add se block for resnet50

model_zoo/official/cv/resnet/eval.py

@@ -38,17 +38,20 @@ de.config.set_seed(1)
 
 if args_opt.net == "resnet50":
     from src.resnet import resnet50 as resnet
-
     if args_opt.dataset == "cifar10":
         from src.config import config1 as config
         from src.dataset import create_dataset1 as create_dataset
     else:
         from src.config import config2 as config
         from src.dataset import create_dataset2 as create_dataset
-else:
+elif args_opt.net == "resnet101":
     from src.resnet import resnet101 as resnet
     from src.config import config3 as config
     from src.dataset import create_dataset3 as create_dataset
+else:
+    from src.resnet import se_resnet50 as resnet
+    from src.config import config4 as config
+    from src.dataset import create_dataset4 as create_dataset
 
 if __name__ == '__main__':
     target = args_opt.device_target

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

@@ -16,13 +16,13 @@
 
 if [ $# != 4 ] && [ $# != 5 ]
 then 
-	echo "Usage: sh run_distribute_train.sh [resnet50|resnet101] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+	echo "Usage: sh run_distribute_train.sh [resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
 exit 1
 fi
 
-if [ $1 != "resnet50" ] && [ $1 != "resnet101" ]
+if [ $1 != "resnet50" ] && [ $1 != "resnet101" ] && [ $1 != "se-resnet50" ]
 then 
-    echo "error: the selected net is neither resnet50 nor resnet101"
+    echo "error: the selected net is neither resnet50 nor resnet101 and se-resnet50"
 exit 1
 fi
 
@@ -38,6 +38,11 @@ then
 exit 1
 fi
 
+if [ $1 == "se-resnet50" ] && [ $2 == "cifar10" ]
+then
+    echo "error: evaluating se-resnet50 with cifar10 dataset is unsupported now!"
+exit 1
+fi
 
 get_real_path(){
   if [ "${1:0:1}" == "/" ]; then

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

@@ -16,13 +16,13 @@
 
 if [ $# != 4 ]
 then 
-    echo "Usage: sh run_eval.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]"
+    echo "Usage: sh run_eval.sh [resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]"
 exit 1
 fi
 
-if [ $1 != "resnet50" ] && [ $1 != "resnet101" ]
+if [ $1 != "resnet50" ] && [ $1 != "resnet101" ] && [ $1 != "se-resnet50" ]
 then 
-    echo "error: the selected net is neither resnet50 nor resnet101"
+    echo "error: the selected net is neither resnet50 nor resnet101 nor se-resnet50"
 exit 1
 fi
 
@@ -38,6 +38,11 @@ then
 exit 1
 fi
 
+if [ $1 == "se-resnet50" ] && [ $2 == "cifar10" ]
+then
+    echo "error: evaluating se-resnet50 with cifar10 dataset is unsupported now!"
+exit 1
+fi
 
 get_real_path(){
   if [ "${1:0:1}" == "/" ]; then

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

@@ -16,13 +16,13 @@
 
 if [ $# != 3 ] && [ $# != 4 ]
 then 
-    echo "Usage: sh run_standalone_train.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+    echo "Usage: sh run_standalone_train.sh [resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
 exit 1
 fi
 
-if [ $1 != "resnet50" ] && [ $1 != "resnet101" ]
+if [ $1 != "resnet50" ] && [ $1 != "resnet101" ] && [ $1 != "se-resnet50" ]
 then 
-    echo "error: the selected net is neither resnet50 nor resnet101"
+    echo "error: the selected net is neither resnet50 nor resnet101 and se-resnet50"
 exit 1
 fi
 
@@ -38,6 +38,11 @@ then
 exit 1
 fi
 
+if [ $1 == "se-resnet50" ] && [ $2 == "cifar10" ]
+then
+    echo "error: evaluating se-resnet50 with cifar10 dataset is unsupported now!"
+exit 1
+fi
 
 get_real_path(){
   if [ "${1:0:1}" == "/" ]; then

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

@@ -50,12 +50,12 @@ config2 = ed({
     "keep_checkpoint_max": 10,
     "save_checkpoint_path": "./",
     "warmup_epochs": 0,
-    "lr_decay_mode": "cosine",
+    "lr_decay_mode": "linear",
     "use_label_smooth": True,
     "label_smooth_factor": 0.1,
     "lr_init": 0,
-    "lr_max": 0.1
-
+    "lr_max": 0.1,
+    "lr_end": 0.0
 })
 
 # config for resent101, imagenet2012
@@ -77,3 +77,25 @@ config3 = ed({
     "label_smooth_factor": 0.1,
     "lr": 0.1
 })
+
+# config for se-resnet50, imagenet2012
+config4 = ed({
+    "class_num": 1001,
+    "batch_size": 32,
+    "loss_scale": 1024,
+    "momentum": 0.9,
+    "weight_decay": 1e-4,
+    "epoch_size": 28,
+    "pretrain_epoch_size": 1,
+    "save_checkpoint": True,
+    "save_checkpoint_epochs": 4,
+    "keep_checkpoint_max": 10,
+    "save_checkpoint_path": "./",
+    "warmup_epochs": 3,
+    "lr_decay_mode": "cosine",
+    "use_label_smooth": True,
+    "label_smooth_factor": 0.1,
+    "lr_init": 0.0,
+    "lr_max": 0.3,
+    "lr_end": 0.0001
+})

