import CPMWithLoss, CPMTrainOneStepWithLossScaleCell, VirtualDatasetOneInputCell, \
diff --git a/official/nlp/dgu/src/utils.py b/official/nlp/dgu/src/utils.py
index 474bd2b7e..0d8174f4e 100644
--- a/official/nlp/dgu/src/utils.py
+++ b/official/nlp/dgu/src/utils.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -23,7 +23,7 @@ import os
import numpy as np
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
import mindspore.nn as nn
import mindspore.ops as P
diff --git a/official/nlp/duconv/src/dataset.py b/official/nlp/duconv/src/dataset.py
index be6752663..4d8e69c99 100644
--- a/official/nlp/duconv/src/dataset.py
+++ b/official/nlp/duconv/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ Data loader
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
from mindspore import log as logger
def create_dataset(batch_size, device_num=1, rank=0, num_workers=8, do_shuffle=True,
diff --git a/official/nlp/emotect/src/dataset.py b/official/nlp/emotect/src/dataset.py
index 7adeac9f3..e8be1e68f 100644
--- a/official/nlp/emotect/src/dataset.py
+++ b/official/nlp/emotect/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@ Data operations, will be used in run_pretrain.py
"""
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
def create_classification_dataset(batch_size=1,
repeat_count=1,
diff --git a/official/nlp/ernie/src/dataset.py b/official/nlp/ernie/src/dataset.py
index 15b6c61dc..d672304a3 100644
--- a/official/nlp/ernie/src/dataset.py
+++ b/official/nlp/ernie/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ Data operations, will be used in run_pretrain.py
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
from mindspore import log as logger
from .config import cfg
diff --git a/official/nlp/fasttext/eval.py b/official/nlp/fasttext/eval.py
index d4ca9d23a..92f28e027 100644
--- a/official/nlp/fasttext/eval.py
+++ b/official/nlp/fasttext/eval.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,7 +24,7 @@ from mindspore.common.tensor import Tensor
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as deC
+import mindspore.dataset.transforms as deC
from mindspore import context
from src.fasttext_model import FastText
diff --git a/official/nlp/gnmt_v2/src/dataset/load_dataset.py b/official/nlp/gnmt_v2/src/dataset/load_dataset.py
index 8af9fe84c..79a690daf 100644
--- a/official/nlp/gnmt_v2/src/dataset/load_dataset.py
+++ b/official/nlp/gnmt_v2/src/dataset/load_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -15,7 +15,7 @@
"""Dataset loader to feed into model."""
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as deC
+import mindspore.dataset.transforms as deC
def _load_dataset(input_files, batch_size, sink_mode=False,
diff --git a/official/nlp/gpt/src/dataset.py b/official/nlp/gpt/src/dataset.py
index a2b3ec638..29c9fbbad 100644
--- a/official/nlp/gpt/src/dataset.py
+++ b/official/nlp/gpt/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@ Create dataset for training and evaluating
import os
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
import mindspore.common.dtype as mstype
diff --git a/official/nlp/gru/src/dataset.py b/official/nlp/gru/src/dataset.py
index d63d5c2cc..6de44d1c9 100644
--- a/official/nlp/gru/src/dataset.py
+++ b/official/nlp/gru/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@
import numpy as np
import mindspore.common.dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as deC
+import mindspore.dataset.transforms as deC
from model_utils.config import config
de.config.set_seed(1)
diff --git a/official/nlp/mass/src/dataset/load_dataset.py b/official/nlp/mass/src/dataset/load_dataset.py
index 879ccf41c..377b6123b 100644
--- a/official/nlp/mass/src/dataset/load_dataset.py
+++ b/official/nlp/mass/src/dataset/load_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -15,7 +15,7 @@
"""Dataset loader to feed into model."""
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as deC
+import mindspore.dataset.transforms as deC
def _load_dataset(input_files, batch_size, epoch_count=1,
diff --git a/official/nlp/pangu_alpha/src/dataset.py b/official/nlp/pangu_alpha/src/dataset.py
index b18fd397f..8e803d82d 100644
--- a/official/nlp/pangu_alpha/src/dataset.py
+++ b/official/nlp/pangu_alpha/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@ Create dataset for training and evaluating
import os
import numpy as np
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
import mindspore.common.dtype as mstype
from mindspore import context
diff --git a/official/nlp/prophetnet/src/dataset/load_dataset.py b/official/nlp/prophetnet/src/dataset/load_dataset.py
index e585f50b9..84aaf94a7 100644
--- a/official/nlp/prophetnet/src/dataset/load_dataset.py
+++ b/official/nlp/prophetnet/src/dataset/load_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -15,7 +15,7 @@
"""Dataset loader to feed into model."""
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as deC
+import mindspore.dataset.transforms as deC
def _load_dataset(input_files, batch_size, epoch_count=1,
diff --git a/official/nlp/tinybert/src/dataset.py b/official/nlp/tinybert/src/dataset.py
index 2b023f699..62a0523c4 100644
--- a/official/nlp/tinybert/src/dataset.py
+++ b/official/nlp/tinybert/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@ import os
from enum import Enum
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
class DataType(Enum):
diff --git a/official/nlp/transformer/eval.py b/official/nlp/transformer/eval.py
index 07a30c5d6..e3e6f367f 100644
--- a/official/nlp/transformer/eval.py
+++ b/official/nlp/transformer/eval.py
@@ -23,7 +23,7 @@ from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
from mindspore.train.model import Model
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as deC
+import mindspore.dataset.transforms as deC
from src.transformer_model import TransformerModel
from src.model_utils.config import config
diff --git a/official/nlp/transformer/src/dataset.py b/official/nlp/transformer/src/dataset.py
index 551639072..4728db94e 100644
--- a/official/nlp/transformer/src/dataset.py
+++ b/official/nlp/transformer/src/dataset.py
@@ -33,7 +33,7 @@ def create_transformer_dataset(rank_size=1, rank_id=0, do_shuffle="true", datase
"target_sos_ids", "target_sos_mask",
"target_eos_ids", "target_eos_mask"],
shuffle=(do_shuffle == "true"), num_shards=rank_size, shard_id=rank_id)
- type_cast_op = de.transforms.c_transforms.TypeCast(ms.int32)
+ type_cast_op = de.transforms.transforms.TypeCast(ms.int32)
ds = ds.map(operations=type_cast_op, input_columns="source_eos_ids")
ds = ds.map(operations=type_cast_op, input_columns="source_eos_mask")
ds = ds.map(operations=type_cast_op, input_columns="target_sos_ids")
diff --git a/research/audio/ctcmodel/src/dataset.py b/research/audio/ctcmodel/src/dataset.py
index e0cc55926..117a3cc9d 100644
--- a/research/audio/ctcmodel/src/dataset.py
+++ b/research/audio/ctcmodel/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,7 +16,7 @@
"""Dataset preprocessing."""
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
import mindspore.common.dtype as mstype
diff --git a/research/audio/speech_transformer/src/dataset.py b/research/audio/speech_transformer/src/dataset.py
index 1ae5a5ee6..5354e39f6 100644
--- a/research/audio/speech_transformer/src/dataset.py
+++ b/research/audio/speech_transformer/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ from pathlib import Path
import mindspore.common.dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as deC
+import mindspore.dataset.transforms as deC
import numpy as np
from .model_utils.config import config
diff --git a/research/cv/3dcnn/src/dataset.py b/research/cv/3dcnn/src/dataset.py
index 0610c9b06..1879601b0 100644
--- a/research/cv/3dcnn/src/dataset.py
+++ b/research/cv/3dcnn/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ from nibabel import load as load_nii
import mindspore.dataset as ds
import mindspore.common.dtype as mstype
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.transforms as C2
def norm(image):
diff --git a/research/cv/APDrawingGAN/src/data/aligned_dataset.py b/research/cv/APDrawingGAN/src/data/aligned_dataset.py
index 894c6c9b7..5e912e722 100644
--- a/research/cv/APDrawingGAN/src/data/aligned_dataset.py
+++ b/research/cv/APDrawingGAN/src/data/aligned_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ import os.path
import random
import csv
import cv2
-import mindspore.dataset.vision.py_transforms as P_VISION
+import mindspore.dataset.vision as vision
import mindspore.ops as ops
from mindspore import Tensor
from mindspore import dtype as mstype
@@ -139,8 +139,8 @@ def init_AB(opt, AB_path):
(opt.loadSize, opt.loadSize), Image.BICUBIC)
B = AB.crop((w2, 0, w, h)).resize(
(opt.loadSize, opt.loadSize), Image.BICUBIC)
- A = P_VISION.ToTensor()(A)
- B = P_VISION.ToTensor()(B)
+ A = vision.ToTensor()(A)
+ B = vision.ToTensor()(B)
w_offset = random.randint(
0, max(0, opt.loadSize - opt.fineSize - 1))
h_offset = random.randint(
@@ -151,8 +151,8 @@ def init_AB(opt, AB_path):
B = B[:, h_offset:h_offset + opt.fineSize,
w_offset:w_offset + opt.fineSize]
- A = P_VISION.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(A)
- B = P_VISION.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(B)
+ A = vision.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), is_hwc=False)(A)
+ B = vision.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), is_hwc=False)(B)
return A, B
def regions_process(opt, regions, feats, item, A, B, input_nc, output_nc):
@@ -270,7 +270,7 @@ class AlignedDataset(BaseDataset):
bgdir = self.opt.bg_dir
bgpath = os.path.join(bgdir, basen[:-4] + '.png')
im_bg = Image.open(bgpath)
- mask2 = P_VISION.ToTensor()(im_bg) # mask out background
+ mask2 = vision.ToTensor()(im_bg) # mask out background
if flipped:
mask2 = np.take(mask2, idx, axis=2)
diff --git a/research/cv/APDrawingGAN/src/data/base_dataset.py b/research/cv/APDrawingGAN/src/data/base_dataset.py
index cd0c4d8f4..f782c1cd2 100644
--- a/research/cv/APDrawingGAN/src/data/base_dataset.py
+++ b/research/cv/APDrawingGAN/src/data/base_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -15,8 +15,8 @@
"""base dataset"""
from PIL import Image
-import mindspore.dataset.vision.py_transforms as py_trans
-from mindspore.dataset.transforms.py_transforms import Compose
+import mindspore.dataset.vision as vision
+from mindspore.dataset.transforms.transforms import Compose
from mindspore.dataset.vision import Inter
@@ -45,17 +45,17 @@ def get_transform(opt):
transform_list = []
if opt.resize_or_crop == 'resize_and_crop':
osize = [opt.loadSize, opt.fineSize]
- transform_list.append(py_trans.Resize(osize, Inter.BICUBIC)) # PIL
- transform_list.append(py_trans.RandomCrop(opt.fineSize)) # PIL
+ transform_list.append(vision.Resize(osize, Inter.BICUBIC)) # PIL
+ transform_list.append(vision.RandomCrop(opt.fineSize)) # PIL
elif opt.resize_or_crop == 'crop':
- transform_list.append(py_trans.RandomCrop(opt.fineSize))
+ transform_list.append(vision.RandomCrop(opt.fineSize))
elif opt.resize_or_crop == 'scale_width':
transform_list.append(
lambda img: __scale_width(img, opt.fineSize))
elif opt.resize_or_crop == 'scale_width_and_crop':
transform_list.append(
lambda img: __scale_width(img, opt.loadSize))
- transform_list.append(py_trans.RandomCrop(opt.fineSize))
+ transform_list.append(vision.RandomCrop(opt.fineSize))
elif opt.resize_or_crop == 'none':
transform_list.append(
lambda img: __adjust(img))
@@ -63,11 +63,11 @@ def get_transform(opt):
raise ValueError('--resize_or_crop %s is not a valid option.' % opt.resize_or_crop)
if opt.isTrain and not opt.no_flip:
- transform_list.append(py_trans.RandomHorizontalFlip())
+ transform_list.append(vision.RandomHorizontalFlip())
- transform_list += [py_trans.ToTensor(),
- py_trans.Normalize((0.5, 0.5, 0.5),
- (0.5, 0.5, 0.5))]
+ transform_list += [vision.ToTensor(),
+ vision.Normalize((0.5, 0.5, 0.5),
+ (0.5, 0.5, 0.5), is_hwc=False)]
return Compose(transform_list)
# just modify the width and height to be multiple of 4
diff --git a/research/cv/AVA_cifar/src/datasets.py b/research/cv/AVA_cifar/src/datasets.py
index 6761f9090..df2d7a6d3 100644
--- a/research/cv/AVA_cifar/src/datasets.py
+++ b/research/cv/AVA_cifar/src/datasets.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,9 +16,9 @@
import numpy as np
import mindspore.dataset as ds
-import mindspore.dataset.vision.py_transforms as transforms
-import mindspore.dataset.transforms.c_transforms as C
-from mindspore.dataset.transforms.py_transforms import Compose
+import mindspore.dataset.transforms as data_trans
+import mindspore.dataset.vision as vision
+from mindspore.dataset.transforms.transforms import Compose
from mindspore.common import dtype as mstype
from src.RandAugment import RandAugment
from src.autoaugment import CIFAR10Policy
@@ -32,39 +32,39 @@ class CIFAR10Dataset():
if not training:
trsfm = Compose([
- transforms.ToTensor(),
- transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+ vision.ToTensor(),
+ vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010), is_hwc=False),
])
else:
if not use_third_trsfm:
trsfm = Compose([
- transforms.ToPIL(),
- transforms.RandomResizedCrop(size=32, scale=(0.2, 1.)),
- transforms.RandomColorAdjust(0.4, 0.4, 0.4, 0.4),
- transforms.RandomGrayscale(prob=0.2),
- transforms.RandomHorizontalFlip(),
- transforms.ToTensor(),
- transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+ vision.ToPIL(),
+ vision.RandomResizedCrop(size=32, scale=(0.2, 1.)),
+ vision.RandomColorAdjust(0.4, 0.4, 0.4, 0.4),
+ vision.RandomGrayscale(prob=0.2),
+ vision.RandomHorizontalFlip(),
+ vision.ToTensor(),
+ vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010), is_hwc=False),
])
else:
if use_auto_augment:
trsfm = Compose([
- transforms.ToPIL(),
- transforms.RandomResizedCrop(size=32, scale=(0.2, 1.)),
- transforms.RandomHorizontalFlip(),
+ vision.ToPIL(),
+ vision.RandomResizedCrop(size=32, scale=(0.2, 1.)),
+ vision.RandomHorizontalFlip(),
CIFAR10Policy(),
- transforms.ToTensor(),
- transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+ vision.ToTensor(),
+ vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010), is_hwc=False),
])
else:
rand_augment = RandAugment(n=2, m=10)
trsfm = Compose([
- transforms.ToPIL(),
- transforms.RandomResizedCrop(size=32, scale=(0.2, 1.)),
- transforms.RandomHorizontalFlip(),
+ vision.ToPIL(),
+ vision.RandomResizedCrop(size=32, scale=(0.2, 1.)),
+ vision.RandomHorizontalFlip(),
rand_augment,
- transforms.ToTensor(),
- transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+ vision.ToTensor(),
+ vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010), is_hwc=False),
])
self.trsfm = trsfm
@@ -83,7 +83,7 @@ class CIFAR10Dataset():
num_shards=self.device_num, shard_id=self.device_id)
ds_ = ds_.map(input_columns=["image"], operations=self.trsfm)
- typecast_op = C.TypeCast(mstype.int32)
+ typecast_op = data_trans.TypeCast(mstype.int32)
ds_ = ds_.map(input_columns=["label"], operations=typecast_op)
return ds_
diff --git a/research/cv/AVA_hpa/src/datasets.py b/research/cv/AVA_hpa/src/datasets.py
index 7225593e7..9394785d1 100644
--- a/research/cv/AVA_hpa/src/datasets.py
+++ b/research/cv/AVA_hpa/src/datasets.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,9 +19,9 @@ from collections import Counter
from PIL import Image
import numpy as np
import pandas as pd
-import mindspore.dataset.vision.py_transforms as transforms
+import mindspore.dataset.vision as vision
from mindspore.dataset import GeneratorDataset
-from mindspore.dataset.transforms.py_transforms import Compose
+from mindspore.dataset.transforms.transforms import Compose
from src.RandAugment import RandAugment
# split train val test = 4:1:5
@@ -40,35 +40,35 @@ class TransformOnImg:
self.mode = mode
rand_augment = RandAugment(n=2, m=10)
self.trsfm_basic = Compose([
- transforms.ToPIL(),
- transforms.Resize(256),
- transforms.RandomResizedCrop(size=224, scale=(0.2, 1.)),
- transforms.RandomColorAdjust(0.4, 0.4, 0.4, 0),
- transforms.RandomHorizontalFlip(),
- transforms.ToTensor(),
- transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+ vision.ToPIL(),
+ vision.Resize(256),
+ vision.RandomResizedCrop(size=224, scale=(0.2, 1.)),
+ vision.RandomColorAdjust(0.4, 0.4, 0.4, 0),
+ vision.RandomHorizontalFlip(),
+ vision.ToTensor(),
+ vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010), is_hwc=False),
])
self.trsfm_aux = Compose([
- transforms.ToPIL(),
- transforms.Resize(256),
- transforms.RandomResizedCrop(size=224, scale=(0.2, 1.)),
- transforms.RandomHorizontalFlip(),
+ vision.ToPIL(),
+ vision.Resize(256),
+ vision.RandomResizedCrop(size=224, scale=(0.2, 1.)),
+ vision.RandomHorizontalFlip(),
rand_augment,
- transforms.ToTensor(),
- transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+ vision.ToTensor(),
+ vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010), is_hwc=False),
])
self.trsfm_train = Compose([
- transforms.ToPIL(),
- transforms.Resize(256),
- transforms.RandomResizedCrop(size=224, scale=(0.2, 1.)),
- transforms.ToTensor(),
- transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+ vision.ToPIL(),
+ vision.Resize(256),
+ vision.RandomResizedCrop(size=224, scale=(0.2, 1.)),
+ vision.ToTensor(),
+ vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010), is_hwc=False),
])
self.trsfm = Compose([
- transforms.ToPIL(),
- transforms.Resize(224),
- transforms.ToTensor(),
- transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+ vision.ToPIL(),
+ vision.Resize(224),
+ vision.ToTensor(),
+ vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010), is_hwc=False),
])
def __call__(self, img, use_aux=False):
diff --git a/research/cv/AlignedReID++/src/dataset_loader.py b/research/cv/AlignedReID++/src/dataset_loader.py
index a3dba42f7..34ac5bbc3 100644
--- a/research/cv/AlignedReID++/src/dataset_loader.py
+++ b/research/cv/AlignedReID++/src/dataset_loader.py
@@ -1,5 +1,5 @@
"""get the dataset"""
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,8 +21,8 @@ import os.path as osp
from PIL import Image
from mindspore.dataset import GeneratorDataset
-from mindspore.dataset.transforms.py_transforms import Compose
-import mindspore.dataset.vision.py_transforms as P1
+from mindspore.dataset.transforms.transforms import Compose
+import mindspore.dataset.vision as vision
from .import data_manager
from .import samplers
@@ -159,9 +159,9 @@ def create_train_dataset(real_path, args, rank_id, rank_size):
transform_train = [
decode,
Random2DTranslation(args.height, args.width),
- P1.RandomHorizontalFlip(0.5),
- P1.ToTensor(),
- P1.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ vision.RandomHorizontalFlip(0.5),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False),
RandomErasing()
]
transform_train = Compose(transform_train)
@@ -186,9 +186,9 @@ def create_test_dataset(real_path, args):
transform_test = [
decode,
- P1.Resize((args.height, args.width)),
- P1.ToTensor(),
- P1.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+ vision.Resize((args.height, args.width)),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False)
]
transform_test = Compose(transform_test)
@@ -212,4 +212,4 @@ def create_test_dataset(real_path, args):
galleryloader = galleryloader.batch(batch_size=32, drop_remainder=True)
return queryloader, galleryloader, dataset.num_train_pids
-
\ No newline at end of file
+
diff --git a/research/cv/AlignedReID/src/dataset.py b/research/cv/AlignedReID/src/dataset.py
index 18afc481d..9014b2467 100644
--- a/research/cv/AlignedReID/src/dataset.py
+++ b/research/cv/AlignedReID/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@ import pickle
from collections import defaultdict
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
import numpy as np
from PIL import Image
diff --git a/research/cv/AlphaPose/infer/sdk/postprocess/src/dataset.py b/research/cv/AlphaPose/infer/sdk/postprocess/src/dataset.py
index 7265dfeba..baa3310d4 100644
--- a/research/cv/AlphaPose/infer/sdk/postprocess/src/dataset.py
+++ b/research/cv/AlphaPose/infer/sdk/postprocess/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -26,7 +26,7 @@ import numpy as np
import cv2
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from src.utils.transforms import fliplr_joints, get_affine_transform, affine_transform
from src.config import config
diff --git a/research/cv/AlphaPose/src/dataset.py b/research/cv/AlphaPose/src/dataset.py
index 7265dfeba..baa3310d4 100644
--- a/research/cv/AlphaPose/src/dataset.py
+++ b/research/cv/AlphaPose/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -26,7 +26,7 @@ import numpy as np
import cv2
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from src.utils.transforms import fliplr_joints, get_affine_transform, affine_transform
from src.config import config
diff --git a/research/cv/AttGAN/src/data.py b/research/cv/AttGAN/src/data.py
index 72cb898c2..75f6a6881 100644
--- a/research/cv/AttGAN/src/data.py
+++ b/research/cv/AttGAN/src/data.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import numpy as np
from PIL import Image
import mindspore.dataset as de
-import mindspore.dataset.vision.py_transforms as py_vision
-from mindspore.dataset.transforms import py_transforms
+import mindspore.dataset.vision as vision
+from mindspore.dataset.transforms.transforms import Compose
from src.utils import DistributedSampler
@@ -40,11 +40,11 @@ class Custom:
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
- transform = [py_vision.ToPIL()]
- transform.append(py_vision.Resize([128, 128]))
- transform.append(py_vision.ToTensor())
- transform.append(py_vision.Normalize(mean=mean, std=std))
- transform = py_transforms.Compose(transform)
+ transform = [vision.ToPIL()]
+ transform.append(vision.Resize([128, 128]))
+ transform.append(vision.ToTensor())
+ transform.append(vision.Normalize(mean=mean, std=std), is_hwc=False)
+ transform = Compose(transform)
self.transform = transform
self.images = np.array([images]) if images.size == 1 else images[0:]
self.labels = np.array([labels]) if images.size == 1 else labels[0:]
@@ -108,12 +108,12 @@ def get_loader(data_root, attr_path, selected_attrs, crop_size=170, image_size=1
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
- transform = [py_vision.ToPIL()]
- transform.append(py_vision.CenterCrop((crop_size, crop_size)))
- transform.append(py_vision.Resize([image_size, image_size]))
- transform.append(py_vision.ToTensor())
- transform.append(py_vision.Normalize(mean=mean, std=std))
- transform = py_transforms.Compose(transform)
+ transform = [vision.ToPIL()]
+ transform.append(vision.CenterCrop((crop_size, crop_size)))
+ transform.append(vision.Resize([image_size, image_size]))
+ transform.append(vision.ToTensor())
+ transform.append(vision.Normalize(mean=mean, std=std), is_hwc=False)
+ transform = Compose(transform)
dataset = CelebA(data_root, attr_path, image_size, mode, selected_attrs, transform, split_point=split_point)
diff --git a/research/cv/AttentionCluster/make_dataset.py b/research/cv/AttentionCluster/make_dataset.py
index af3b92140..d8e864ca7 100644
--- a/research/cv/AttentionCluster/make_dataset.py
+++ b/research/cv/AttentionCluster/make_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@ import os
import numpy as np
import mindspore
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as c_trans
+import mindspore.dataset.vision as c_trans
import mindspore.nn as nn
import mindspore.context as context
import mindspore.common as common
diff --git a/research/cv/AutoSlim/src/dataset.py b/research/cv/AutoSlim/src/dataset.py
