diff --git a/research/cv/LEO/README.md b/research/cv/LEO/README.md
index 20cb4db100fe56ed6960c1e5b3ea90926bc8d9ff..919e97a62981a993bb55bb11c954f6853a1debe2 100644
--- a/research/cv/LEO/README.md
+++ b/research/cv/LEO/README.md
@@ -105,8 +105,9 @@ LEO由以下几个模块组成，分类器，编码器，关系网络和编码
# 环境要求
-- 硬件（GPU）
+- 硬件（GPU or Ascend）
- 使用GPU处理器来搭建硬件环境。
+ - 使用Ascend处理器来搭建硬件环境。
- 框架
- [MindSpore](https://www.mindspore.cn/install/en)
- 如需查看详情，请参见如下资源：
@@ -130,6 +131,24 @@ LEO由以下几个模块组成，分类器，编码器，关系网络和编码
bash scripts/run_eval_gpu.sh [DATA_PATH] [DATA_NAME] [NUM_TR_EXAMPLES_PER_CLASS] [CKPT_FILE]
# 运行评估示例
bash scripts/run_eval_gpu.sh /home/mindspore/dataset/embeddings/ miniImageNet 1 ./ckpt/1P_mini_1/xxx.ckpt
+
+ ```
+
+- Ascend处理器环境运行
+
+ ```bash
+ # 运行训练示例
+ bash scripts/run_train_gpu.sh [DEVICE_ID] [DEVICE_TARGET] [DATA_PATH] [DATA_NAME] [NUM_TR_EXAMPLES_PER_CLASS] [SAVE_PATH]
+ # 例如：
+ bash scripts/run_train_ascend.sh 6 Ascend /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpts/1P_mini_5
+ # 运行分布式训练示例
+ bash scripts/run_distribution_ascend.sh [RANK_TABLE_FILE] [DEVICE_TARGET] [DATA_PATH] [DATA_NAME] [NUM_TR_EXAMPLES_PER_CLASS] [SAVE_PATH]
+ # 例如：
+ bash scripts/run_distribution_ascend.sh ./hccl_8p_01234567_127.0.0.1.json Ascend /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpts/8P_mini_5
+ # 运行评估示例
+ bash scripts/run_eval_gpu.sh [DEVICE_ID] [DATA_PATH] [CKPT_FILE]
+ # 例如
+ bash scripts/run_eval_ascend.sh 4 Ascend /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpt/1P_mini_5/xxx.ckpt
```
以上为第一个实验示例，其余三个实验请参考训练部分。
@@ -144,8 +163,11 @@ LEO由以下几个模块组成，分类器，编码器，关系网络和编码
├─ train.py # 训练脚本
├─ eval.py # 评估脚本
├─ scripts
- │ ├─ run_eval_gpu.sh # 启动评估
- │ └─ run_train_gpu.sh # 启动训练
+ │ ├─ run_distribution_ascend.sh # 启动8卡Ascend训练
+ │ ├─ run_eval_ascend.sh # ascend启动评估
+ │ ├─ run_eval_gpu.sh # gpu启动评估
+ │ ├─ run_train_ascend.sh # ascend启动训练
+ │ └─ run_train_gpu.sh # gpu启动训练
├─ src
│ ├─ data.py # 数据处理
│ ├─ model.py # LEO模型
@@ -211,7 +233,7 @@ LEO由以下几个模块组成，分类器，编码器，关系网络和编码
outer_lr: 0.004 #超参
gradient_threshold: 0.1
gradient_norm_threshold: 0.1
- total_steps: 100000
+ total_steps: 200000
```
更多配置细节请参考config文件夹，**启动训练之前请根据不同的实验设置上述超参数。**
@@ -221,13 +243,13 @@ LEO由以下几个模块组成，分类器，编码器，关系网络和编码
- 四个实验设置不同的超参
| 超参 | miniImageNet 1-shot | miniImageNet 5-shot | tieredImageNet 1-shot | tieredImageNet 5-shot |
-| ------------------------------ | ------------------- | ------------------- | --------------------- | --------------------- |
+| ------------------------------ |---------------------|---------------------|-----------------------| --------------------- |
| `dropout` | 0.3 | 0.3 | 0.2 | 0.3 |
| `kl_weight` | 0.001 | 0.001 | 0 | 0.001 |
| `encoder_penalty_weight` | 1E-9 | 2.66E-7 | 5.7E-1 | 5.7E-6 |
| `l2_penalty_weight` | 0.0001 | 8.5E-6 | 5.10E-6 | 3.6E-10 |
-| `orthogonality_penalty_weight` | 303.0 | 0.00152 | 4.88E-1 | 0.188 |
-| `outer_lr` | 0.004 | 0.004 | 0.004 | 0.0025 |
+| `orthogonality_penalty_weight` | 303.0 | 0.00152 | 4.88E-1 | 0.188 |
+| `outer_lr` | 0.005 | 0.005 | 0.005 | 0.0025 |
### 训练
@@ -240,6 +262,15 @@ LEO由以下几个模块组成，分类器，编码器，关系网络和编码
bash scripts/run_train_gpu.sh 1 /home/mindspore/dataset/embeddings/ tieredImageNet 5 ./ckpt/1P_tiered_5
```
+- 配置好上述参数后，AScend环境运行
+
+ ```bash
+ bash scripts/run_train_ascend.sh 6 Ascend /home/mindspore/dataset/embeddings/ miniImageNet 1 ./ckpts/1P_mini_1
+ bash scripts/run_train_ascend.sh 6 Ascend /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpts/1P_mini_5
+ bash scripts/run_train_ascend.sh 6 Ascend /home/mindspore/dataset/embeddings/ tieredImageNet 1 ./ckpt/1P_tiered_1
+ bash scripts/run_train_ascend.sh 6 Ascend /home/mindspore/dataset/embeddings/ tieredImageNet 5 ./ckpt/1P_tiered_5
+ ```
+
训练将在后台运行，您可以通过`1P_miniImageNet_1_train.log`等日志文件查看训练过程。
训练结束后，您可在 ` ./ckpt/1P_mini_1` 等checkpoint文件夹下找到检查点文件。
@@ -256,6 +287,15 @@ LEO由以下几个模块组成，分类器，编码器，关系网络和编码
bash scripts/run_train_gpu.sh 8 /home/mindspore/dataset/embeddings/ tieredImageNet 5 ./ckpt/8P_tiered_5
```
+- 配置好上述参数后，Ascend环境运行
+
+ ```bash
+ bash scripts/run_distribution_ascend.sh ./hccl_8p_01234567_127.0.0.1.json Ascend /home/mindspore/dataset/embeddings/ miniImageNet 1 ./ckpts/8P_mini_1
+ bash scripts/run_distribution_ascend.sh ./hccl_8p_01234567_127.0.0.1.json Ascend /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpts/8P_mini_5
