diff --git a/official/cv/resnet/README_CN.md b/official/cv/resnet/README_CN.md
index f0bbae40b2046238ed71578f611180bccbb7bca7..67d50369b9e8b56c5a2b53a1e691c57bb3e1ebd5 100644
--- a/official/cv/resnet/README_CN.md
+++ b/official/cv/resnet/README_CN.md
@@ -208,6 +208,7 @@ bash run_eval_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH] [CONFIG_PATH]
鈹溾攢鈹€ resnet18_imagenet2012_config_gpu.yaml
鈹溾攢鈹€ resnet34_imagenet2012_config.yaml
鈹溾攢鈹€ resnet50_cifar10_config.yaml
+ 鈹溾攢鈹€ resnet34_cpu_config.yaml
鈹溾攢鈹€ resnet50_imagenet2012_Boost_config.yaml # 楂樻€ц兘鐗堟湰锛氭€ц兘鎻愰珮瓒呰繃10%鑰岀簿搴︿笅闄嶅皯浜�1%
鈹溾攢鈹€ resnet50_imagenet2012_Ascend_Thor_config.yaml
鈹溾攢鈹€ resnet50_imagenet2012_config.yaml
@@ -226,6 +227,7 @@ bash run_eval_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH] [CONFIG_PATH]
鈹溾攢鈹€ run_standalone_train_gpu.sh # 鍚姩GPU鍗曟満璁粌锛堝崟鍗★級
鈹斺攢鈹€ cache_util.sh # 浣跨敤鍗曡妭鐐圭珐瀛樼殑甯姪鍑芥暟
鈹溾攢鈹€ src
+ 鈹溾攢鈹€ data_split.py # 鍒囧垎杩佺Щ鏁版嵁闆嗚剼鏈紙cpu锛�
鈹溾攢鈹€ dataset.py # 鏁版嵁棰勫鐞�
鈹溾攢鈹€ eval_callback.py # 璁粌鏃舵帹鐞嗗洖璋冨嚱鏁�
鈹溾攢鈹€ CrossEntropySmooth.py # ImageNet2012鏁版嵁闆嗙殑鎹熷け瀹氫箟
@@ -236,6 +238,9 @@ bash run_eval_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH] [CONFIG_PATH]
鈹溾攢鈹€ device_adapter.py # 璁惧閰嶇疆
鈹溾攢鈹€ local_adapter.py # 鏈湴璁惧閰嶇疆
鈹斺攢鈹€ moxing_adapter.py # modelarts璁惧閰嶇疆
+ 鈹溾攢鈹€ fine_tune.py # 杩佺Щ璁粌缃戠粶锛坈pu锛�
+ 鈹溾攢鈹€ quick_start.py # quick start婕旂ず鏂囦欢锛坈pu锛�
+ 鈹溾攢鈹€ requirements.txt # 绗笁鏂逛緷璧�
鈹溾攢鈹€ eval.py # 璇勪及缃戠粶
鈹斺攢鈹€ train.py # 璁粌缃戠粶
```
@@ -475,6 +480,48 @@ bash run_standalone_train_gpu.sh [DATASET_PATH] [CONFIG_PATH] [RUN_EVAL](optiona
鍦ㄨ缁冪粨鏉熷悗锛屽彲浠ラ€夋嫨鍏抽棴缂撳瓨鏈嶅姟鍣ㄦ垨涓嶅叧闂畠浠ョ户缁负鏈潵鐨勬帹鐞嗘彁渚涚紦瀛樻湇鍔°€�
+## 杩佺Щ璁粌杩囩▼
+
+### 杩佺Щ鏁版嵁闆嗗鐞�
+
+[鏍规嵁鎻愪緵鐨勬暟鎹泦閾炬帴涓嬭浇鏁版嵁闆哴(https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz ),灏嗗垏鍒嗘暟鎹泦鑴氭湰data_split.py鏀剧疆鍦ㄤ笅杞藉ソ鐨刦lower_photos鐩綍涓嬶紝杩愯鍚庝細鐢熸垚train鏂囦欢澶瑰強test鏂囦欢澶癸紝灏唗rain鏂囦欢澶瑰強test鏂囦欢澶逛繚瀛樺埌鏂板缓鏂囦欢澶筪atasets閲�.