index 82d921f7b..6f8d7e3d8 100644
--- a/research/cv/AutoSlim/src/dataset.py
+++ b/research/cv/AutoSlim/src/dataset.py
@@ -18,8 +18,7 @@ Produce the dataset
import os
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as c_vision
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.vision as vision
def data_transforms(args):
"""get transform of dataset"""
@@ -40,18 +39,18 @@ def data_transforms(args):
std = [0.229, 0.224, 0.225]
crop_scale = 0.25
jitter_param = 0.4
- train_transforms = [c_vision.RandomCropDecodeResize(224, scale=(crop_scale, 1.0)),
- c_vision.RandomColorAdjust(brightness=jitter_param,
- contrast=jitter_param,
- saturation=jitter_param),
- c_vision.RandomHorizontalFlip(),
- c_vision.HWC2CHW(),
+ train_transforms = [vision.RandomCropDecodeResize(224, scale=(crop_scale, 1.0)),
+ vision.RandomColorAdjust(brightness=jitter_param,
+ contrast=jitter_param,
+ saturation=jitter_param),
+ vision.RandomHorizontalFlip(),
+ vision.HWC2CHW(),
]
- val_transforms = [py_vision.Decode(),
- py_vision.Resize(256),
- py_vision.CenterCrop(224),
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std)
+ val_transforms = [vision.Decode(True),
+ vision.Resize(256),
+ vision.CenterCrop(224),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False)
]
return train_transforms, val_transforms
diff --git a/research/cv/CBAM/src/data.py b/research/cv/CBAM/src/data.py
index 960b51725..7a425b1b3 100644
--- a/research/cv/CBAM/src/data.py
+++ b/research/cv/CBAM/src/data.py
@@ -22,7 +22,7 @@ import numpy as np
from mindspore.communication.management import get_rank, get_group_size
import mindspore.dataset as de
import mindspore.common.dtype as mstype
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
def _get_rank_info(run_distribute):
diff --git a/research/cv/CGAN/src/dataset.py b/research/cv/CGAN/src/dataset.py
index 00e805587..e2b687cb3 100644
--- a/research/cv/CGAN/src/dataset.py
+++ b/research/cv/CGAN/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@ import os
import numpy as np
import mindspore.dataset as ds
from mindspore.common import dtype as mstype
-import mindspore.dataset.transforms.c_transforms as CT
+import mindspore.dataset.transforms as CT
from mindspore.communication.management import get_rank, get_group_size
diff --git a/research/cv/CMT/src/dataset.py b/research/cv/CMT/src/dataset.py
index a27c6ecb2..3bc5b667c 100644
--- a/research/cv/CMT/src/dataset.py
+++ b/research/cv/CMT/src/dataset.py
@@ -16,11 +16,10 @@
import os
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.py_transforms as pytrans
+import mindspore.dataset.transforms as C2
-from mindspore.dataset.transforms.py_transforms import Compose
-import mindspore.dataset.vision.c_transforms as C
+from mindspore.dataset.transforms.transforms import Compose
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, repeat_num=1, infer_910=True, device_id=0, batch_size=128):
@@ -59,13 +58,13 @@ def create_dataset(dataset_path, do_train, repeat_num=1, infer_910=True, device_
]
else:
trans = [
- pytrans.Decode(),
- pytrans.Resize(235),
- pytrans.CenterCrop(224)
+ C.Decode(True),
+ C.Resize(235),
+ C.CenterCrop(224)
]
trans += [
- pytrans.ToTensor(),
- pytrans.Normalize(mean=mean, std=std),
+ C.ToTensor(),
+ C.Normalize(mean=mean, std=std, is_hwc=False),
]
trans = Compose(trans)
diff --git a/research/cv/CascadeRCNN/src/dataset.py b/research/cv/CascadeRCNN/src/dataset.py
index 657c9619d..8fd59ebd1 100644
--- a/research/cv/CascadeRCNN/src/dataset.py
+++ b/research/cv/CascadeRCNN/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -23,7 +23,7 @@ from numpy import random
import cv2
import mmcv
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from src.config import config
diff --git a/research/cv/CycleGAN/src/dataset/cyclegan_dataset.py b/research/cv/CycleGAN/src/dataset/cyclegan_dataset.py
index ec2a889ba..2371995e0 100644
--- a/research/cv/CycleGAN/src/dataset/cyclegan_dataset.py
+++ b/research/cv/CycleGAN/src/dataset/cyclegan_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ import multiprocessing
import numpy as np
from PIL import Image
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from .distributed_sampler import DistributedSampler
IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.tif', '.tiff']
diff --git a/research/cv/DBPN/src/dataset/dataset.py b/research/cv/DBPN/src/dataset/dataset.py
index a9c57d850..a7f426930 100644
--- a/research/cv/DBPN/src/dataset/dataset.py
+++ b/research/cv/DBPN/src/dataset/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@ import os
import random
import numpy as np
from PIL import Image, ImageOps
-import mindspore.dataset.vision.py_transforms as P
+import mindspore.dataset.vision as V
from mindspore import dataset as de, context
from mindspore.context import ParallelMode
from mindspore.communication import get_rank, get_group_size
@@ -173,10 +173,10 @@ def create_train_dataset(dataset, args):
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
trans = [
- P.ToTensor(),
+ V.ToTensor(),
]
if args.isgan:
- trans.append(P.Normalize(mean=mean, std=std))
+ trans.append(V.Normalize(mean=mean, std=std, is_hwc=False))
train_ds = train_ds.map(operations=trans, input_columns=['target_image'])
train_ds = train_ds.map(operations=trans, input_columns=['input_image'])
train_ds = train_ds.map(operations=trans, input_columns=['bicubic_image'])
@@ -215,9 +215,9 @@ def create_val_dataset(dataset, args):
if not args.vgg:
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
- trans = [P.ToTensor()]
+ trans = [V.ToTensor()]
if args.isgan:
- trans.append(P.Normalize(mean=mean, std=std))
+ trans.append(V.Normalize(mean=mean, std=std, is_hwc=False))
val_ds = val_ds.map(operations=trans, input_columns=["target_image"])
val_ds = val_ds.map(operations=trans, input_columns=["input_image"])
val_ds = val_ds.map(operations=trans, input_columns=["bicubic_image"])
diff --git a/research/cv/DDAG/eval.py b/research/cv/DDAG/eval.py
index cc907a049..9a37a8e7c 100644
--- a/research/cv/DDAG/eval.py
+++ b/research/cv/DDAG/eval.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,12 +21,12 @@ import argparse
import psutil
import numpy as np
import mindspore.dataset as ds
-import mindspore.dataset.vision.py_transforms as py_trans
+import mindspore.dataset.vision as vision
from mindspore import context, load_checkpoint, load_param_into_net, DatasetHelper
from mindspore.context import ParallelMode
from mindspore.communication.management import init, get_group_size
-from mindspore.dataset.transforms.py_transforms import Compose
+from mindspore.dataset.transforms.transforms import Compose
from src.dataset import SYSUDatasetGenerator, RegDBDatasetGenerator, TestData
from src.dataset import process_gallery_sysu, process_query_sysu, process_test_regdb
from src.evalfunc import test
@@ -246,9 +246,9 @@ if __name__ == "__main__":
transform_test = Compose(
[
decode,
- py_trans.Resize((args.img_h, args.img_w)),
- py_trans.ToTensor(),
- py_trans.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+ vision.Resize((args.img_h, args.img_w)),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False)
]
)
diff --git a/research/cv/DDAG/train.py b/research/cv/DDAG/train.py
index 7c35b7357..ef393d619 100644
--- a/research/cv/DDAG/train.py
+++ b/research/cv/DDAG/train.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,12 +24,12 @@ import numpy as np
import mindspore as ms
import mindspore.nn as nn
import mindspore.dataset as ds
-import mindspore.dataset.vision.py_transforms as py_trans
+import mindspore.dataset.vision as vision
from mindspore import context, load_checkpoint, load_param_into_net, save_checkpoint, DatasetHelper, Tensor
from mindspore.context import ParallelMode
from mindspore.communication import init, get_group_size, get_rank
-from mindspore.dataset.transforms.py_transforms import Compose
+from mindspore.dataset.transforms.transforms import Compose
from mindspore.nn import SGD, Adam
@@ -375,35 +375,35 @@ if __name__ == "__main__":
transform_train_rgb = Compose(
[
decode,
- py_trans.Pad(10),
- py_trans.RandomCrop((args.img_h, args.img_w)),
- py_trans.RandomGrayscale(prob=0.5),
- py_trans.RandomHorizontalFlip(),
- py_trans.ToTensor(),
- py_trans.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
- py_trans.RandomErasing(prob=0.5)
+ vision.Pad(10),
+ vision.RandomCrop((args.img_h, args.img_w)),
+ vision.RandomGrayscale(prob=0.5),
+ vision.RandomHorizontalFlip(),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False),
+ vision.RandomErasing(prob=0.5)
]
)
transform_train_ir = Compose(
[
decode,
- py_trans.Pad(10),
- py_trans.RandomCrop((args.img_h, args.img_w)),
- py_trans.RandomGrayscale(prob=0.5),
- py_trans.RandomHorizontalFlip(),
- py_trans.ToTensor(),
- py_trans.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
- py_trans.RandomErasing(prob=0.5)
+ vision.Pad(10),
+ vision.RandomCrop((args.img_h, args.img_w)),
+ vision.RandomGrayscale(prob=0.5),
+ vision.RandomHorizontalFlip(),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False),
+ vision.RandomErasing(prob=0.5)
]
)
transform_test = Compose(
[
decode,
- py_trans.Resize((args.img_h, args.img_w)),
- py_trans.ToTensor(),
- py_trans.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+ vision.Resize((args.img_h, args.img_w)),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False)
]
)
diff --git a/research/cv/DDRNet/src/data/imagenet.py b/research/cv/DDRNet/src/data/imagenet.py
index b4d01c858..3f176dc05 100644
--- a/research/cv/DDRNet/src/data/imagenet.py
+++ b/research/cv/DDRNet/src/data/imagenet.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,9 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from src.data.augment.auto_augment import rand_augment_transform
from src.data.augment.mixup import Mixup
@@ -90,29 +89,29 @@ def create_dataset_imagenet(dataset_dir, args, repeat_num=1, training=True):
aa_params["interpolation"] = _pil_interp(interpolation)
transform_img = [
vision.Decode(),
- py_vision.ToPIL(),
+ vision.ToPIL(),
RandomResizedCropAndInterpolation(size=args.image_size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
interpolation=interpolation),
- py_vision.RandomHorizontalFlip(prob=0.5),
+ vision.RandomHorizontalFlip(prob=0.5),
]
if isinstance(auto_augment, str) and auto_augment.startswith('rand'):
transform_img += [rand_augment_transform(auto_augment, aa_params)]
else:
- transform_img += [py_vision.RandomColorAdjust(args.color_jitter, args.color_jitter, args.color_jitter)]
+ transform_img += [vision.RandomColorAdjust(args.color_jitter, args.color_jitter, args.color_jitter)]
transform_img += [
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std)]
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False)]
if args.re_prob > 0.:
transform_img += [RandomErasing(args.re_prob, mode=args.re_mode, max_count=args.re_count)]
else:
# test transform complete
transform_img = [
vision.Decode(),
- py_vision.ToPIL(),
+ vision.ToPIL(),
Resize(int(args.image_size / args.crop_pct), interpolation="bicubic"),
- py_vision.CenterCrop(image_size),
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std)
+ vision.CenterCrop(image_size),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False)
]
transform_label = C.TypeCast(mstype.int32)
diff --git a/research/cv/DRNet/src/dataset.py b/research/cv/DRNet/src/dataset.py
index 1da459994..04802ca69 100644
--- a/research/cv/DRNet/src/dataset.py
+++ b/research/cv/DRNet/src/dataset.py
@@ -16,9 +16,8 @@
import os
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.transforms.py_transforms as py_transforms
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as vision
from mindspore.dataset.vision import Inter
def create_dataset(dataset_path, do_train, repeat_num=1, infer_910=True, device_id=0, batch_size=128):
@@ -55,16 +54,16 @@ def create_dataset(dataset_path, do_train, repeat_num=1, infer_910=True, device_
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
- decode_p = py_vision.Decode()
- resize_p = py_vision.Resize(int(256), interpolation=Inter.BILINEAR)
- center_crop_p = py_vision.CenterCrop(224)
- totensor = py_vision.ToTensor()
- normalize_p = py_vision.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
- trans = py_transforms.Compose([decode_p, resize_p, center_crop_p, totensor, normalize_p])
+ decode_p = vision.Decode(True)
+ resize_p = vision.Resize(int(256), interpolation=Inter.BILINEAR)
+ center_crop_p = vision.CenterCrop(224)
+ totensor = vision.ToTensor()
+ normalize_p = vision.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], is_hwc=False)
+ trans = C2.Compose([decode_p, resize_p, center_crop_p, totensor, normalize_p])
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=8)
ds = ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=8)
ds = ds.batch(batch_size, drop_remainder=True)
return ds
-
\ No newline at end of file
+
diff --git a/research/cv/DeepID/src/dataset.py b/research/cv/DeepID/src/dataset.py
index 7541a7ff4..328410d7a 100644
--- a/research/cv/DeepID/src/dataset.py
+++ b/research/cv/DeepID/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,8 +20,8 @@ import csv
import numpy as np
from PIL import Image
-import mindspore.dataset.vision.py_transforms as py_vision
-import mindspore.dataset.transforms.py_transforms as py_transforms
+import mindspore.dataset.vision as vision
+import mindspore.dataset.transforms as data_trans
import mindspore.dataset as de
@@ -112,12 +112,12 @@ def get_loader(data_root, mode='train'):
"""Build and return a data loader."""
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
- transform = [py_vision.ToPIL()]
+ transform = [vision.ToPIL()]
if mode == 'train':
- transform.append(py_vision.RandomHorizontalFlip())
- transform.append(py_vision.ToTensor())
- transform.append(py_vision.Normalize(mean=mean, std=std))
- transform = py_transforms.Compose(transform)
+ transform.append(vision.RandomHorizontalFlip())
+ transform.append(vision.ToTensor())
+ transform.append(vision.Normalize(mean=mean, std=std, is_hwc=False))
+ transform = data_trans.Compose(transform)
dataset = Youtube(data_root, mode, transform=transform)
diff --git a/research/cv/EfficientDet_d0/src/dataset.py b/research/cv/EfficientDet_d0/src/dataset.py
index 537857e52..5c5f87644 100644
--- a/research/cv/EfficientDet_d0/src/dataset.py
+++ b/research/cv/EfficientDet_d0/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ import os
import numpy as np
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from pycocotools.coco import COCO
from src.config import config
diff --git a/research/cv/FDA-BNN/src/dataset.py b/research/cv/FDA-BNN/src/dataset.py
index 84177de83..c8f22b5aa 100755
--- a/research/cv/FDA-BNN/src/dataset.py
+++ b/research/cv/FDA-BNN/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,14 +17,12 @@ import math
import os
import numpy as np
-import mindspore.dataset.vision.py_transforms as py_vision
-import mindspore.dataset.transforms.py_transforms as py_transforms
-import mindspore.dataset.transforms.c_transforms as c_transforms
+import mindspore.dataset.vision as vision
+import mindspore.dataset.transforms as data_trans
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
from mindspore.communication.management import get_rank, get_group_size
from mindspore.dataset.vision import Inter
-import mindspore.dataset.vision.c_transforms as vision
# values that should remain constant
@@ -55,24 +53,24 @@ def create_dataset(batch_size, train_data_url='', workers=8, distributed=False,
"""Create ImageNet training dataset"""
if not os.path.exists(train_data_url):
raise ValueError('Path not exists')
- decode_op = py_vision.Decode()
- type_cast_op = c_transforms.TypeCast(mstype.int32)
+ decode_op = vision.Decode(True)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
- random_resize_crop_bicubic = py_vision.RandomResizedCrop(size=(input_size, input_size),
- scale=SCALE, ratio=RATIO,
- interpolation=Inter.BICUBIC)
- random_horizontal_flip_op = py_vision.RandomHorizontalFlip(0.5)
+ random_resize_crop_bicubic = vision.RandomResizedCrop(size=(input_size, input_size),
+ scale=SCALE, ratio=RATIO,
+ interpolation=Inter.BICUBIC)
+ random_horizontal_flip_op = vision.RandomHorizontalFlip(0.5)
adjust_range = (max(0, 1 - color_jitter), 1 + color_jitter)
- random_color_jitter_op = py_vision.RandomColorAdjust(brightness=adjust_range,
- contrast=adjust_range,
- saturation=adjust_range)
- to_tensor = py_vision.ToTensor()
- normalize_op = py_vision.Normalize(
- IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+ random_color_jitter_op = vision.RandomColorAdjust(brightness=adjust_range,
+ contrast=adjust_range,
+ saturation=adjust_range)
+ to_tensor = vision.ToTensor()
+ normalize_op = vision.Normalize(
+ IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, is_hwc=False)
# assemble all the transforms
- image_ops = py_transforms.Compose([decode_op, random_resize_crop_bicubic,
- random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op])
+ image_ops = data_trans.Compose([decode_op, random_resize_crop_bicubic,
+ random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op])
rank_id = get_rank() if distributed else 0
rank_size = get_group_size() if distributed else 1
@@ -122,16 +120,16 @@ def create_dataset_val(batch_size=128, val_data_url='', workers=8, distributed=F
else:
scale_size = int(math.floor(input_size / DEFAULT_CROP_PCT))
- type_cast_op = c_transforms.TypeCast(mstype.int32)
- decode_op = py_vision.Decode()
- resize_op = py_vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
- center_crop = py_vision.CenterCrop(size=input_size)
- to_tensor = py_vision.ToTensor()
- normalize_op = py_vision.Normalize(
- IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
+ decode_op = vision.Decode(True)
+ resize_op = vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
+ center_crop = vision.CenterCrop(size=input_size)
+ to_tensor = vision.ToTensor()
+ normalize_op = vision.Normalize(
+ IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, is_hwc=False)
- image_ops = py_transforms.Compose([decode_op, resize_op, center_crop,
- to_tensor, normalize_op])
+ image_ops = data_trans.Compose([decode_op, resize_op, center_crop,
+ to_tensor, normalize_op])
dataset = dataset.map(input_columns=["label"], operations=type_cast_op,
num_parallel_workers=workers)
@@ -180,9 +178,9 @@ def create_dataset_cifar10(data_home, repeat_num=1, training=True, cifar_cfg=Non
random_horizontal_op = vision.RandomHorizontalFlip()
resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR
rescale_op = vision.Rescale(1.0 / 255.0, 0.0)
- normalize_op = vision.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+ normalize_op = vision.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), is_hwc=True)
changeswap_op = vision.HWC2CHW()
- type_cast_op = c_transforms.TypeCast(mstype.int32)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
c_trans = []
if training:
diff --git a/research/cv/FaceAttribute/preprocess.py b/research/cv/FaceAttribute/preprocess.py
index cbe80e324..1a2ebb578 100644
--- a/research/cv/FaceAttribute/preprocess.py
+++ b/research/cv/FaceAttribute/preprocess.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,8 +16,8 @@
import os
import mindspore.dataset as de
-import mindspore.dataset.vision.py_transforms as F
-import mindspore.dataset.transforms.py_transforms as F2
+import mindspore.dataset.vision as F
+import mindspore.dataset.transforms as F2
from model_utils.config import config
@@ -28,10 +28,10 @@ def eval_data_generator(args):
dst_h = args.dst_h
batch_size = 1
#attri_num = args.attri_num
- transform_img = F2.Compose([F.Decode(),
+ transform_img = F2.Compose([F.Decode(True)),
F.Resize((dst_w, dst_h)),
F.ToTensor(),
- F.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+ F.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), is_hwc=False)])
de_dataset = de.MindDataset(mindrecord_path + "0", columns_list=["image", "label"])
de_dataset = de_dataset.map(input_columns="image", operations=transform_img, num_parallel_workers=args.workers,
diff --git a/research/cv/FaceAttribute/src/dataset_eval.py b/research/cv/FaceAttribute/src/dataset_eval.py
index 2167a4b2f..f4ed3cd9f 100644
--- a/research/cv/FaceAttribute/src/dataset_eval.py
+++ b/research/cv/FaceAttribute/src/dataset_eval.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -14,8 +14,8 @@
# ============================================================================
"""Face attribute dataset for eval"""
import mindspore.dataset as de
-import mindspore.dataset.vision.py_transforms as F
-import mindspore.dataset.transforms.py_transforms as F2
+import mindspore.dataset.vision as F
+import mindspore.dataset.transforms as F2
__all__ = ['data_generator_eval']
@@ -27,10 +27,10 @@ def data_generator_eval(args):
dst_h = args.dst_h
batch_size = 1
attri_num = args.attri_num
- transform_img = F2.Compose([F.Decode(),
+ transform_img = F2.Compose([F.Decode(True),
F.Resize((dst_w, dst_h)),
F.ToTensor(),
- F.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+ F.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), is_hwc=False)])
de_dataset = de.MindDataset(mindrecord_path + "0", columns_list=["image", "label"])
de_dataset = de_dataset.map(input_columns="image", operations=transform_img, num_parallel_workers=args.workers,
diff --git a/research/cv/FaceAttribute/src/dataset_train.py b/research/cv/FaceAttribute/src/dataset_train.py
index bbd210a35..79617bdb7 100644
--- a/research/cv/FaceAttribute/src/dataset_train.py
+++ b/research/cv/FaceAttribute/src/dataset_train.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -14,8 +14,8 @@
# ============================================================================
"""Face attribute dataset for train"""
import mindspore.dataset as de
-import mindspore.dataset.vision.py_transforms as F
-import mindspore.dataset.transforms.py_transforms as F2
+import mindspore.dataset.vision as F
+import mindspore.dataset.transforms as F2
__all__ = ['data_generator']
@@ -28,11 +28,11 @@ def data_generator(args):
batch_size = args.per_batch_size
attri_num = args.attri_num
max_epoch = args.max_epoch
- transform_img = F2.Compose([F.Decode(),
+ transform_img = F2.Compose([F.Decode(True)),
F.Resize((dst_w, dst_h)),
F.RandomHorizontalFlip(prob=0.5),
F.ToTensor(),
- F.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+ F.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), is_hwc=False)])
de_dataset = de.MindDataset(mindrecord_path + "0", columns_list=["image", "label"], num_shards=args.world_size,
shard_id=args.local_rank)
diff --git a/research/cv/FaceDetection/preprocess.py b/research/cv/FaceDetection/preprocess.py
index 8f9961b26..205d31531 100644
--- a/research/cv/FaceDetection/preprocess.py
+++ b/research/cv/FaceDetection/preprocess.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,7 +16,7 @@
import os
import numpy as np
from PIL import Image
-import mindspore.dataset.vision.py_transforms as P
+import mindspore.dataset.vision as V
import mindspore.dataset as de
from model_utils.config import config
@@ -30,7 +30,7 @@ class SingleScaleTrans_Infer:
def __call__(self, imgs, ann, image_names, image_size, batch_info):
- decode = P.Decode()
+ decode = V.Decode(True)
ret_imgs = []
ret_anno = []
diff --git a/research/cv/FaceDetection/src/data_preprocess.py b/research/cv/FaceDetection/src/data_preprocess.py
index 1eba91758..8c1e7ebaa 100644
--- a/research/cv/FaceDetection/src/data_preprocess.py
+++ b/research/cv/FaceDetection/src/data_preprocess.py
@@ -1,4 +1,4 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -15,7 +15,7 @@
"""Face detection yolov3 data pre-process."""