+ bash scripts/run_distribution_ascend.sh ./hccl_8p_01234567_127.0.0.1.json Ascend /home/mindspore/dataset/embeddings/ tieredImageNet 1 ./ckpts/8P_tired_1
+ bash scripts/run_distribution_ascend.sh ./hccl_8p_01234567_127.0.0.1.json Ascend /home/mindspore/dataset/embeddings/ tieredImageNet 5 ./ckpts/8P_tired_5
+ ```
+
与单卡训练一样，可以在`8P_miniImageNet_1_train.log`文件查看训练过程，并在默认`./ckpt/8P_mini_1`等checkpoint文件夹下找到检查点文件。
## 评估过程
@@ -273,6 +313,15 @@ LEO由以下几个模块组成，分类器，编码器，关系网络和编码
bash scripts/run_eval_gpu.sh /home/mindspore/dataset/embeddings/ tieredImageNet 5 ./ckpt/1P_tiered_5/xxx.ckpt
```
+- Ascend环境运行
+
+ ```bash
+ bash scripts/run_eval_ascend.sh 0 Ascend /home/mindspore/dataset/embeddings/ miniImageNet 1 ./ckpt/1P_mini_1/xxx.ckpt
+ bash scripts/run_eval_ascend.sh 0 Ascend /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpt/1P_mini_5/xxx.ckpt
+ bash scripts/run_eval_ascend.sh 0 Ascend /home/mindspore/dataset/embeddings/ tieredImageNet 1 ./ckpt/1P_tiered_1/xxx.ckpt
+ bash scripts/run_eval_ascend.sh 0 Ascend /home/mindspore/dataset/embeddings/ tieredImageNet 5 ./ckpt/1P_tiered_5/xxx.ckpt
+ ```
+
评估将在后台运行，您可以通过`1P_miniImageNet_1_eval.log`等日志文件查看评估过程。
# 模型描述
@@ -283,19 +332,19 @@ LEO由以下几个模块组成，分类器，编码器，关系网络和编码
- 训练参数
-| 参数 | LEO |
-| -------------| ----------------------------------------------------------- |
-| 资源 | NVIDIA GeForce RTX 3090；CUDA核心 10496个；显存 24GB |
-| 上传日期 | 2022-03-27 |
-| MindSpore版本 | 1.7.0 |
-| 数据集 | miniImageNet |
-| 优化器 | Adam |
-| 损失函数 | Cross Entropy Loss |
-| 输出 | 准确率 |
-| 损失 | GANLoss,L1Loss,localLoss,DTLoss |
-| 微调检查点 | 672KB (.ckpt文件) |
+| 参数 | LEO | Ascend |
+| -------------| ----------------------------------------------------------- |-----------------------------------------------|
+| 资源 | NVIDIA GeForce RTX 3090；CUDA核心 10496个；显存 24GB | Ascend 910; CPU 24cores; 显存 256G; OS Euler2.8 |
+| 上传日期 | 2022-03-27 | 2022-06-12 |
+| MindSpore版本 | 1.7.0 | 1.5.0 |
+| 数据集 | miniImageNet | miniImageNet |
+| 优化器 | Adam | Adam |
+| 损失函数 | Cross Entropy Loss | Cross Entropy Loss |
+| 输出 | 准确率 | 准确率 |
+| 损失 | GANLoss,L1Loss,localLoss,DTLoss | GANLoss,L1Loss,localLoss,DTLoss |
+| 微调检查点 | 672KB (.ckpt文件) | 672KB (.ckpt文件) |
-- 评估性能
+- GPU评估性能
| 实验 | miniImageNet 1-shot | miniImageNet 5-shot | tieredImageNet 1-shot | tieredImageNet 5-shot |
| ----- | ------------------- | ------------------- | --------------------- | --------------------- |
@@ -306,13 +355,13 @@ LEO由以下几个模块组成，分类器，编码器，关系网络和编码
- 评估参数
-| 参数 | LEO |
-| ------------ | ----------------------------------------------------------- |
-| 资源 | NVIDIA GeForce RTX 3090；CUDA核心 10496个；显存 24GB |
-| 上传日期 | 2022-03-27 |
-| MindSpore版本 | 1.7.0 |
-| 数据集 | miniImageNet |
-| 输出 | 准确率 |
+| 参数 | LEO | Ascend |
+| ------------ | ----------------------------------------------------------- |-----------------------------------------------|
+| 资源 | NVIDIA GeForce RTX 3090；CUDA核心 10496个；显存 24GB | Ascend 910; CPU 24cores; 显存 256G; OS Euler2.8 |
+| 上传日期 | 2022-03-27 | 2022-06-12 |
+| MindSpore版本 | 1.7.0 |1.5.0 |
+| 数据集 | miniImageNet | miniImageNet |
+| 输出 | 准确率 | 准确率 |
- 评估精度
diff --git a/research/cv/LEO/config/LEO-N5-K1_miniImageNet_config.yaml b/research/cv/LEO/config/LEO-N5-K1_miniImageNet_config.yaml
index 4675844d43431e25a14419e6c9a27059280d0985..71b2aee874a5c2cf56ced06157ac6994f49c20c2 100644
--- a/research/cv/LEO/config/LEO-N5-K1_miniImageNet_config.yaml
+++ b/research/cv/LEO/config/LEO-N5-K1_miniImageNet_config.yaml
@@ -2,6 +2,8 @@
enable_modelarts: False
data_url: ""
train_url: ""
+ckpt_url: 'ckpt files'
+result_url: 'infer result files'
checkpoint_url: ""
device_target: "GPU"
device_num: 1
@@ -36,10 +38,10 @@ metatrain_batch_size: 12
metavalid_batch_size: 200
metatest_batch_size: 200
num_steps_limit: int(1e5)
-outer_lr: 0.004 # parameters
+outer_lr: 0.005 # parameters
gradient_threshold: 0.1
gradient_norm_threshold: 0.1
-total_steps: 100000
+total_steps: 200000
# Model Description
model_name: LEO
diff --git a/research/cv/LEO/config/LEO-N5-K1_tieredImageNet_config.yaml b/research/cv/LEO/config/LEO-N5-K1_tieredImageNet_config.yaml
index e636703461fe85dbe38e0ec99805af5f6c757fdb..8255b4107905e985d5a1efd363c8bb875a01f61c 100644
--- a/research/cv/LEO/config/LEO-N5-K1_tieredImageNet_config.yaml
+++ b/research/cv/LEO/config/LEO-N5-K1_tieredImageNet_config.yaml
@@ -2,6 +2,8 @@
enable_modelarts: False
