support xception lr slice and fix usability problems

model_zoo/official/cv/xception/README.md

@@ -81,9 +81,10 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
     ├─config.py                       # parameter configuration
     ├─dataset.py                      # data preprocessing
     ├─Xception.py                     # network definition
-    ├─CrossEntropySmooth.py           # Customized CrossEntropy loss function
+    ├─loss.py                         # Customized CrossEntropy loss function
     └─lr_generator.py                 # learning rate generator
   ├─train.py                          # train net
+  ├─export.py                         # export net
   └─eval.py                           # eval net
 
 ```
@@ -110,7 +111,6 @@ Major parameters in train.py and config.py are:
 'lr_init': 0.00004                 # initiate learning rate
 'lr_max': 0.4                      # max bound of learning rate
 'lr_end': 0.00004                  # min bound of learning rate
-"weight_init": 'xavier_uniform'    # Weight initialization mode
 ```
 
 ## [Training process](#contents)
@@ -149,13 +149,13 @@ sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
 
 ### Result
 
-Training result will be stored in the example path. Checkpoints will be stored at `. /model_0` by default, and training log will be redirected to `log.txt` like followings.
+Training result will be stored in the example path. Checkpoints will be stored at `. /ckpt_0` by default, and training log will be redirected to `log.txt` like followings.
 
 ``` shell
-epoch: [  0/250], step:[ 1250/ 1251], loss:[4.761/5.613], time:[529.305], lr:[0.400]
-epoch time: 1128662.862, per step time: 902.209, avg loss: 5.609
-epoch: [  1/250], step:[ 1250/ 1251], loss:[4.164/4.318], time:[503.708], lr:[0.398]
-epoch time: 889163.081, per step time: 710.762, avg loss: 4.312
+epoch: 1 step: 1251, loss is 4.8427444
+epoch time: 701242.350 ms, per step time: 560.545 ms
+epoch: 2 step: 1251, loss is 4.0637593
+epoch time: 598591.422 ms, per step time: 478.490ms
 ```
 
 ## [Eval process](#contents)
@@ -199,18 +199,19 @@ result: {'Loss': 1.7797744848789312, 'Top_1_Acc': 0.7985777243589743, 'Top_5_Acc
 | -------------------------- | ---------------------------------------------- |
 | Model Version              | Xception                                       |
 | Resource                   | HUAWEI CLOUD Modelarts                         |
-| uploaded Date              | 11/15/2020                                     |
-| MindSpore Version          | 1.0.0                                          |
+| uploaded Date              | 12/10/2020                                     |
+| MindSpore Version          | 1.1.0                                          |
 | Dataset                    | 1200k images                                   |
 | Batch_size                 | 128                                            |
 | Training Parameters        | src/config.py                                  |
 | Optimizer                  | Momentum                                       |
 | Loss Function              | CrossEntropySmooth                             |
 | Loss                       | 1.78                                           |
-| Accuracy (8p)              | Top1[79.9%] Top5[94.9%]                        |
-| Total time (8p)            | 63h                                            |
+| Accuracy (8p)              | Top1[79.8%] Top5[94.8%]                        |
+| Per step time (8p)         | 479 ms/step                                    |
+| Total time (8p)            | 42h                                            |
 | Params (M)                 | 180M                                           |
-| Scripts                    | [Xception script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/Xception) |
+| Scripts                    | [Xception script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/xception) |
 
 #### Inference Performance
 
@@ -231,4 +232,4 @@ In `dataset.py`, we set the seed inside `create_dataset` function. We also use r
 
 # [ModelZoo Homepage](#contents)
 
-Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
+Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

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

@@ -27,13 +27,10 @@ echo "avg_core_per_rank" $avg_core_per_rank
 echo "core_gap" $core_gap
 for((i=0;i<RANK_SIZE;i++))
 do
-    start=`expr $i \* $avg_core_per_rank`
     export DEVICE_ID=$i
     export RANK_ID=$i
     export DEPLOY_MODE=0
     export GE_USE_STATIC_MEMORY=1
-    end=`expr $start \+ $core_gap`
-    cmdopt=$start"-"$end
 
     rm -rf train_parallel$i
     mkdir ./train_parallel$i
@@ -42,7 +39,7 @@ do
     echo "start training for rank $i, device $DEVICE_ID"
 
     env > env.log
-    taskset -c $cmdopt python ../train.py  \
+    python ../train.py  \
     --is_distributed \
     --device_target=Ascend \
     --dataset_path=$DATA_DIR > log.txt 2>&1 &

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

@@ -18,8 +18,14 @@ export DEVICE_ID=$1
 DATA_DIR=$2
 PATH_CHECKPOINT=$3
 
-python ./eval.py  \
+rm -rf eval_output
+mkdir ./eval_output
+cd ./eval_output || exit
+echo "start evaluating model..."
+
+python ../eval.py  \
     --device_target=Ascend \
     --device_id=$DEVICE_ID \
     --checkpoint_path=$PATH_CHECKPOINT \
     --dataset_path=$DATA_DIR > eval.log 2>&1 &
+cd ../