import multiprocessing
import numpy as np
-import mindspore.dataset.vision.py_transforms as P
+import mindspore.dataset.vision as V
import mindspore.dataset as de
from src.transforms import RandomCropLetterbox, RandomFlip, HSVShift, ResizeLetterbox
@@ -31,10 +31,10 @@ class SingleScaleTrans:
def __call__(self, imgs, ann, image_names, image_size, batch_info):
size = self.resize
- decode = P.Decode()
+ decode = P.Decode(True)
resize_letter_box_op = ResizeLetterbox(input_dim=size)
- to_tensor = P.ToTensor()
+ to_tensor = V.ToTensor()
ret_imgs = []
ret_anno = []
@@ -204,7 +204,7 @@ def preprocess_fn(image, annotation):
anchors = config.anchors
anchors_mask = config.anchors_mask
- decode = P.Decode()
+ decode = P.Decode(True)
random_crop_letter_box_op = RandomCropLetterbox(jitter=jitter, input_dim=size)
random_flip_op = RandomFlip(flip)
hsv_shift_op = HSVShift(hue, sat, val)
diff --git a/research/cv/FaceNet/src/LFWDataset.py b/research/cv/FaceNet/src/LFWDataset.py
index cda486727..7cc5444a1 100644
--- a/research/cv/FaceNet/src/LFWDataset.py
+++ b/research/cv/FaceNet/src/LFWDataset.py
@@ -17,8 +17,7 @@
import os
import numpy as np
from PIL import Image
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.vision.py_transforms as P
+import mindspore.dataset.vision as C
import mindspore.dataset as de
from mindspore.common import set_seed
set_seed(0)
@@ -94,9 +93,9 @@ class LFWDataset:
def get_lfw_dataloader(eval_root_dir, eval_pairs_path, eval_batch_size):
data_transforms = [C.RandomResize(size=(224, 224)),
- P.ToPIL(),
- P.ToTensor(),
- P.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])]
+ C.ToPIL(),
+ C.ToTensor(),
+ C.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], is_hwc=False)]
face_dataset = LFWDataset(data_dir=eval_root_dir, pairs_path=eval_pairs_path)
diff --git a/research/cv/FaceNet/src/data_loader.py b/research/cv/FaceNet/src/data_loader.py
index 41968e5d5..abe10b2d3 100644
--- a/research/cv/FaceNet/src/data_loader.py
+++ b/research/cv/FaceNet/src/data_loader.py
@@ -18,8 +18,7 @@ import os
import csv
import numpy as np
from PIL import Image
-import mindspore.dataset.vision.py_transforms as P
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
import mindspore.dataset as de
@@ -68,17 +67,17 @@ def get_dataloader(train_root_dir, valid_root_dir,
'train': [
C.RandomResize(size=(224, 224)),
C.RandomHorizontalFlip(),
- P.ToTensor(),
- P.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])],
+ C.ToTensor(),
+ C.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], is_hwc=False)],
'train_valid': [
C.RandomResize(size=(224, 224)),
C.RandomHorizontalFlip(),
- P.ToTensor(),
- P.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])],
+ C.ToTensor(),
+ C.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], is_hwc=False)],
'valid': [
C.RandomResize(size=(224, 224)),
- P.ToTensor(),
- P.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])]}
+ C.ToTensor(),
+ C.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], is_hwc=False)]}
dataset_column_names = ["anc_img", "pos_img", "neg_img", "pos_class", "neg_class"]
diff --git a/research/cv/FaceNet/src/data_loader_generate_triplets_online.py b/research/cv/FaceNet/src/data_loader_generate_triplets_online.py
index b12537e89..337b200ab 100644
--- a/research/cv/FaceNet/src/data_loader_generate_triplets_online.py
+++ b/research/cv/FaceNet/src/data_loader_generate_triplets_online.py
@@ -18,8 +18,7 @@ import os
import numpy as np
import pandas as pd
from PIL import Image
-import mindspore.dataset.vision.py_transforms as P
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
import mindspore.dataset as de
@@ -107,17 +106,17 @@ def get_dataloader(train_root_dir, valid_root_dir,
'train': [
C.RandomResize(size=(224, 224)),
C.RandomHorizontalFlip(),
- P.ToTensor(),
- P.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])],
+ C.ToTensor(),
+ C.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], is_hwc=False)],
'train_valid': [
C.RandomResize(size=(224, 224)),
C.RandomHorizontalFlip(),
- P.ToTensor(),
- P.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])],
+ C.ToTensor(),
+ C.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], is_hwc=False)],
'valid': [
C.RandomResize(size=(224, 224)),
- P.ToTensor(),
- P.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])]}
+ C.ToTensor(),
+ C.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], is_hwc=False)]}
dataset_column_names = ["anc_img", "pos_img", "neg_img", "pos_class", "neg_class"]
diff --git a/research/cv/FaceQualityAssessment/src/dataset.py b/research/cv/FaceQualityAssessment/src/dataset.py
index 8ccf54460..149ae3410 100644
--- a/research/cv/FaceQualityAssessment/src/dataset.py
+++ b/research/cv/FaceQualityAssessment/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@ import numpy as np
from PIL import Image, ImageFile
import mindspore.dataset as ds
-import mindspore.dataset.vision.py_transforms as F
+import mindspore.dataset.vision as F
warnings.filterwarnings('ignore')
ImageFile.LOAD_TRUNCATED_IMAGES = True
diff --git a/research/cv/FaceRecognition/eval.py b/research/cv/FaceRecognition/eval.py
index 6c5dba139..0ca97bfce 100644
--- a/research/cv/FaceRecognition/eval.py
+++ b/research/cv/FaceRecognition/eval.py
@@ -21,8 +21,8 @@ import numpy as np
import cv2
from mindspore.common import dtype as mstype
-import mindspore.dataset.transforms.py_transforms as transforms
-import mindspore.dataset.vision.py_transforms as vision
+import mindspore.dataset.transforms as transforms
+import mindspore.dataset.vision as vision
import mindspore.dataset as de
from mindspore import Tensor, context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
@@ -266,9 +266,8 @@ def run_eval(args):
args.logger.info('INFO, graph compile finished, time used:{:.2f}s, start calculate img embedding'.
format(compile_time_used))
- img_transforms = transforms.Compose([vision.ToTensor(), vision.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
-
-
+ img_transforms = transforms.Compose([vision.ToTensor(),
+ vision.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), is_hwc=False)])
#for test images
args.logger.info('INFO, start step1, calculate test img embedding, weight file = {}'.format(args.weight))
diff --git a/research/cv/FaceRecognition/src/dataset_factory.py b/research/cv/FaceRecognition/src/dataset_factory.py
index 0cf1ad0d3..64b0fb49f 100644
--- a/research/cv/FaceRecognition/src/dataset_factory.py
+++ b/research/cv/FaceRecognition/src/dataset_factory.py
@@ -18,8 +18,8 @@ import math
import numpy as np
import mindspore.dataset as de
-import mindspore.dataset.vision.py_transforms as F
-import mindspore.dataset.transforms.py_transforms as F2
+import mindspore.dataset.vision as F
+import mindspore.dataset.transforms as F2
from src.custom_dataset import DistributedCustomSampler, CustomDataset
@@ -27,7 +27,7 @@ __all__ = ['get_de_dataset']
def get_de_dataset(args):
'''get_de_dataset'''
- lbl_transforms = [F.ToType(np.int32)]
+ lbl_transforms = [F2.TypeCast(np.int32)]
transform_label = F2.Compose(lbl_transforms)
drop_remainder = True
@@ -35,7 +35,7 @@ def get_de_dataset(args):
transforms = [F.ToPIL(),
F.RandomHorizontalFlip(),
F.ToTensor(),
- F.Normalize(mean=[0.5], std=[0.5])]
+ F.Normalize(mean=[0.5], std=[0.5], is_hwc=False)]
transform = F2.Compose(transforms)
cache_path = os.path.join('cache', os.path.basename(args.data_dir), 'data_cache.pkl')
if args.device_target == 'GPU' and args.local_rank != 0:
diff --git a/research/cv/FaceRecognitionForTracking/eval.py b/research/cv/FaceRecognitionForTracking/eval.py
index 110e37c50..aba67c818 100644
--- a/research/cv/FaceRecognitionForTracking/eval.py
+++ b/research/cv/FaceRecognitionForTracking/eval.py
@@ -21,8 +21,8 @@ import numpy as np
from PIL import Image
from tqdm import tqdm
-import mindspore.dataset.vision.py_transforms as V
-import mindspore.dataset.transforms.py_transforms as T
+import mindspore.dataset.vision as V
+import mindspore.dataset.transforms as T
from mindspore import context, Tensor
from mindspore.train.serialization import load_checkpoint, load_param_into_net
@@ -98,7 +98,7 @@ def load_images(paths, batch_size=128):
resize = V.Resize((96, 64))
transform = T.Compose([
V.ToTensor(),
- V.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
+ V.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5], is_hwc=False)])
for i, _ in enumerate(paths):
im = Image.open(paths[i])
im = resize(im)
diff --git a/research/cv/FaceRecognitionForTracking/preprocess.py b/research/cv/FaceRecognitionForTracking/preprocess.py
index dad3c2795..140134811 100644
--- a/research/cv/FaceRecognitionForTracking/preprocess.py
+++ b/research/cv/FaceRecognitionForTracking/preprocess.py
@@ -1,4 +1,4 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ import argparse
import numpy as np
from PIL import Image
-import mindspore.dataset.vision.py_transforms as V
-import mindspore.dataset.transforms.py_transforms as T
+import mindspore.dataset.vision as V
+import mindspore.dataset.transforms as T
def load_images(paths, batch_size=1):
@@ -28,7 +28,7 @@ def load_images(paths, batch_size=1):
resize = V.Resize((96, 64))
transform = T.Compose([
V.ToTensor(),
- V.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
+ V.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5], is_hwc=False)])
for i, _ in enumerate(paths):
im = Image.open(paths[i])
im = resize(im)
diff --git a/research/cv/FaceRecognitionForTracking/src/dataset.py b/research/cv/FaceRecognitionForTracking/src/dataset.py
index 5cea07502..3ffd35795 100644
--- a/research/cv/FaceRecognitionForTracking/src/dataset.py
+++ b/research/cv/FaceRecognitionForTracking/src/dataset.py
@@ -19,8 +19,8 @@ from PIL import ImageFile
from mindspore import dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as VC
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.vision as VC
+import mindspore.dataset.transforms as C
sys.path.append('./')
sys.path.append('../data/')
diff --git a/research/cv/GENet_Res50/src/dataset.py b/research/cv/GENet_Res50/src/dataset.py
index 3f032c27b..3396298b7 100644
--- a/research/cv/GENet_Res50/src/dataset.py
+++ b/research/cv/GENet_Res50/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ create train or eval dataset.
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
diff --git a/research/cv/HRNetW48_cls/src/dataset.py b/research/cv/HRNetW48_cls/src/dataset.py
index 9d417f545..43f0d27e8 100644
--- a/research/cv/HRNetW48_cls/src/dataset.py
+++ b/research/cv/HRNetW48_cls/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,8 +16,8 @@
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
diff --git a/research/cv/HireMLP/src/dataset.py b/research/cv/HireMLP/src/dataset.py
index a27c6ecb2..3bc5b667c 100644
--- a/research/cv/HireMLP/src/dataset.py
+++ b/research/cv/HireMLP/src/dataset.py
@@ -16,11 +16,10 @@
import os
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.py_transforms as pytrans
+import mindspore.dataset.transforms as C2
-from mindspore.dataset.transforms.py_transforms import Compose
-import mindspore.dataset.vision.c_transforms as C
+from mindspore.dataset.transforms.transforms import Compose
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, repeat_num=1, infer_910=True, device_id=0, batch_size=128):
@@ -59,13 +58,13 @@ def create_dataset(dataset_path, do_train, repeat_num=1, infer_910=True, device_
]
else:
trans = [
- pytrans.Decode(),
- pytrans.Resize(235),
- pytrans.CenterCrop(224)
+ C.Decode(True),
+ C.Resize(235),
+ C.CenterCrop(224)
]
trans += [
- pytrans.ToTensor(),
- pytrans.Normalize(mean=mean, std=std),
+ C.ToTensor(),
+ C.Normalize(mean=mean, std=std, is_hwc=False),
]
trans = Compose(trans)
diff --git a/research/cv/HourNAS/src/dataset.py b/research/cv/HourNAS/src/dataset.py
index 663fa35f4..1ec1f68ff 100644
--- a/research/cv/HourNAS/src/dataset.py
+++ b/research/cv/HourNAS/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,14 +17,12 @@ import math
import os
import numpy as np
-import mindspore.dataset.vision.py_transforms as py_vision
-import mindspore.dataset.transforms.py_transforms as py_transforms
-import mindspore.dataset.transforms.c_transforms as c_transforms
+import mindspore.dataset.vision as vision
+import mindspore.dataset.transforms as data_trans
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
from mindspore.communication.management import get_rank, get_group_size
from mindspore.dataset.vision import Inter
-import mindspore.dataset.vision.c_transforms as vision
# values that should remain constant
DEFAULT_CROP_PCT = 0.875
@@ -54,24 +52,24 @@ def create_dataset(batch_size, train_data_url='', workers=8, distributed=False,
"""Create ImageNet training dataset"""
if not os.path.exists(train_data_url):
raise ValueError('Path not exists')
- decode_op = py_vision.Decode()
- type_cast_op = c_transforms.TypeCast(mstype.int32)
+ decode_op = vision.Decode(True)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
- random_resize_crop_bicubic = py_vision.RandomResizedCrop(size=(input_size, input_size),
- scale=SCALE, ratio=RATIO,
- interpolation=Inter.BICUBIC)
- random_horizontal_flip_op = py_vision.RandomHorizontalFlip(0.5)
+ random_resize_crop_bicubic = vision.RandomResizedCrop(size=(input_size, input_size),
+ scale=SCALE, ratio=RATIO,
+ interpolation=Inter.BICUBIC)
+ random_horizontal_flip_op = vision.RandomHorizontalFlip(0.5)
adjust_range = (max(0, 1 - color_jitter), 1 + color_jitter)
- random_color_jitter_op = py_vision.RandomColorAdjust(brightness=adjust_range,
- contrast=adjust_range,
- saturation=adjust_range)
- to_tensor = py_vision.ToTensor()
- normalize_op = py_vision.Normalize(
- IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+ random_color_jitter_op = vision.RandomColorAdjust(brightness=adjust_range,
+ contrast=adjust_range,
+ saturation=adjust_range)
+ to_tensor = vision.ToTensor()
+ normalize_op = vision.Normalize(
+ IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, is_hwc=False)
# assemble all the transforms
- image_ops = py_transforms.Compose([decode_op, random_resize_crop_bicubic,
- random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op])
+ image_ops = data_trans.Compose([decode_op, random_resize_crop_bicubic,
+ random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op])
rank_id = get_rank() if distributed else 0
rank_size = get_group_size() if distributed else 1
@@ -120,16 +118,16 @@ def create_dataset_val(batch_size=128, val_data_url='', workers=8, distributed=F
else:
scale_size = int(math.floor(input_size / DEFAULT_CROP_PCT))
- type_cast_op = c_transforms.TypeCast(mstype.int32)
- decode_op = py_vision.Decode()
- resize_op = py_vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
- center_crop = py_vision.CenterCrop(size=input_size)
- to_tensor = py_vision.ToTensor()
- normalize_op = py_vision.Normalize(
- IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
+ decode_op = vision.Decode(True)
+ resize_op = vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
+ center_crop = vision.CenterCrop(size=input_size)
+ to_tensor = vision.ToTensor()
+ normalize_op = vision.Normalize(
+ IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, is_hwc=False)
- image_ops = py_transforms.Compose([decode_op, resize_op, center_crop,
- to_tensor, normalize_op])
+ image_ops = data_trans.Compose([decode_op, resize_op, center_crop,
+ to_tensor, normalize_op])
dataset = dataset.map(input_columns=["label"], operations=type_cast_op,
num_parallel_workers=workers)
@@ -175,10 +173,10 @@ def create_dataset_cifar10(data_home, repeat_num=1, training=True, cifar_cfg=Non
random_horizontal_op = vision.RandomHorizontalFlip()
resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR
rescale_op = vision.Rescale(1.0 / 255.0, 0.0)
- #normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
- normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.24703233, 0.24348505, 0.26158768))
+ #normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010), is_hwc=True)
+ normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.24703233, 0.24348505, 0.26158768), is_hwc=True)
changeswap_op = vision.HWC2CHW()
- type_cast_op = c_transforms.TypeCast(mstype.int32)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
c_trans = []
if training:
diff --git a/research/cv/ICNet/Res50V1_PRE/src/dataset.py b/research/cv/ICNet/Res50V1_PRE/src/dataset.py
index 3f032c27b..3396298b7 100644
--- a/research/cv/ICNet/Res50V1_PRE/src/dataset.py
+++ b/research/cv/ICNet/Res50V1_PRE/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ create train or eval dataset.
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
diff --git a/research/cv/ICNet/eval.py b/research/cv/ICNet/eval.py
index c48be4d8e..21d0a21e0 100644
--- a/research/cv/ICNet/eval.py
+++ b/research/cv/ICNet/eval.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,7 +24,7 @@ import mindspore.ops as ops
from mindspore import load_param_into_net
from mindspore import load_checkpoint
from mindspore import Tensor
-import mindspore.dataset.vision.py_transforms as transforms
+import mindspore.dataset.vision as vision
parser = argparse.ArgumentParser(description="ICNet Evaluation")
parser.add_argument("--dataset_path", type=str, default="/data/cityscapes/", help="dataset path")
@@ -98,8 +98,8 @@ class Evaluator:
def _img_transform(self, image):
"""img_transform"""
- to_tensor = transforms.ToTensor()
- normalize = transforms.Normalize([.485, .456, .406], [.229, .224, .225])
+ to_tensor = vision.ToTensor()
+ normalize = vision.Normalize([.485, .456, .406], [.229, .224, .225], is_hwc=False)
image = to_tensor(image)
image = normalize(image)
return image
diff --git a/research/cv/ICNet/src/cityscapes_mindrecord.py b/research/cv/ICNet/src/cityscapes_mindrecord.py
index 0ccc783dd..a3acc652e 100644
--- a/research/cv/ICNet/src/cityscapes_mindrecord.py
+++ b/research/cv/ICNet/src/cityscapes_mindrecord.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -22,8 +22,8 @@ from PIL import ImageOps
from PIL import ImageFilter
import mindspore.dataset as de
from mindspore.mindrecord import FileWriter
-import mindspore.dataset.vision.py_transforms as transforms
-import mindspore.dataset.transforms.py_transforms as tc
+import mindspore.dataset.vision as transforms
+import mindspore.dataset.transforms as tc
def _get_city_pairs(folder, split='train'):
@@ -103,7 +103,8 @@ def _sync_transform(img, mask):
def _class_to_index(mask):
"""class to index"""
- # reference: https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py
+ # Reference:
+ # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py
_key = np.array([-1, -1, -1, -1, -1, -1,
-1, -1, 0, 1, -1, -1,
2, 3, 4, -1, -1, -1,
@@ -136,7 +137,7 @@ def _img_mask_transform(img, mask):
"""img and mask transform"""
input_transform = tc.Compose([
transforms.ToTensor(),
- transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])
+ transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), is_hwc=False)])
img = _img_transform(img)
mask = _mask_transform(mask)
img = input_transform(img)
diff --git a/research/cv/ICNet/src/visualize.py b/research/cv/ICNet/src/visualize.py
index 5748181e9..61adc9d70 100644
--- a/research/cv/ICNet/src/visualize.py
+++ b/research/cv/ICNet/src/visualize.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ import mindspore.ops as ops
from mindspore import Tensor
from mindspore import load_param_into_net
from mindspore import load_checkpoint
-import mindspore.dataset.vision.py_transforms as transforms
+import mindspore.dataset.vision as vision
from models.icnet import ICNet
__all__ = ['get_color_palette', 'set_img_color',
@@ -30,8 +30,8 @@ __all__ = ['get_color_palette', 'set_img_color',
def _img_transform(img):
"""img_transform"""
- totensor = transforms.ToTensor()
- normalize = transforms.Normalize([.485, .456, .406], [.229, .224, .225])
+ totensor = vision.ToTensor()
+ normalize = vision.Normalize([.485, .456, .406], [.229, .224, .225], is_hwc=False)
img = totensor(img)
img = normalize(img)
return img
diff --git a/research/cv/ISyNet/src/dataset.py b/research/cv/ISyNet/src/dataset.py
index 06d86d1c9..940c0acc4 100644
--- a/research/cv/ISyNet/src/dataset.py
+++ b/research/cv/ISyNet/src/dataset.py
@@ -19,9 +19,8 @@ import os
import numpy as np
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.py_transforms as P
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.dataset.vision import Inter
from mindspore.communication.management import init, get_rank, get_group_size
from src.model_utils.config import config
@@ -83,7 +82,7 @@ def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, target=
]
if autoaugment:
trans += [
- P.ToPIL(),
+ C.ToPIL(),
ImageNetPolicy(),
ToNumpy(),
]
@@ -171,7 +170,7 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
]
if autoaugment:
trans += [
- P.ToPIL(),
+ C.ToPIL(),
ImageNetPolicy(),
ToNumpy(),
]
@@ -267,7 +266,7 @@ def create_dataset_pynative(dataset_path, do_train, repeat_num=1, batch_size=32,
]
if autoaugment:
trans += [
- P.ToPIL(),
+ C.ToPIL(),
ImageNetPolicy(),
ToNumpy(),
]
@@ -351,7 +350,7 @@ def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target=
]
if autoaugment:
trans += [
- P.ToPIL(),
+ C.ToPIL(),
ImageNetPolicy(),
ToNumpy(),
]
@@ -437,7 +436,7 @@ def create_dataset4(dataset_path, do_train, repeat_num=1, batch_size=32, target=
]
if autoaugment:
trans += [
- P.ToPIL(),
+ C.ToPIL(),
ImageNetPolicy(),
ToNumpy(),
]
diff --git a/research/cv/ISyNet/src/transform.py b/research/cv/ISyNet/src/transform.py
index 400e8b211..d3ea07319 100644
--- a/research/cv/ISyNet/src/transform.py
+++ b/research/cv/ISyNet/src/transform.py
@@ -16,7 +16,7 @@
random augment class
"""
import numpy as np
-import mindspore.dataset.vision.py_transforms as P
+import mindspore.dataset.vision as vision
from src import transform_utils
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
@@ -37,9 +37,9 @@ class RandAugment:
# assert the imgs object are pil_images
ret_imgs = []
ret_labels = []
- py_to_pil_op = P.ToPIL()
- to_tensor = P.ToTensor()
- normalize_op = P.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+ py_to_pil_op = vision.ToPIL()
+ to_tensor = vision.Tensor()
+ normalize_op = vision.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, is_hwc=False)
rand_augment_ops = transform_utils.rand_augment_transform(self.config_str, self.hparams)
for i, image in enumerate(imgs):
img_pil = py_to_pil_op(image)
diff --git a/research/cv/ISyNet/utils/preprocess_310.py b/research/cv/ISyNet/utils/preprocess_310.py
index 38c855977..897d2e5d4 100644
--- a/research/cv/ISyNet/utils/preprocess_310.py
+++ b/research/cv/ISyNet/utils/preprocess_310.py
@@ -18,8 +18,8 @@ import argparse
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
PARSER = argparse.ArgumentParser(description="ISyNet preprocess")
PARSER.add_argument("--data_path", type=str, required=True, help="data path.")