data_url: ""
train_url: ""
+ckpt_url: 'ckpt files'
+result_url: 'infer result files'
checkpoint_url: ""
device_target: "GPU"
device_num: 1
@@ -36,10 +38,10 @@ metatrain_batch_size: 12
metavalid_batch_size: 200
metatest_batch_size: 200
num_steps_limit: int(1e5)
-outer_lr: 0.004 # parameters
+outer_lr: 0.005 # parameters
gradient_threshold: 0.1
gradient_norm_threshold: 0.1
-total_steps: 100000
+total_steps: 200000
# Model Description
model_name: LEO
diff --git a/research/cv/LEO/config/LEO-N5-K5_miniImageNet_config.yaml b/research/cv/LEO/config/LEO-N5-K5_miniImageNet_config.yaml
index 38e74815ab4a1d06640e0b8ce491ce4633a75004..d3e5838d20a887e61cc2b5d60dde3e82c35979a5 100644
--- a/research/cv/LEO/config/LEO-N5-K5_miniImageNet_config.yaml
+++ b/research/cv/LEO/config/LEO-N5-K5_miniImageNet_config.yaml
@@ -2,6 +2,8 @@
enable_modelarts: False
data_url: ""
train_url: ""
+ckpt_url: 'ckpt files'
+result_url: 'infer result files'
checkpoint_url: ""
device_target: "GPU"
device_num: 1
@@ -20,10 +22,11 @@ inner_unroll_length: 5
finetuning_unroll_length: 5
num_latents: 64
inner_lr_init: 1.0
-finetuning_lr_init: 0.001
+finetuning_lr_init: 0.0005
dropout_rate: 0.3 # parameters
kl_weight: 0.001 # parameters
-encoder_penalty_weight: 2.66E-7 # parameters
+encoder_penalty_weight: 2.66E-7 # parameters origin
+
l2_penalty_weight: 8.5E-6 # parameters
orthogonality_penalty_weight: 0.00152 # parameters
# ==============================================================================
@@ -36,15 +39,15 @@ metatrain_batch_size: 12
metavalid_batch_size: 200
metatest_batch_size: 200
num_steps_limit: int(1e5)
-outer_lr: 0.004 # parameters
+outer_lr: 0.005 # parameters origin
gradient_threshold: 0.1
gradient_norm_threshold: 0.1
-total_steps: 100000
+total_steps: 200000
# Model Description
model_name: LEO
file_name: 'leo'
-file_format: 'MINDIR' # ['AIR', 'MINDIR']
+file_format: 'AIR'
---
@@ -54,6 +57,8 @@ data_url: 'Dataset url for obs'
train_url: 'Training output url for obs'
data_path: 'Dataset path for local'
output_path: 'Training output path for local'
+ckpt_url: 'ckpt files'
+result_url: 'infer result files'
device_target: 'Target device type'
enable_profiling: 'Whether enable profiling while training, default: False'
diff --git a/research/cv/LEO/config/LEO-N5-K5_tieredImageNet_config.yaml b/research/cv/LEO/config/LEO-N5-K5_tieredImageNet_config.yaml
index 92056d7b6b3ac468dbc60235ca6dcf7c28ecf5a9..97f7cb50eec33b9ad0600a9fc6007fc97226cd8c 100644
--- a/research/cv/LEO/config/LEO-N5-K5_tieredImageNet_config.yaml
+++ b/research/cv/LEO/config/LEO-N5-K5_tieredImageNet_config.yaml
@@ -2,6 +2,8 @@
enable_modelarts: False
data_url: ""
train_url: ""
+ckpt_url: 'ckpt files'
+result_url: 'infer result files'
checkpoint_url: ""
device_target: "GPU"
device_num: 1
@@ -39,7 +41,7 @@ num_steps_limit: int(1e5)
outer_lr: 0.0025 # parameters
gradient_threshold: 0.1
gradient_norm_threshold: 0.1
-total_steps: 100000
+total_steps: 200000
# Model Description
model_name: LEO
diff --git a/research/cv/LEO/eval.py b/research/cv/LEO/eval.py
index 62169c7b41d8500b16cd951635eaf39dd390e96a..167a1f7ce5938cafaf3574f0fb36fc1a0779da53 100644
--- a/research/cv/LEO/eval.py
+++ b/research/cv/LEO/eval.py
@@ -35,7 +35,7 @@ def eval_leo(init_config, inner_model_config, outer_model_config):
total_test_steps = 100
data_utils = data.Data_Utils(
- train=False, seed=100, way=outer_model_config['num_classes'],
+ train=False, seed=1, way=outer_model_config['num_classes'],
shot=outer_model_config['num_tr_examples_per_class'],
data_path=init_config['data_path'], dataset_name=init_config['dataset_name'],
embedding_crop=init_config['embedding_crop'],
@@ -75,6 +75,7 @@ if __name__ == '__main__':
initConfig = config.get_init_config()
inner_model_Config = config.get_inner_model_config()
outer_model_Config = config.get_outer_model_config()
+ args = config.get_config(get_args=True)
print("===============inner_model_config=================")
for key, value in inner_model_Config.items():
@@ -84,5 +85,16 @@ if __name__ == '__main__':
print(key+": "+str(value))
context.set_context(mode=context.GRAPH_MODE, device_target=initConfig['device_target'])
+ if args.enable_modelarts:
+ import moxing as mox
+
+ mox.file.copy_parallel(
+ src_url=args.data_url, dst_url='/cache/dataset/device_' + os.getenv('DEVICE_ID'))
