modify

model_zoo/deeplabv3/src/backbone/__init__.py

@@ -0,0 +1,8 @@
+from .resnet_deeplab import Subsample, DepthwiseConv2dNative, SpaceToBatch, BatchToSpace, ResNetV1, \
+	RootBlockBeta, resnet50_dl
+	
+__all__= [
+	"Subsample", "DepthwiseConv2dNative", "SpaceToBatch", "BatchToSpace", "ResNetV1", "RootBlockBeta",
+	"resnet50_dl"
+]
+	

model_zoo/deeplabv3/src/backbone/resnet_deeplab.py

@@ -532,3 +532,6 @@ class RootBlockBeta(nn.Cell):
         x = self.conv2(x)
         x = self.conv3(x)
         return x
+        
+class resnet50_dl(fine_tune_batch_norm=False):
+    return ResNetV1(fine_tune_batch_norm)

model_zoo/deeplabv3/src/ei_datasest.py

@@ -17,7 +17,7 @@ import abc
 import os
 import time
 
-from .utils.adapter import get_manifest_samples, get_raw_samples, read_image
+from .utils.adapter import get_raw_samples, read_image
 
 
 class BaseDataset(object):
@@ -62,29 +62,6 @@ class BaseDataset(object):
         pass
 
 
-class HwVocManifestDataset(BaseDataset):
-    """
-    Create dataset with manifest data.
-
-    Args:
-        data_url (str): The path of data.
-        usage (str): Whether to use train or eval (default='train').
-
-    Returns:
-        Dataset.
-    """
-
-    def __init__(self, data_url, usage="train"):
-        super().__init__(data_url, usage)
-
-    def _load_samples(self):
-        try:
-            self.samples = get_manifest_samples(self.data_url, self.usage)
-        except Exception as e:
-            print("load HwVocManifestDataset samples failed!!!")
-            raise e
-
-
 class HwVocRawDataset(BaseDataset):
     """
     Create dataset with raw data.

model_zoo/deeplabv3/src/md_dataset.py

@@ -17,7 +17,7 @@ from PIL import Image
 import mindspore.dataset as de
 import mindspore.dataset.transforms.vision.c_transforms as C
 
-from .ei_dataset import HwVocManifestDataset, HwVocRawDataset
+from .ei_dataset import HwVocRawDataset
 from .utils import custom_transforms as tr
 
 
@@ -77,10 +77,7 @@ def create_dataset(args, data_url, epoch_num=1, batch_size=1, usage="train"):
         Dataset.
     """
     # create iter dataset
-    if data_url.endswith(".manifest"):
-        dataset = HwVocManifestDataset(data_url, usage=usage)
-    else:
-        dataset = HwVocRawDataset(data_url, usage=usage)
+    dataset = HwVocRawDataset(data_url, usage=usage)
     dataset_len = len(dataset)
 
     # wrapped with GeneratorDataset
@@ -100,5 +97,4 @@ def create_dataset(args, data_url, epoch_num=1, batch_size=1, usage="train"):
     dataset = dataset.repeat(count=epoch_num)
     dataset.map_model = 4
 
-    dataset.__loop_size__ = 1
     return dataset

model_zoo/deeplabv3/train.py

@@ -87,13 +87,13 @@ 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(crop_size.seg_num_classes, [args_opt.batch_size,3,args_opt.crop_size,args_opt.crop_size],
-                                     infer_scale_sizes=crop_size.eval_scales, atrous_rates=crop_size.atrous_rates,
-                                     decoder_output_stride=crop_size.decoder_output_stride, output_stride = crop_size.output_stride,
-                                     fine_tune_batch_norm=crop_size.fine_tune_batch_norm, image_pyramid = crop_size.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(crop_size.seg_num_classes, crop_size.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=args_opt.learning_rate, momentum=args_opt.momentum, weight_decay=args_opt.weight_decay)
+    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)