+
+### 杩佺Щ璁粌Ckpt鑾峰彇
+
+[鏍规嵁鎻愪緵鐨凜kpt閾炬帴涓嬭浇鏁版嵁闆哴(https://download.mindspore.cn/models/r1.5/ ),Ckpt鏂囦欢鍚嶇О涓衡€渞esnet34_ascend_v170_imagenet2012_official_cv_top1acc73.61_top5acc91.74.ckpt鈥濓紝涓嬭浇鍚庡瓨鏀惧湪fine_tune.py鍚岃矾寰勪笅銆�
+
+### 鐢ㄦ硶
+
+鎮ㄥ彲浠ラ€氳繃python鑴氭湰寮€濮嬭缁冿細
+
+```shell
+python fine_tune.py --config_path ./config/resnet34_cpu_config.yaml
+```
+
+### 缁撴灉
+
+- 浣跨敤flower_photos鎹泦璁粌ResNet34
+
+```text
+# 杩佺Щ璁粌缁撴灉锛圕PU锛�
+epoch: 1 step: 1, loss is 1.5975518
+epoch: 1 step: 2, loss is 1.5453123
+epoch: 1 step: 3, loss is 1.3293151
+epoch: 1 step: 4, loss is 1.491757
+epoch: 1 step: 5, loss is 1.3044931
+...
+```
+
+## 杩佺Щ璁粌鎺ㄧ悊杩囩▼
+
+### 鐢ㄦ硶
+
+鎮ㄥ彲浠ラ€氳繃python鑴氭湰寮€濮嬫帹鐞�(闇€瑕佸厛鍒皉esnet34_cpu_config.yaml閰嶇疆鏂囦欢涓皢ckpt_path璁句负鏈€濂界殑ckpt鏂囦欢璺緞):
+
+```shell
+python eval.py --config_path ./cpu_default_config.yaml --data_path ./dataset/flower_photos/test
+```
+
## 缁杩囩▼
### 鐢ㄦ硶
@@ -1039,6 +1086,26 @@ result:{'top_1_accuracy': 0.928385416666666} prune_rate=0.45 ckpt=~/resnet50_cif
| 寰皟妫€鏌ョ偣| 166M锛�.ckpt鏂囦欢锛� |
| 閰嶇疆鏂囦欢 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config)|
+#### flower_photos涓婄殑ResNet34
+
+| 鍙傛暟 | CPU |
+| -------------------------- |-------------------------------------------------------------------------------|
+| 妯″瀷鐗堟湰 | ResNet34 |
+| 璧勬簮 | CPU 3.40GHz锛�4鏍革紱鍐呭瓨 8G锛涚郴缁� win7 |
+| 涓婁紶鏃ユ湡 | 2022-08-30 |
+| MindSpore鐗堟湰 | 1.8.1 |
+| 鏁版嵁闆� | flower_photos |
+| 璁粌鍙傛暟 | epoch=10, steps per epoch=85, batch_size = 32 |
+| 浼樺寲鍣� | Momentum |
+| 鎹熷け鍑芥暟 | Softmax浜ゅ弶鐔� |
+| 杈撳嚭 | 姒傜巼 |
+| 鎹熷け | 0.32727173 |
+| 閫熷害 | 6859姣/姝� |
+| 鎬绘椂闀� | 70鍒嗛挓 |
+| 鍙傛暟(M) | 20.28 |
+| 寰皟妫€鏌ョ偣| 81M锛�.ckpt鏂囦欢锛� |
+| 閰嶇疆鏂囦欢 | [閾炬帴](https://gitee.com/mindspore/models/tree/master/official/cv/resnet/config) |
+
#### ImageNet2012涓婄殑ResNet101
| 鍙傛暟 | Ascend 910 | GPU |
diff --git a/official/cv/resnet/config/resnet34_cpu_config.yaml b/official/cv/resnet/config/resnet34_cpu_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f3969b0666b72447c1efbe5e6372b8a8cabd49c0
--- /dev/null
+++ b/official/cv/resnet/config/resnet34_cpu_config.yaml
@@ -0,0 +1,95 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: False
+# Url for modelarts
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+# Path for local
+run_distribute: False
+enable_profiling: False
+data_path: "./datasets/"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path/"
+device_target: "CPU"
+checkpoint_path: "./resnet34_ascend_v170_imagenet2012_official_cv_top1acc73.61_top5acc91.74.ckpt"
+checkpoint_file_path: "./best_acc.ckpt"
+
+# ==============================================================================
+# Training options
+optimizer: "Momentum"
+infer_label: ""
+class_num: 5
+batch_size: 32
+loss_scale: 1024
+momentum: 0.9
+weight_decay: 0.0001
+epoch_size: 10
+pretrain_epoch_size: 0
+save_checkpoint: True
+save_checkpoint_epochs: 5
+keep_checkpoint_max: 10
+warmup_epochs: 5
+learning_rate: 0.001