+ train_dataset_path = os.path.join('/cache/dataset/device_' + os.getenv('DEVICE_ID'), "embeddings")
+ ckpt_path = '/home/work/user-job-dir/checkpoint.ckpt'
+ mox.file.copy(args.ckpt_url, ckpt_path)
+ initConfig['data_path'] = train_dataset_path
+ initConfig['ckpt_file'] = ckpt_path
+
eval_leo(initConfig, inner_model_Config, outer_model_Config)
diff --git a/research/cv/LEO/export.py b/research/cv/LEO/export.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c755a3c392feb180c60a45d429b2945a1a5cb64
--- /dev/null
+++ b/research/cv/LEO/export.py
@@ -0,0 +1,91 @@
+# 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.
+# ============================================================================
+
+import os
+import src.data as data
+import src.outerloop as outerloop
+import model_utils.config as config
+import mindspore as ms
+from mindspore import context, Tensor, export
+from mindspore import load_checkpoint, load_param_into_net
+import numpy as np
+
+os.environ['GLOG_v'] = "3"
+os.environ['GLOG_log_dir'] = '/var/log'
+
+
+def export_leo(init_config, inner_model_config, outer_model_config):
+ inner_lr_init = inner_model_config['inner_lr_init']
+ finetuning_lr_init = inner_model_config['finetuning_lr_init']
+
+ data_utils = data.Data_Utils(
+ train=False, seed=100, way=outer_model_config['num_classes'],
+ shot=outer_model_config['num_tr_examples_per_class'],
+ data_path=init_config['data_path'], dataset_name=init_config['dataset_name'],
+ embedding_crop=init_config['embedding_crop'],
+ batchsize=outer_model_config['metatrain_batch_size'],
+ val_batch_size=outer_model_config['metavalid_batch_size'],
+ test_batch_size=outer_model_config['metatest_batch_size'],
+ meta_val_steps=outer_model_config['num_val_examples_per_class'], embedding_size=640, verbose=True)
+
+ test_outer_loop = outerloop.OuterLoop(
+ batchsize=outer_model_config['metavalid_batch_size'], input_size=640,
+ latent_size=inner_model_config['num_latents'],
+ way=outer_model_config['num_classes'], shot=outer_model_config['num_tr_examples_per_class'],
+ dropout=inner_model_config['dropout_rate'], kl_weight=inner_model_config['kl_weight'],
+ encoder_penalty_weight=inner_model_config['encoder_penalty_weight'],
+ orthogonality_weight=inner_model_config['orthogonality_penalty_weight'],
+ inner_lr_init=inner_lr_init, finetuning_lr_init=finetuning_lr_init,
+ inner_step=inner_model_config['inner_unroll_length'],
+ finetune_inner_step=inner_model_config['finetuning_unroll_length'], is_meta_training=False)
+
+ parm_dict = load_checkpoint(init_config['ckpt_file'])
+ load_param_into_net(test_outer_loop, parm_dict)
+
+ batch = data_utils.get_batch('test')
+ print(batch['train']['input'].shape) # [200,5,5,640]
+ print(batch['train']['input'].dtype) # Float32
+ print(batch['train']['target'].shape) # [200,5,5,1]
+ print(batch['train']['target'].dtype) # Int64
+ print(batch['val']['input'].shape) # [200,5,15,640]
+ print(batch['val']['input'].dtype) # Float32
+ print(batch['val']['target'].shape) # [200,5,15,1]
+ print(batch['val']['target'].dtype) # Int64
+ train_input = Tensor(np.zeros(batch['train']['input'].shape), ms.float32)
+ train_target = Tensor(np.zeros(batch['train']['target'].shape), ms.int64)
+ val_input = Tensor(np.zeros(batch['val']['input'].shape), ms.float32)
+ val_target = Tensor(np.zeros(batch['val']['target'].shape), ms.int64)
+ result_name = "LEO-" + init_config['dataset_name'] + str(outer_model_config['num_classes']) +\
+ "N" + str(outer_model_config['num_tr_examples_per_class']) + "K"
+ export(test_outer_loop, train_input, train_target, val_input, val_target,
+ file_name=result_name, file_format="MINDIR")
+
+
+if __name__ == '__main__':
+ initConfig = config.get_init_config()
+ inner_model_Config = config.get_inner_model_config()
+ outer_model_Config = config.get_outer_model_config()
+
+ print("===============inner_model_config=================")
+ for key, value in inner_model_Config.items():
+ print(key + ": " + str(value))
+ print("===============outer_model_config=================")
+ for key, value in outer_model_Config.items():
+ print(key + ": " + str(value))
+
+ context.set_context(mode=context.GRAPH_MODE, device_target=initConfig['device_target'])
+
+ export_leo(initConfig, inner_model_Config, outer_model_Config)
+ print("successfully export LEO model!")
