add cpu model scripts

model_zoo/research/cv/FaceRecognition/README.md

@@ -55,8 +55,9 @@ The directory structure is as follows:
 
 # [Environment Requirements](#contents)
 
-- Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor.
+- Hardware（Ascend, CPU）
+    - Prepare hardware environment with Ascend processor. It also supports the use of CPU processor to prepare the
+    hardware environment.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
@@ -76,9 +77,12 @@ The entire code structure is as following:
   │   ├── run_distribute_train_base.sh      // shell script for distributed training on Ascend
   │   ├── run_distribute_train_beta.sh      // shell script for distributed training on Ascend
   │   ├── run_eval.sh                       // shell script for evaluation on Ascend
+  │   ├── run_eval_cpu.sh                   // shell script for evaluation on CPU
   │   ├── run_export.sh                     // shell script for exporting air model
   │   ├── run_standalone_train_base.sh      // shell script for standalone training on Ascend
   │   ├── run_standalone_train_beta.sh      // shell script for standalone training on Ascend
+  │   ├── run_train_base_cpu.sh             // shell script for training on CPU
+  │   ├── run_train_btae_cpu.sh             // shell script for training on CPU
   ├── src
   │   ├── backbone
   │   │   ├── head.py                       // head unit
@@ -100,8 +104,11 @@ The entire code structure is as following:
   │   ├── local_adapter.py                  // local adapter
   │   ├── moxing_adapter.py                 // moxing adapter
   ├─ base_config.yaml                       // parameter configuration
+  ├─ base_config_cpu.yaml                   // parameter configuration
   ├─ beta_config.yaml                       // parameter configuration
+  ├─ beta_config_cpu.yaml                   // parameter configuration
   ├─ inference_config.yaml                  // parameter configuration
+  ├─ inference_config_cpu.yaml              // parameter configuration
   ├─ train.py                               // training scripts
   ├─ eval.py                                // evaluation scripts
   └─ export.py                              // export air model
@@ -111,7 +118,7 @@ The entire code structure is as following:
 
 ### Train
 
-- Stand alone mode
+- Stand alone mode(Ascend)
 
     - base model
 
@@ -171,6 +178,36 @@ The entire code structure is as following:
       sh run_distribute_train_beta.sh ./rank_table_8p.json
       ```
 
+- Stand alone mode(CPU)
+
+    - base model
+
+      ```bash
+      cd ./scripts
+      sh run_train_base_cpu.sh
+      ```
+
+      for example:
+
+      ```bash
+      cd ./scripts
+      sh run_train_base_cpu.sh
+      ```
+
+    - beta model
+
+      ```bash
+      cd ./scripts
+      sh run_train_beta_cpu.sh
+      ```
+
+      for example:
+
+      ```bash
+      cd ./scripts
+      sh run_train_beta_cpu.sh
+      ```
+
 - ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training as follows)
 
     - base model

model_zoo/research/cv/FaceRecognition/base_config_cpu.yaml

@@ -0,0 +1,76 @@
+# 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
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+device_target: "CPU"
+enable_profiling: False
+
+# ==============================================================================
+# Training options
+train_stage: "base"
+is_distributed: 0
+
+# dataset related
+data_dir: "/cache/data/face_recognition_dataset/train_dataset/"
+num_classes: 1
+per_batch_size: 64
+need_modelarts_dataset_unzip: True
+
+# network structure related
+backbone: "r100"
+use_se: 1
+emb_size: 512
+act_type: "relu"
+fp16: 1
+pre_bn: 1
+inference: 0
+use_drop: 1
+nc_16: 1
+
+# loss related
+margin_a: 1.0
+margin_b: 0.2
+margin_m: 0.3
+margin_s: 64
+
+# optimizer related
+lr: 0.01
+lr_scale: 1
+lr_epochs: "8,14,18"
+weight_decay: 0.0002
+momentum: 0.9
+max_epoch: 20
+pretrained: ""
+warmup_epochs: 0
+
+# distributed parameter
+local_rank: 0
+world_size: 1
+model_parallel: 0
+
+# logging related
+log_interval: 100
+ckpt_path: "outputs"
+max_ckpts: -1
+dynamic_init_loss_scale: 65536
+ckpt_steps: 1000
+
+---
+
+# Help description for each configuration
+enable_modelarts: "Whether training on modelarts, default: False"
+data_url: "Url for modelarts"
+train_url: "Url for modelarts"
+data_path: "The location of the input data."
+output_path: "The location of the output file."
+device_target: 'Target device type'
+enable_profiling: 'Whether enable profiling while training, default: False'
+
+train_stage: "Train stage, base or beta"
+is_distributed: "If multi device"