diff --git a/research/cv/Inception-v2/src/dataset.py b/research/cv/Inception-v2/src/dataset.py
index 59a03b8e7..1ebd2a9f5 100644
--- a/research/cv/Inception-v2/src/dataset.py
+++ b/research/cv/Inception-v2/src/dataset.py
@@ -19,8 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
def create_dataset_cifar10(dataset_path, cfg, training, repeat_num=1):
diff --git a/research/cv/JDE/eval_detect.py b/research/cv/JDE/eval_detect.py
index 9425f78ca..f4487ec2d 100644
--- a/research/cv/JDE/eval_detect.py
+++ b/research/cv/JDE/eval_detect.py
@@ -23,7 +23,7 @@ from mindspore import dataset as ds
from mindspore.common import set_seed
from mindspore.communication.management import get_group_size
from mindspore.communication.management import get_rank
-from mindspore.dataset.vision import py_transforms as PY
+from mindspore.dataset.vision import transforms as vision
from mindspore.train.serialization import load_checkpoint
from cfg.config import config as default_config
@@ -69,7 +69,7 @@ def main(
opt.dataset_root,
test_paths,
augment=False,
- transforms=PY.ToTensor(),
+ transforms=vision.ToTensor(),
config=opt,
)
diff --git a/research/cv/JDE/train.py b/research/cv/JDE/train.py
index da70b3718..dbce644f2 100644
--- a/research/cv/JDE/train.py
+++ b/research/cv/JDE/train.py
@@ -25,7 +25,7 @@ from mindspore.communication.management import get_group_size
from mindspore.communication.management import get_rank
from mindspore.communication.management import init
from mindspore.context import ParallelMode
-from mindspore.dataset.vision import py_transforms as PY
+from mindspore.dataset.vision import transforms as vision
from mindspore.train.callback import CheckpointConfig
from mindspore.train.callback import LossMonitor
from mindspore.train.callback import ModelCheckpoint
@@ -177,7 +177,7 @@ if __name__ == "__main__":
trainset_paths,
k_max=config.k_max,
augment=True,
- transforms=PY.ToTensor(),
+ transforms=vision.ToTensor(),
config=config,
)
diff --git a/research/cv/LightCNN/src/dataset.py b/research/cv/LightCNN/src/dataset.py
index 46192a0a0..110d14dd0 100644
--- a/research/cv/LightCNN/src/dataset.py
+++ b/research/cv/LightCNN/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import cv2
import numpy as np
import mindspore.dataset as ds
-import mindspore.dataset.vision.py_transforms as py_vision
-from mindspore.dataset.transforms.py_transforms import Compose
+import mindspore.dataset.vision as vision
+from mindspore.dataset.transforms.transforms import Compose
def img_loader(path):
@@ -67,19 +67,19 @@ def create_dataset(mode, data_url, data_list, batch_size, resize_size=144,
if mode == 'Train':
shuffle = True
drop_last = True
- image_ops = Compose([py_vision.ToPIL(),
- py_vision.Resize(resize_size),
- py_vision.RandomCrop(input_size),
- py_vision.RandomHorizontalFlip(),
- py_vision.ToTensor()])
+ image_ops = Compose([vision.ToPIL(),
+ vision.Resize(resize_size),
+ vision.RandomCrop(input_size),
+ vision.RandomHorizontalFlip(),
+ vision.ToTensor()])
elif mode == 'Val':
shuffle = False
drop_last = False
- image_ops = Compose([py_vision.ToPIL(),
- py_vision.Resize(resize_size),
- py_vision.CenterCrop(input_size),
- py_vision.ToTensor()])
+ image_ops = Compose([vision.ToPIL(),
+ vision.Resize(resize_size),
+ vision.CenterCrop(input_size),
+ vision.ToTensor()])
dataset_generator = ImageList(root=data_url, fileList=data_list)
diff --git a/research/cv/MGN/src/dataset.py b/research/cv/MGN/src/dataset.py
index 2d4e8696f..f4ce073a5 100644
--- a/research/cv/MGN/src/dataset.py
+++ b/research/cv/MGN/src/dataset.py
@@ -20,7 +20,7 @@ import random
import re
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
import numpy as np
from PIL import Image
diff --git a/research/cv/MVD/eval.py b/research/cv/MVD/eval.py
index 8151b8e6c..af9c2a75d 100644
--- a/research/cv/MVD/eval.py
+++ b/research/cv/MVD/eval.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,10 +21,10 @@ import argparse
import numpy as np
import psutil
import mindspore.dataset as ds
-import mindspore.dataset.vision.py_transforms as py_trans
+import mindspore.dataset.vision as vision
from mindspore import context, load_checkpoint, load_param_into_net, DatasetHelper
-from mindspore.dataset.transforms.py_transforms import Compose
+from mindspore.dataset.transforms.transforms import Compose
from PIL import Image
@@ -216,9 +216,9 @@ if __name__ == "__main__":
transform_test = Compose(
[
decode,
- py_trans.Resize((args.img_h, args.img_w)),
- py_trans.ToTensor(),
- py_trans.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+ vision.Resize((args.img_h, args.img_w)),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False)
]
)
diff --git a/research/cv/MVD/train.py b/research/cv/MVD/train.py
index ea0d5f1c4..c6ad55320 100644
--- a/research/cv/MVD/train.py
+++ b/research/cv/MVD/train.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,13 +24,13 @@ from tqdm import tqdm
import mindspore as ms
import mindspore.ops as P
import mindspore.dataset as ds
-import mindspore.dataset.vision.py_transforms as py_trans
+import mindspore.dataset.vision as vision
from mindspore import context, load_checkpoint, \
load_param_into_net, save_checkpoint, DatasetHelper
from mindspore.context import ParallelMode
from mindspore.communication.management import init, get_group_size
-from mindspore.dataset.transforms.py_transforms import Compose
+from mindspore.dataset.transforms.transforms import Compose
from mindspore.nn import SGD, Adam
from mindspore import nn
@@ -289,33 +289,33 @@ if __name__ == "__main__":
transform_train_rgb = Compose(
[
decode,
- py_trans.RandomCrop((args.img_h, args.img_w)),
- py_trans.RandomGrayscale(prob=0.5),
- py_trans.RandomHorizontalFlip(),
- py_trans.ToTensor(),
- py_trans.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
- py_trans.RandomErasing(prob=0.5)
+ vision.RandomCrop((args.img_h, args.img_w)),
+ vision.RandomGrayscale(prob=0.5),
+ vision.RandomHorizontalFlip(),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False),
+ vision.RandomErasing(prob=0.5)
]
)
transform_train_ir = Compose(
[
decode,
- py_trans.RandomCrop((args.img_h, args.img_w)),
- # py_trans.RandomGrayscale(prob=0.5),
- py_trans.RandomHorizontalFlip(),
- py_trans.ToTensor(),
- py_trans.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
- py_trans.RandomErasing(prob=0.5)
+ vision.RandomCrop((args.img_h, args.img_w)),
+ # vision.RandomGrayscale(prob=0.5),
+ vision.RandomHorizontalFlip(),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False),
+ vision.RandomErasing(prob=0.5)
]
)
transform_test = Compose(
[
decode,
- py_trans.Resize((args.img_h, args.img_w)),
- py_trans.ToTensor(),
- py_trans.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+ vision.Resize((args.img_h, args.img_w)),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False)
]
)
diff --git a/research/cv/ManiDP/src/dataset.py b/research/cv/ManiDP/src/dataset.py
index 220fd0147..58aa330bd 100644
--- a/research/cv/ManiDP/src/dataset.py
+++ b/research/cv/ManiDP/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,14 +17,12 @@ import math
import os
import numpy as np
-import mindspore.dataset.vision.py_transforms as py_vision
-import mindspore.dataset.transforms.py_transforms as py_transforms
-import mindspore.dataset.transforms.c_transforms as c_transforms
+import mindspore.dataset.vision as vision
+import mindspore.dataset.transforms as data_trans
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
from mindspore.communication.management import get_rank, get_group_size
from mindspore.dataset.vision import Inter
-import mindspore.dataset.vision.c_transforms as vision
# values that should remain constant
@@ -55,24 +53,24 @@ def create_dataset(batch_size, train_data_url='', workers=8, distributed=False,
"""Create ImageNet training dataset"""
if not os.path.exists(train_data_url):
raise ValueError('Path not exists')
- decode_op = py_vision.Decode()
- type_cast_op = c_transforms.TypeCast(mstype.int32)
+ decode_op = vision.Decode(True)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
- random_resize_crop_bicubic = py_vision.RandomResizedCrop(size=(input_size, input_size),
- scale=SCALE, ratio=RATIO,
- interpolation=Inter.BICUBIC)
- random_horizontal_flip_op = py_vision.RandomHorizontalFlip(0.5)
+ random_resize_crop_bicubic = vision.RandomResizedCrop(size=(input_size, input_size),
+ scale=SCALE, ratio=RATIO,
+ interpolation=Inter.BICUBIC)
+ random_horizontal_flip_op = vision.RandomHorizontalFlip(0.5)
adjust_range = (max(0, 1 - color_jitter), 1 + color_jitter)
- random_color_jitter_op = py_vision.RandomColorAdjust(brightness=adjust_range,
- contrast=adjust_range,
- saturation=adjust_range)
- to_tensor = py_vision.ToTensor()
- normalize_op = py_vision.Normalize(
- IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+ random_color_jitter_op = vision.RandomColorAdjust(brightness=adjust_range,
+ contrast=adjust_range,
+ saturation=adjust_range)
+ to_tensor = vision.ToTensor()
+ normalize_op = vision.Normalize(
+ IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, is_hwc=False)
# assemble all the transforms
- image_ops = py_transforms.Compose([decode_op, random_resize_crop_bicubic,
- random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op])
+ image_ops = data_trans.Compose([decode_op, random_resize_crop_bicubic,
+ random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op])
rank_id = get_rank() if distributed else 0
rank_size = get_group_size() if distributed else 1
@@ -121,16 +119,16 @@ def create_dataset_val(batch_size=128, val_data_url='', workers=8, distributed=F
else:
scale_size = int(math.floor(input_size / DEFAULT_CROP_PCT))
- type_cast_op = c_transforms.TypeCast(mstype.int32)
- decode_op = py_vision.Decode()
- resize_op = py_vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
- center_crop = py_vision.CenterCrop(size=input_size)
- to_tensor = py_vision.ToTensor()
- normalize_op = py_vision.Normalize(
- IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
+ decode_op = vision.Decode(True)
+ resize_op = vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
+ center_crop = vision.CenterCrop(size=input_size)
+ to_tensor = vision.ToTensor()
+ normalize_op = vision.Normalize(
+ IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, is_hwc=False)
- image_ops = py_transforms.Compose([decode_op, resize_op, center_crop,
- to_tensor, normalize_op])
+ image_ops = data_trans.Compose([decode_op, resize_op, center_crop,
+ to_tensor, normalize_op])
dataset = dataset.map(input_columns=["label"], operations=type_cast_op,
num_parallel_workers=workers)
@@ -178,7 +176,7 @@ def create_dataset_cifar10(data_home, repeat_num=1, training=True, cifar_cfg=Non
rescale_op = vision.Rescale(1.0 / 255.0, 0.0)
normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.2471, 0.2435, 0.2616))
changeswap_op = vision.HWC2CHW()
- type_cast_op = c_transforms.TypeCast(mstype.int32)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
c_trans = []
if training:
diff --git a/research/cv/MaskedFaceRecognition/test_dataset.py b/research/cv/MaskedFaceRecognition/test_dataset.py
index 33720e01c..f35c23b7e 100644
--- a/research/cv/MaskedFaceRecognition/test_dataset.py
+++ b/research/cv/MaskedFaceRecognition/test_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@ create train or eval dataset.
"""
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.vision.c_transforms as CV
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.vision as CV
+import mindspore.dataset.transforms as C
from config import config
from dataset.Dataset import Dataset
diff --git a/research/cv/MaskedFaceRecognition/train_dataset.py b/research/cv/MaskedFaceRecognition/train_dataset.py
index 0d9d9664f..c3f8e5d92 100644
--- a/research/cv/MaskedFaceRecognition/train_dataset.py
+++ b/research/cv/MaskedFaceRecognition/train_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@ create train or eval dataset.
"""
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.vision.c_transforms as CV
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.vision as CV
+import mindspore.dataset.transforms as C
from config import config
from dataset.MGDataset import DistributedPKSampler, MGDataset
diff --git a/research/cv/NFNet/src/data/imagenet.py b/research/cv/NFNet/src/data/imagenet.py
index 8edae82eb..213425e7e 100644
--- a/research/cv/NFNet/src/data/imagenet.py
+++ b/research/cv/NFNet/src/data/imagenet.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,9 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from mindspore.dataset.vision.utils import Inter
from src.data.augment.auto_augment import _pil_interp, rand_augment_transform
@@ -95,12 +94,12 @@ def create_dataset_imagenet(dataset_dir, args, repeat_num=1, training=True):
vision.RandomCropDecodeResize(input_size, scale=(0.08, 1.0), ratio=(3 / 4, 4 / 3),
interpolation=Inter.BICUBIC),
vision.RandomHorizontalFlip(prob=0.5),
- py_vision.ToPIL()
+ vision.ToPIL()
]
transform_img += [rand_augment_transform(auto_augment, aa_params)]
transform_img += [
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False),
RandomErasing(args.re_prob, mode=args.re_mode, max_count=args.re_count)
]
else:
diff --git a/research/cv/Neighbor2Neighbor/src/dataset.py b/research/cv/Neighbor2Neighbor/src/dataset.py
index cf438509a..5cda4531b 100644
--- a/research/cv/Neighbor2Neighbor/src/dataset.py
+++ b/research/cv/Neighbor2Neighbor/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ import glob
import numpy as np
import PIL.Image as Image
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.vision as CV
class DataLoader_Imagenet_val:
'''DataLoader_Imagenet_val'''
diff --git a/research/cv/PAMTRI/MultiTaskNet/preprocess.py b/research/cv/PAMTRI/MultiTaskNet/preprocess.py
index 26bd80b31..dc872e137 100644
--- a/research/cv/PAMTRI/MultiTaskNet/preprocess.py
+++ b/research/cv/PAMTRI/MultiTaskNet/preprocess.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@ import argparse
from pathlib import Path
import mindspore.dataset as ds
import mindspore.common.dtype as mstype
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.transforms as C2
from src.dataset.data_manager import DatasetManager
from src.dataset.data_loader import ImageDataset
from src.dataset.transforms import Compose_Keypt, Resize_Keypt, ToTensor_Keypt, Normalize_Keypt
diff --git a/research/cv/PAMTRI/MultiTaskNet/src/dataset/dataset.py b/research/cv/PAMTRI/MultiTaskNet/src/dataset/dataset.py
index ef92874e6..e34b6bbf8 100644
--- a/research/cv/PAMTRI/MultiTaskNet/src/dataset/dataset.py
+++ b/research/cv/PAMTRI/MultiTaskNet/src/dataset/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,7 +16,7 @@
import os
import mindspore.dataset as ds
import mindspore.common.dtype as mstype
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.transforms as C2
from .data_manager import DatasetManager
from .data_loader import ImageDataset
diff --git a/research/cv/PAMTRI/MultiTaskNet/src/dataset/transforms.py b/research/cv/PAMTRI/MultiTaskNet/src/dataset/transforms.py
index e57f28f2a..303fb4314 100644
--- a/research/cv/PAMTRI/MultiTaskNet/src/dataset/transforms.py
+++ b/research/cv/PAMTRI/MultiTaskNet/src/dataset/transforms.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ import random
import collections
import cv2
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.vision as vision
if sys.version_info < (3, 3):
Iterable = collections.Iterable
@@ -54,7 +54,7 @@ class ToTensor_Keypt():
In the other cases, tensors are returned without scaling.
"""
def __init__(self):
- self.to_tensor = py_vision.ToTensor()
+ self.to_tensor = vision.ToTensor()
def __call__(self, img, vkeypt):
"""
@@ -104,7 +104,7 @@ class Normalize_Keypt():
self.mean.extend([mean_avg] * (channels_new - channels_orig))
self.std.extend([std_avg] * (channels_new - channels_orig))
- normalize = py_vision.Normalize(self.mean, self.std)
+ normalize = vision.Normalize(self.mean, self.std, is_hwc=False)
return normalize(tensor)
class Resize_Keypt():
diff --git a/research/cv/PAMTRI/PoseEstNet/src/dataset/dataset.py b/research/cv/PAMTRI/PoseEstNet/src/dataset/dataset.py
index 75a78a144..26031cd94 100644
--- a/research/cv/PAMTRI/PoseEstNet/src/dataset/dataset.py
+++ b/research/cv/PAMTRI/PoseEstNet/src/dataset/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ import copy
import json
from pathlib import Path
import mindspore.dataset as ds
-import mindspore.dataset.vision.py_transforms as py_vision
-from mindspore.dataset.transforms.py_transforms import Compose
+import mindspore.dataset.vision as vision
+from mindspore.dataset.transforms.transforms import Compose
from .veri import VeRiDataset
@@ -42,8 +42,8 @@ def create_dataset(cfg, data_dir, is_train=True):
"joints", "joints_vis"], num_parallel_workers=1, shuffle=False, num_shards=1, shard_id=0)
trans = Compose([
- py_vision.ToTensor(),
- py_vision.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+ vision.ToTensor(),
+ vision.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), is_hwc=False)
])
dataset = dataset.map(operations=trans, input_columns="input", num_parallel_workers=8)
diff --git a/research/cv/PAMTRI/PoseEstNet/trans.py b/research/cv/PAMTRI/PoseEstNet/trans.py
index 5f6c377c8..cb5a4b4ab 100644
--- a/research/cv/PAMTRI/PoseEstNet/trans.py
+++ b/research/cv/PAMTRI/PoseEstNet/trans.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,10 +21,10 @@ python trans.py --cfg config.yaml --ckpt_path Your.ckpt --data_dir datapath
import os
import argparse
import mindspore.dataset as ds
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.vision as vision
from mindspore import context
-from mindspore.dataset.transforms.py_transforms import Compose
+from mindspore.dataset.transforms.transforms import Compose
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.model import get_pose_net
@@ -62,8 +62,8 @@ if __name__ == '__main__':
num_parallel_workers=1, shuffle=False, num_shards=1, shard_id=0)
trans = Compose([
- py_vision.ToTensor(),
- py_vision.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+ vision.ToTensor(),
+ vision.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), is_hwc=False)
])
test_dataloader = test_dataloader.map(operations=trans, input_columns="input", num_parallel_workers=1)
diff --git a/research/cv/PDarts/src/dataset.py b/research/cv/PDarts/src/dataset.py
index e08fd37c9..2309590cd 100644
--- a/research/cv/PDarts/src/dataset.py
+++ b/research/cv/PDarts/src/dataset.py
@@ -15,8 +15,8 @@
"""Read train and eval data"""
import mindspore.dataset as ds
from mindspore.common import dtype as mstype
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from mindspore.dataset.vision.utils import Inter
diff --git a/research/cv/Pix2Pix/src/dataset/pix2pix_dataset.py b/research/cv/Pix2Pix/src/dataset/pix2pix_dataset.py
index 2e4e2111c..66efd4c18 100644
--- a/research/cv/Pix2Pix/src/dataset/pix2pix_dataset.py
+++ b/research/cv/Pix2Pix/src/dataset/pix2pix_dataset.py
@@ -22,7 +22,7 @@ import numpy as np
from PIL import Image
import mindspore
from mindspore import dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from src.utils.config import config
class pix2pixDataset():
diff --git a/research/cv/ReIDStrongBaseline/src/dataset.py b/research/cv/ReIDStrongBaseline/src/dataset.py
index 25fd91b51..6955853c0 100644
--- a/research/cv/ReIDStrongBaseline/src/dataset.py
+++ b/research/cv/ReIDStrongBaseline/src/dataset.py
@@ -18,7 +18,7 @@ import math
import random
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
import numpy as np
from PIL import Image
diff --git a/research/cv/RefineDet/src/dataset.py b/research/cv/RefineDet/src/dataset.py
index 9a400aa3f..bac268b49 100644
--- a/research/cv/RefineDet/src/dataset.py
+++ b/research/cv/RefineDet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -23,7 +23,7 @@ import numpy as np
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from .box_utils import jaccard_numpy, refinedet_bboxes_encode, box_init
diff --git a/research/cv/RefineNet/src/dataset.py b/research/cv/RefineNet/src/dataset.py
index 50f68bf46..22b40d204 100644
--- a/research/cv/RefineNet/src/dataset.py
+++ b/research/cv/RefineNet/src/dataset.py
@@ -15,7 +15,7 @@
""" dataset """
import numpy as np
import cv2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
import mindspore.dataset as ds
from mindspore.common import set_seed
cv2.setNumThreads(0)
diff --git a/research/cv/ResNeSt50/src/datasets/autoaug.py b/research/cv/ResNeSt50/src/datasets/autoaug.py
index 1eb4c5a1b..bfc1acd66 100644
--- a/research/cv/ResNeSt50/src/datasets/autoaug.py
+++ b/research/cv/ResNeSt50/src/datasets/autoaug.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@
import random
import numpy as np
import PIL
-import mindspore.dataset.vision.py_transforms as py_trans
+import mindspore.dataset.vision as vision
RESAMPLE_MODE = PIL.Image.BICUBIC
@@ -175,8 +175,8 @@ class RandAugment:
self.n = n
self.m = m
self.augment_list = rand_augment_list()
- self.to_pil = py_trans.ToPIL()
- self.to_tensor = py_trans.ToTensor()
+ self.to_pil = vision.ToPIL()
+ self.to_tensor = vision.ToTensor()
self.from_pil = from_pil
self.as_pil = as_pil
diff --git a/research/cv/ResNeSt50/src/datasets/dataset.py b/research/cv/ResNeSt50/src/datasets/dataset.py
index 3fbea0409..3aaa5e8e8 100644
--- a/research/cv/ResNeSt50/src/datasets/dataset.py
+++ b/research/cv/ResNeSt50/src/datasets/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,9 +16,8 @@
import os
import mindspore.dataset as dataset
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as V_C
-import mindspore.dataset.vision.py_transforms as P_C
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as V_C
from mindspore.common import dtype as mstype
from src.datasets.autoaug import RandAugment
@@ -44,10 +43,10 @@ def ImageNet(root, mode,
V_C.RandomResizedCrop(crop_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
V_C.RandomHorizontalFlip(prob=0.5),
V_C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
- P_C.ToPIL(),
+ V_C.ToPIL(),
RandAugment(2, 12, True, True),
- P_C.ToTensor(),
- P_C.Normalize(mean=mean, std=std)]
+ V_C.ToTensor(),
+ V_C.Normalize(mean=mean, std=std, is_hwc=False)]
else:
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
@@ -55,7 +54,7 @@ def ImageNet(root, mode,
V_C.Decode(),
V_C.Resize((320, 320)),
V_C.CenterCrop(256),
- V_C.Normalize(mean=mean, std=std),
+ V_C.Normalize(mean=mean, std=std, is_hwc=True),
V_C.HWC2CHW()]
else:
transform_img = transform
diff --git a/research/cv/SE-Net/src/dataset.py b/research/cv/SE-Net/src/dataset.py
index 0b8671e27..22750915f 100644
--- a/research/cv/SE-Net/src/dataset.py
+++ b/research/cv/SE-Net/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ create train or eval dataset.
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False):
diff --git a/research/cv/SE_ResNeXt50/src/dataset.py b/research/cv/SE_ResNeXt50/src/dataset.py
index 85f97ed70..9e12fefc0 100644
--- a/research/cv/SE_ResNeXt50/src/dataset.py
+++ b/research/cv/SE_ResNeXt50/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from src.config import imagenet_cfg
diff --git a/research/cv/SPPNet/src/dataset.py b/research/cv/SPPNet/src/dataset.py
index 108114c96..36aad4725 100644
--- a/research/cv/SPPNet/src/dataset.py
+++ b/research/cv/SPPNet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ Produce the dataset
import os
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.vision as CV
from mindspore.communication.management import get_rank, get_group_size
diff --git a/research/cv/STGAN/modelarts/dataset/celeba.py b/research/cv/STGAN/modelarts/dataset/celeba.py
index bf65edaab..c342793e1 100644
--- a/research/cv/STGAN/modelarts/dataset/celeba.py
+++ b/research/cv/STGAN/modelarts/dataset/celeba.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@ import os
import multiprocessing
import numpy as np
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore import context
from mindspore.context import ParallelMode
diff --git a/research/cv/STGAN/src/dataset/celeba.py b/research/cv/STGAN/src/dataset/celeba.py
index bf65edaab..c342793e1 100644
--- a/research/cv/STGAN/src/dataset/celeba.py
+++ b/research/cv/STGAN/src/dataset/celeba.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@ import os
import multiprocessing
import numpy as np
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore import context
from mindspore.context import ParallelMode
diff --git a/research/cv/SiamFC/ModelArts/start_train.py b/research/cv/SiamFC/ModelArts/start_train.py
index 303e8df7c..99f0074c4 100644
--- a/research/cv/SiamFC/ModelArts/start_train.py
+++ b/research/cv/SiamFC/ModelArts/start_train.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -30,7 +30,7 @@ from mindspore import nn
from mindspore.train import Model
from mindspore import Tensor
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
-import mindspore.dataset.transforms.py_transforms as py_transforms
+import mindspore.dataset.transforms as data_trans
from mindspore.train.serialization import load_checkpoint, export, load_param_into_net
from src.config import config
from src.create_lmdb import create_lmdb
@@ -104,12 +104,12 @@ def train(args):
set_seed(1234)
random_crop_size = config.instance_size - 2 * config.total_stride
- train_z_transforms = py_transforms.Compose([
+ train_z_transforms = data_trans.Compose([
RandomStretch(),
CenterCrop((config.exemplar_size, config.exemplar_size)),
ToTensor()
])
- train_x_transforms = py_transforms.Compose([
+ train_x_transforms = data_trans.Compose([
RandomStretch(),
RandomCrop((random_crop_size, random_crop_size),
config.max_translate),
diff --git a/research/cv/SiamFC/train.py b/research/cv/SiamFC/train.py
index c38b288d2..f4f2a6338 100644
--- a/research/cv/SiamFC/train.py
+++ b/research/cv/SiamFC/train.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -26,7 +26,7 @@ import mindspore.dataset as ds
from mindspore import nn
from mindspore.train import Model
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
-import mindspore.dataset.transforms.py_transforms as py_transforms
+import mindspore.dataset.transforms as data_trans
from src.config import config
from src.alexnet import SiameseAlexNet
from src.dataset import ImagnetVIDDataset
@@ -44,12 +44,12 @@ def train(data_dir):
set_seed(1234)
random_crop_size = config.instance_size - 2 * config.total_stride
- train_z_transforms = py_transforms.Compose([
+ train_z_transforms = data_trans.Compose([
RandomStretch(),
CenterCrop((config.exemplar_size, config.exemplar_size)),
ToTensor()
])
- train_x_transforms = py_transforms.Compose([
+ train_x_transforms = data_trans.Compose([
RandomStretch(),
RandomCrop((random_crop_size, random_crop_size),
config.max_translate),
diff --git a/research/cv/StarGAN/src/dataset.py b/research/cv/StarGAN/src/dataset.py
index 22813c290..0d31e4564 100644
--- a/research/cv/StarGAN/src/dataset.py
+++ b/research/cv/StarGAN/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import multiprocessing
import numpy as np
from PIL import Image
-import mindspore.dataset.vision.py_transforms as py_vision
-import mindspore.dataset.transforms.py_transforms as py_transforms
+import mindspore.dataset.vision as vision
+import mindspore.dataset.transforms as data_trans
import mindspore.dataset as de
from src.utils import DistributedSampler
@@ -146,14 +146,14 @@ def get_loader(data_root, attr_path, selected_attrs, crop_size=178, image_size=1
"""Build and return a data loader."""
