!3719 resnet101 changes for GPU

Merge pull request !3719 from RobinGrosman/master

model_zoo/official/cv/resnet/eval.py

@@ -58,11 +58,8 @@ if __name__ == '__main__':
     context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False, device_id=device_id)
 
     # create dataset
-    if args_opt.net == "resnet50":
-        dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
-                                 target=target)
-    else:
-        dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
+    dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
+                             target=target)
     step_size = dataset.get_dataset_size()
 
     # define net

model_zoo/official/cv/resnet/scripts/run_distribute_train_gpu.sh

@@ -0,0 +1,93 @@
+#!/bin/bash
+# Copyright 2020 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 ] && [ $# != 4 ]
+then 
+	echo "Usage: sh run_distribute_train_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012]  [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+exit 1
+fi
+
+if [ $1 != "resnet50" ] && [ $1 != "resnet101" ]
+then 
+    echo "error: the selected net is neither resnet50 nor resnet101"
+exit 1
+fi
+
+if [ $2 != "cifar10" ] && [ $2 != "imagenet2012" ]
+then 
+    echo "error: the selected dataset is neither cifar10 nor imagenet2012"
+exit 1
+fi
+
+if [ $1 == "resnet101" ] && [ $2 == "cifar10" ]
+then 
+    echo "error: training resnet101 with cifar10 dataset is unsupported now!"
+exit 1
+fi
+
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $3)
+
+if [ $# == 4 ]
+then 
+    PATH2=$(get_real_path $4)
+fi
+
+
+if [ ! -d $PATH2 ]
+then 
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi 
+
+if [ $# == 5 ] && [ ! -f $PATH2 ]
+then
+    echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=8
+export RANK_SIZE=8
+
+rm -rf ./train_parallel
+mkdir ./train_parallel
+cp ../*.py ./train_parallel
+cp *.sh ./train_parallel
+cp -r ../src ./train_parallel
+cd ./train_parallel || exit
+
+if [ $# == 3 ]
+then	    
+        mpirun --allow-run-as-root -n $RANK_SIZE \
+	      python train.py --net=$1 --dataset=$2 --run_distribute=True \
+	      --device_num=$DEVICE_NUM --device_target="GPU" --dataset_path=$PATH1 &> log &
+fi
+    
+if [ $# == 4 ]
+then
+        mpirun --allow-run-as-root -n $RANK_SIZE \
+          python train.py --net=$1 --dataset=$2 --run_distribute=True \
+	  --device_num=$DEVICE_NUM --device_target="GPU" --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
+fi

model_zoo/official/cv/resnet/scripts/run_standalone_train_gpu.sh

@@ -0,0 +1,95 @@
+#!/bin/bash
+# Copyright 2020 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 ] && [ $# != 4 ]
+then 
+    echo "Usage: sh run_standalone_train.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+exit 1
+fi
+
+if [ $1 != "resnet50" ] && [ $1 != "resnet101" ]
+then 
+    echo "error: the selected net is neither resnet50 nor resnet101"
+exit 1
+fi
+
+if [ $2 != "cifar10" ] && [ $2 != "imagenet2012" ]
+then 
+    echo "error: the selected dataset is neither cifar10 nor imagenet2012"
+exit 1
+fi
+
+if [ $1 == "resnet101" ] && [ $2 == "cifar10" ]
+then 
+    echo "error: training resnet101 with cifar10 dataset is unsupported now!"
+exit 1
+fi
+
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $3)
+
+if [ $# == 4 ]
+then
+    PATH2=$(get_real_path $4)
+fi
+
+if [ ! -d $PATH1 ]
+then 
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ $# == 4 ] && [ ! -f $PATH2 ]
+then
+    echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_ID=0
+export RANK_SIZE=1
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ../*.py ./train
+cp *.sh ./train
+cp -r ../src ./train
+cd ./train || exit
+echo "start training for device $DEVICE_ID"
+env > env.log
+if [ $# == 3 ]
+then
+    python train.py --net=$1 --dataset=$2 --device_target="GPU" --dataset_path=$PATH1 &> log &
+fi
+
+if [ $# == 4 ]
+then
+    python train.py --net=$1 --dataset=$2 --device_target="GPU" --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
+fi
+cd ..

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

@@ -139,7 +139,7 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
     return ds
 
 
-def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32):
+def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
     """
     create a train or eval imagenet2012 dataset for resnet101
     Args:
@@ -158,36 +158,26 @@ def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32):
     else:
         ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
                                      num_shards=device_num, shard_id=rank_id)
-    resize_height = 224
-    rescale = 1.0 / 255.0
-    shift = 0.0
+    image_size = 224
+    mean = [0.475 * 255, 0.451 * 255, 0.392 * 255]
+    std = [0.275 * 255, 0.267 * 255, 0.278 * 255]
 
     # define map operations
-    decode_op = C.Decode()
-
-    random_resize_crop_op = C.RandomResizedCrop(resize_height, (0.08, 1.0), (0.75, 1.33), max_attempts=100)
-    horizontal_flip_op = C.RandomHorizontalFlip(rank_id / (rank_id + 1))
-    resize_op_256 = C.Resize((256, 256))
-    center_crop = C.CenterCrop(224)
-    rescale_op = C.Rescale(rescale, shift)
-    normalize_op = C.Normalize((0.475, 0.451, 0.392), (0.275, 0.267, 0.278))
-    changeswap_op = C.HWC2CHW()
-
     if do_train:
-        trans = [decode_op,
-                 random_resize_crop_op,
-                 horizontal_flip_op,
-                 rescale_op,
-                 normalize_op,
-                 changeswap_op]
-
+        trans = [
+            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomHorizontalFlip(rank_id/ (rank_id +1)),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
     else:
-        trans = [decode_op,
-                 resize_op_256,
-                 center_crop,
-                 rescale_op,
-                 normalize_op,
-                 changeswap_op]
+        trans = [
+            C.Decode(),
+            C.Resize(256),
+            C.CenterCrop(image_size),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
 
     type_cast_op = C2.TypeCast(mstype.int32)
 

model_zoo/official/cv/resnet/train.py

@@ -86,12 +86,8 @@ if __name__ == '__main__':
             ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
 
     # create dataset
-    if args_opt.net == "resnet50":
-        dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1,
-                                 batch_size=config.batch_size, target=target)
-    else:
-        dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1,
-                                 batch_size=config.batch_size)
+    dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1,
+                             batch_size=config.batch_size, target=target)
     step_size = dataset.get_dataset_size()
 
     # define net
@@ -122,7 +118,7 @@ if __name__ == '__main__':
             lr = get_lr(lr_init=config.lr_init, lr_end=0.0, lr_max=config.lr_max, warmup_epochs=config.warmup_epochs,
                         total_epochs=config.epoch_size, steps_per_epoch=step_size, lr_decay_mode='cosine')
     else:
-        lr = warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, 120,
+        lr = warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, config.epoch_size,
                                         config.pretrain_epoch_size * step_size)
     lr = Tensor(lr)
 
@@ -147,9 +143,13 @@ if __name__ == '__main__':
                       amp_level="O2", keep_batchnorm_fp32=False)
     else:
         # GPU target
-        loss = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction='mean')
-        opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum)
+        loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean", is_grad=False,
+                                             smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
+        opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay)
         model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
+        ##Mixed precision
+        #model = Model(net, loss_fn=loss, optimizer=opt,  metrics={'acc'},
+        #              amp_level="O2", keep_batchnorm_fp32=True)
 
     # define callbacks
     time_cb = TimeMonitor(data_size=step_size)