!14739 Add MobileNetV1 CPU Script

From: @wuxuejian
Reviewed-by: @oacjiewen,@liangchenghui
Signed-off-by: @liangchenghui

model_zoo/official/cv/mobilenetv1/README.md

@@ -99,6 +99,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 You can start training using python or shell scripts. The usage of shell scripts as follows:
 
 - Ascend: sh run_distribute_train.sh [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH] (optional)
+- CPU: sh run_train_CPU.sh [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH] (optional)
 
 For distributed training, a hccl configuration file with JSON format needs to be created in advance.
 
@@ -109,10 +110,12 @@ Please follow the instructions in the link [hccn_tools](https://gitee.com/mindsp
 ```shell
 # training example
   python:
-      Ascend: python train.py --platform Ascend --dataset_path [TRAIN_DATASET_PATH]
+      Ascend: python train.py --device_target Ascend --dataset_path [TRAIN_DATASET_PATH]
+      CPU: python train.py --device_target CPU --dataset_path [TRAIN_DATASET_PATH]
 
   shell:
      Ascend: sh run_distribute_train.sh [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
+     CPU: sh run_train_CPU.sh [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
 ```
 
 ### Result
@@ -133,6 +136,7 @@ Epoch time: 150950.623, per step time: 120.664
 You can start training using python or shell scripts.If the train method is train or fine tune, should not input the `[CHECKPOINT_PATH]` The usage of shell scripts as follows:
 
 - Ascend: sh run_eval.sh [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
+- CPU: sh run_eval_CPU.sh [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
 
 ### Launch
 
@@ -140,9 +144,11 @@ You can start training using python or shell scripts.If the train method is trai
 # eval example
   python:
       Ascend: python eval.py --dataset [cifar10|imagenet2012] --dataset_path [VAL_DATASET_PATH] --pretrain_ckpt [CHECKPOINT_PATH]
+      CPU: python eval.py --dataset [cifar10|imagenet2012] --dataset_path [VAL_DATASET_PATH] --pretrain_ckpt [CHECKPOINT_PATH] --device_target CPU
 