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
- transform = [py_vision.ToPIL()]
+ transform = [vision.ToPIL()]
if mode == 'train':
- transform.append(py_vision.RandomHorizontalFlip())
- transform.append(py_vision.CenterCrop(crop_size))
- transform.append(py_vision.Resize([image_size, image_size]))
- transform.append(py_vision.ToTensor())
- transform.append(py_vision.Normalize(mean=mean, std=std))
- transform = py_transforms.Compose(transform)
+ transform.append(vision.RandomHorizontalFlip())
+ transform.append(vision.CenterCrop(crop_size))
+ transform.append(vision.Resize([image_size, image_size]))
+ transform.append(vision.ToTensor())
+ transform.append(vision.Normalize(mean=mean, std=std, is_hwc=False))
+ transform = data_trans.Compose(transform)
if dataset == 'CelebA':
dataset = CelebA(data_root, attr_path, selected_attrs, transform, mode)
diff --git a/research/cv/TCN/src/dataset.py b/research/cv/TCN/src/dataset.py
index f68fe3e6d..413923a1a 100644
--- a/research/cv/TCN/src/dataset.py
+++ b/research/cv/TCN/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@
"""
import numpy as np
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as CV
from mindspore import dtype as mstype
np.random.seed(0)
diff --git a/research/cv/TNT/src/data/imagenet.py b/research/cv/TNT/src/data/imagenet.py
index c0fb4832e..95cb688a7 100644
--- a/research/cv/TNT/src/data/imagenet.py
+++ b/research/cv/TNT/src/data/imagenet.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,9 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from mindspore.dataset.vision.utils import Inter
from src.data.augment.auto_augment import _pil_interp, rand_augment_transform
@@ -94,12 +93,12 @@ def create_dataset_imagenet(dataset_dir, args, repeat_num=1, training=True):
vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(3 / 4, 4 / 3),
interpolation=Inter.BICUBIC),
vision.RandomHorizontalFlip(prob=0.5),
- py_vision.ToPIL()
+ vision.ToPIL()
]
transform_img += [rand_augment_transform(auto_augment, aa_params)]
transform_img += [
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False),
RandomErasing(args.re_prob, mode=args.re_mode, max_count=args.re_count)
]
else:
diff --git a/research/cv/U-GAT-IT/src/dataset/dataset.py b/research/cv/U-GAT-IT/src/dataset/dataset.py
index 9a12bb4ab..fade60c4c 100644
--- a/research/cv/U-GAT-IT/src/dataset/dataset.py
+++ b/research/cv/U-GAT-IT/src/dataset/dataset.py
@@ -22,7 +22,7 @@ import math
import numpy as np
from PIL import Image
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.vision as vision
import mindspore.dataset as ds
from mindspore.communication.management import get_rank, get_group_size
@@ -30,18 +30,18 @@ from mindspore.communication.management import get_rank, get_group_size
def TrainDataLoader(img_size, data_path, dataset, batch_size, distributed):
""" DataLoader """
train_transform = [
- py_vision.ToPIL(),
- py_vision.RandomHorizontalFlip(),
- py_vision.Resize((img_size + 30, img_size + 30)),
- py_vision.RandomCrop(img_size),
- py_vision.ToTensor(),
- py_vision.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+ vision.ToPIL(),
+ vision.RandomHorizontalFlip(),
+ vision.Resize((img_size + 30, img_size + 30)),
+ vision.RandomCrop(img_size),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], is_hwc=False),
]
test_transform = [
- py_vision.ToPIL(),
- py_vision.Resize((img_size, img_size)),
- py_vision.ToTensor(),
- py_vision.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+ vision.ToPIL(),
+ vision.Resize((img_size, img_size)),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], is_hwc=False),
]
rank_size = 1
if distributed:
@@ -76,10 +76,10 @@ def TrainDataLoader(img_size, data_path, dataset, batch_size, distributed):
def TestDataLoader(img_size, data_path, dataset):
""" DataLoader """
test_transform = [
- py_vision.ToPIL(),
- py_vision.Resize((img_size, img_size)),
- py_vision.ToTensor(),
- py_vision.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+ vision.ToPIL(),
+ vision.Resize((img_size, img_size)),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], is_hwc=False),
]
testA_generator = GetDatasetGenerator(os.path.join(data_path, dataset), 'test')
testA = ds.GeneratorDataset(testA_generator, ["image_A", "image_B"], shuffle=False, num_parallel_workers=12)
diff --git a/research/cv/UNet3+/src/dataset.py b/research/cv/UNet3+/src/dataset.py
index 277178821..eb8ccb3df 100644
--- a/research/cv/UNet3+/src/dataset.py
+++ b/research/cv/UNet3+/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ from skimage.io import imread
from skimage import color
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.vision as CV
class Dataset:
'''Dataset'''
diff --git a/research/cv/VehicleNet/src/dataset.py b/research/cv/VehicleNet/src/dataset.py
index 58fc5ba38..04bd4ea81 100644
--- a/research/cv/VehicleNet/src/dataset.py
+++ b/research/cv/VehicleNet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -22,9 +22,8 @@ from mindspore.mindrecord import FileWriter
import mindspore.dataset as ds
from mindspore.dataset.vision import Inter
import mindspore.common.dtype as mstype
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.py_transforms as P_C
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
class Dataset:
"""Dataset"""
@@ -151,9 +150,9 @@ def create_vehiclenet_dataset(mindrecord_file, batch_size=1, device_num=1, is_tr
if is_training:
if use_aug:
- py_to_pil_op = P_C.ToPIL()
+ py_to_pil_op = C.ToPIL()
autoaugment_op = ImageNetPolicy()
- to_tensor_op = P_C.ToTensor()
+ to_tensor_op = C.ToTensor()
transforms_list += [py_to_pil_op, autoaugment_op, to_tensor_op]
resized_op = C.Resize([train_inputsize, train_inputsize], interpolation=Inter.BICUBIC)
diff --git a/research/cv/ViG/src/data/imagenet.py b/research/cv/ViG/src/data/imagenet.py
index 0e4ad3790..8fb4e3cb1 100644
--- a/research/cv/ViG/src/data/imagenet.py
+++ b/research/cv/ViG/src/data/imagenet.py
@@ -19,9 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from mindspore.dataset.vision.utils import Inter
from src.data.augment.auto_augment import _pil_interp, rand_augment_transform
@@ -94,12 +93,12 @@ def create_dataset_imagenet(dataset_dir, args, repeat_num=1, training=True):
vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(3 / 4, 4 / 3),
interpolation=Inter.BICUBIC),
vision.RandomHorizontalFlip(prob=0.5),
- py_vision.ToPIL()
+ vision.ToPIL()
]
transform_img += [rand_augment_transform(auto_augment, aa_params)]
transform_img += [
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False),
RandomErasing(args.re_prob, mode=args.re_mode, max_count=args.re_count)
]
else:
diff --git a/research/cv/Yolact++/src/dataset.py b/research/cv/Yolact++/src/dataset.py
index 6566291c4..e7b277e3b 100644
--- a/research/cv/Yolact++/src/dataset.py
+++ b/research/cv/Yolact++/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,7 +20,7 @@ import mmcv
import numpy as np
from numpy import random
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from src.config import MEANS
from src.config import yolact_plus_resnet50_config as cfg
diff --git a/research/cv/advanced_east/src/dataset.py b/research/cv/advanced_east/src/dataset.py
index ea040089c..74949f617 100644
--- a/research/cv/advanced_east/src/dataset.py
+++ b/research/cv/advanced_east/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ dataset.
import os
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.vision as vision
from mindspore.mindrecord import FileWriter
import numpy as np
from PIL import Image, ImageFile
diff --git a/research/cv/arcface/src/dataset.py b/research/cv/arcface/src/dataset.py
index 5cef4e2f3..b7ce783e3 100644
--- a/research/cv/arcface/src/dataset.py
+++ b/research/cv/arcface/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ python dataset.py
import os
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
diff --git a/research/cv/augvit/src/c10_dataset.py b/research/cv/augvit/src/c10_dataset.py
index b6042682f..ad36d6e66 100644
--- a/research/cv/augvit/src/c10_dataset.py
+++ b/research/cv/augvit/src/c10_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ create train or eval dataset.
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as c_transforms
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as data_trans
+import mindspore.dataset.vision as vision
def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch_size=1):
"""
@@ -63,7 +63,7 @@ def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch
rescale_op = vision.Rescale(1.0 / 255.0, 0.0)
normalize_op = vision.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
changeswap_op = vision.HWC2CHW()
- type_cast_op = c_transforms.TypeCast(mstype.int32)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
c_trans = []
if do_train:
diff --git a/research/cv/autoaugment/src/dataset/autoaugment/aug.py b/research/cv/autoaugment/src/dataset/autoaugment/aug.py
index 2c6a33b5e..83bda35cb 100644
--- a/research/cv/autoaugment/src/dataset/autoaugment/aug.py
+++ b/research/cv/autoaugment/src/dataset/autoaugment/aug.py
@@ -18,7 +18,7 @@ The Augment operator.
import random
-import mindspore.dataset.vision.py_transforms as py_trans
+import mindspore.dataset.vision as vision
from .third_party.policies import good_policies
from .third_party.policies import svhn_good_policies
@@ -61,8 +61,8 @@ class Augment:
self.policies = policies
self.oc = OperatorClasses()
- self.to_pil = py_trans.ToPIL()
- self.to_tensor = py_trans.ToTensor()
+ self.to_pil = vision.ToPIL()
+ self.to_tensor = vision.ToTensor()
self.enable_basic = enable_basic
self.random_crop = self.oc.RandomCrop(None)
@@ -73,7 +73,7 @@ class Augment:
self.as_pil = as_pil
self.normalize = None
if mean is not None and std is not None:
- self.normalize = py_trans.Normalize(mean, std)
+ self.normalize = vision.Normalize(mean, std, is_hwc=False)
def _apply(self, name, prob, level, img):
if random.random() > prob:
diff --git a/research/cv/autoaugment/src/dataset/autoaugment/ops/__init__.py b/research/cv/autoaugment/src/dataset/autoaugment/ops/__init__.py
index f001d2520..fba0aa2e9 100644
--- a/research/cv/autoaugment/src/dataset/autoaugment/ops/__init__.py
+++ b/research/cv/autoaugment/src/dataset/autoaugment/ops/__init__.py
@@ -16,7 +16,7 @@
Package initialization for custom PIL operators.
"""
-from mindspore.dataset.vision import py_transforms
+from mindspore.dataset.vision import transforms
from .crop import RandomCrop
from .cutout import RandomCutout
@@ -41,9 +41,9 @@ from .transform import (
class OperatorClasses:
"""OperatorClasses gathers all unary-image transformations listed in the
- Table 6 of https://arxiv.org/abs/1805.09501 and uses discrte levels for
- these transformations (The Sample Pairing transformation is an
- exception, which involes multiple images from a single mini-batch and
+ Table 6 of https://arxiv.org/abs/1805.09501 and uses discrete levels for
+ these transformations. (The Sample Pairing transformation is an
+ exception, which involves multiple images from a single mini-batch and
is not exploited in this implementation.)
Additionally, there are RandomHorizontalFlip and RandomCrop.
@@ -56,9 +56,9 @@ class OperatorClasses:
self.TranslateX = self.decorate(TranslateX, max_val=10, rounding=True)
self.TranslateY = self.decorate(TranslateY, max_val=10, rounding=True)
- self.AutoContrast = self.decorate(py_transforms.AutoContrast)
- self.Invert = self.decorate(py_transforms.Invert)
- self.Equalize = self.decorate(py_transforms.Equalize)
+ self.AutoContrast = self.decorate(transforms.AutoContrast)
+ self.Invert = self.decorate(transforms.Invert)
+ self.Equalize = self.decorate(transforms.Equalize)
self.Solarize = self.decorate(
Solarize, max_val=256, rounding=True, post=lambda x: 256 - x)
@@ -76,7 +76,7 @@ class OperatorClasses:
self.Cutout = self.decorate(RandomCutout, max_val=20, rounding=True)
self.RandomHorizontalFlip = self.decorate(
- py_transforms.RandomHorizontalFlip)
+ transforms.RandomHorizontalFlip)
self.RandomCrop = self.decorate(RandomCrop)
def vars(self):
diff --git a/research/cv/autoaugment/src/dataset/autoaugment/ops/crop.py b/research/cv/autoaugment/src/dataset/autoaugment/ops/crop.py
index dd42ad5d3..137236bea 100644
--- a/research/cv/autoaugment/src/dataset/autoaugment/ops/crop.py
+++ b/research/cv/autoaugment/src/dataset/autoaugment/ops/crop.py
@@ -16,17 +16,17 @@
RandomCrop operator.
"""
-from mindspore.dataset.vision import py_transforms
+from mindspore.dataset.vision import transforms
from mindspore.dataset.vision import py_transforms_util
from mindspore.dataset.vision import utils
-class RandomCrop(py_transforms.RandomCrop):
+class RandomCrop(transforms.RandomCrop):
"""
- RandomCrop inherits from py_transforms.RandomCrop but derives/uses the
+ RandomCrop inherits from transforms.RandomCrop but derives/uses the
original image size as the output size.
- Please refer to py_transforms.RandomCrop for argument specifications.
+ Please refer to transforms.RandomCrop for argument specifications.
"""
def __init__(self, padding=4, pad_if_needed=False,
diff --git a/research/cv/autoaugment/src/dataset/autoaugment/ops/cutout.py b/research/cv/autoaugment/src/dataset/autoaugment/ops/cutout.py
index 8d3e2d594..7286d24a3 100644
--- a/research/cv/autoaugment/src/dataset/autoaugment/ops/cutout.py
+++ b/research/cv/autoaugment/src/dataset/autoaugment/ops/cutout.py
@@ -21,7 +21,7 @@ import random
class RandomCutout:
"""
- RandomCutout is similar to py_transforms.Cutout but is simplified and
+ RandomCutout is similar to transforms.CutOut but is simplified and
crafted for PIL images.
Args:
diff --git a/research/cv/autoaugment/src/dataset/autoaugment/ops/ops_test.py b/research/cv/autoaugment/src/dataset/autoaugment/ops/ops_test.py
index ebe85e632..b4575363a 100644
--- a/research/cv/autoaugment/src/dataset/autoaugment/ops/ops_test.py
+++ b/research/cv/autoaugment/src/dataset/autoaugment/ops/ops_test.py
@@ -19,7 +19,7 @@ Visualization for testing purposes.
import matplotlib.pyplot as plt
import mindspore.dataset as ds
-import mindspore.dataset.vision.py_transforms as py_trans
+import mindspore.dataset.vision as vision
from mindspore import context
context.set_context(mode=context.PYNATIVE_MODE, device_target='CPU')
@@ -36,7 +36,7 @@ def compare(data_path, trans, output_path='./ops_test.png'):
# Apply transformations
dataset_augmented = dataset_orig.map(
- operations=[py_trans.ToPIL()] + trans + [py_trans.ToTensor()],
+ operations=[vision.ToPIL()] + trans + [vision.ToTensor()],
input_columns=['image'],
)
diff --git a/research/cv/autoaugment/src/dataset/autoaugment/ops/transform.py b/research/cv/autoaugment/src/dataset/autoaugment/ops/transform.py
index c69a7401c..7974af711 100644
--- a/research/cv/autoaugment/src/dataset/autoaugment/ops/transform.py
+++ b/research/cv/autoaugment/src/dataset/autoaugment/ops/transform.py
@@ -21,7 +21,6 @@ import random
from PIL import Image, __version__
-from mindspore.dataset.vision.py_transforms import DE_PY_INTER_MODE
from mindspore.dataset.vision.py_transforms_util import (
augment_error_message,
is_pil,
@@ -46,7 +45,7 @@ class ShearX:
raise TypeError('shear must be a single number.')
self.shear = shear
- self.resample = DE_PY_INTER_MODE[resample]
+ self.resample = resample
self.fill_value = fill_value
def __call__(self, img):
@@ -91,7 +90,7 @@ class ShearY:
raise TypeError('shear must be a single number.')
self.shear = shear
- self.resample = DE_PY_INTER_MODE[resample]
+ self.resample = resample
self.fill_value = fill_value
def __call__(self, img):
@@ -136,7 +135,7 @@ class TranslateX:
raise TypeError('translate must be a single number.')
self.translate = translate
- self.resample = DE_PY_INTER_MODE[resample]
+ self.resample = resample
self.fill_value = fill_value
def __call__(self, img):
@@ -181,7 +180,7 @@ class TranslateY:
raise TypeError('Translate must be a single number.')
self.translate = translate
- self.resample = DE_PY_INTER_MODE[resample]
+ self.resample = resample
self.fill_value = fill_value
def __call__(self, img):
@@ -212,12 +211,12 @@ class TranslateY:
class Rotate:
"""
- Rotate is similar to py_vision.RandomRotation but uses a fixed degree.
+ Rotate is similar to mindspore.dataset.vision.transform's RandomRotation but uses a fixed degree.
Args:
degree (int): the degree to rotate.
- Please refer to py_transforms.RandomRotation for more argument
+ Please refer to mindspore.dataset.vision.transforms Rotation for more argument
specifications.
"""
@@ -229,7 +228,7 @@ class Rotate:
raise TypeError('degree must be a single number.')
