modify

model_zoo/deeplabv3/evaluation.py

-#!/bin/bash
 # Copyright 2020 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,21 +13,13 @@
 # limitations under the License.
 # ============================================================================
 """evaluation."""
-import os, time
 import argparse
 from mindspore import context
-from mindspore import log as logger
-from mindspore.communication.management import init
-import mindspore.nn as nn
-from mindspore.nn.optim.momentum import Momentum
-from mindspore.train.loss_scale_manager import FixedLossScaleManager
-from mindspore import Model, ParallelMode
-import argparse
+from mindspore import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.train.callback import Callback,CheckpointConfig, ModelCheckpoint, TimeMonitor
 from src.md_dataset import create_dataset
 from src.losses import OhemLoss
-from src.miou_precision import MiouPrecision 
+from src.miou_precision import MiouPrecision
 from src.deeplabv3 import deeplabv3_resnet50
 from src.config import config
 parser = argparse.ArgumentParser(description="Deeplabv3 evaluation")
@@ -44,15 +35,16 @@ print(args_opt)
 if __name__ == "__main__":
     args_opt.crop_size = config.crop_size
     args_opt.base_size = config.crop_size
-    eval_dataset = create_dataset(args_opt, args_opt.data_url, args_opt.epoch_size, args_opt.batch_size, usage="eval")   
-    net =  deeplabv3_resnet50(config.seg_num_classes, [args_opt.batch_size,3,args_opt.crop_size,args_opt.crop_size],
-                                     infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
-                                     decoder_output_stride=config.decoder_output_stride, output_stride = config.output_stride,
-                                     fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid = config.image_pyramid)
+    eval_dataset = create_dataset(args_opt, args_opt.data_url, args_opt.epoch_size, args_opt.batch_size, usage="eval")
+    net =  deeplabv3_resnet50(config.seg_num_classes, [args_opt.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
+                              infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
+                              decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
+                              fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
     param_dict = load_checkpoint(args_opt.checkpoint_url)
     load_param_into_net(net, param_dict)
     mIou = MiouPrecision(config.seg_num_classes)
-    metrics={'mIou':mIou}
+    metrics = {'mIou': mIou}
     loss = OhemLoss(config.seg_num_classes, config.ignore_label)
     model = Model(net, loss, metrics=metrics)
-    model.eval(eval_dataset)
\ No newline at end of file
+    model.eval(eval_dataset)
+    
\ No newline at end of file

model_zoo/deeplabv3/train.py

-#!/bin/bash
 # Copyright 2020 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,18 +13,13 @@
 # limitations under the License.
 # ============================================================================
 """train."""
-import os, time
 import argparse
 from mindspore import context
-from mindspore import log as logger
 from mindspore.communication.management import init
-import mindspore.nn as nn
 from mindspore.nn.optim.momentum import Momentum
-from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore import Model, ParallelMode
-import argparse
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.train.callback import Callback,CheckpointConfig, ModelCheckpoint, TimeMonitor
+from mindspore.train.callback import Callback, CheckpointConfig, ModelCheckpoint, TimeMonitor
 from src.md_dataset import create_dataset
 from src.losses import OhemLoss
 from src.deeplabv3 import deeplabv3_resnet50
@@ -40,8 +34,7 @@ parser.add_argument("--device_id", type=int, default=0, help="Device id, default
 parser.add_argument('--checkpoint_url', default=None, help='Checkpoint path')
 parser.add_argument("--enable_save_ckpt", type=str, default="true", help="Enable save checkpoint, default is true.")
 parser.add_argument('--max_checkpoint_num', type=int, default=5, help='Max checkpoint number.')
-parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, "
-                                                                                "default is 1000.")
+parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, default is 1000.")
 parser.add_argument("--save_checkpoint_num", type=int, default=1, help="Save checkpoint numbers, default is 1.")
 args_opt = parser.parse_args()
 print(args_opt)
@@ -63,22 +56,22 @@ class LossCallBack(Callback):
         cb_params = run_context.original_args()
         print("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
                                                            str(cb_params.net_outputs)))
-def model_fine_tune(flags, net, fix_weight_layer):
+def model_fine_tune(flags, train_net, fix_weight_layer):
     checkpoint_path = flags.checkpoint_url
     if checkpoint_path is None:
         return
     param_dict = load_checkpoint(checkpoint_path)
-    load_param_into_net(net, param_dict)
-    for para in net.trainable_params():
+    load_param_into_net(train_net, param_dict)
+    for para in train_net.trainable_params():
         if fix_weight_layer in para.name:
-            para.requires_grad=False
+            para.requires_grad = False
 if __name__ == "__main__":
     if args_opt.distribute == "true":
         context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True)
         init()
     args_opt.base_size = config.crop_size
     args_opt.crop_size = config.crop_size
-    train_dataset = create_dataset(args_opt, args_opt.data_url, args_opt.epoch_size, args_opt.batch_size, usage="train")   
+    train_dataset = create_dataset(args_opt, args_opt.data_url, args_opt.epoch_size, args_opt.batch_size, usage="train")
     dataset_size = train_dataset.get_dataset_size()
     time_cb = TimeMonitor(data_size=dataset_size)
     callback = [time_cb, LossCallBack()]
@@ -87,13 +80,14 @@ if __name__ == "__main__":
                                      keep_checkpoint_max=args_opt.save_checkpoint_num)
         ckpoint_cb = ModelCheckpoint(prefix='checkpoint_deeplabv3', config=config_ck)
         callback.append(ckpoint_cb)
-    net =  deeplabv3_resnet50(config.seg_num_classes, [args_opt.batch_size,3,args_opt.crop_size,args_opt.crop_size],
-                                     infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
-                                     decoder_output_stride=config.decoder_output_stride, output_stride = config.output_stride,
-                                     fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid = config.image_pyramid)
+    net =  deeplabv3_resnet50(config.seg_num_classes, [args_opt.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
+                              infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
+                              decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
+                              fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
     net.set_train()
     model_fine_tune(args_opt, net, 'layer')
     loss = OhemLoss(config.seg_num_classes, config.ignore_label)
     opt = Momentum(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'depth' not in x.name and 'bias' not in x.name, net.trainable_params()), learning_rate=config.learning_rate, momentum=config.momentum, weight_decay=config.weight_decay)
     model = Model(net, loss, opt)
-    model.train(args_opt.epoch_size, train_dataset, callback)
\ No newline at end of file
+    model.train(args_opt.epoch_size, train_dataset, callback)
+    
\ No newline at end of file