model_zoo/research/cv/FaceRecognition/beta_config_cpu.yaml

@@ -0,0 +1,76 @@
+# 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
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+device_target: "CPU"
+enable_profiling: False
+
+# ==============================================================================
+# Training options
+train_stage: "beta"
+is_distributed: 0
+
+# dataset related
+data_dir: "/cache/data/face_recognition_dataset/train_dataset/"
+num_classes: 1
+per_batch_size: 64
+need_modelarts_dataset_unzip: True
+
+# network structure related
+backbone: "r100"
+use_se: 0
+emb_size: 256
+act_type: "relu"
+fp16: 1
+pre_bn: 0
+inference: 0
+use_drop: 1
+nc_16: 1
+
+# loss related
+margin_a: 1.0
+margin_b: 0.2
+margin_m: 0.3
+margin_s: 64
+
+# optimizer related
+lr: 0.04
+lr_scale: 1
+lr_epochs: "8,14,18"
+weight_decay: 0.0002
+momentum: 0.9
+max_epoch: 20
+pretrained: "your_pretrained_model"
+warmup_epochs: 0
+
+# distributed parameter
+local_rank: 0
+world_size: 1
+model_parallel: 0
+
+# logging related
+log_interval: 100
+ckpt_path: "outputs"
+max_ckpts: -1
+dynamic_init_loss_scale: 65536
+ckpt_steps: 1000
+
+---
+
+# Help description for each configuration
+enable_modelarts: "Whether training on modelarts, default: False"
+data_url: "Url for modelarts"
+train_url: "Url for modelarts"
+data_path: "The location of the input data."
+output_path: "The location of the output file."
+device_target: 'Target device type'
+enable_profiling: 'Whether enable profiling while training, default: False'
+
+train_stage: "Train stage, base or beta"
+is_distributed: "If multi device"

model_zoo/research/cv/FaceRecognition/eval.py

@@ -33,7 +33,7 @@ from model_utils.config import config
 from model_utils.moxing_adapter import moxing_wrapper
 from model_utils.device_adapter import get_device_id, get_device_num, get_rank_id
 
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=get_device_id())
+context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=get_device_id())
 
 
 class TxtDataset():

model_zoo/research/cv/FaceRecognition/inference_config_cpu.yaml

@@ -0,0 +1,60 @@
+# 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
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+device_target: "CPU"
+enable_profiling: False
+
+# ==============================================================================
+# Training options
+
+# distributed parameter
+is_distributed: 0
+local_rank: 0
+world_size: 1
+
+# test weight
+weight: 'your_test_model'
+test_dir: '/cache/data/face_recognition_dataset/'
+need_modelarts_dataset_unzip: True
+
+# model define
+backbone: "r100"
+use_se: 0
+emb_size: 256
+act_type: "relu"
+fp16: 1
+pre_bn: 0
+inference: 1
+use_drop: 0
+
+# test and dis batch size
+test_batch_size: 128
+dis_batch_size: 512
+
+# log
+log_interval: 100
+ckpt_path: "outputs/models"
+
+# test and dis image list
+test_img_predix: ""
+test_img_list: ""
+dis_img_predix: ""
+dis_img_list: ""
+
+---
+
+# Help description for each configuration
+enable_modelarts: "Whether training on modelarts, default: False"
+data_url: "Url for modelarts"
+train_url: "Url for modelarts"
+data_path: "The location of the input data."
+output_path: "The location of the output file."
+device_target: 'Target device type'
+enable_profiling: 'Whether enable profiling while training, default: False'