diff --git a/research/cv/LEO/model_utils/config.py b/research/cv/LEO/model_utils/config.py
index ac01fc5da7d8eec3cdbb7b6c67fe393a111deff7..461d5ba854fe9bd9957a8bc8825606396dec5eb0 100644
--- a/research/cv/LEO/model_utils/config.py
+++ b/research/cv/LEO/model_utils/config.py
@@ -56,6 +56,8 @@ def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path="LEO-N5-K
helper = {} if helper is None else helper
choices = {} if choices is None else choices
for item in cfg:
+ if item in ("dataset_name", "num_tr_examples_per_class"):
+ continue
if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict):
help_description = helper[item] if item in helper else "Please reference to {}".format(cfg_path)
choice = choices[item] if item in choices else None
@@ -110,20 +112,28 @@ def merge(args, cfg):
return cfg
-def get_config():
+def get_config(get_args=False):
"""
Get Config according to the yaml file and cli arguments.
"""
parser = argparse.ArgumentParser(description="default name", add_help=False)
- config_dir = os.path.join(os.path.abspath(os.getcwd()), "config")
- config_name = "LEO-N5-K" + str(os.getenv("NUM_TR_EXAMPLES_PER_CLASS")) \
- + "_" + os.getenv("DATA_NAME") + "_config.yaml"
- parser.add_argument("--config_path", type=str,
- default=os.path.join(config_dir, config_name),
- help="Config file path")
+
+ parser.add_argument("--num_tr_examples_per_class", type=int,
+ default=5,
+ help="num_tr_examples_per_class")
+ parser.add_argument("--dataset_name", type=str,
+ default="miniImageNet",
+ help="dataset_name")
path_args, _ = parser.parse_known_args()
- default, helper, choices = parse_yaml(path_args.config_path)
- args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path)
+ config_name = "LEO-N5-K" + str(path_args.num_tr_examples_per_class) \
+ + "_" + path_args.dataset_name + "_config.yaml"
+ config_path = os.path.join(os.path.abspath(os.path.join(__file__, "../..")), "config", config_name)
+
+
+ default, helper, choices = parse_yaml(config_path)
+ args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=config_path)
+ if get_args:
+ return args
final_config = merge(args, default)
return Config(final_config)
diff --git a/research/cv/LEO/scripts/export.sh b/research/cv/LEO/scripts/export.sh
new file mode 100644
index 0000000000000000000000000000000000000000..16872d73272dfe062e1138071cdede17ee04629d
--- /dev/null
+++ b/research/cv/LEO/scripts/export.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+# 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.
+if [[ $# -ne 6 ]]; then
+ echo "=============================================================================================================="
+ echo "Please run the script as: "
+ echo "bash scripts/run_eval_gpu.sh [DEVICE_NUM] [DEVICE_TARGET] [DATA_PATH] [DATA_NAME] [NUM_TR_EXAMPLES_PER_CLASS] [CKPT_FILE] "
+ echo "For example: bash scripts/export.sh 1 Ascend ../leo/leo-mindspore/embeddings miniImageNet 5 ./ckpts/xxx.ckpt "
+ echo "=============================================================================================================="
+ exit 1;
+fi
+
+export GLOG_v=3
+export DEVICE_ID=$1
+export DEVICE_TARGET=$2
+export DATA_PATH=$3
+export DATA_NAME=$4
+export NUM_TR_EXAMPLES_PER_CLASS=$5
+export CKPT_FILE=$6
+
+nohup python export.py --device_target $DEVICE_TARGET \
+ --data_path $DATA_PATH \
+ --dataset_name $DATA_NAME \
+ --num_tr_examples_per_class $NUM_TR_EXAMPLES_PER_CLASS \
+ --ckpt_file $CKPT_FILE > export.log 2>&1 &
diff --git a/research/cv/LEO/scripts/run_distribution_ascend.sh b/research/cv/LEO/scripts/run_distribution_ascend.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2f15b7fb5354f15144badabc4aa294343de0a04c
--- /dev/null
+++ b/research/cv/LEO/scripts/run_distribution_ascend.sh
@@ -0,0 +1,71 @@
+#!/bin/bash
+# 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.