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

@@ -16,7 +16,13 @@
 
 export DEVICE_ID=$1
 DATA_DIR=$2
-python ./train.py  \
+
+rm -rf train_standalone
+mkdir ./train_standalone
+cd ./train_standalone || exit
+echo "start training standalone on device $DEVICE_ID"
+
+python ../train.py  \
     --device_target=Ascend \
     --dataset_path=$DATA_DIR > log.txt 2>&1 &
-
+cd ../

model_zoo/official/cv/xception/src/Xception.py

@@ -15,15 +15,14 @@
 """Xception."""
 import mindspore.nn as nn
 import mindspore.ops.operations as P
-from src.config import config
 
 class SeparableConv2d(nn.Cell):
     def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0):
         super(SeparableConv2d, self).__init__()
         self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size, stride, group=in_channels, pad_mode='pad',
-                               padding=padding, weight_init=config.weight_init)
+                               padding=padding, weight_init='xavier_uniform')
         self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, pad_mode='valid',
-                                   weight_init=config.weight_init)
+                                   weight_init='xavier_uniform')
 
     def construct(self, x):
         x = self.conv1(x)
@@ -37,7 +36,7 @@ class Block(nn.Cell):
 
         if out_filters != in_filters or strides != 1:
             self.skip = nn.Conv2d(in_filters, out_filters, 1, stride=strides, pad_mode='valid', has_bias=False,
-                                  weight_init=config.weight_init)
+                                  weight_init='xavier_uniform')
             self.skipbn = nn.BatchNorm2d(out_filters, momentum=0.9)
         else:
             self.skip = None
@@ -96,10 +95,10 @@ class Xception(nn.Cell):
         """
         super(Xception, self).__init__()
         self.num_classes = num_classes
-        self.conv1 = nn.Conv2d(3, 32, 3, 2, pad_mode='valid', weight_init=config.weight_init)
+        self.conv1 = nn.Conv2d(3, 32, 3, 2, pad_mode='valid', weight_init='xavier_uniform')
         self.bn1 = nn.BatchNorm2d(32, momentum=0.9)
         self.relu = nn.ReLU()
-        self.conv2 = nn.Conv2d(32, 64, 3, pad_mode='valid', weight_init=config.weight_init)
+        self.conv2 = nn.Conv2d(32, 64, 3, pad_mode='valid', weight_init='xavier_uniform')
         self.bn2 = nn.BatchNorm2d(64, momentum=0.9)
 
         # Entry flow

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

@@ -36,6 +36,5 @@ config = ed({
     "label_smooth_factor": 0.1,
     "lr_init": 0.00004,
     "lr_max": 0.4,
-    "lr_end": 0.00004,
-    "weight_init": 'xavier_uniform'
+    "lr_end": 0.00004
 })

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

@@ -17,7 +17,7 @@ import math
 import numpy as np
 
 
-def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
+def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode, global_step=0):
     """
     generate learning rate array
 
@@ -82,6 +82,6 @@ def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch
                 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)
+    lr_each_step = np.array(lr_each_step[global_step:]).astype(np.float32)
 
     return lr_each_step

model_zoo/official/cv/xception/train.py

@@ -59,7 +59,8 @@ if __name__ == '__main__':
         else:
             rank = 0
             group_size = 1
-            context.set_context(device_id=0)
+            if os.getenv('DEVICE_ID', "not_set").isdigit():
+                context.set_context(device_id=int(os.getenv('DEVICE_ID')))
 
         # define network
         net = xception(class_num=config.class_num)
@@ -88,7 +89,8 @@ if __name__ == '__main__':
                            warmup_epochs=config.warmup_epochs,
                            total_epochs=config.epoch_size,
                            steps_per_epoch=step_size,
-                           lr_decay_mode=config.lr_decay_mode))
+                           lr_decay_mode=config.lr_decay_mode,
+                           global_step=config.finish_epoch * step_size))
 
         # define optimization
         opt = Momentum(net.trainable_params(), lr, config.momentum, config.weight_decay, config.loss_scale)
@@ -100,7 +102,7 @@ if __name__ == '__main__':
         # define callbacks
         cb = [TimeMonitor(), LossMonitor()]
         if config.save_checkpoint:
-            save_ckpt_path = os.path.join(config.save_checkpoint_path, 'model_' + str(rank) + '/')
+            save_ckpt_path = os.path.join(config.save_checkpoint_path, 'ckpt_' + str(rank) + '/')
             config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
                                          keep_checkpoint_max=config.keep_checkpoint_max)
             ckpt_cb = ModelCheckpoint(f"Xception-rank{rank}", directory=save_ckpt_path, config=config_ck)