!18907 Fix acc of resnet34

Merge pull request !18907 from zhouyaqiang0/fix_resnet34

model_zoo/official/cv/resnet/eval.py

@@ -35,9 +35,11 @@ args_opt = parser.parse_args()
 
 set_seed(1)
 
-if args_opt.net in ("resnet18", "resnet50"):
+if args_opt.net in ("resnet18", "resnet34", "resnet50"):
     if args_opt.net == "resnet18":
         from src.resnet import resnet18 as resnet
+    if args_opt.net == "resnet34":
+        from src.resnet import resnet34 as resnet
     if args_opt.net == "resnet50":
         from src.resnet import resnet50 as resnet
     if args_opt.dataset == "cifar10":
@@ -46,10 +48,6 @@ if args_opt.net in ("resnet18", "resnet50"):
     else:
         from src.config import config2 as config
         from src.dataset import create_dataset2 as create_dataset
-elif args_opt.net == "resnet34":
-    from src.resnet import resnet34 as resnet
-    from src.config import config_resnet34 as config
-    from src.dataset import create_dataset2 as create_dataset
 elif args_opt.net == "resnet101":
     from src.resnet import resnet101 as resnet
     from src.config import config3 as config

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

@@ -153,24 +153,3 @@ config_thor_gpu = ed({
     "damping_decay": 0.5467,
     "frequency": 834,
 })
-
-# config for resnet34, imagenet2012, Ascend
-config_resnet34 = ed({
-    "class_num": 1001,
-    "batch_size": 256,
-    "loss_scale": 1024,
-    "momentum": 0.9,
-    "weight_decay": 1e-4,
-    "epoch_size": 90,
-    "pretrain_epoch_size": 0,
-    "save_checkpoint": True,
-    "save_checkpoint_epochs": 5,
-    "keep_checkpoint_max": 1,
-    "save_checkpoint_path": "./",
-    "warmup_epochs": 0,
-    "optimizer": 'Momentum',
-    "use_label_smooth": True,
-    "label_smooth_factor": 0.1,
-    "lr_init": 0.0,
-    "lr_max": 1.0,
-    "lr_end": 0.0})

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

@@ -238,20 +238,3 @@ def get_thor_damping(global_step, damping_init, decay_rate, total_epochs, steps_
     damping_each_step = np.array(damping_each_step).astype(np.float32)
     damping_now = damping_each_step[current_step:]
     return damping_now
-
-def get_resnet34_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
-
-    total_steps = steps_per_epoch * total_epochs
-    warmup_steps = steps_per_epoch * warmup_epochs
-
-    lr_each_step = []
-    for i in range(total_steps):
-        if i < warmup_steps:
-            lr = lr_init + (lr_max - lr_init) * i / warmup_steps
-        else:
-            lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
-        lr_each_step.append(lr)
-
-    lr_each_step = np.array(lr_each_step).astype(np.float32)
-
-    return lr_each_step

model_zoo/official/cv/resnet/train.py

@@ -32,7 +32,7 @@ from mindspore.parallel import set_algo_parameters
 import mindspore.nn as nn
 import mindspore.common.initializer as weight_init
 import mindspore.log as logger
-from src.lr_generator import get_lr, warmup_cosine_annealing_lr, get_resnet34_lr
+from src.lr_generator import get_lr, warmup_cosine_annealing_lr
 from src.CrossEntropySmooth import CrossEntropySmooth
 from src.config import cfg
 from src.eval_callback import EvalCallBack
@@ -69,9 +69,11 @@ args_opt = parser.parse_args()
 
 set_seed(1)
 
-if args_opt.net in ("resnet18", "resnet50"):
+if args_opt.net in ("resnet18", "resnet34", "resnet50"):
     if args_opt.net == "resnet18":
         from src.resnet import resnet18 as resnet
+    if args_opt.net == "resnet34":
+        from src.resnet import resnet34 as resnet
     if args_opt.net == "resnet50":
         from src.resnet import resnet50 as resnet
     if args_opt.dataset == "cifar10":
@@ -83,10 +85,6 @@ if args_opt.net in ("resnet18", "resnet50"):
             from src.dataset import create_dataset2 as create_dataset
         else:
             from src.dataset import create_dataset_pynative as create_dataset
-elif args_opt.net == "resnet34":
-    from src.resnet import resnet34 as resnet
-    from src.config import config_resnet34 as config
-    from src.dataset import create_dataset2 as create_dataset
 elif args_opt.net == "resnet101":
     from src.resnet import resnet101 as resnet
     from src.config import config3 as config
@@ -199,13 +197,6 @@ if __name__ == '__main__':
         else:
             lr = warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, config.epoch_size,
                                             config.pretrain_epoch_size * step_size)
-    if args_opt.net == "resnet34":
-        lr = get_resnet34_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 = Tensor(lr)
 
     # define opt
@@ -233,7 +224,7 @@ if __name__ == '__main__':
     metrics = {"acc"}
     if args_opt.run_distribute:
         metrics = {'acc': DistAccuracy(batch_size=config.batch_size, device_num=args_opt.device_num)}
-    if (args_opt.net not in ("resnet18", "resnet50", "resnet101", "se-resnet50")) or \
+    if (args_opt.net not in ("resnet18", "resnet34", "resnet50", "resnet101", "se-resnet50")) or \
         args_opt.parameter_server or target == "CPU":
         ## fp32 training
         model = Model(net, loss_fn=loss, optimizer=opt, metrics=metrics, eval_network=dist_eval_network)