   shell:
       Ascend: sh run_eval.sh [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
+      CPU: sh run_eval_CPU.sh [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
 ```
 
 > checkpoint can be produced in training process.

model_zoo/official/cv/mobilenetv1/eval.py

@@ -45,7 +45,7 @@ if __name__ == '__main__':
 
     # init context
     context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
-    if target != "GPU":
+    if target == "Ascend":
         device_id = int(os.getenv('DEVICE_ID'))
         context.set_context(device_id=device_id)
 

model_zoo/official/cv/mobilenetv1/scripts/run_eval_cpu.sh

@@ -0,0 +1,64 @@
+#!/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.
+# ============================================================================
+
+if [ $# != 3 ]
+then 
+    echo "Usage: bash run_eval_cpu.sh [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]"
+exit 1
+fi
+
+if [ $1 != "cifar10" ] && [ $1 != "imagenet2012" ]
+then 
+    echo "error: the selected dataset is neither cifar10 nor imagenet2012"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $2)
+PATH2=$(get_real_path $3)
+
+
+if [ ! -d $PATH1 ]
+then 
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi 
+
+if [ ! -f $PATH2 ]
+then 
+    echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
+exit 1
+fi 
+
+if [ -d "eval" ];
+then
+    rm -rf ./eval
+fi
+mkdir ./eval
+cp ../*.py ./eval
+cp *.sh ./eval
+cp -r ../src ./eval
+cd ./eval || exit
+env > env.log
+python eval.py --dataset=$1 --dataset_path=$PATH1 --checkpoint_path=$PATH2 --device_target=CPU &> log &
+cd ..

model_zoo/official/cv/mobilenetv1/scripts/run_train_cpu.sh

@@ -0,0 +1,75 @@
+#!/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.
+# ============================================================================
+
+if [ $# != 2 ] && [ $# != 3 ]
+then 
+    echo "Usage: bash run_train_cpu.sh [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+exit 1
+fi
+
+if [ $1 != "cifar10" ] && [ $1 != "imagenet2012" ]
+then 
+    echo "error: the selected dataset is neither cifar10 nor imagenet2012"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $2)
+
+if [ $# == 3 ]
+then
+    PATH2=$(get_real_path $3)
+fi
+
+if [ ! -d $PATH1 ]
+then 
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ $# == 3 ] && [ ! -f $PATH2 ]
+then
+    echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ../*.py ./train
+cp *.sh ./train
+cp -r ../src ./train
+cd ./train || exit
+env > env.log
+if [ $# == 2 ]
+then
+    python train.py --dataset=$1 --dataset_path=$PATH1 --device_target=CPU &> log &
+fi
+
+if [ $# == 3 ]
+then
+    python train.py --dataset=$1 --dataset_path=$PATH1 --pre_trained=$PATH2 --device_target=CPU &> log &
+fi
+cd ..

model_zoo/official/cv/mobilenetv1/src/dataset.py

@@ -16,12 +16,15 @@
 create train or eval dataset.
 """
 import os
+from multiprocessing import cpu_count
 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
 from mindspore.communication.management import init, get_rank, get_group_size
 
+THREAD_NUM = 12 if cpu_count() >= 12 else 8
+
 
 def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
     """
@@ -38,15 +41,17 @@ def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, target=
     """
     if target == "Ascend":
         device_num, rank_id = _get_rank_info()
-    else:
+    elif target == "GPU":
         init()
         rank_id = get_rank()
         device_num = get_group_size()
+    else:
+        device_num = 1
 
     if device_num == 1:
-        data_set = ds.Cifar10Dataset(dataset_path, num_parallel_workers=12, shuffle=True)
+        data_set = ds.Cifar10Dataset(dataset_path, num_parallel_workers=THREAD_NUM, shuffle=True)
     else:
-        data_set = ds.Cifar10Dataset(dataset_path, num_parallel_workers=12, shuffle=True,
+        data_set = ds.Cifar10Dataset(dataset_path, num_parallel_workers=THREAD_NUM, shuffle=True,
                                      num_shards=device_num, shard_id=rank_id)
 
     # define map operations
@@ -66,8 +71,8 @@ def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, target=
 
     type_cast_op = C2.TypeCast(mstype.int32)
 
-    data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=12)
-    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=THREAD_NUM)
+    data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=THREAD_NUM)
 
     # apply batch operations
     data_set = data_set.batch(batch_size, drop_remainder=True)
@@ -99,9 +104,9 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
         device_num = get_group_size()
 
     if device_num == 1:
-        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=12, shuffle=True)
+        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=THREAD_NUM, shuffle=True)
     else:
-        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=12, shuffle=True,
+        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=THREAD_NUM, shuffle=True,
                                          num_shards=device_num, shard_id=rank_id)
 
     image_size = 224
@@ -127,8 +132,8 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
 
     type_cast_op = C2.TypeCast(mstype.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)
+    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)
 
     # apply batch operations
     data_set = data_set.batch(batch_size, drop_remainder=True)

model_zoo/official/cv/mobilenetv1/train.py

@@ -116,38 +116,28 @@ if __name__ == '__main__':
         else:
             no_decayed_params.append(param)
 
-    group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
-                    {'params': no_decayed_params},
-                    {'order_params': net.trainable_params()}]
-    opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
-    # define loss, model
     if target == "Ascend":
-        if args_opt.dataset == "imagenet2012":
-            if not config.use_label_smooth:
-                config.label_smooth_factor = 0.0
-            loss = CrossEntropySmooth(sparse=True, reduction="mean",
-                                      smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
-        else:
-            loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
-        loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
-        model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
-                      amp_level="O2", keep_batchnorm_fp32=False)
+        group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
+                        {'params': no_decayed_params},
+                        {'order_params': net.trainable_params()}]
+        opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
     else:
-        # GPU target
-        if args_opt.dataset == "imagenet2012":
-            if not config.use_label_smooth:
-                config.label_smooth_factor = 0.0
-            loss = CrossEntropySmooth(sparse=True, reduction="mean",
-                                      smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
-        else:
-            loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
-
         opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay,
                        config.loss_scale)
-        loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
-        # Mixed precision
+    # define loss, model
+    if args_opt.dataset == "imagenet2012":
+        if not config.use_label_smooth:
+            config.label_smooth_factor = 0.0
+        loss = CrossEntropySmooth(sparse=True, reduction="mean",
+                                  smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
+    else:
+        loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+    loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
+    if target != "CPU":
         model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
                       amp_level="O2", keep_batchnorm_fp32=False)
+    else:
+        model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
 
     # define callbacks
     time_cb = TimeMonitor(data_size=step_size)