+# ============================================================================
+# an simple tutorial as follows, more parameters can be setting
+
+if [ $# != 6 ]
+then
+ echo "Usage: bash scripts/run_distribution_ascend.sh [RANK_TABLE_FILE] [DEVICE_TARGET] [DATA_PATH] [DATA_NAME] [NUM_TR_EXAMPLES_PER_CLASS] [SAVE_PATH]"
+ echo "For example: bash scripts/run_distribution_ascend.sh ./hccl_8p_01234567_127.0.0.1.json Ascend /home/jialing/leo/leo-mindspore/embeddings miniImageNet 5 ./ckpts/8P_mini_5
+"
+exit 1
+fi
+
+if [ ! -f $1 ]
+then
+ echo "error: RANK_TABLE_FILE=$1 is not a file"
+exit 1
+fi
+
+get_real_path(){
+ if [ "${1:0:1}" == "/" ]; then
+ echo "$1"
+ else
+ echo "$(realpath -m $PWD/$1)"
+ fi
+}
+
+ulimit -u unlimited
+export DEVICE_NUM=8
+export RANK_SIZE=8
+RANK_TABLE_FILE=$(realpath $1)
+export RANK_TABLE_FILE
+echo "RANK_TABLE_FILE=${RANK_TABLE_FILE}"
+
+export DEVICE_TARGET=$2
+export DATA_PATH=$3
+export DATA_NAME=$4
+export NUM_TR_EXAMPLES_PER_CLASS=$5
+export SAVE_PATH=$6
+
+export SERVER_ID=0
+rank_start=$((DEVICE_NUM * SERVER_ID))
+for((i=0; i<${DEVICE_NUM}; i++))
+do
+ export DEVICE_ID=$i
+ export RANK_ID=$((rank_start + i))
+ rm -rf ./train_parallel$i
+ mkdir ./train_parallel$i
+ cp -r ./src ./train_parallel$i
+ cp -r ./config ./train_parallel$i
+ cp -r ./model_utils ./train_parallel$i
+ cp ./train.py ./train_parallel$i
+ echo "start training for rank $RANK_ID, device $DEVICE_ID"
+ cd ./train_parallel$i ||exit
+ env > env.log
+ nohup python -u train.py --device_target $DEVICE_TARGET --data_path $DATA_PATH --dataset_name $DATA_NAME --num_tr_examples_per_class $NUM_TR_EXAMPLES_PER_CLASS --save_path $SAVE_PATH >log_distribution_ascend 2>&1 &
+ cd ..
+done
+
diff --git a/research/cv/LEO/scripts/run_eval_ascend.sh b/research/cv/LEO/scripts/run_eval_ascend.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7781e3d2a3dfe43635eefccd4b0d7da84a098f30
--- /dev/null
+++ b/research/cv/LEO/scripts/run_eval_ascend.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+# 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.
+if [[ $# -ne 6 ]]; then
+ echo "=============================================================================================================="
+ echo "Please run the script as: "
+ echo "bash scripts/run_eval_gpu.sh [DEVICE_ID] [DEVICE_TARGET] [DATA_PATH] [DATA_NAME] [NUM_TR_EXAMPLES_PER_CLASS] [CKPT_FILE] "
+ echo "For example: bash scripts/run_eval_ascend.sh 4 Ascend ../leo/leo-mindspore/embeddings miniImageNet 5 ./ckpt/1P_mini_5/xxx.ckpt "
+ echo "=============================================================================================================="
+ exit 1;
+fi
+
+export GLOG_v=3
+export DEVICE_ID=$1
+export DEVICE_TARGET=$2
+export DATA_PATH=$3
+export DATA_NAME=$4
+export NUM_TR_EXAMPLES_PER_CLASS=$5
+export CKPT_FILE=$6
+
+nohup python eval.py --device_target $DEVICE_TARGET \
+ --data_path $DATA_PATH \
+ --dataset_name $DATA_NAME \
+ --num_tr_examples_per_class $NUM_TR_EXAMPLES_PER_CLASS \
+ --ckpt_file $CKPT_FILE > ${DATA_NAME}_${NUM_TR_EXAMPLES_PER_CLASS}_eval.log 2>&1 &
diff --git a/research/cv/LEO/scripts/run_eval_gpu.sh b/research/cv/LEO/scripts/run_eval_gpu.sh
index c3ced3c0941e805654ce905bdc15866ca363397e..bfb8d3373b9a410ccadc1897614656d57772b036 100644
--- a/research/cv/LEO/scripts/run_eval_gpu.sh
+++ b/research/cv/LEO/scripts/run_eval_gpu.sh
@@ -12,14 +12,18 @@
# 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.
-echo "============================================================================================================================"
-echo "Please run the script as: "
-echo "bash scripts/run_eval_gpu.sh [DATA_PATH] [DATA_NAME] [NUM_TR_EXAMPLES_PER_CLASS] [CKPT_FILE] "
-echo "For example: bash scripts/run_eval_gpu.sh /home/mindspore/dataset/embeddings/ miniImageNet 1 ./ckpt/1P_mini_1/xxx.ckpt "
-echo "============ bash scripts/run_eval_gpu.sh /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpt/1P_mini_5/xxx.ckpt "
-echo "============ bash scripts/run_eval_gpu.sh /home/mindspore/dataset/embeddings/ tieredImageNet 1 ./ckpt/1P_tiered_1/xxx.ckpt "
-echo "============ bash scripts/run_eval_gpu.sh /home/mindspore/dataset/embeddings/ tieredImageNet 5 ./ckpt/1P_tiered_5/xxx.ckpt "
-echo "============================================================================================================================"
+if [[ $# -ne 6 ]]; then
+ echo "============================================================================================================================"
+ echo "Please run the script as: "
+ echo "bash scripts/run_eval_gpu.sh [DATA_PATH] [DATA_NAME] [NUM_TR_EXAMPLES_PER_CLASS] [CKPT_FILE] "
+ echo "For example: bash scripts/run_eval_gpu.sh /home/mindspore/dataset/embeddings/ miniImageNet 1 ./ckpt/1P_mini_1/xxx.ckpt "
+ echo "============ bash scripts/run_eval_gpu.sh /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpt/1P_mini_5/xxx.ckpt "
+ echo "============ bash scripts/run_eval_gpu.sh /home/mindspore/dataset/embeddings/ tieredImageNet 1 ./ckpt/1P_tiered_1/xxx.ckpt "
+ echo "============ bash scripts/run_eval_gpu.sh /home/mindspore/dataset/embeddings/ tieredImageNet 5 ./ckpt/1P_tiered_5/xxx.ckpt "
+ echo "============================================================================================================================"
+ exit 1;
+fi
+
export GLOG_v=3
export DEVICE_TARGET=GPU
export DATA_PATH=$1
diff --git a/research/cv/LEO/scripts/run_train_ascend.sh b/research/cv/LEO/scripts/run_train_ascend.sh
new file mode 100644
index 0000000000000000000000000000000000000000..bd42217f8d1c8c9850044caa1c9400e5f30e1792
--- /dev/null
+++ b/research/cv/LEO/scripts/run_train_ascend.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+# 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.