model_zoo/research/cv/FaceRecognition/scripts/run_eval_cpu.sh

@@ -0,0 +1,38 @@
+#!/bin/bash
+# Copyright 2021 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.
+# ============================================================================
+
+dirname_path=$(dirname "$(pwd)")
+echo ${dirname_path}
+export PYTHONPATH=${dirname_path}:$PYTHONPATH
+
+USE_DEVICE_ID=0
+echo 'start device '$USE_DEVICE_ID
+dev=`expr $USE_DEVICE_ID + 0`
+export DEVICE_ID=$dev
+
+EXECUTE_PATH=$(pwd)
+echo *******************EXECUTE_PATH= $EXECUTE_PATH
+if [ -d "${EXECUTE_PATH}/log_inference" ]; then
+  echo "[INFO] Delete old log_inference log files"
+  rm -rf ${EXECUTE_PATH}/log_inference
+fi
+mkdir ${EXECUTE_PATH}/log_inference
+
+cd ${EXECUTE_PATH}/log_inference || exit
+env > ${EXECUTE_PATH}/log_inference/face_recognition.log
+python ${EXECUTE_PATH}/../eval.py --config_path=${EXECUTE_PATH}/../inference_config_cpu.yaml &> ${EXECUTE_PATH}/log_inference/face_recognition.log &
+
+echo "[INFO] Start inference..."

model_zoo/research/cv/FaceRecognition/scripts/run_train_base_cpu.sh

@@ -0,0 +1,45 @@
+#!/bin/bash
+# Copyright 2021 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.
+# ============================================================================
+
+dirname_path=$(dirname "$(pwd)")
+echo ${dirname_path}
+export PYTHONPATH=${dirname_path}:$PYTHONPATH
+
+
+USE_DEVICE_ID=0
+dev=`expr $USE_DEVICE_ID + 0`
+export DEVICE_ID=$dev
+
+EXECUTE_PATH=$(pwd)
+echo *******************EXECUTE_PATH= $EXECUTE_PATH
+if [ -d "${EXECUTE_PATH}/log_standalone_graph" ]; then
+  echo "[INFO] Delete old data_standalone log files"
+  rm -rf ${EXECUTE_PATH}/log_standalone_graph
+fi
+mkdir ${EXECUTE_PATH}/log_standalone_graph
+
+
+rm -rf ${EXECUTE_PATH}/data_standalone_log_$USE_DEVICE_ID
+mkdir -p ${EXECUTE_PATH}/data_standalone_log_$USE_DEVICE_ID
+cd ${EXECUTE_PATH}/data_standalone_log_$USE_DEVICE_ID || exit
+
+env > ${EXECUTE_PATH}/log_standalone_graph/face_recognition_$USE_DEVICE_ID.log
+python ${EXECUTE_PATH}/../train.py \
+    --config_path=${EXECUTE_PATH}/../base_config_cpu.yaml \
+    --train_stage=base \
+    --is_distributed=0 &> ${EXECUTE_PATH}/log_standalone_graph/face_recognition_$USE_DEVICE_ID.log &
+
+echo "[INFO] Start training..."

