modify format in vgg16

model_zoo/official/cv/vgg16/scripts/run_distribute_train_gpu.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_distribute_train_gpu.sh DATA_PATH"
 echo "for example: bash run_distribute_train_gpu.sh /path/ImageNet2012/train"
 echo "=============================================================================================================="

model_zoo/official/cv/vgg16/scripts/run_eval.sh

@@ -0,0 +1,32 @@
+#!/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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "bash run_eval.sh DATA_PATH DATASET_TYPE DEVICE_TYPE CHECKPOINT_PATH"
+echo "for example: bash run_eval.sh /path/ImageNet2012/train cifar10 Ascend /path/a.ckpt "
+echo "=============================================================================================================="
+
+DATA_PATH=&1
+DATASET_TYPE=$2
+DEVICE_TYPE=$3
+CHECKPOINT_PATH=$4
+
+python eval.py \
+    --data_path=$DATA_PATH \
+    --dataset=$DATASET_TYPE \
+    --device_target=$DEVICE_TYPE \
+    --pre_trained=$CHECKPOINT_PATH > output.eval.log 2>&1 &

model_zoo/official/cv/vgg16/train.py

@@ -18,7 +18,6 @@ python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
 """
 import argparse
 import datetime
-import time
 import os
 import random
 
@@ -29,7 +28,7 @@ from mindspore import Tensor
 from mindspore import context
 from mindspore.communication.management import init, get_rank, get_group_size
 from mindspore.nn.optim.momentum import Momentum
-from mindspore.train.callback import Callback, ModelCheckpoint, CheckpointConfig
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.train.model import Model, ParallelMode
 from mindspore.train.serialization import load_param_into_net, load_checkpoint
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
@@ -49,63 +48,6 @@ random.seed(1)
 np.random.seed(1)
 
 
-class ProgressMonitor(Callback):
-    """monitor loss and time"""
-    def __init__(self, args_param):
-        super(ProgressMonitor, self).__init__()
-        self.me_epoch_start_time = 0
-        self.me_epoch_start_step_num = 0
-        self.args = args_param
-        self.ckpt_history = []
-
-    def begin(self, run_context):
-        self.args.logger.info('start network train...')
-
-    def epoch_begin(self, run_context):
-        pass
-
-    def epoch_end(self, run_context):
-        """
-        Called after each epoch finished.
-
-        Args:
-            run_context (RunContext): Include some information of the model.
-        """
-        cb_params = run_context.original_args()
-        me_step = cb_params.cur_step_num - 1
-
-        real_epoch = me_step // self.args.steps_per_epoch
-        time_used = time.time() - self.me_epoch_start_time
-        fps_mean = self.args.per_batch_size * (me_step-self.me_epoch_start_step_num) * self.args.group_size / time_used
-        self.args.logger.info('epoch[{}], iter[{}], loss:{}, mean_fps:{:.2f}'
-                              'imgs/sec'.format(real_epoch, me_step, cb_params.net_outputs, fps_mean))
-
-        if self.args.rank_save_ckpt_flag:
-            import glob
-            ckpts = glob.glob(os.path.join(self.args.outputs_dir, '*.ckpt'))
-            for ckpt in ckpts:
-                ckpt_fn = os.path.basename(ckpt)
-                if not ckpt_fn.startswith('{}-'.format(self.args.rank)):
-                    continue
-                if ckpt in self.ckpt_history:
-                    continue
-                self.ckpt_history.append(ckpt)
-                self.args.logger.info('epoch[{}], iter[{}], loss:{}, ckpt:{},'
-                                      'ckpt_fn:{}'.format(real_epoch, me_step, cb_params.net_outputs, ckpt, ckpt_fn))
-
-        self.me_epoch_start_step_num = me_step
-        self.me_epoch_start_time = time.time()
-
-    def step_begin(self, run_context):
-        pass
-
-    def step_end(self, run_context, *me_args):
-        pass
-
-    def end(self, run_context):
-        self.args.logger.info('end network train...')
-
-
 def parse_args(cloud_args=None):
     """parameters"""
     parser = argparse.ArgumentParser('mindspore classification training')
@@ -279,9 +221,10 @@ if __name__ == '__main__':
         loss_scale_manager = FixedLossScaleManager(args.loss_scale, drop_overflow_update=False)
         model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager, amp_level="O2")
 
-    # checkpoint save
-    progress_cb = ProgressMonitor(args)
-    callbacks = [progress_cb,]
+    # define callbacks
+    time_cb = TimeMonitor(data_size=batch_num)
+    loss_cb = LossMonitor(per_print_times=batch_num)
+    callbacks = [time_cb, loss_cb]
     if args.rank_save_ckpt_flag:
         ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval * args.steps_per_epoch,
                                        keep_checkpoint_max=args.ckpt_save_max)