+if [[ $# -ne 6 ]]; then
+ echo "=============================================================================================================="
+ echo "Please run the script as: "
+ echo "bash scripts/run_train_gpu.sh [DEVICE_ID] [DEVICE_TARGET] [DATA_PATH] [DATA_NAME] [NUM_TR_EXAMPLES_PER_CLASS] [SAVE_PATH] "
+ echo "For example: bash scripts/run_train_ascend.sh 6 Ascend /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpts/1P_mini_5"
+ echo "=============================================================================================================="
+ exit 1;
+fi
+
+export DEVICE_ID=$1
+export DEVICE_TARGET=$2
+export DATA_PATH=$3
+export DATA_NAME=$4
+export NUM_TR_EXAMPLES_PER_CLASS=$5
+export SAVE_PATH=$6
+
+export GLOG_v=3
+export DEVICE_ID=$DEVICE_ID
+nohup python -u train.py --device_target $DEVICE_TARGET --data_path $DATA_PATH --dataset_name $DATA_NAME --num_tr_examples_per_class $NUM_TR_EXAMPLES_PER_CLASS --save_path $SAVE_PATH > ${DEVICE_NUM}P_${DATA_NAME}_${NUM_TR_EXAMPLES_PER_CLASS}_train.log 2>&1 &
diff --git a/research/cv/LEO/scripts/run_train_gpu.sh b/research/cv/LEO/scripts/run_train_gpu.sh
index 4c3d099dd960ec9b853cad12b829ebf52f7707d8..2161d4a84d19df757a29020f403efeaf9494bcf9 100644
--- a/research/cv/LEO/scripts/run_train_gpu.sh
+++ b/research/cv/LEO/scripts/run_train_gpu.sh
@@ -12,20 +12,24 @@
# 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.
-echo "===================================================================================================================="
-echo "Please run the script as: "
-echo "bash scripts/run_train_gpu.sh [DEVICE_NUM] [DATA_PATH] [DATA_NAME] [NUM_TR_EXAMPLES_PER_CLASS] [SAVE_PATH] "
-echo "For example: bash scripts/run_train_gpu.sh 1 /home/mindspore/dataset/embeddings/ miniImageNet 1 ./ckpt/1P_mini_1"
-echo " ============bash scripts/run_train_gpu.sh 1 /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpt/1P_mini_5"
-echo " ============bash scripts/run_train_gpu.sh 1 /home/mindspore/dataset/embeddings/ tieredImageNet 1 ./ckpt/1P_tiered_1"
-echo " ============bash scripts/run_train_gpu.sh 1 /home/mindspore/dataset/embeddings/ tieredImageNet 5 ./ckpt/1P_tiered_5"
-echo "===================================================================================================================="
-echo "Please run distributed training script as: "
-echo "For example: bash scripts/run_train_gpu.sh 8 /home/mindspore/dataset/embeddings/ miniImageNet 1 ./ckpt/8P_mini_1 "
-echo " ============bash scripts/run_train_gpu.sh 8 /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpt/8P_mini_5"
-echo " ============bash scripts/run_train_gpu.sh 8 /home/mindspore/dataset/embeddings/ tieredImageNet 1 ./ckpt/8P_tiered_1"
-echo " ============bash scripts/run_train_gpu.sh 8 /home/mindspore/dataset/embeddings/ tieredImageNet 5 ./ckpt/8P_tiered_5"
-echo "===================================================================================================================="
+if [[ $# -ne 6 ]]; then
+ echo "===================================================================================================================="
+ echo "Please run the script as: "
+ echo "bash scripts/run_train_gpu.sh [DEVICE_NUM] [DATA_PATH] [DATA_NAME] [NUM_TR_EXAMPLES_PER_CLASS] [SAVE_PATH] "
+ echo "For example: bash scripts/run_train_gpu.sh 1 /home/mindspore/dataset/embeddings/ miniImageNet 1 ./ckpt/1P_mini_1"
+ echo " ============bash scripts/run_train_gpu.sh 1 /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpt/1P_mini_5"
+ echo " ============bash scripts/run_train_gpu.sh 1 /home/mindspore/dataset/embeddings/ tieredImageNet 1 ./ckpt/1P_tiered_1"
+ echo " ============bash scripts/run_train_gpu.sh 1 /home/mindspore/dataset/embeddings/ tieredImageNet 5 ./ckpt/1P_tiered_5"
+ echo "===================================================================================================================="
+ echo "Please run distributed training script as: "
+ echo "For example: bash scripts/run_train_gpu.sh 8 /home/mindspore/dataset/embeddings/ miniImageNet 1 ./ckpt/8P_mini_1 "
+ echo " ============bash scripts/run_train_gpu.sh 8 /home/mindspore/dataset/embeddings/ miniImageNet 5 ./ckpt/8P_mini_5"
+ echo " ============bash scripts/run_train_gpu.sh 8 /home/mindspore/dataset/embeddings/ tieredImageNet 1 ./ckpt/8P_tiered_1"
+ echo " ============bash scripts/run_train_gpu.sh 8 /home/mindspore/dataset/embeddings/ tieredImageNet 5 ./ckpt/8P_tiered_5"
+ echo "===================================================================================================================="
+ exit 1;
+fi
+
export DEVICE_NUM=$1
export DEVICE_TARGET=GPU
export DATA_PATH=$2
diff --git a/research/cv/LEO/train.py b/research/cv/LEO/train.py
index 812955ac41deee8f0113213d6d304aae0c477b63..574e0a72234e3711cf33b87d1ecbfd70fd72c169 100644
--- a/research/cv/LEO/train.py
+++ b/research/cv/LEO/train.py