model_zoo/research/cv/FaceRecognition/scripts/run_train_beta_cpu.sh

@@ -0,0 +1,44 @@
+#!/bin/bash
+# Copyright 2021 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.
+# ============================================================================
+
+dirname_path=$(dirname "$(pwd)")
+echo ${dirname_path}
+export PYTHONPATH=${dirname_path}:$PYTHONPATH
+
+USE_DEVICE_ID=0
+dev=`expr $USE_DEVICE_ID + 0`
+export DEVICE_ID=$dev
+
+EXECUTE_PATH=$(pwd)
+echo *******************EXECUTE_PATH= $EXECUTE_PATH
+if [ -d "${EXECUTE_PATH}/log_standalone_graph" ]; then
+  echo "[INFO] Delete old data_stanalone log files"
+  rm -rf ${EXECUTE_PATH}/log_standalone_graph
+fi
+mkdir ${EXECUTE_PATH}/log_standalone_graph
+
+
+rm -rf ${EXECUTE_PATH}/data_standalone_log_$USE_DEVICE_ID
+mkdir -p ${EXECUTE_PATH}/data_standalone_log_$USE_DEVICE_ID
+cd ${EXECUTE_PATH}/data_standalone_log_$USE_DEVICE_ID || exit
+
+env > ${EXECUTE_PATH}/log_standalone_graph/face_recognition_$USE_DEVICE_ID.log
+python ${EXECUTE_PATH}/../train.py \
+    --config_path=${EXECUTE_PATH}/../beta_config_cpu.yaml \
+    --train_stage=beta \
+    --is_distributed=0 &> ${EXECUTE_PATH}/log_standalone_graph/face_recognition_$USE_DEVICE_ID.log &
+
+echo "[INFO] Start training..."

model_zoo/research/cv/FaceRecognition/src/custom_dataset.py

@@ -21,6 +21,7 @@ from collections import defaultdict
 import numpy as np
 
 from PIL import Image, ImageFile
+from utils.config import config
 from mindspore.communication.management import get_group_size, get_rank
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 
@@ -56,9 +57,14 @@ class DistributedCustomSampler:
         self.epoch_gen = 1
 
     def _sample_(self, indices):
+        """sample"""
         sampled = []
+
         for indice in indices:
             sampled_id = indice
+            if config.device_target == 'CPU':
+                if self.k >= len(sampled_id):
+                    continue
             sampled.extend(np.random.choice(self.dataset.id2range[sampled_id][:], self.k).tolist())
 
         return sampled

model_zoo/research/cv/FaceRecognition/src/dataset_factory.py

@@ -21,6 +21,7 @@ import mindspore.dataset as de
 import mindspore.dataset.vision.py_transforms as F
 import mindspore.dataset.transforms.py_transforms as F2
 
+from utils.config import config
 from src.custom_dataset import DistributedCustomSampler, CustomDataset
 
 __all__ = ['get_de_dataset']
@@ -44,9 +45,12 @@ def get_de_dataset(args):
         os.makedirs(os.path.dirname(cache_path))
     dataset = CustomDataset(args.data_dir, cache_path, args.is_distributed)
     args.logger.info("dataset len:{}".format(dataset.__len__()))
-    sampler = DistributedCustomSampler(dataset, num_replicas=args.world_size, rank=args.local_rank,
-                                       is_distributed=args.is_distributed)
-    de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
+    if config.device_target == 'Ascend':
+        sampler = DistributedCustomSampler(dataset, num_replicas=args.world_size, rank=args.local_rank,
+                                           is_distributed=args.is_distributed)
+        de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
+    elif config.device_target == 'CPU':
+        de_dataset = de.GeneratorDataset(dataset, ["image", "label"])
     args.logger.info("after sampler de_dataset datasize :{}".format(de_dataset.get_dataset_size()))
     de_dataset = de_dataset.map(input_columns="image", operations=transform)
     de_dataset = de_dataset.map(input_columns="label", operations=transform_label)

model_zoo/research/cv/FaceRecognition/train.py

@@ -41,7 +41,7 @@ from model_utils.config import config
 from model_utils.device_adapter import get_device_id, get_device_num, get_rank_id
 
 mindspore.common.seed.set_seed(1)
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False,
+context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, save_graphs=False,
                     device_id=get_device_id(), reserve_class_name_in_scope=False, enable_auto_mixed_precision=False)
 
 class DistributedHelper(Cell):