+lr_decay_mode: "poly"
+lr_init: 0.01
+lr_end: 0.00001
+lr_max: 0.1
+lars_epsilon: 0.0
+lars_coefficient: 0.001
+
+net_name: "resnet34"
+dataset: "flower_photos"
+device_num: 1
+pre_trained: ""
+run_eval: False
+eval_dataset_path: ""
+parameter_server: False
+filter_weight: False
+save_best_ckpt: True
+eval_start_epoch: 40
+eval_interval: 1
+enable_cache: False
+cache_session_id: ""
+mode_name: "GRAPH"
+boost_mode: "O0"
+conv_init: "XavierUniform"
+dense_init: "TruncatedNormal"
+train_image_size: 224
+eval_image_size: 224
+
+# Export options
+device_id: 0
+width: 224
+height: 224
+file_name: "resnet34"
+file_format: "MINDIR"
+ckpt_file: ""
+network_dataset: "resnet34_flower_photos"
+
+# Retrain options
+save_graphs: False
+save_graphs_path: "./graphs"
+has_trained_epoch: 0
+has_trained_step: 0
+
+# postprocess resnet inference
+result_path: ''
+label_path: ''
+
+---
+# Help description for each configuration
+enable_modelarts: "Whether training on modelarts, default: False"
+data_url: "Dataset url for obs"
+checkpoint_url: "The location of checkpoint for obs"
+data_path: "Dataset path for local"
+output_path: "Training output path for local"
+load_path: "The location of checkpoint for obs"
+device_target: "Target device type, available: [Ascend, GPU, CPU]"
+enable_profiling: "Whether enable profiling while training, default: False"
+num_classes: "Class for dataset"
+batch_size: "Batch size for training and evaluation"
+epoch_size: "Total training epochs."
+checkpoint_path: "The location of the checkpoint file."
+checkpoint_file_path: "The location of the checkpoint file."
+save_graphs: "Whether save graphs during training, default: False."
+save_graphs_path: "Path to save graphs."
diff --git a/official/cv/resnet/eval.py b/official/cv/resnet/eval.py
index e3e40e3c7f64b4c47d9c3fad5646cdbfefba1020..8cab2d99ff0f93d2a5d4bfdcc36e3d1b5c457b8c 100644
--- a/official/cv/resnet/eval.py
+++ b/official/cv/resnet/eval.py
@@ -35,6 +35,7 @@ if config.net_name in ("resnet18", "resnet34", "resnet50", "resnet152"):
from src.dataset import create_dataset1 as create_dataset
else:
from src.dataset import create_dataset2 as create_dataset
+
elif config.net_name == "resnet101":
from src.resnet import resnet101 as resnet
from src.dataset import create_dataset3 as create_dataset
@@ -46,7 +47,6 @@ else:
def eval_net():
"""eval net"""
target = config.device_target
-
# init context
ms.set_context(mode=ms.GRAPH_MODE, device_target=target, save_graphs=False)
if target == "Ascend":
diff --git a/official/cv/resnet/fine_tune.py b/official/cv/resnet/fine_tune.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3dba578c40db6d9625159bdfe633f761e19960a
--- /dev/null
+++ b/official/cv/resnet/fine_tune.py
@@ -0,0 +1,166 @@
+# Copyright 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.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""train resnet34."""