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

@@ -22,7 +22,6 @@ import mindspore.dataset.transforms.vision.c_transforms as C
 import mindspore.dataset.transforms.c_transforms as C2
 from mindspore.communication.management import init, get_rank, get_group_size
 
-
 def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
     """
     create a train or evaluate cifar10 dataset for resnet50
@@ -191,6 +190,59 @@ def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target=
 
     return ds
 
+def create_dataset4(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
+    """
+    create a train or eval imagenet2012 dataset for se-resnet50
+
+    Args:
+        dataset_path(string): the path of dataset.
+        do_train(bool): whether dataset is used for train or eval.
+        repeat_num(int): the repeat times of dataset. Default: 1
+        batch_size(int): the batch size of dataset. Default: 32
+        target(str): the device target. Default: Ascend
+
+    Returns:
+        dataset
+    """
+    if target == "Ascend":
+        device_num, rank_id = _get_rank_info()
+    if device_num == 1:
+        ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=12, shuffle=True)
+    else:
+        ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=12, shuffle=True,
+                                     num_shards=device_num, shard_id=rank_id)
+    image_size = 224
+    mean = [123.68, 116.78, 103.94]
+    std = [1.0, 1.0, 1.0]
+
+    # define map operations
+    if do_train:
+        trans = [
+            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomHorizontalFlip(prob=0.5),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+    else:
+        trans = [
+            C.Decode(),
+            C.Resize(292),
+            C.CenterCrop(256),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+
+    type_cast_op = C2.TypeCast(mstype.int32)
+    ds = ds.map(input_columns="image", num_parallel_workers=12, operations=trans)
+    ds = ds.map(input_columns="label", num_parallel_workers=12, operations=type_cast_op)
+
+    # apply batch operations
+    ds = ds.batch(batch_size, drop_remainder=True)
+
+    # apply dataset repeat operation
+    ds = ds.repeat(repeat_num)
+
+    return ds
 
 def _get_rank_info():
     """

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

@@ -62,6 +62,18 @@ def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch
                 if lr < 0.0:
                     lr = 0.0
             lr_each_step.append(lr)
+    elif lr_decay_mode == 'cosine':
+        decay_steps = total_steps - warmup_steps
+        for i in range(total_steps):
+            if i < warmup_steps:
+                lr_inc = (float(lr_max) - float(lr_init)) / float(warmup_steps)
+                lr = float(lr_init) + lr_inc * (i + 1)
+            else:
+                linear_decay = (total_steps - i) / decay_steps
+                cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * 0.47 * i / decay_steps))
+                decayed = linear_decay * cosine_decay + 0.00001
+                lr = lr_max * decayed
+            lr_each_step.append(lr)
     else:
         for i in range(total_steps):
             if i < warmup_steps:

model_zoo/official/cv/resnet/train.py

@@ -50,17 +50,21 @@ de.config.set_seed(1)
 
 if args_opt.net == "resnet50":
     from src.resnet import resnet50 as resnet
-
     if args_opt.dataset == "cifar10":
         from src.config import config1 as config
         from src.dataset import create_dataset1 as create_dataset
     else:
         from src.config import config2 as config
         from src.dataset import create_dataset2 as create_dataset
-else:
+elif args_opt.net == "resnet101":
     from src.resnet import resnet101 as resnet
     from src.config import config3 as config
     from src.dataset import create_dataset3 as create_dataset
+else:
+    from src.resnet import se_resnet50 as resnet
+    from src.config import config4 as config
+    from src.dataset import create_dataset4 as create_dataset
+
 
 if __name__ == '__main__':
     target = args_opt.device_target
@@ -74,7 +78,7 @@ if __name__ == '__main__':
             context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
             context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                               mirror_mean=True)
-            if args_opt.net == "resnet50":
+            if args_opt.net == "resnet50" or args_opt.net == "se-resnet50":
                 auto_parallel_context().set_all_reduce_fusion_split_indices([85, 160])
             else:
                 auto_parallel_context().set_all_reduce_fusion_split_indices([180, 313])
@@ -112,14 +116,10 @@ if __name__ == '__main__':
                                                                     cell.weight.dtype)
 
     # init lr
-    if args_opt.net == "resnet50":
-        if args_opt.dataset == "cifar10":
-            lr = get_lr(lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
-                        warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size, steps_per_epoch=step_size,
-                        lr_decay_mode='poly')
-        else:
-            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')
+    if args_opt.net == "resnet50" or args_opt.net == "se-resnet50":
+        lr = get_lr(lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
+                    warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size, steps_per_epoch=step_size,
+                    lr_decay_mode=config.lr_decay_mode)
     else:
         lr = warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, config.epoch_size,
                                         config.pretrain_epoch_size * step_size)