self.degree = degree
- self.resample = DE_PY_INTER_MODE[resample]
+ self.resample = resample
self.expand = expand
self.center = center
self.fill_value = fill_value
diff --git a/research/cv/autoaugment/src/dataset/cifar10.py b/research/cv/autoaugment/src/dataset/cifar10.py
index 25724a9cf..166c78659 100644
--- a/research/cv/autoaugment/src/dataset/cifar10.py
+++ b/research/cv/autoaugment/src/dataset/cifar10.py
@@ -20,8 +20,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
from mindspore.communication.management import get_group_size
from mindspore.communication.management import get_rank
from mindspore.communication.management import init
diff --git a/research/cv/autoaugment/src/dataset/svhn_dataset.py b/research/cv/autoaugment/src/dataset/svhn_dataset.py
index fa95bf169..070b877ef 100644
--- a/research/cv/autoaugment/src/dataset/svhn_dataset.py
+++ b/research/cv/autoaugment/src/dataset/svhn_dataset.py
@@ -20,8 +20,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
from mindspore.communication.management import get_group_size
from mindspore.communication.management import get_rank
from mindspore.communication.management import init
diff --git a/research/cv/cait/src/data/imagenet.py b/research/cv/cait/src/data/imagenet.py
index 1539daa2c..2eb8354c8 100644
--- a/research/cv/cait/src/data/imagenet.py
+++ b/research/cv/cait/src/data/imagenet.py
@@ -19,9 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from src.data.augment.auto_augment import rand_augment_transform
from src.data.augment.mixup import Mixup
@@ -91,26 +90,26 @@ def create_dataset_imagenet(dataset_dir, args, repeat_num=1, training=True):
assert auto_augment.startswith('rand')
transform_img = [
vision.Decode(),
- py_vision.ToPIL(),
+ vision.ToPIL(),
RandomResizedCropAndInterpolation(size=args.image_size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
interpolation=interpolation),
- py_vision.RandomHorizontalFlip(prob=0.5),
+ vision.RandomHorizontalFlip(prob=0.5),
]
transform_img += [rand_augment_transform(auto_augment, aa_params)]
transform_img += [
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std)]
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False)]
if args.re_prob > 0.:
transform_img += [RandomErasing(args.re_prob, mode=args.re_mode, max_count=args.re_count)]
else:
# test transform complete
transform_img = [
vision.Decode(),
- py_vision.ToPIL(),
+ vision.ToPIL(),
Resize(int(args.image_size / args.crop_pct), interpolation="bicubic"),
- py_vision.CenterCrop(image_size),
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std)
+ vision.CenterCrop(image_size),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False)
]
transform_label = C.TypeCast(mstype.int32)
diff --git a/research/cv/cct/src/data/cifar10.py b/research/cv/cct/src/data/cifar10.py
index dceac7f86..dbfe0dcdb 100644
--- a/research/cv/cct/src/data/cifar10.py
+++ b/research/cv/cct/src/data/cifar10.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from src.data.augment.auto_augment import rand_augment_transform
from src.data.augment.mixup import Mixup
@@ -89,15 +89,15 @@ def create_dataset_cifar10(dataset_dir, args, repeat_num=1, training=True):
auto_augment = args.auto_augment
assert auto_augment.startswith('rand')
transform_img = [
- py_vision.ToPIL(),
+ vision.ToPIL(),
RandomResizedCropAndInterpolation(size=args.image_size, scale=(0.8, 1.0), ratio=(3. / 4., 4. / 3.),
interpolation=interpolation),
- py_vision.RandomHorizontalFlip(prob=0.5),
+ vision.RandomHorizontalFlip(prob=0.5),
]
transform_img += [rand_augment_transform(auto_augment, aa_params)]
transform_img += [
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std)]
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False)]
if args.re_prob > 0.:
transform_img += [RandomErasing(args.re_prob, mode=args.re_mode, max_count=args.re_count)]
else:
@@ -105,10 +105,10 @@ def create_dataset_cifar10(dataset_dir, args, repeat_num=1, training=True):
std = [0.2470, 0.2435, 0.2616]
# test transform complete
transform_img = [
- py_vision.ToPIL(),
+ vision.ToPIL(),
Resize(int(image_size), interpolation="bicubic"),
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std)
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False)
]
transform_label = C.TypeCast(mstype.int32)
diff --git a/research/cv/cct/src/data/imagenet.py b/research/cv/cct/src/data/imagenet.py
index e512c685c..7d1f0a2f3 100644
--- a/research/cv/cct/src/data/imagenet.py
+++ b/research/cv/cct/src/data/imagenet.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,9 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from src.data.augment.auto_augment import rand_augment_transform
from src.data.augment.mixup import Mixup
@@ -92,26 +91,26 @@ def create_dataset_imagenet(dataset_dir, args, repeat_num=1, training=True):
assert auto_augment.startswith('rand')
transform_img = [
vision.Decode(),
- py_vision.ToPIL(),
+ vision.ToPIL(),
RandomResizedCropAndInterpolation(size=args.image_size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
interpolation=interpolation),
- py_vision.RandomHorizontalFlip(prob=0.5),
+ vision.RandomHorizontalFlip(prob=0.5),
]
transform_img += [rand_augment_transform(auto_augment, aa_params)]
transform_img += [
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std)]
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False)]
if args.re_prob > 0.:
transform_img += [RandomErasing(args.re_prob, mode=args.re_mode, max_count=args.re_count)]
else:
# test transform complete
transform_img = [
vision.Decode(),
- py_vision.ToPIL(),
+ vision.ToPIL(),
Resize(int(args.image_size / args.crop_pct), interpolation="bicubic"),
- py_vision.CenterCrop(image_size),
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std)
+ vision.CenterCrop(image_size),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False)
]
transform_label = C.TypeCast(mstype.int32)
diff --git a/research/cv/convnext/src/data/imagenet.py b/research/cv/convnext/src/data/imagenet.py
index 7aa5c2c63..5a0cdcffe 100644
--- a/research/cv/convnext/src/data/imagenet.py
+++ b/research/cv/convnext/src/data/imagenet.py
@@ -19,9 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from mindspore.dataset.vision.utils import Inter
from src.data.augment.auto_augment import pil_interp, rand_augment_transform
@@ -94,12 +93,12 @@ def create_dataset_imagenet(dataset_dir, args, repeat_num=1, training=True):
vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(3 / 4, 4 / 3),
interpolation=Inter.PILCUBIC),
vision.RandomHorizontalFlip(prob=0.5),
- py_vision.ToPIL()
+ vision.ToPIL()
]
transform_img += [rand_augment_transform(auto_augment, aa_params)]
transform_img += [
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False),
RandomErasing(args.re_prob, mode=args.re_mode, max_count=args.re_count)
]
else:
diff --git a/research/cv/dcgan/src/dataset.py b/research/cv/dcgan/src/dataset.py
index 74a467a75..4e0c06772 100644
--- a/research/cv/dcgan/src/dataset.py
+++ b/research/cv/dcgan/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ import numpy as np
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from src.config import dcgan_imagenet_cfg, dcgan_cifar10_cfg
diff --git a/research/cv/delf/src/data_augmentation_parallel.py b/research/cv/delf/src/data_augmentation_parallel.py
index 1428b0727..fe5571299 100755
--- a/research/cv/delf/src/data_augmentation_parallel.py
+++ b/research/cv/delf/src/data_augmentation_parallel.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@ import os
from mindspore import dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
def create_dataset(data_path, image_size=321, batch_size=32, seed=0, augmentation=True, repeat=True):
"""create dataset"""
diff --git a/research/cv/ecolite/src/transforms.py b/research/cv/ecolite/src/transforms.py
index 06e4d14be..c72ee0100 100644
--- a/research/cv/ecolite/src/transforms.py
+++ b/research/cv/ecolite/src/transforms.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@ import numbers
import math
from PIL import Image, ImageOps
import numpy as np
-from mindspore.dataset.vision import py_transforms as py_trans
+import mindspore.dataset.vision as vision
class GroupRandomCrop:
@@ -55,7 +55,7 @@ class GroupCenterCrop:
"""GroupCenterCrop"""
def __init__(self, size):
- self.worker = py_trans.CenterCrop(size)
+ self.worker = vision.CenterCrop(size)
def __call__(self, img_group):
return [self.worker(img) for img in img_group]
diff --git a/research/cv/efficientnet-b0/src/dataset.py b/research/cv/efficientnet-b0/src/dataset.py
index 56602a36c..64906ba88 100644
--- a/research/cv/efficientnet-b0/src/dataset.py
+++ b/research/cv/efficientnet-b0/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@ Data operations, will be used in train.py and eval.py
"""
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, batch_size=16, device_num=1, rank=0):
diff --git a/research/cv/efficientnet-b1/src/dataset.py b/research/cv/efficientnet-b1/src/dataset.py
index 1c87e9973..373f0995f 100644
--- a/research/cv/efficientnet-b1/src/dataset.py
+++ b/research/cv/efficientnet-b1/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,8 +16,8 @@
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
diff --git a/research/cv/efficientnet-b2/src/dataset.py b/research/cv/efficientnet-b2/src/dataset.py
index c72f7fd3b..696400856 100644
--- a/research/cv/efficientnet-b2/src/dataset.py
+++ b/research/cv/efficientnet-b2/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@ Data operations, will be used in train.py and eval.py
"""
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, batch_size=16, device_num=1, rank=0):
diff --git a/research/cv/efficientnet-b3/src/dataset.py b/research/cv/efficientnet-b3/src/dataset.py
index 6c1347d02..5c571624e 100644
--- a/research/cv/efficientnet-b3/src/dataset.py
+++ b/research/cv/efficientnet-b3/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@ Data operations, will be used in train.py and eval.py
"""
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, batch_size=16, device_num=1, rank=0):
diff --git a/research/cv/efficientnetv2/src/data/imagenet_finetune.py b/research/cv/efficientnetv2/src/data/imagenet_finetune.py
index 600615183..1029f0081 100644
--- a/research/cv/efficientnetv2/src/data/imagenet_finetune.py
+++ b/research/cv/efficientnetv2/src/data/imagenet_finetune.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
import numpy as np
from .data_utils.moxing_adapter import sync_data
diff --git a/research/cv/eppmvsnet/src/blendedmvs.py b/research/cv/eppmvsnet/src/blendedmvs.py
index 21acd1e04..123a6ba0c 100644
--- a/research/cv/eppmvsnet/src/blendedmvs.py
+++ b/research/cv/eppmvsnet/src/blendedmvs.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -22,7 +22,7 @@ import cv2
import numpy as np
from PIL import Image
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.vision as vision
from src.utils import read_pfm
@@ -202,15 +202,15 @@ class BlendedMVSDataset:
def define_transforms(self):
if self.training_tag and self.split == 'train': # you can add augmentation here
self.transform = Compose([
- py_vision.ToTensor(),
- py_vision.Normalize(mean=[0.485, 0.456, 0.406],
- std=[0.229, 0.224, 0.225]),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225], is_hwc=False),
])
else:
self.transform = Compose([
- py_vision.ToTensor(),
- py_vision.Normalize(mean=[0.485, 0.456, 0.406],
- std=[0.229, 0.224, 0.225]),
+ vision.ToTensor(),
+ vision.Normalize(mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225], is_hwc=False),
])
def __len__(self):
diff --git a/research/cv/faster_rcnn_dcn/src/dataset.py b/research/cv/faster_rcnn_dcn/src/dataset.py
index 1f104189d..23697df6a 100644
--- a/research/cv/faster_rcnn_dcn/src/dataset.py
+++ b/research/cv/faster_rcnn_dcn/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -22,7 +22,7 @@ from numpy import random
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
diff --git a/research/cv/fishnet99/src/dataset.py b/research/cv/fishnet99/src/dataset.py
index a5e72f198..8be01542b 100644
--- a/research/cv/fishnet99/src/dataset.py
+++ b/research/cv/fishnet99/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from src.config import imagenet_cfg
diff --git a/research/cv/ghostnet/src/dataset.py b/research/cv/ghostnet/src/dataset.py
index 05acf36e1..28a272e3f 100644
--- a/research/cv/ghostnet/src/dataset.py
+++ b/research/cv/ghostnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,8 +16,8 @@
import os
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, repeat_num=1, infer_910=True, device_id=0, batch_size=128):
diff --git a/research/cv/ghostnet_quant/src/dataset.py b/research/cv/ghostnet_quant/src/dataset.py
index edee462b4..6c56662cb 100644
--- a/research/cv/ghostnet_quant/src/dataset.py
+++ b/research/cv/ghostnet_quant/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,9 +18,8 @@ create train or eval dataset.
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.vision.c_transforms as C
-import mindspore.dataset.transforms.vision.py_transforms as P
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.dataset.transforms.vision import Inter
@@ -73,18 +72,18 @@ def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch
change_swap_op = C.HWC2CHW()
# define python operations
- decode_p = P.Decode()
+ decode_p = C.Decode(True)
if model == 'ghostnet-600':
s = 274
c = 240
else:
s = 256
c = 224
- resize_p = P.Resize(s, interpolation=Inter.BICUBIC)
- center_crop_p = P.CenterCrop(c)
- totensor = P.ToTensor()
- normalize_p = P.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
- composeop = P.ComposeOp(
+ resize_p = C.Resize(s, interpolation=Inter.BICUBIC)
+ center_crop_p = C.CenterCrop(c)
+ totensor = C.ToTensor()
+ normalize_p = C.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), is_hwc=False)
+ composeop = C.ComposeOp(
[decode_p, resize_p, center_crop_p, totensor, normalize_p])
if do_train:
trans = [resize_crop_op, horizontal_flip_op, color_op,
diff --git a/research/cv/glore_res/src/autoaugment.py b/research/cv/glore_res/src/autoaugment.py
index 35ef907c1..0cc9e6569 100644
--- a/research/cv/glore_res/src/autoaugment.py
+++ b/research/cv/glore_res/src/autoaugment.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -15,8 +15,8 @@
"""define autoaugment"""
import os
import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.c_transforms as c_transforms
-import mindspore.dataset.vision.c_transforms as c_vision
+import mindspore.dataset.transforms as data_trans
+import mindspore.dataset.vision as vision
from mindspore import dtype as mstype
from mindspore.communication.management import init, get_rank, get_group_size
@@ -34,101 +34,101 @@ def int_parameter(level, maxval):
def shear_x(level):
v = float_parameter(level, 0.3)
- return c_transforms.RandomChoice(
- [c_vision.RandomAffine(degrees=0, shear=(-v, -v)), c_vision.RandomAffine(degrees=0, shear=(v, v))])
+ return data_trans.RandomChoice(
+ [vision.RandomAffine(degrees=0, shear=(-v, -v)), vision.RandomAffine(degrees=0, shear=(v, v))])
def shear_y(level):
v = float_parameter(level, 0.3)
- return c_transforms.RandomChoice(
- [c_vision.RandomAffine(degrees=0, shear=(0, 0, -v, -v)), c_vision.RandomAffine(degrees=0, shear=(0, 0, v, v))])
+ return data_trans.RandomChoice(
+ [vision.RandomAffine(degrees=0, shear=(0, 0, -v, -v)), vision.RandomAffine(degrees=0, shear=(0, 0, v, v))])
def translate_x(level):
v = float_parameter(level, 150 / 331)
- return c_transforms.RandomChoice(
- [c_vision.RandomAffine(degrees=0, translate=(-v, -v)), c_vision.RandomAffine(degrees=0, translate=(v, v))])
+ return data_trans.RandomChoice(
+ [vision.RandomAffine(degrees=0, translate=(-v, -v)), vision.RandomAffine(degrees=0, translate=(v, v))])
def translate_y(level):
v = float_parameter(level, 150 / 331)
- return c_transforms.RandomChoice([c_vision.RandomAffine(degrees=0, translate=(0, 0, -v, -v)),
- c_vision.RandomAffine(degrees=0, translate=(0, 0, v, v))])
+ return data_trans.RandomChoice([vision.RandomAffine(degrees=0, translate=(0, 0, -v, -v)),
+ vision.RandomAffine(degrees=0, translate=(0, 0, v, v))])
def color_impl(level):
v = float_parameter(level, 1.8) + 0.1
- return c_vision.RandomColor(degrees=(v, v))
+ return vision.RandomColor(degrees=(v, v))
def rotate_impl(level):
v = int_parameter(level, 30)
- return c_transforms.RandomChoice(
- [c_vision.RandomRotation(degrees=(-v, -v)), c_vision.RandomRotation(degrees=(v, v))])
+ return data_trans.RandomChoice(
+ [vision.RandomRotation(degrees=(-v, -v)), vision.RandomRotation(degrees=(v, v))])
def solarize_impl(level):
level = int_parameter(level, 256)
v = 256 - level
- return c_vision.RandomSolarize(threshold=(0, v))
+ return vision.RandomSolarize(threshold=(0, v))
def posterize_impl(level):
level = int_parameter(level, 4)
v = 4 - level
- return c_vision.RandomPosterize(bits=(v, v))
+ return vision.RandomPosterize(bits=(v, v))
def contrast_impl(level):
v = float_parameter(level, 1.8) + 0.1
- return c_vision.RandomColorAdjust(contrast=(v, v))
+ return vision.RandomColorAdjust(contrast=(v, v))
def autocontrast_impl(level):
- return c_vision.AutoContrast()
+ return vision.AutoContrast()
def sharpness_impl(level):
v = float_parameter(level, 1.8) + 0.1
- return c_vision.RandomSharpness(degrees=(v, v))
+ return vision.RandomSharpness(degrees=(v, v))
def brightness_impl(level):
v = float_parameter(level, 1.8) + 0.1
- return c_vision.RandomColorAdjust(brightness=(v, v))
+ return vision.RandomColorAdjust(brightness=(v, v))
# define the Auto Augmentation policy
imagenet_policy = [
[(posterize_impl(8), 0.4), (rotate_impl(9), 0.6)],
[(solarize_impl(5), 0.6), (autocontrast_impl(5), 0.6)],
- [(c_vision.Equalize(), 0.8), (c_vision.Equalize(), 0.6)],
+ [(vision.Equalize(), 0.8), (vision.Equalize(), 0.6)],
[(posterize_impl(7), 0.6), (posterize_impl(6), 0.6)],
- [(c_vision.Equalize(), 0.4), (solarize_impl(4), 0.2)],
+ [(vision.Equalize(), 0.4), (solarize_impl(4), 0.2)],
- [(c_vision.Equalize(), 0.4), (rotate_impl(8), 0.8)],
- [(solarize_impl(3), 0.6), (c_vision.Equalize(), 0.6)],
- [(posterize_impl(5), 0.8), (c_vision.Equalize(), 1.0)],
+ [(vision.Equalize(), 0.4), (rotate_impl(8), 0.8)],
+ [(solarize_impl(3), 0.6), (vision.Equalize(), 0.6)],
+ [(posterize_impl(5), 0.8), (vision.Equalize(), 1.0)],
[(rotate_impl(3), 0.2), (solarize_impl(8), 0.6)],
- [(c_vision.Equalize(), 0.6), (posterize_impl(6), 0.4)],
+ [(vision.Equalize(), 0.6), (posterize_impl(6), 0.4)],
[(rotate_impl(8), 0.8), (color_impl(0), 0.4)],
- [(rotate_impl(9), 0.4), (c_vision.Equalize(), 0.6)],
- [(c_vision.Equalize(), 0.0), (c_vision.Equalize(), 0.8)],
- [(c_vision.Invert(), 0.6), (c_vision.Equalize(), 1.0)],
+ [(rotate_impl(9), 0.4), (vision.Equalize(), 0.6)],
+ [(vision.Equalize(), 0.0), (vision.Equalize(), 0.8)],
+ [(vision.Invert(), 0.6), (vision.Equalize(), 1.0)],
[(color_impl(4), 0.6), (contrast_impl(8), 1.0)],
[(rotate_impl(8), 0.8), (color_impl(2), 1.0)],
[(color_impl(8), 0.8), (solarize_impl(7), 0.8)],
- [(sharpness_impl(7), 0.4), (c_vision.Invert(), 0.6)],
- [(shear_x(5), 0.6), (c_vision.Equalize(), 1.0)],
- [(color_impl(0), 0.4), (c_vision.Equalize(), 0.6)],
+ [(sharpness_impl(7), 0.4), (vision.Invert(), 0.6)],
+ [(shear_x(5), 0.6), (vision.Equalize(), 1.0)],
+ [(color_impl(0), 0.4), (vision.Equalize(), 0.6)],
- [(c_vision.Equalize(), 0.4), (solarize_impl(4), 0.2)],
+ [(vision.Equalize(), 0.4), (solarize_impl(4), 0.2)],
[(solarize_impl(5), 0.6), (autocontrast_impl(5), 0.6)],
- [(c_vision.Invert(), 0.6), (c_vision.Equalize(), 1.0)],
+ [(vision.Invert(), 0.6), (vision.Equalize(), 1.0)],
[(color_impl(4), 0.6), (contrast_impl(8), 1.0)],
- [(c_vision.Equalize(), 0.8), (c_vision.Equalize(), 0.6)],
+ [(vision.Equalize(), 0.8), (vision.Equalize(), 0.6)],
]
@@ -153,19 +153,19 @@ def autoaugment(dataset_path, repeat_num=1, batch_size=32, target="Ascend"):
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
trans = [
- c_vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+ vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
]
post_trans = [
- c_vision.RandomHorizontalFlip(prob=0.5),
- c_vision.Normalize(mean=mean, std=std),
- c_vision.HWC2CHW()
+ vision.RandomHorizontalFlip(prob=0.5),
+ vision.Normalize(mean=mean, std=std),
+ vision.HWC2CHW()
]
dataset = ds.map(operations=trans, input_columns="image")
- dataset = dataset.map(operations=c_vision.RandomSelectSubpolicy(imagenet_policy), input_columns=["image"])
+ dataset = dataset.map(operations=vision.RandomSelectSubpolicy(imagenet_policy), input_columns=["image"])
dataset = dataset.map(operations=post_trans, input_columns="image")
- type_cast_op = c_transforms.TypeCast(mstype.int32)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
dataset = dataset.map(operations=type_cast_op, input_columns="label")
# apply the batch operation
dataset = dataset.batch(batch_size, drop_remainder=True)
diff --git a/research/cv/glore_res/src/dataset.py b/research/cv/glore_res/src/dataset.py
index b7a5fdc9c..9a6ecc0d0 100644
--- a/research/cv/glore_res/src/dataset.py
+++ b/research/cv/glore_res/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,9 +18,9 @@ create train or eval dataset.
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.dataset.vision import Inter
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
from src.transform import RandAugment
from src.config import config
diff --git a/research/cv/glore_res/src/transform.py b/research/cv/glore_res/src/transform.py
index cba6ea73a..83939be14 100644
--- a/research/cv/glore_res/src/transform.py
+++ b/research/cv/glore_res/src/transform.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,7 +16,7 @@
random augment class
"""
import numpy as np
-import mindspore.dataset.vision.py_transforms as P
+import mindspore.dataset.vision as V
from src import transform_utils
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
@@ -37,9 +37,9 @@ class RandAugment:
# assert the imgs object are pil_images
ret_imgs = []
ret_labels = []
- py_to_pil_op = P.ToPIL()
- to_tensor = P.ToTensor()
- normalize_op = P.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+ py_to_pil_op = V.ToPIL()
+ to_tensor = V.ToTensor()
+ normalize_op = V.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, is_hwc=False)
rand_augment_ops = transform_utils.rand_augment_transform(self.config_str, self.hparams)
for i, image in enumerate(imgs):
img_pil = py_to_pil_op(image)
diff --git a/research/cv/hardnet/src/dataset.py b/research/cv/hardnet/src/dataset.py
index 1955ca545..227033753 100644
--- a/research/cv/hardnet/src/dataset.py
+++ b/research/cv/hardnet/src/dataset.py
@@ -18,8 +18,8 @@ Data operations, will be used in train.py and eval.py
import os
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
def create_dataset_ImageNet(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
diff --git a/research/cv/hed/src/dataset.py b/research/cv/hed/src/dataset.py
index a74551d2a..842d1438d 100644
--- a/research/cv/hed/src/dataset.py
+++ b/research/cv/hed/src/dataset.py
@@ -19,8 +19,8 @@ import cv2
import numpy as np
import mindspore
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C2
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.vision as C2
+import mindspore.dataset.transforms as C
mindspore.set_seed(1)
def prepare_image_cv2(im):
diff --git a/research/cv/ibnnet/src/dataset.py b/research/cv/ibnnet/src/dataset.py
index fef7aae68..62c6c4308 100644
--- a/research/cv/ibnnet/src/dataset.py
+++ b/research/cv/ibnnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ python dataset.py
import os
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
diff --git a/research/cv/inception_resnet_v2/src/dataset.py b/research/cv/inception_resnet_v2/src/dataset.py
index 81912007b..0316efc28 100644
--- a/research/cv/inception_resnet_v2/src/dataset.py
+++ b/research/cv/inception_resnet_v2/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, config=None):
diff --git a/research/cv/lresnet100e_ir/src/dataset.py b/research/cv/lresnet100e_ir/src/dataset.py
index d0ddade90..a03dadfb5 100644
--- a/research/cv/lresnet100e_ir/src/dataset.py
+++ b/research/cv/lresnet100e_ir/src/dataset.py
@@ -15,8 +15,8 @@
"""Create train or eval dataset."""
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
def create_dataset(dataset_path, do_train, img_shape, repeat_num=1, batch_size=32, run_distribute=False):
diff --git a/research/cv/mae/src/datasets/dataset.py b/research/cv/mae/src/datasets/dataset.py
index d1bcd1664..bb9ab3112 100644
--- a/research/cv/mae/src/datasets/dataset.py
+++ b/research/cv/mae/src/datasets/dataset.py
@@ -23,9 +23,8 @@ import numpy as np
import mindspore.dataset as de
import mindspore.common.dtype as mstype
from mindspore.dataset.vision.utils import Inter
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.vision.py_transforms as P
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from src.datasets.mixup import Mixup
from src.datasets.random_erasing import RandomErasing
@@ -111,12 +110,12 @@ def create_dataset(dataset_path,
C.RandomCropDecodeResize(image_size, scale=(crop_min, 1.0), ratio=(3 / 4, 4 / 3),
interpolation=interpolation),
C.RandomHorizontalFlip(prob=hflip),
- P.ToPIL()
+ C.ToPIL()
]
trans += [rand_augment_transform(auto_augment, aa_params)]
trans += [
- P.ToTensor(),
- P.Normalize(mean=mean, std=std),
+ C.ToTensor(),
+ C.Normalize(mean=mean, std=std, is_hwc=False),
RandomErasing(probability=re_prop, mode=re_mode, max_count=re_count)
]
@@ -127,7 +126,7 @@ def create_dataset(dataset_path,
C.Decode(),
C.Resize(int(256 / 224 * image_size), interpolation=interpolation),
C.CenterCrop(image_size),
- C.Normalize(mean=mean, std=std),
+ C.Normalize(mean=mean, std=std, is_hwc=True),
C.HWC2CHW()
]
diff --git a/research/cv/mae/src/datasets/imagenet.py b/research/cv/mae/src/datasets/imagenet.py
index 3c6894daa..e39fc40c6 100644
--- a/research/cv/mae/src/datasets/imagenet.py
+++ b/research/cv/mae/src/datasets/imagenet.py
@@ -21,7 +21,7 @@ from PIL import Image
import mindspore.dataset as de
from mindspore.dataset.vision import Inter
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
class DataLoader:
diff --git a/research/cv/meta-baseline/src/data/mini_Imagenet.py b/research/cv/meta-baseline/src/data/mini_Imagenet.py
index 23275afed..da52ceda5 100644
--- a/research/cv/meta-baseline/src/data/mini_Imagenet.py
+++ b/research/cv/meta-baseline/src/data/mini_Imagenet.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ MiniImageNet
import os
import pickle
import numpy as np
-import mindspore.dataset.vision.py_transforms as py_transforms
-from mindspore.dataset.transforms.py_transforms import Compose
+import mindspore.dataset.vision as vision
+from mindspore.dataset.transforms.transforms import Compose
from PIL import Image
@@ -45,19 +45,19 @@ class MiniImageNet:
label = [x - min_label for x in label]
image_size = 84
- normalize = py_transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+ normalize = vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False)
if split == 'train':
self.transforms = Compose([
decode,
- py_transforms.RandomCrop(image_size, padding=4),
- py_transforms.ToTensor(),
+ vision.RandomCrop(image_size, padding=4),
+ vision.ToTensor(),
normalize
])
else:
self.transforms = Compose([
decode,
- py_transforms.Resize(image_size),
- py_transforms.ToTensor(),
+ vision.Resize(image_size),
+ vision.ToTensor(),
normalize
])
data = [self.transforms(x)[0] for x in data]
diff --git a/research/cv/metric_learn/src/dataset.py b/research/cv/metric_learn/src/dataset.py
index 882363fd0..f2d4b75d4 100644
--- a/research/cv/metric_learn/src/dataset.py
+++ b/research/cv/metric_learn/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@ create train or eval dataset.