+
+import os
+import mindspore as ms
+import mindspore.nn as nn
+
+from mindspore.train.model import Model
+from mindspore.train.callback import TimeMonitor
+from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
+from train import LossCallBack
+from src.resnet import resnet34
+from src.dataset import create_dataset2
+from src.model_utils.config import config
+from src.eval_callback import EvalCallBack
+
+
+ms.set_context(mode=ms.GRAPH_MODE, device_target=config.device_target, save_graphs=False)
+
+
+def import_data():
+ dataset_train = create_dataset2(dataset_path=os.path.join(config.data_path, "train"), do_train=True,
+ batch_size=config.batch_size, train_image_size=config.train_image_size,
+ eval_image_size=config.eval_image_size, target=config.device_target,
+ distribute=False, enable_cache=False, cache_session_id=None)
+ dataset_val = create_dataset2(dataset_path=os.path.join(config.data_path, "test"), do_train=True,
+ batch_size=config.batch_size, train_image_size=config.train_image_size,
+ eval_image_size=config.eval_image_size, target=config.device_target,
+ distribute=False, enable_cache=False, cache_session_id=None)
+ #
+ data = next(dataset_train.create_dict_iterator())
+ images = data["image"]
+ labels = data["label"]
+ print("Tensor of image", images.shape)
+ print("Labels:", labels)
+
+ return dataset_train, dataset_val
+
+
+# define the head layer
+class DenseHead(nn.Cell):
+ def __init__(self, input_channel, num_classes):
+ super(DenseHead, self).__init__()
+ self.dense = nn.Dense(input_channel, num_classes)
+
+ def construct(self, x):
+ return self.dense(x)
+
+
+def filter_checkpoint_parameter_by_list(origin_dict, param_filter):
+ """remove useless parameters according to filter_list"""
+ for key in list(origin_dict.keys()):
+ for name in param_filter:
+ if name in key:
+ print("Delete parameter from checkpoint: ", key)
+ del origin_dict[key]
+ break
+
+
+def init_weight(net, param_dict):
+ """init_weight"""
+ if param_dict:
+ if config.filter_weight:
+ filter_list = [x.name for x in net.end_point.get_parameters()]
+ filter_checkpoint_parameter_by_list(param_dict, filter_list)
+ ms.load_param_into_net(net, param_dict)
+
+
+def eval_net(net, dataset):
+ """eval net"""
+ net.set_train(False)
+
+ loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+
+ # define model
+ model = ms.Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
+
+ # eval model
+ res = model.eval(dataset)
+ print("result:", res)
+
+
+def apply_eval(eval_param):
+ eval_model = eval_param["model"]
+ eval_ds = eval_param["dataset"]
+ metrics_name = eval_param["metrics_name"]
+ res = eval_model.eval(eval_ds)
+ print("res:", res)
+ return res[metrics_name]
+
+
+def run_eval(model, ckpt_save_dir, eval_dataset, cb):
+ """run_eval"""
+ eval_param_dict = {"model": model, "dataset": eval_dataset, "metrics_name": "top_1_accuracy"}
+ eval_cb = EvalCallBack(apply_eval, eval_param_dict, interval=1,
+ eval_start_epoch=0, save_best_ckpt=True,
+ ckpt_directory=ckpt_save_dir, besk_ckpt_name="best_acc.ckpt",
+ metrics_name="acc")
+ cb += [eval_cb]
+
+
+def finetune_train():
+ dataset_train, data_val = import_data()
+
+ ckpt_param_dict = ms.load_checkpoint(config.checkpoint_path)
+ net = resnet34(class_num=1001)
+ init_weight(net=net, param_dict=ckpt_param_dict)
+ print("net parameter:")
+ for param in net.get_parameters():
+ print("param:", param)
+
+ # fully Connected layer the size of the input layer
+ src_head = net.end_point
+ in_channels = src_head.in_channels
+ # the number of output channels is 5
+ head = DenseHead(in_channels, config.class_num)