@@ -19,8 +19,10 @@ import model_utils.config as config
import src.data as data
import src.outerloop as outerloop
from src.trainonestepcell import TrainOneStepCell
+import mindspore
+import mindspore.nn as nn
from mindspore import context
-from mindspore import save_checkpoint, load_param_into_net, load_checkpoint
+from mindspore import save_checkpoint, load_param_into_net
from mindspore.communication.management import init
from mindspore.context import ParallelMode
@@ -29,12 +31,35 @@ os.environ['GLOG_v'] = "3"
os.environ['GLOG_log_dir'] = '/var/log'
+def save_checkpoint_to_file(if_save_checkpoint, val_accs, best_acc, step, val_losses, init_config, train_outer_loop):
+ if if_save_checkpoint:
+ if not sum(val_accs) / len(val_accs) < best_acc:
+ best_acc = sum(val_accs) / len(val_accs)
+ model_name = '%dk_%4.4f_%4.4f_model.ckpt' % (
+ (step // 1000 + 1),
+ sum(val_losses) / len(val_losses),
+ sum(val_accs) / len(val_accs))
+
+ check_dir(init_config['save_path'])
+
+ if args.enable_modelarts:
+ save_checkpoint_path = '/cache/train_output/device_' + \
+ os.getenv('DEVICE_ID') + '/'
+ save_checkpoint_path = '/cache/train_output/'
+ if not os.path.exists(save_checkpoint_path):
+ os.makedirs(save_checkpoint_path)
+ save_checkpoint(train_outer_loop, os.path.join(save_checkpoint_path, model_name))
+ else:
+ save_checkpoint(train_outer_loop, os.path.join(init_config['save_path'], model_name))
+ print('Saved checkpoint %s...' % model_name)
+
+
def train_leo(init_config, inner_model_config, outer_model_config):
inner_lr_init = inner_model_config['inner_lr_init']
finetuning_lr_init = inner_model_config['finetuning_lr_init']
total_train_steps = outer_model_config['total_steps']
- val_every_step = 5000
+ val_every_step = 3000
total_val_steps = 100
if_save_checkpoint = True
best_acc = 0
@@ -72,8 +97,11 @@ def train_leo(init_config, inner_model_config, outer_model_config):
inner_step=inner_model_config['inner_unroll_length'],
finetune_inner_step=inner_model_config['finetuning_unroll_length'], is_meta_training=True)
- parm_dict = load_checkpoint('./resource/leo_ms_init.ckpt')
- load_param_into_net(train_outer_loop, parm_dict)
+ if context.get_context("device_target") == "Ascend":
+ train_outer_loop.to_float(mindspore.float32)
+ for _, cell in train_outer_loop.cells_and_names():
+ if isinstance(cell, nn.Dense):
+ cell.to_float(mindspore.float16)
train_net = TrainOneStepCell(train_outer_loop,
outer_model_config['outer_lr'],
@@ -105,8 +133,8 @@ def train_leo(init_config, inner_model_config, outer_model_config):
train_net.group_params[0]['params'][1].T.asnumpy(),
val_acc.asnumpy(), now_t-old_t))
- if step % val_every_step == 4999:
- print('5000 step average time: %4.4f second...'%(sum_steptime/5000))
+ if step % val_every_step == 2999:
+ print('3000 step average time: %4.4f second...'%(sum_steptime/3000))
sum_steptime = 0
val_losses = []
@@ -128,18 +156,8 @@ def train_leo(init_config, inner_model_config, outer_model_config):
(sum(val_losses)/len(val_losses), sum(val_accs)/len(val_accs)))
print('=' * 50)
- if if_save_checkpoint:
- if not sum(val_accs)/len(val_accs) < best_acc:
- best_acc = sum(val_accs)/len(val_accs)
- model_name = '%dk_%4.4f_%4.4f_model.ckpt' % (
- (step//1000+1),
- sum(val_losses)/len(val_losses),
- sum(val_accs)/len(val_accs))
-
- check_dir(init_config['save_path'])
-
- save_checkpoint(train_outer_loop, os.path.join(init_config['save_path'], model_name))
- print('Saved checkpoint %s...'%model_name)
+ save_checkpoint_to_file(if_save_checkpoint, val_accs, best_acc, step, val_losses,
+ init_config, train_outer_loop)
if step == (total_train_steps-1):
train_end = time.time()
@@ -166,6 +184,8 @@ if __name__ == '__main__':
initConfig = config.get_init_config()
inner_model_Config = config.get_inner_model_config()
outer_model_Config = config.get_outer_model_config()
+ args = config.get_config(get_args=True)
+
print("===============inner_model_config=================")
for key, value in inner_model_Config.items():
@@ -175,10 +195,21 @@ if __name__ == '__main__':
print(key+": "+str(value))
context.set_context(mode=context.GRAPH_MODE, device_target=initConfig['device_target'])
- if initConfig['device_num'] > 1:
+ if args.enable_modelarts:
+ import moxing as mox
+
+ mox.file.copy_parallel(
+ src_url=args.data_url, dst_url='/cache/dataset/device_' + os.getenv('DEVICE_ID'))
+ train_dataset_path = os.path.join('/cache/dataset/device_' + os.getenv('DEVICE_ID'), "embeddings")
+ initConfig['data_path'] = train_dataset_path
+
+ elif initConfig['device_num'] > 1:
init('nccl')
context.set_auto_parallel_context(device_num=initConfig['device_num'],
parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True)
train_leo(initConfig, inner_model_Config, outer_model_Config)
+ if args.enable_modelarts:
+ mox.file.copy_parallel(
+ src_url='/cache/train_output', dst_url=args.train_url)