"""
import mindspore.common.dtype as mstype
import mindspore.dataset as dss
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.dataset.vision import Inter
def create_dataset0(dataset_generator, do_train, batch_size=80, device_num=1, rank_id=0):
diff --git a/research/cv/mnasnet/src/dataset.py b/research/cv/mnasnet/src/dataset.py
index e9797e9ae..191a15a4d 100644
--- a/research/cv/mnasnet/src/dataset.py
+++ b/research/cv/mnasnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@ Data operations, will be used in train.py and eval.py
"""
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, batch_size=16, device_num=1, rank=0):
diff --git a/research/cv/mobilenetV3_small_x1_0/src/dataset.py b/research/cv/mobilenetV3_small_x1_0/src/dataset.py
index 5d3dd274e..626654039 100644
--- a/research/cv/mobilenetV3_small_x1_0/src/dataset.py
+++ b/research/cv/mobilenetV3_small_x1_0/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import multiprocessing
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
def create_dataset(dataset_path, do_train, batch_size=16, device_num=1, rank=0):
diff --git a/research/cv/mobilenetv3_large/src/dataset.py b/research/cv/mobilenetv3_large/src/dataset.py
index 49ebdacc5..eb28ad6ea 100644
--- a/research/cv/mobilenetv3_large/src/dataset.py
+++ b/research/cv/mobilenetv3_large/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -15,8 +15,8 @@
"""Create train or eval dataset."""
import mindspore.common.dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import get_rank, get_group_size
def create_dataset(dataset_path, do_train, config, repeat_num=1, batch_size=32, run_distribute=True):
diff --git a/research/cv/nas-fpn/src/dataset.py b/research/cv/nas-fpn/src/dataset.py
index 36c018347..239d7de55 100644
--- a/research/cv/nas-fpn/src/dataset.py
+++ b/research/cv/nas-fpn/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ import os
import numpy as np
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from src.model_utils.config import config
from src.box_utils import jaccard_numpy, retinanet_bboxes_encode
diff --git a/research/cv/nima_vgg16/src/MyDataset.py b/research/cv/nima_vgg16/src/MyDataset.py
index 36b8e9b80..39f7bb1d5 100644
--- a/research/cv/nima_vgg16/src/MyDataset.py
+++ b/research/cv/nima_vgg16/src/MyDataset.py
@@ -20,9 +20,9 @@ import cv2
import numpy as np
from mindspore import dataset as ds
from mindspore import dtype as mstype
-from mindspore.dataset.transforms import c_transforms as t_ct
+from mindspore.dataset.transforms import transforms as t_ct
from mindspore.dataset.vision import Inter
-from mindspore.dataset.vision import c_transforms as v_ct
+from mindspore.dataset.vision import transforms as v_ct
class Dataset:
diff --git a/research/cv/ntsnet/src/dataset.py b/research/cv/ntsnet/src/dataset.py
index 9e0d068d0..6b1d0d017 100644
--- a/research/cv/ntsnet/src/dataset.py
+++ b/research/cv/ntsnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -15,7 +15,7 @@
"""ntsnet dataset"""
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.vision as vision
from mindspore.dataset.vision import Inter
diff --git a/research/cv/ntsnet/src/dataset_gpu.py b/research/cv/ntsnet/src/dataset_gpu.py
index a33d39d76..daab745cb 100644
--- a/research/cv/ntsnet/src/dataset_gpu.py
+++ b/research/cv/ntsnet/src/dataset_gpu.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@
import os
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.vision as vision
from mindspore.dataset.vision import Inter
from src.config_gpu import config
diff --git a/research/cv/osnet/model_utils/transforms.py b/research/cv/osnet/model_utils/transforms.py
index 779d5e89f..d1fd32e60 100644
--- a/research/cv/osnet/model_utils/transforms.py
+++ b/research/cv/osnet/model_utils/transforms.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License Version 2.0(the "License");
# you may not use this file except in compliance with the License.
@@ -17,8 +17,8 @@
import math
import random
-from mindspore.dataset.vision.c_transforms import Resize, Rescale, Normalize, HWC2CHW, RandomHorizontalFlip
-from mindspore.dataset.transforms.c_transforms import Compose
+from mindspore.dataset.vision import Resize, Rescale, Normalize, HWC2CHW, RandomHorizontalFlip
+from mindspore.dataset.transforms import Compose
class RandomErasing():
diff --git a/research/cv/pcb_rpp/src/dataset.py b/research/cv/pcb_rpp/src/dataset.py
index 6cc911977..04214054a 100644
--- a/research/cv/pcb_rpp/src/dataset.py
+++ b/research/cv/pcb_rpp/src/dataset.py
@@ -24,9 +24,9 @@ import numpy as np
from mindspore import dataset as ds
from mindspore.common import dtype as mstype
from mindspore.communication.management import init, get_rank, get_group_size
-from mindspore.dataset.transforms import c_transforms as C2
+from mindspore.dataset.transforms import transforms as C2
from mindspore.dataset.vision import Inter
-from mindspore.dataset.vision import c_transforms as C
+from mindspore.dataset.vision import transforms as C
from mindspore.mindrecord import FileWriter
from src import datasets
diff --git a/research/cv/pnasnet/src/dataset.py b/research/cv/pnasnet/src/dataset.py
index e991ae495..e3c8adc1a 100644
--- a/research/cv/pnasnet/src/dataset.py
+++ b/research/cv/pnasnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ Data operations, will be used in train.py and eval.py
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, rank, group_size,
num_parallel_workers=8, batch_size=128,
diff --git a/research/cv/proxylessnas/src/dataset.py b/research/cv/proxylessnas/src/dataset.py
index f8e335f71..1bab7079f 100644
--- a/research/cv/proxylessnas/src/dataset.py
+++ b/research/cv/proxylessnas/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ Data operations, will be used in train.py and eval.py
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, rank, group_size,
num_parallel_workers=8, batch_size=128,
diff --git a/research/cv/ras/src/dataset_test.py b/research/cv/ras/src/dataset_test.py
index 4a6b64138..53e0d2462 100644
--- a/research/cv/ras/src/dataset_test.py
+++ b/research/cv/ras/src/dataset_test.py
@@ -1,5 +1,5 @@
"""
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@
import os
import numpy as np
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
import mindspore.dataset as ds
from PIL import Image
diff --git a/research/cv/ras/src/dataset_train.py b/research/cv/ras/src/dataset_train.py
index 9e6770e53..73ae09830 100644
--- a/research/cv/ras/src/dataset_train.py
+++ b/research/cv/ras/src/dataset_train.py
@@ -1,5 +1,5 @@
"""
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@
import os
import numpy as np
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
import mindspore.dataset as ds
from mindspore.communication import get_rank, get_group_size
from PIL import Image
diff --git a/research/cv/rcnn/eval.py b/research/cv/rcnn/eval.py
index d16327831..cc34f042d 100644
--- a/research/cv/rcnn/eval.py
+++ b/research/cv/rcnn/eval.py
@@ -25,7 +25,7 @@ from operator import itemgetter
import cv2
import mindspore
import mindspore.dataset
-import mindspore.dataset.vision.c_transforms as c_trans
+import mindspore.dataset.vision as c_trans
from mindspore import load_param_into_net, load_checkpoint, ops
import numpy as np
from tqdm import tqdm
diff --git a/research/cv/relationnet/src/dataset.py b/research/cv/relationnet/src/dataset.py
index 02c86b0a3..516f3c3b3 100644
--- a/research/cv/relationnet/src/dataset.py
+++ b/research/cv/relationnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ import random
import os
from PIL import Image
import numpy as np
-import mindspore.dataset.vision.py_transforms as py_vision
-from mindspore.dataset.transforms.py_transforms import Compose
+import mindspore.dataset.vision as vision
+from mindspore.dataset.transforms.transforms import Compose
from mindspore import Tensor
@@ -176,8 +176,8 @@ class ClassBalancedSampler():
def get_data_loader(task, num_per_class=1, split='train', shuffle=True, rotation=0, flip=None):
'''get dataloader'''
mean, std = [0.92206], [0.08426]
- transform = Compose([py_vision.ToTensor(), # numpy HWC-> Tensor CHW
- py_vision.Normalize(mean=mean, std=std)])
+ transform = Compose([vision.ToTensor(), # numpy HWC-> Tensor CHW
+ vision.Normalize(mean=mean, std=std, is_hwc=False)])
dataset = Omniglot(task, split=split, transform=transform, rotation=rotation, flip=flip)
if split == 'train':
diff --git a/research/cv/renas/src/dataset.py b/research/cv/renas/src/dataset.py
index c411d5b70..899927a4c 100644
--- a/research/cv/renas/src/dataset.py
+++ b/research/cv/renas/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,14 +17,12 @@ import math
import os
import numpy as np
-import mindspore.dataset.vision.py_transforms as py_vision
-import mindspore.dataset.transforms.py_transforms as py_transforms
-import mindspore.dataset.transforms.c_transforms as c_transforms
+import mindspore.dataset.vision as vision
+import mindspore.dataset.transforms as data_trans
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
from mindspore.communication.management import get_rank, get_group_size
from mindspore.dataset.vision import Inter
-import mindspore.dataset.vision.c_transforms as vision
# values that should remain constant
@@ -55,24 +53,24 @@ def create_dataset(batch_size, train_data_url='', workers=8, distributed=False,
"""Create ImageNet training dataset"""
if not os.path.exists(train_data_url):
raise ValueError('Path not exists')
- decode_op = py_vision.Decode()
- type_cast_op = c_transforms.TypeCast(mstype.int32)
+ decode_op = vision.Decode(True)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
- random_resize_crop_bicubic = py_vision.RandomResizedCrop(size=(input_size, input_size),
- scale=SCALE, ratio=RATIO,
- interpolation=Inter.BICUBIC)
- random_horizontal_flip_op = py_vision.RandomHorizontalFlip(0.5)
+ random_resize_crop_bicubic = vision.RandomResizedCrop(size=(input_size, input_size),
+ scale=SCALE, ratio=RATIO,
+ interpolation=Inter.BICUBIC)
+ random_horizontal_flip_op = vision.RandomHorizontalFlip(0.5)
adjust_range = (max(0, 1 - color_jitter), 1 + color_jitter)
- random_color_jitter_op = py_vision.RandomColorAdjust(brightness=adjust_range,
- contrast=adjust_range,
- saturation=adjust_range)
- to_tensor = py_vision.ToTensor()
- normalize_op = py_vision.Normalize(
- IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+ random_color_jitter_op = vision.RandomColorAdjust(brightness=adjust_range,
+ contrast=adjust_range,
+ saturation=adjust_range)
+ to_tensor = vision.ToTensor()
+ normalize_op = vision.Normalize(
+ IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, is_hwc=False)
# assemble all the transforms
- image_ops = py_transforms.Compose([decode_op, random_resize_crop_bicubic,
- random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op])
+ image_ops = data_trans.Compose([decode_op, random_resize_crop_bicubic,
+ random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op])
rank_id = get_rank() if distributed else 0
rank_size = get_group_size() if distributed else 1
@@ -121,16 +119,16 @@ def create_dataset_val(batch_size=128, val_data_url='', workers=8, distributed=F
else:
scale_size = int(math.floor(input_size / DEFAULT_CROP_PCT))
- type_cast_op = c_transforms.TypeCast(mstype.int32)
- decode_op = py_vision.Decode()
- resize_op = py_vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
- center_crop = py_vision.CenterCrop(size=input_size)
- to_tensor = py_vision.ToTensor()
- normalize_op = py_vision.Normalize(
- IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
+ decode_op = vision.Decode(True)
+ resize_op = vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
+ center_crop = vision.CenterCrop(size=input_size)
+ to_tensor = vision.ToTensor()
+ normalize_op = vision.Normalize(
+ IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, is_hwc=False)
- image_ops = py_transforms.Compose([decode_op, resize_op, center_crop,
- to_tensor, normalize_op])
+ image_ops = data_trans.Compose([decode_op, resize_op, center_crop,
+ to_tensor, normalize_op])
dataset = dataset.map(input_columns=["label"], operations=type_cast_op,
num_parallel_workers=workers)
@@ -176,9 +174,9 @@ def create_dataset_cifar10(data_home, repeat_num=1, training=True, cifar_cfg=Non
random_horizontal_op = vision.RandomHorizontalFlip()
resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR
rescale_op = vision.Rescale(1.0 / 255.0, 0.0)
- normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
+ normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010), is_hwc=True)
changeswap_op = vision.HWC2CHW()
- type_cast_op = c_transforms.TypeCast(mstype.int32)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
c_trans = []
if training:
diff --git a/research/cv/repvgg/src/data/imagenet.py b/research/cv/repvgg/src/data/imagenet.py
index fabaef259..c8a6762f2 100644
--- a/research/cv/repvgg/src/data/imagenet.py
+++ b/research/cv/repvgg/src/data/imagenet.py
@@ -19,9 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from mindspore.dataset.vision.utils import Inter
from src.data.augment.auto_augment import pil_interp, rand_augment_transform
@@ -93,13 +92,13 @@ def create_dataset_imagenet(dataset_dir, args, repeat_num=1, training=True):
vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(3 / 4, 4 / 3),
interpolation=Inter.PILCUBIC),
vision.RandomHorizontalFlip(prob=0.5),
- py_vision.ToPIL()
+ vision.ToPIL()
]
if auto_augment != "None":
transform_img += [rand_augment_transform(auto_augment, aa_params)]
transform_img += [
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False),
RandomErasing(args.re_prob, mode=args.re_mode, max_count=args.re_count)
]
else:
diff --git a/research/cv/res2net/src/dataset.py b/research/cv/res2net/src/dataset.py
index 3959fd266..8b4ee28f5 100644
--- a/research/cv/res2net/src/dataset.py
+++ b/research/cv/res2net/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ create train or eval dataset.
import multiprocessing
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, train_image_size=224, eval_image_size=224,
diff --git a/research/cv/res2net/src/dataset_infer.py b/research/cv/res2net/src/dataset_infer.py
index 98114b7c7..1b894af18 100644
--- a/research/cv/res2net/src/dataset_infer.py
+++ b/research/cv/res2net/src/dataset_infer.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
import numpy as np
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
from src.model_utils.config import config
diff --git a/research/cv/res2net_faster_rcnn/src/dataset.py b/research/cv/res2net_faster_rcnn/src/dataset.py
index 090452458..7e7c421b7 100644
--- a/research/cv/res2net_faster_rcnn/src/dataset.py
+++ b/research/cv/res2net_faster_rcnn/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -22,7 +22,7 @@ from numpy import random
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
diff --git a/research/cv/res2net_yolov3/src/yolo_dataset.py b/research/cv/res2net_yolov3/src/yolo_dataset.py
index 6dac2bad3..b97fd5e83 100644
--- a/research/cv/res2net_yolov3/src/yolo_dataset.py
+++ b/research/cv/res2net_yolov3/src/yolo_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ from PIL import Image
import numpy as np
from pycocotools.coco import COCO
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.vision as CV
from src.distributed_sampler import DistributedSampler
from src.transforms import reshape_fn, MultiScaleTrans
diff --git a/research/cv/resnet3d/src/dataset.py b/research/cv/resnet3d/src/dataset.py
index 4bfffda6b..f5034cee2 100644
--- a/research/cv/resnet3d/src/dataset.py
+++ b/research/cv/resnet3d/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@ import os
import mindspore.dataset as ds
import mindspore.common.dtype as mstype
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import get_rank, get_group_size
from .videodataset import DatasetGenerator
diff --git a/research/cv/resnet3d/src/pil_transforms.py b/research/cv/resnet3d/src/pil_transforms.py
index 270a3e3fb..cfd0f0fa7 100644
--- a/research/cv/resnet3d/src/pil_transforms.py
+++ b/research/cv/resnet3d/src/pil_transforms.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@ transforms by PIL.
"""
import numpy as np
-import mindspore.dataset.vision.py_transforms as py_trans
+import mindspore.dataset.vision as vision
class PILTrans:
@@ -27,16 +27,16 @@ class PILTrans:
def __init__(self, opt, mean, std):
super(PILTrans).__init__()
- self.to_pil = py_trans.ToPIL()
+ self.to_pil = vision.ToPIL()
self.random_resized_crop = \
- py_trans.RandomResizedCrop(opt.sample_size, scale=(opt.train_crop_min_scale, 1.0),
- ratio=(opt.train_crop_min_ratio, 1.0 / opt.train_crop_min_ratio))
- self.random_horizontal_flip = py_trans.RandomHorizontalFlip(prob=0.5)
- self.color = py_trans.RandomColorAdjust(0.4, 0.4, 0.4, 0.1)
- self.normalize = py_trans.Normalize(mean=mean, std=std)
- self.to_tensor = py_trans.ToTensor()
- self.resize = py_trans.Resize(opt.sample_size)
- self.center_crop = py_trans.CenterCrop(opt.sample_size)
+ vision.RandomResizedCrop(opt.sample_size, scale=(opt.train_crop_min_scale, 1.0),
+ ratio=(opt.train_crop_min_ratio, 1.0 / opt.train_crop_min_ratio))
+ self.random_horizontal_flip = vision.RandomHorizontalFlip(prob=0.5)
+ self.color = vision.RandomColorAdjust(0.4, 0.4, 0.4, 0.1)
+ self.normalize = vision.Normalize(mean=mean, std=std, is_hwc=False)
+ self.to_tensor = vision.ToTensor()
+ self.resize = vision.Resize(opt.sample_size)
+ self.center_crop = vision.CenterCrop(opt.sample_size)
self.opt = opt
def __call__(self, data, labels, batchInfo):
@@ -72,11 +72,11 @@ class EvalPILTrans:
def __init__(self, opt, mean, std):
super(EvalPILTrans).__init__()
- self.to_pil = py_trans.ToPIL()
- self.resize = py_trans.Resize(opt.sample_size)
- self.center_crop = py_trans.CenterCrop(opt.sample_size)
- self.normalize = py_trans.Normalize(mean=mean, std=std)
- self.to_tensor = py_trans.ToTensor()
+ self.to_pil = vision.ToPIL()
+ self.resize = vision.Resize(opt.sample_size)
+ self.center_crop = vision.CenterCrop(opt.sample_size)
+ self.normalize = vision.Normalize(mean=mean, std=std, is_hwc=False)
+ self.to_tensor = vision.ToTensor()
def __call__(self, data, labels, batchInfo):
data = data[0]
diff --git a/research/cv/resnet50_adv_pruning/src/pet_dataset.py b/research/cv/resnet50_adv_pruning/src/pet_dataset.py
index 8eb9eaecc..de6adced7 100644
--- a/research/cv/resnet50_adv_pruning/src/pet_dataset.py
+++ b/research/cv/resnet50_adv_pruning/src/pet_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,10 +18,8 @@ create train or eval dataset.
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.vision.py_transforms as P
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.transforms.py_transforms as P2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.dataset.vision import Inter
@@ -74,12 +72,12 @@ def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch
change_swap_op = C.HWC2CHW()
# define python operations
- decode_p = P.Decode()
- resize_p = P.Resize(256, interpolation=Inter.BILINEAR)
- center_crop_p = P.CenterCrop(224)
- totensor = P.ToTensor()
- normalize_p = P.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
- composeop = P2.Compose(
+ decode_p = C.Decode(True)
+ resize_p = C.Resize(256, interpolation=Inter.BILINEAR)
+ center_crop_p = C.CenterCrop(224)
+ totensor = C.ToTensor()
+ normalize_p = C.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), is_hwc=False)
+ composeop = C2.Compose(
[decode_p, resize_p, center_crop_p, totensor, normalize_p])
if do_train:
trans = [resize_crop_op, horizontal_flip_op, color_op,
diff --git a/research/cv/resnet50_bam/src/dataset.py b/research/cv/resnet50_bam/src/dataset.py
index db662754f..6ec6428d7 100644
--- a/research/cv/resnet50_bam/src/dataset.py
+++ b/research/cv/resnet50_bam/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from src.config import imagenet_cfg
diff --git a/research/cv/resnetv2/src/dataset.py b/research/cv/resnetv2/src/dataset.py
index 784e564e9..cb3ff1d68 100644
--- a/research/cv/resnetv2/src/dataset.py
+++ b/research/cv/resnetv2/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,8 +16,8 @@
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
def create_dataset1(dataset_path, do_train=True, repeat_num=1, batch_size=32, target="Ascend", distribute=False):
diff --git a/research/cv/resnetv2_50_frn/src/dataset.py b/research/cv/resnetv2_50_frn/src/dataset.py
index 97221b5b5..b1166b11c 100644
--- a/research/cv/resnetv2_50_frn/src/dataset.py
+++ b/research/cv/resnetv2_50_frn/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ Data operations, will be used in train.py and eval.py
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
def create_dataset(dataset_path, do_train, rank, group_size,
num_parallel_workers=8, batch_size=128,
diff --git a/research/cv/resnext152_64x4d/src/dataset.py b/research/cv/resnext152_64x4d/src/dataset.py
index 712bd775a..b7f1534e6 100644
--- a/research/cv/resnext152_64x4d/src/dataset.py
+++ b/research/cv/resnext152_64x4d/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ dataset processing.
import os
from mindspore.common import dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as V_C
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as V_C
from PIL import Image, ImageFile
from src.utils.sampler import DistributedSampler
diff --git a/research/cv/retinanet_resnet101/src/dataset.py b/research/cv/retinanet_resnet101/src/dataset.py
index 375f5337b..d86f6d8fd 100644
--- a/research/cv/retinanet_resnet101/src/dataset.py
+++ b/research/cv/retinanet_resnet101/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,7 +24,7 @@ import numpy as np
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from .model_utils.config import config
from .box_utils import jaccard_numpy, retinanet_bboxes_encode
diff --git a/research/cv/retinanet_resnet152/src/dataset.py b/research/cv/retinanet_resnet152/src/dataset.py
index f1dad5f44..f9f186612 100644
--- a/research/cv/retinanet_resnet152/src/dataset.py
+++ b/research/cv/retinanet_resnet152/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,7 +24,7 @@ import numpy as np
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from .model_utils.config import config
from .box_utils import jaccard_numpy, retinanet_bboxes_encode
diff --git a/research/cv/rfcn/src/dataset.py b/research/cv/rfcn/src/dataset.py
index a148b7fd3..dcc009b19 100644
--- a/research/cv/rfcn/src/dataset.py
+++ b/research/cv/rfcn/src/dataset.py
@@ -23,7 +23,7 @@ from numpy import random
import cv2
from PIL import Image
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
def bbox_overlaps(bboxes1, bboxes2, mode='iou'):
diff --git a/research/cv/simple_baselines/src/dataset.py b/research/cv/simple_baselines/src/dataset.py
index 84796c054..af06907cb 100644
--- a/research/cv/simple_baselines/src/dataset.py
+++ b/research/cv/simple_baselines/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -26,7 +26,7 @@ import numpy as np
import cv2
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from src.utils.transforms import fliplr_joints, get_affine_transform, affine_transform
ds.config.set_seed(1) # Set Random Seed
diff --git a/research/cv/single_path_nas/src/dataset.py b/research/cv/single_path_nas/src/dataset.py
index ac51ad69e..eac12de24 100644
--- a/research/cv/single_path_nas/src/dataset.py
+++ b/research/cv/single_path_nas/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ Data operations, will be used in train.py and eval.py
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from src.config import imagenet_cfg
diff --git a/research/cv/sknet/src/dataset.py b/research/cv/sknet/src/dataset.py
index 7a67fb74a..611a93736 100644
--- a/research/cv/sknet/src/dataset.py
+++ b/research/cv/sknet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.vision as C
from mindspore.communication.management import get_group_size, get_rank, init
diff --git a/research/cv/squeezenet/src/dataset.py b/research/cv/squeezenet/src/dataset.py
index 38eaef30f..e778661b4 100644
--- a/research/cv/squeezenet/src/dataset.py
+++ b/research/cv/squeezenet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ create train or eval dataset.
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
diff --git a/research/cv/squeezenet1_1/src/dataset.py b/research/cv/squeezenet1_1/src/dataset.py
index 81bf48ced..033f8a699 100644
--- a/research/cv/squeezenet1_1/src/dataset.py
+++ b/research/cv/squeezenet1_1/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,8 +18,8 @@ create train or eval dataset of imagenet and cifar10.
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
def create_dataset_imagenet(dataset_path,
do_train,
diff --git a/research/cv/ssc_resnet50/src/dataset.py b/research/cv/ssc_resnet50/src/dataset.py
index 3ef167ceb..b714dbee4 100644
--- a/research/cv/ssc_resnet50/src/dataset.py
+++ b/research/cv/ssc_resnet50/src/dataset.py
@@ -23,8 +23,8 @@ import logging
import numpy as np
from PIL import Image
from PIL import ImageFile
-import mindspore.dataset.vision.py_transforms as py_vision
-import mindspore.dataset.transforms.py_transforms as py_trans
+import mindspore.dataset.vision as vision
+import mindspore.dataset.transforms as data_trans
import mindspore.ops
import mindspore.dataset as de
@@ -91,12 +91,12 @@ class CoMatchDatasetImageNet:
self.samples = samples
logging.info("sample len: %d", len(self.samples))
- self.random_resize_crop = py_vision.RandomResizedCrop(224, scale=(0.2, 1.))