+ # reset the fully connected layer
+ net.end_point = head
+
+ print("net.get_parameters():", net.get_parameters())
+ print("net.trainable_params():", net.trainable_params())
+ # freeze all parameters except the last layer
+ for param in net.get_parameters():
+ if param.name not in ["end_point.dense.weight", "end_point.dense.bias"]:
+ param.requires_grad = False
+ if param.name == "end_point.dense.weight":
+ param.name = "end_point.weight"
+ if param.name == "end_point.dense.bias":
+ param.name = "end_point.bias"
+
+ # define optimizer and loss function
+ opt = nn.Momentum(params=net.trainable_params(), learning_rate=config.learning_rate, momentum=config.momentum)
+ loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+
+ # instantiating the model
+ model = Model(net, loss, opt, metrics={'top_1_accuracy', 'top_5_accuracy'})
+
+ # define callbacks
+ step_size = dataset_train.get_dataset_size()
+ time_cb = TimeMonitor(data_size=step_size)
+ loss_cb = LossCallBack(config.has_trained_epoch)
+ cb = [time_cb, loss_cb]
+
+ run_eval(model, "./", data_val, cb)
+
+ num_epochs = config.epoch_size
+ model.train(num_epochs, dataset_train, callbacks=cb)
+
+ eval_net(net, data_val)
+
+
+if __name__ == '__main__':
+ finetune_train()
diff --git a/official/cv/resnet/quick_start.py b/official/cv/resnet/quick_start.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca96298f98ae21c1c84a7a8f3bf8f5d2eeb737c5
--- /dev/null
+++ b/official/cv/resnet/quick_start.py
@@ -0,0 +1,105 @@
+# Copyright 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.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""quick start"""
+# ## This paper mainly visualizes the prediction data, uses the model to predict, and visualizes the prediction results.
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from mindspore import Tensor
+from mindspore.train import Model
+from mindspore import load_checkpoint, load_param_into_net
+from src.dataset import create_dataset2
+from src.resnet import resnet34
+from src.model_utils.config import config
+
+
+# class_name corresponds to label,and labels are marked in the order of folders
+class_name = {0: "daisy", 1: "dandelion", 2: "roses", 3: "sunflowers", 4: "tulips"}
+
+
+# define visual prediction data functions锛�
+def visual_input_data(val_dataset):
+ data = next(val_dataset.create_dict_iterator())
+ images = data["image"]
+ labels = data["label"]
+ print("Tensor of image", images.shape)
+ print("Labels:", labels)
+ plt.figure(figsize=(15, 7))
+ for i in range(len(labels)):
+ # get the image and its corresponding label
+ data_image = images[i].asnumpy()
+ # data_label = labels[i]
+ # process images for display
+ data_image = np.transpose(data_image, (1, 2, 0))
+ mean = np.array([0.485, 0.456, 0.406])
+ std = np.array([0.229, 0.224, 0.225])
+ data_image = std * data_image + mean
+ data_image = np.clip(data_image, 0, 1)
+ # display image
+ plt.subplot(4, 8, i+1)
+ plt.imshow(data_image)
+ plt.title(class_name[int(labels[i].asnumpy())], fontsize=10)
+ plt.axis("off")
+
+ plt.show()
+
+
+# define visualize_model()锛寁isualize model prediction
+def visualize_model(best_ckpt_path, val_ds):
+ net = resnet34(class_num=config.class_num)
+ # load model parameters
+ param_dict = load_checkpoint(best_ckpt_path)
+ load_param_into_net(net, param_dict)
+ model = Model(net)
+ # load the data of the validation set for validation
+ data = next(val_ds.create_dict_iterator())
+ images = data["image"].asnumpy()
+ labels = data["label"].asnumpy()
+ flower_class_name = {0: "daisy", 1: "dandelion", 2: "roses", 3: "sunflowers", 4: "tulips"}
