Adjust NASNet-A-Mobile training setting

model_zoo/official/cv/nasnet/README.md

@@ -40,7 +40,7 @@ Parameters for both training and evaluating can be set in config.py
 'rank': 0,                       # local rank of distributed
 'group_size': 1,                 # world size of distributed
 'work_nums': 8,                  # number of workers to read the data
-'epoch_size': 250,               # total epoch numbers
+'epoch_size': 500,               # total epoch numbers
 'keep_checkpoint_max': 100,      # max numbers to keep checkpoints
 'ckpt_path': './checkpoint/',    # save checkpoint path
 'is_save_on_master': 1           # save checkpoint on rank0, distributed parameters

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

@@ -23,9 +23,9 @@ nasnet_a_mobile_config_gpu = edict({
     'rank': 0,
     'group_size': 1,
     'work_nums': 8,
-    'epoch_size': 312,
+    'epoch_size': 500,
     'keep_checkpoint_max': 100,
-    'ckpt_path': './',
+    'ckpt_path': './checkpoint/',
     'is_save_on_master': 0,
 
     ### Dataset Config

model_zoo/official/cv/nasnet/train.py

@@ -28,7 +28,7 @@ from mindspore.common import set_seed
 
 from src.config import nasnet_a_mobile_config_gpu as cfg
 from src.dataset import create_dataset
-from src.nasnet_a_mobile import NASNetAMobileWithLoss, NASNetAMobileTrainOneStepWithClipGradient
+from src.nasnet_a_mobile import NASNetAMobile, CrossEntropy
 from src.lr_generator import get_lr
 
 
@@ -68,10 +68,13 @@ if __name__ == '__main__':
     batches_per_epoch = dataset.get_dataset_size()
 
     # network
-    net_with_loss = NASNetAMobileWithLoss(cfg)
+    net = NASNetAMobile(cfg.num_classes)
     if args_opt.resume:
         ckpt = load_checkpoint(args_opt.resume)
-        load_param_into_net(net_with_loss, ckpt)
+        load_param_into_net(net, ckpt)
+
+    #loss
+    loss = CrossEntropy(smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes, factor=cfg.aux_factor)
 
     # learning rate schedule
     lr = get_lr(lr_init=cfg.lr_init, lr_decay_rate=cfg.lr_decay_rate,
@@ -82,20 +85,18 @@ if __name__ == '__main__':
     # optimizer
     decayed_params = []
     no_decayed_params = []
-    for param in net_with_loss.trainable_params():
+    for param in net.trainable_params():
         if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
             decayed_params.append(param)
         else:
             no_decayed_params.append(param)
     group_params = [{'params': decayed_params, 'weight_decay': cfg.weight_decay},
                     {'params': no_decayed_params},
-                    {'order_params': net_with_loss.trainable_params()}]
+                    {'order_params': net.trainable_params()}]
     optimizer = RMSProp(group_params, lr, decay=cfg.rmsprop_decay, weight_decay=cfg.weight_decay,
                         momentum=cfg.momentum, epsilon=cfg.opt_eps, loss_scale=cfg.loss_scale)
 
-    net_with_grads = NASNetAMobileTrainOneStepWithClipGradient(net_with_loss, optimizer)
+    model = Model(net, loss_fn=loss, optimizer=optimizer)
-    net_with_grads.set_train()
-    model = Model(net_with_grads)
 
     print("============== Starting Training ==============")
     loss_cb = LossMonitor(per_print_times=batches_per_epoch)