- self.random_horizontal_flip = py_vision.RandomHorizontalFlip()
- self.to_tensor = py_vision.ToTensor()
- self.normalize = py_vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
- self.random_apply = py_trans.RandomApply([py_vision.RandomColorAdjust(0.4, 0.4, 0.4, 0.1)], prob=0.8)
- self.random_grayscale = py_vision.RandomGrayscale(prob=0.2)
+ self.random_resize_crop = vision.RandomResizedCrop(224, scale=(0.2, 1.))
+ self.random_horizontal_flip = vision.RandomHorizontalFlip()
+ self.to_tensor = vision.ToTensor()
+ self.normalize = vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False)
+ self.random_apply = data_trans.RandomApply([vision.RandomColorAdjust(0.4, 0.4, 0.4, 0.1)], prob=0.8)
+ self.random_grayscale = vision.RandomGrayscale(prob=0.2)
self.unlable_randomaugmentMC = RandAugmentMC(int(args.unlabel_randomaug_count),
int(args.unlabel_randomaug_intensity))
@@ -297,10 +297,10 @@ class CoMatchDatasetImageNetTest:
logging.info("sample len: %d", len(self.samples))
# for test
- self.resize = py_vision.Resize(256)
- self.center_crop = py_vision.CenterCrop(224)
- self.to_tensor = py_vision.ToTensor()
- self.normalize = py_vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+ self.resize = vision.Resize(256)
+ self.center_crop = vision.CenterCrop(224)
+ self.to_tensor = vision.ToTensor()
+ self.normalize = vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False)
def __getitem__(self, index):
"""
@@ -362,11 +362,11 @@ class CoMatchSelectSample:
self.samples = samples
# for test
- self.random_resize_crop = py_vision.RandomResizedCrop(224, scale=(0.2, 1.))
- self.random_horizontal_flip = py_vision.RandomHorizontalFlip()
+ self.random_resize_crop = vision.RandomResizedCrop(224, scale=(0.2, 1.))
+ self.random_horizontal_flip = vision.RandomHorizontalFlip()
- self.to_tensor = py_vision.ToTensor()
- self.normalize = py_vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+ self.to_tensor = vision.ToTensor()
+ self.normalize = vision.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], is_hwc=False)
def __getitem__(self, index):
"""
diff --git a/research/cv/ssd_ghostnet/src/dataset.py b/research/cv/ssd_ghostnet/src/dataset.py
index 0350fe910..8eb7fa4dc 100644
--- a/research/cv/ssd_ghostnet/src/dataset.py
+++ b/research/cv/ssd_ghostnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,7 +24,7 @@ import numpy as np
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C2
+import mindspore.dataset.vision as C2
from mindspore.mindrecord import FileWriter
from src.model_utils.config import config
from .box_utils import jaccard_numpy, ssd_bboxes_encode
diff --git a/research/cv/ssd_inception_v2/src/dataset.py b/research/cv/ssd_inception_v2/src/dataset.py
index 0c08dbae5..ebeaf3774 100644
--- a/research/cv/ssd_inception_v2/src/dataset.py
+++ b/research/cv/ssd_inception_v2/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -25,7 +25,7 @@ import xml.etree.ElementTree as et
import cv2
import numpy as np
from mindspore import dataset as de
-from mindspore.dataset.vision import c_transforms as C
+from mindspore.dataset.vision import transforms as C
from mindspore.mindrecord import FileWriter
from src.model_utils.config import config
diff --git a/research/cv/ssd_inceptionv2/src/dataset.py b/research/cv/ssd_inceptionv2/src/dataset.py
index d6aa4ee1b..1a0d2cb71 100644
--- a/research/cv/ssd_inceptionv2/src/dataset.py
+++ b/research/cv/ssd_inceptionv2/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,7 +24,7 @@ import numpy as np
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from .config import config
from .box_utils import jaccard_numpy, ssd_bboxes_encode
diff --git a/research/cv/ssd_mobilenetV2/src/dataset.py b/research/cv/ssd_mobilenetV2/src/dataset.py
index 9b665fa06..c3ab4b7fa 100644
--- a/research/cv/ssd_mobilenetV2/src/dataset.py
+++ b/research/cv/ssd_mobilenetV2/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,7 +24,7 @@ import numpy as np
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from .config import config
from .box_utils import jaccard_numpy, ssd_bboxes_encode
diff --git a/research/cv/ssd_mobilenetV2_FPNlite/src/dataset.py b/research/cv/ssd_mobilenetV2_FPNlite/src/dataset.py
index 1e55f9010..f0a96bb84 100644
--- a/research/cv/ssd_mobilenetV2_FPNlite/src/dataset.py
+++ b/research/cv/ssd_mobilenetV2_FPNlite/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -25,7 +25,7 @@ import cv2
from tqdm import tqdm
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from src.model_utils.config import config as cfg
from .box_utils import jaccard_numpy, ssd_bboxes_encode
diff --git a/research/cv/ssd_resnet34/src/dataset.py b/research/cv/ssd_resnet34/src/dataset.py
index 5b2a64255..26d042dec 100644
--- a/research/cv/ssd_resnet34/src/dataset.py
+++ b/research/cv/ssd_resnet34/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,7 +24,7 @@ import numpy as np
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from .config import config
from .box_utils import jaccard_numpy, ssd_bboxes_encode
diff --git a/research/cv/ssd_resnet50/src/dataset.py b/research/cv/ssd_resnet50/src/dataset.py
index a102b4729..eaed11ad0 100644
--- a/research/cv/ssd_resnet50/src/dataset.py
+++ b/research/cv/ssd_resnet50/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -24,7 +24,7 @@ import numpy as np
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from .config import config
from .box_utils import jaccard_numpy, ssd_bboxes_encode
diff --git a/research/cv/ssd_resnet_34/src/dataset.py b/research/cv/ssd_resnet_34/src/dataset.py
index 5df5d78df..d6f878705 100644
--- a/research/cv/ssd_resnet_34/src/dataset.py
+++ b/research/cv/ssd_resnet_34/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -26,7 +26,7 @@ from xml.etree import ElementTree
import cv2
import numpy as np
from mindspore import dataset as ds
-from mindspore.dataset.vision import c_transforms as C
+from mindspore.dataset.vision import transforms as C
from mindspore.mindrecord import FileWriter
from .box_utils import jaccard_numpy
diff --git a/research/cv/stpm/src/dataset.py b/research/cv/stpm/src/dataset.py
index e8e337855..6bb77547c 100644
--- a/research/cv/stpm/src/dataset.py
+++ b/research/cv/stpm/src/dataset.py
@@ -21,8 +21,8 @@ import numpy as np
from PIL import Image
import mindspore.dataset as ds
from mindspore.dataset.vision import Inter
-import mindspore.dataset.vision.py_transforms as py_vision
-from mindspore.dataset.transforms.py_transforms import Compose
+import mindspore.dataset.vision as vision
+from mindspore.dataset.transforms.transforms import Compose
class MVTecDataset():
@@ -113,15 +113,15 @@ def createDataset(dataset_path, category, save_sample=False, out_size=256, train
std = [0.229, 0.224, 0.225]
data_transforms = Compose([
- py_vision.Resize((out_size, out_size), interpolation=Inter.ANTIALIAS),
- py_vision.CenterCrop(out_size),
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std)
+ vision.Resize((out_size, out_size), interpolation=Inter.ANTIALIAS),
+ vision.CenterCrop(out_size),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False)
])
gt_transforms = Compose([
- py_vision.Resize((out_size, out_size)),
- py_vision.CenterCrop(out_size),
- py_vision.ToTensor()
+ vision.Resize((out_size, out_size)),
+ vision.CenterCrop(out_size),
+ vision.ToTensor()
])
train_data = MVTecDataset(root=os.path.join(dataset_path, category),
diff --git a/research/cv/swin_transformer/src/data/imagenet.py b/research/cv/swin_transformer/src/data/imagenet.py
index 522159871..f1883aef7 100644
--- a/research/cv/swin_transformer/src/data/imagenet.py
+++ b/research/cv/swin_transformer/src/data/imagenet.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,9 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
-import mindspore.dataset.vision.py_transforms as py_vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from mindspore.dataset.vision.utils import Inter
from src.data.augment.auto_augment import _pil_interp, rand_augment_transform
@@ -94,12 +93,12 @@ def create_dataset_imagenet(dataset_dir, args, repeat_num=1, training=True):
vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(3 / 4, 4 / 3),
interpolation=Inter.BICUBIC),
vision.RandomHorizontalFlip(prob=0.5),
- py_vision.ToPIL()
+ vision.ToPIL()
]
transform_img += [rand_augment_transform(auto_augment, aa_params)]
transform_img += [
- py_vision.ToTensor(),
- py_vision.Normalize(mean=mean, std=std),
+ vision.ToTensor(),
+ vision.Normalize(mean=mean, std=std, is_hwc=False),
RandomErasing(args.re_prob, mode=args.re_mode, max_count=args.re_count)
]
else:
@@ -111,14 +110,14 @@ def create_dataset_imagenet(dataset_dir, args, repeat_num=1, training=True):
vision.Decode(),
vision.Resize(int(256 / 224 * image_size), interpolation=Inter.BICUBIC),
vision.CenterCrop(image_size),
- vision.Normalize(mean=mean, std=std),
+ vision.Normalize(mean=mean, std=std, is_hwc=True),
vision.HWC2CHW()
]
else:
transform_img = [
vision.Decode(),
vision.Resize(int(image_size), interpolation=Inter.BICUBIC),
- vision.Normalize(mean=mean, std=std),
+ vision.Normalize(mean=mean, std=std, is_hwc=True),
vision.HWC2CHW()
]
diff --git a/research/cv/textfusenet/src/dataset.py b/research/cv/textfusenet/src/dataset.py
index 47479f06b..a406408b3 100755
--- a/research/cv/textfusenet/src/dataset.py
+++ b/research/cv/textfusenet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -22,7 +22,7 @@ from numpy import random
import cv2
import mmcv
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
from .model_utils.config import config
diff --git a/research/cv/tinynet/src/dataset.py b/research/cv/tinynet/src/dataset.py
index cf49192e7..b2c001d54 100644
--- a/research/cv/tinynet/src/dataset.py
+++ b/research/cv/tinynet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,9 +17,8 @@ import math
import os
import numpy as np
-import mindspore.dataset.vision.py_transforms as py_vision
-import mindspore.dataset.transforms.py_transforms as py_transforms
-import mindspore.dataset.transforms.c_transforms as c_transforms
+import mindspore.dataset.vision as vision
+import mindspore.dataset.transforms as data_trans
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
from mindspore.communication.management import get_rank, get_group_size
@@ -53,24 +52,24 @@ def create_dataset(batch_size, train_data_url='', workers=8, distributed=False,
"""Create ImageNet training dataset"""
if not os.path.exists(train_data_url):
raise ValueError('Path not exists')
- decode_op = py_vision.Decode()
- type_cast_op = c_transforms.TypeCast(mstype.int32)
+ decode_op = vision.Decode(True)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
- random_resize_crop_bicubic = py_vision.RandomResizedCrop(size=(input_size, input_size),
- scale=SCALE, ratio=RATIO,
- interpolation=Inter.BICUBIC)
- random_horizontal_flip_op = py_vision.RandomHorizontalFlip(0.5)
+ random_resize_crop_bicubic = vision.RandomResizedCrop(size=(input_size, input_size),
+ scale=SCALE, ratio=RATIO,
+ interpolation=Inter.BICUBIC)
+ random_horizontal_flip_op = vision.RandomHorizontalFlip(0.5)
adjust_range = (max(0, 1 - color_jitter), 1 + color_jitter)
- random_color_jitter_op = py_vision.RandomColorAdjust(brightness=adjust_range,
- contrast=adjust_range,
- saturation=adjust_range)
- to_tensor = py_vision.ToTensor()
- normalize_op = py_vision.Normalize(
- IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+ random_color_jitter_op = vision.RandomColorAdjust(brightness=adjust_range,
+ contrast=adjust_range,
+ saturation=adjust_range)
+ to_tensor = vision.ToTensor()
+ normalize_op = vision.Normalize(
+ IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, is_hwc=False)
# assemble all the transforms
- image_ops = py_transforms.Compose([decode_op, random_resize_crop_bicubic,
- random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op])
+ image_ops = data_trans.Compose([decode_op, random_resize_crop_bicubic,
+ random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op])
rank_id = get_rank() if distributed else 0
rank_size = get_group_size() if distributed else 1
@@ -119,16 +118,16 @@ def create_dataset_val(batch_size=128, val_data_url='', workers=8, distributed=F
else:
scale_size = int(math.floor(input_size / DEFAULT_CROP_PCT))
- type_cast_op = c_transforms.TypeCast(mstype.int32)
- decode_op = py_vision.Decode()
- resize_op = py_vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
- center_crop = py_vision.CenterCrop(size=input_size)
- to_tensor = py_vision.ToTensor()
- normalize_op = py_vision.Normalize(
- IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+ type_cast_op = data_trans.TypeCast(mstype.int32)
+ decode_op = vision.Decode(True)
+ resize_op = vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
+ center_crop = vision.CenterCrop(size=input_size)
+ to_tensor = vision.ToTensor()
+ normalize_op = vision.Normalize(
+ IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, is_hwc=False)
- image_ops = py_transforms.Compose([decode_op, resize_op, center_crop,
- to_tensor, normalize_op])
+ image_ops = data_trans.Compose([decode_op, resize_op, center_crop,
+ to_tensor, normalize_op])
dataset = dataset.map(input_columns=["label"], operations=type_cast_op,
num_parallel_workers=workers)
diff --git a/research/cv/tracktor/src/dataset.py b/research/cv/tracktor/src/dataset.py
index 0e2afa850..d63c6b1f9 100644
--- a/research/cv/tracktor/src/dataset.py
+++ b/research/cv/tracktor/src/dataset.py
@@ -24,7 +24,7 @@ import os.path as osp
import cv2
import mindspore as ms
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
import numpy as np
from numpy import random
diff --git a/research/cv/u2net/src/data_loader.py b/research/cv/u2net/src/data_loader.py
index 66eb91711..0f0c8e05e 100644
--- a/research/cv/u2net/src/data_loader.py
+++ b/research/cv/u2net/src/data_loader.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -25,7 +25,7 @@ from skimage import io, transform, color
from mindspore import context
from mindspore import dataset as ds
from mindspore.common import dtype as mstype
-import mindspore.dataset.transforms.c_transforms as CC
+import mindspore.dataset.transforms as CC
from mindspore.context import ParallelMode
from mindspore.communication.management import get_rank, get_group_size
diff --git a/research/cv/vgg19/src/dataset.py b/research/cv/vgg19/src/dataset.py
index 93772b806..d6af839cb 100644
--- a/research/cv/vgg19/src/dataset.py
+++ b/research/cv/vgg19/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
from PIL import Image, ImageFile
from mindspore.common import dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from src.utils.sampler import DistributedSampler
ImageFile.LOAD_TRUNCATED_IMAGES = True
diff --git a/research/cv/vit_base/src/dataset.py b/research/cv/vit_base/src/dataset.py
index 645c594c5..75d969c7f 100644
--- a/research/cv/vit_base/src/dataset.py
+++ b/research/cv/vit_base/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
-import mindspore.dataset.vision.c_transforms as vision
+import mindspore.dataset.transforms as C
+import mindspore.dataset.vision as vision
from mindspore.communication.management import get_group_size
from mindspore.communication.management import get_rank
diff --git a/research/cv/wave_mlp/src/dataset.py b/research/cv/wave_mlp/src/dataset.py
index 89c44b541..9449268f6 100644
--- a/research/cv/wave_mlp/src/dataset.py
+++ b/research/cv/wave_mlp/src/dataset.py
@@ -17,15 +17,14 @@ import os
import numpy as np
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.c_transforms as C2
-import mindspore.dataset.vision.py_transforms as pytrans
-import mindspore.dataset.transforms.py_transforms as py_transforms
+import mindspore.dataset.transforms as C2
+import mindspore.dataset.transforms as transforms
-from mindspore.dataset.transforms.py_transforms import Compose
-import mindspore.dataset.vision.c_transforms as C
+from mindspore.dataset.transforms.transforms import Compose
+import mindspore.dataset.vision as C
-class ToNumpy(py_transforms.PyTensorOperation):
+class ToNumpy(transforms.PyTensorOperation):
def __init__(self, output_type=np.float32):
self.output_type = output_type
@@ -81,13 +80,13 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=128):
]
else:
trans = [
- pytrans.Decode(),
- pytrans.Resize(235),
- pytrans.CenterCrop(224)
+ C.Decode(True),
+ C.Resize(235),
+ C.CenterCrop(224)
]
trans += [
- pytrans.ToTensor(),
- pytrans.Normalize(mean=mean, std=std),
+ C.ToTensor(),
+ C.Normalize(mean=mean, std=std, is_hwc=False),
]
trans = Compose(trans)
diff --git a/research/cv/wgan/src/dataset.py b/research/cv/wgan/src/dataset.py
index 6168e0752..de2c14e7b 100644
--- a/research/cv/wgan/src/dataset.py
+++ b/research/cv/wgan/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -15,8 +15,8 @@
""" dataset """
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as c
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as c
+import mindspore.dataset.transforms as C2
import mindspore.common.dtype as mstype
diff --git a/research/cv/wideresnet/src/dataset.py b/research/cv/wideresnet/src/dataset.py
index 2617a656b..ea4fcef9f 100644
--- a/research/cv/wideresnet/src/dataset.py
+++ b/research/cv/wideresnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@ Data operations, will be used in train.py and eval.py
import os
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
diff --git a/research/cv/yolov3_tiny/src/transforms.py b/research/cv/yolov3_tiny/src/transforms.py
index 8a483f822..3417ba8cc 100644
--- a/research/cv/yolov3_tiny/src/transforms.py
+++ b/research/cv/yolov3_tiny/src/transforms.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,7 +17,7 @@ import copy
import random
import cv2
-import mindspore.dataset.vision.py_transforms as PV
+import mindspore.dataset.vision as vision
import numpy as np
from PIL import Image
@@ -566,6 +566,6 @@ class MultiScaleTrans:
def __call__(self, img, anno, input_size, mosaic_flag):
if mosaic_flag[0] == 0:
- img = PV.Decode()(img)
+ img = vision.Decode(True)(img)
img, anno = preprocess_fn(img, anno, self.config, input_size, self.device_num)
return img, anno, np.array(img.shape[0:2])
diff --git a/research/cv/yolov3_tiny/src/yolo_dataset.py b/research/cv/yolov3_tiny/src/yolo_dataset.py
index 1678f9d7e..fd0a25f28 100644
--- a/research/cv/yolov3_tiny/src/yolo_dataset.py
+++ b/research/cv/yolov3_tiny/src/yolo_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ import random
import cv2
import mindspore.dataset as de
-import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.vision as CV
import numpy as np
from PIL import Image
from pycocotools.coco import COCO
diff --git a/research/mm/wukong/src/dataset/dataset.py b/research/mm/wukong/src/dataset/dataset.py
index 4fef4bd98..a15635b4c 100644
--- a/research/mm/wukong/src/dataset/dataset.py
+++ b/research/mm/wukong/src/dataset/dataset.py
@@ -15,8 +15,8 @@
from mindspore import dtype as mstype
import mindspore.dataset as ds
from mindspore.dataset.vision import Inter
-import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.vision as C
+import mindspore.dataset.transforms as C2
def get_wukong_dataset(dataset_path, columns_list, num_parallel_workers, shuffle, num_shards, shard_id, batch_size):
diff --git a/research/nlp/DYR/src/dataset.py b/research/nlp/DYR/src/dataset.py
index 9ec0212c7..3e35e7cd1 100644
--- a/research/nlp/DYR/src/dataset.py
+++ b/research/nlp/DYR/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,7 +20,7 @@ import random
import numpy as np
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
# samples in one block
POS_SIZE = 1
diff --git a/research/nlp/albert/src/dataset.py b/research/nlp/albert/src/dataset.py
index dec0680de..37549dae2 100644
--- a/research/nlp/albert/src/dataset.py
+++ b/research/nlp/albert/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -20,7 +20,7 @@ import math
import numpy as np
import mindspore.common.dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
from mindspore import log as logger
diff --git a/research/nlp/gpt2/src/dataset.py b/research/nlp/gpt2/src/dataset.py
index 7435c52b6..44984954d 100644
--- a/research/nlp/gpt2/src/dataset.py
+++ b/research/nlp/gpt2/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -15,7 +15,7 @@
"""Data operations"""
import mindspore.common.dtype as mstype
import mindspore.dataset as de
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
from .finetune_eval_config import gpt2_net_cfg
diff --git a/research/nlp/hypertext/src/dataset.py b/research/nlp/hypertext/src/dataset.py
index a25135520..4479d240b 100644
--- a/research/nlp/hypertext/src/dataset.py
+++ b/research/nlp/hypertext/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@ import random
from datetime import timedelta
import numpy as np
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
import mindspore.common.dtype as mstype
MAX_VOCAB_SIZE = 5000000
diff --git a/research/nlp/ktnet/src/dataset.py b/research/nlp/ktnet/src/dataset.py
index b2f739e59..66ee89773 100644
--- a/research/nlp/ktnet/src/dataset.py
+++ b/research/nlp/ktnet/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,7 +16,7 @@
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
def create_train_dataset(data_file=None, do_shuffle=True, device_num=1, rank=0, batch_size=1,
diff --git a/research/nlp/luke/src/reading_comprehension/dataLoader.py b/research/nlp/luke/src/reading_comprehension/dataLoader.py
index 95494be55..73a3fc345 100644
--- a/research/nlp/luke/src/reading_comprehension/dataLoader.py
+++ b/research/nlp/luke/src/reading_comprehension/dataLoader.py
@@ -32,7 +32,7 @@ def create_dataset(data_file=None, do_shuffle=True, device_num=1, rank=0, batch_
"start_positions", "end_positions"],
shuffle=do_shuffle, num_shards=device_num, shard_id=rank,
num_samples=num, num_parallel_workers=num_parallel_workers)
- type_int32 = C.c_transforms.TypeCast(mstype.int32)
+ type_int32 = C.TypeCast(mstype.int32)
dataset = dataset.map(operations=type_int32, input_columns="word_ids")
dataset = dataset.map(operations=type_int32, input_columns="word_segment_ids")
dataset = dataset.map(operations=type_int32, input_columns="word_attention_mask")
@@ -57,7 +57,7 @@ def create_eval_dataset(data_file=None, do_shuffle=True, device_num=1, rank=0, b
"example_indices"],
shuffle=do_shuffle, num_shards=device_num, shard_id=rank,
num_samples=num, num_parallel_workers=num_parallel_workers)
- type_int32 = C.c_transforms.TypeCast(mstype.int32)
+ type_int32 = C.TypeCast(mstype.int32)
dataset = dataset.map(operations=type_int32, input_columns="word_ids")
dataset = dataset.map(operations=type_int32, input_columns="word_segment_ids")
dataset = dataset.map(operations=type_int32, input_columns="word_attention_mask")
diff --git a/research/nlp/seq2seq/src/dataset/load_dataset.py b/research/nlp/seq2seq/src/dataset/load_dataset.py
index 5843e0a31..5ada4bd0b 100644
--- a/research/nlp/seq2seq/src/dataset/load_dataset.py
+++ b/research/nlp/seq2seq/src/dataset/load_dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -15,7 +15,7 @@
"""Dataset loader to feed into model."""
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as deC
+import mindspore.dataset.transforms as deC
def _load_dataset(input_files, batch_size, sink_mode=False,
diff --git a/research/recommend/mmoe/src/load_dataset.py b/research/recommend/mmoe/src/load_dataset.py
index 602a26d72..650df81c1 100644
--- a/research/recommend/mmoe/src/load_dataset.py
+++ b/research/recommend/mmoe/src/load_dataset.py
@@ -16,7 +16,7 @@
import os
import mindspore.dataset as de
from mindspore.communication.management import get_rank, get_group_size
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms as C
import mindspore.common.dtype as mstype
diff --git a/utils/model_scaffolding/example/src/dataset.py b/utils/model_scaffolding/example/src/dataset.py
index f659d158a..83d20a7be 100644
--- a/utils/model_scaffolding/example/src/dataset.py
+++ b/utils/model_scaffolding/example/src/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -15,8 +15,8 @@
"""Dataset"""
import mindspore.dataset as ds
-import mindspore.dataset.vision.c_transforms as CV
-import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.vision as CV
+import mindspore.dataset.transforms as C
from mindspore.dataset.vision import Inter
from mindspore.common import dtype as mstype
--
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