+ # prediction image category
+ output = model.predict(Tensor(data['image']))
+ pred = np.argmax(output.asnumpy(), axis=1)
+
+ # display the image and the predicted value of the image
+ plt.figure(figsize=(15, 7))
+ for i in range(len(labels)):
+ plt.subplot(4, 8, i + 1)
+ # if the prediction is correct, it is displayed in blue; if the prediction is wrong, it is displayed in red
+ color = 'blue' if pred[i] == labels[i] else 'red'
+ plt.title('predict:{}'.format(flower_class_name[pred[i]]), color=color)
+ picture_show = np.transpose(images[i], (1, 2, 0))
+ mean = np.array([0.485, 0.456, 0.406])
+ std = np.array([0.229, 0.224, 0.225])
+ picture_show = std * picture_show + mean
+ picture_show = np.clip(picture_show, 0, 1)
+ plt.imshow(picture_show)
+ plt.axis('off')
+
+ plt.show()
+
+
+# the best ckpt file obtained by model tuning is used to predict the images of the validation set
+# (need to go to resnet34_cpu_config.yaml set ckpt_path as the best ckpt file path锛�
+if __name__ == '__main__':
+ # load inference dataset
+ dataset_val = create_dataset2(dataset_path=os.path.join(config.data_path, "flower_photos/"), do_train=True,
+ batch_size=config.batch_size, train_image_size=config.train_image_size,
+ eval_image_size=config.eval_image_size, target=config.device_target,
+ distribute=False, enable_cache=False, cache_session_id=None)
+
+ visual_input_data(dataset_val)
+ # the best ckpt file obtained by model tuning is used to predict the images of the validation set
+ # (need to go to resnet34_cpu_config.yaml set ckpt_path as the best ckpt file path锛�
+ visualize_model('best_acc.ckpt', dataset_val)
diff --git a/official/cv/resnet/requirements.txt b/official/cv/resnet/requirements.txt
index 1d221251d344c253f877e06128344a2daf7d62ba..11617da3e0def093e031188bf4b0e7cd2305f3ae 100644
--- a/official/cv/resnet/requirements.txt
+++ b/official/cv/resnet/requirements.txt
@@ -1,3 +1,4 @@
numpy
scipy
easydict
+matplotlib
\ No newline at end of file
diff --git a/official/cv/resnet/src/data_split.py b/official/cv/resnet/src/data_split.py
new file mode 100644
index 0000000000000000000000000000000000000000..a968c922716a6e7b347bd3fc5151175399e62b06
--- /dev/null
+++ b/official/cv/resnet/src/data_split.py
@@ -0,0 +1,65 @@
+# Copyright 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.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+cpu_cut_data.
+"""
+import os
+import shutil
+
+
+def generate_data():
+ dirs = []
+ path = "./"
+ abs_path = None
+ for abs_path, j, _ in os.walk(path):
+ print("abs_path:", abs_path)
+ if len(j).__trunc__() > 0:
+ dirs.append(j)
+ print(dirs)
+
+ train_folder = os.path.exists("./train")
+ if not train_folder:
+ os.makedirs("./train")
+ test_folder = os.path.exists("./test")
+ if not test_folder:
+ os.makedirs("./test")
+
+ for di in dirs[0]:
+ files = os.listdir(di)
+ train_set = files[: int(len(files) * 3 / 4)]
+ test_set = files[int(len(files) * 3 / 4):]
+ for file in train_set:
+ file_path = "./train/" + di + "/"
+ folder = os.path.exists(file_path)
+ if not folder:
+ os.makedirs(file_path)
+ src_file = "./" + di + "/" + file
+ print("src_file:", src_file)
+ dst_file = file_path + file
+ print("dst_file:", dst_file)
+ shutil.copyfile(src_file, dst_file)
+
+ for file in test_set:
+ file_path = "./test/" + di + "/"
+ folder = os.path.exists(file_path)
+ if not folder:
+ os.makedirs(file_path)
+ src_file = "./" + di + "/" + file
+ dst_file = file_path + file
+ shutil.copyfile(src_file, dst_file)
+
+
+if __name__ == '__main__':
+ generate_data()