fixup

fixup

fixup

model_zoo/research/cv/advanced_east/eval.py

@@ -75,6 +75,7 @@ def parse_args():
     args_opt.max_predict_img_size = cfg.max_predict_img_size
     args_opt.last_model_name = cfg.last_model_name
     args_opt.saved_model_file_path = cfg.saved_model_file_path
+    args_opt.is_train = False
 
     return args_opt
 

model_zoo/research/cv/advanced_east/export.py

@@ -32,6 +32,7 @@ parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"]
 parser.add_argument("--device_target", type=str, default="Ascend",
                     choices=["Ascend", "GPU"], help="device target(default: Ascend)")
 args = parser.parse_args()
+args.is_train = False
 
 context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 if args.device_target == "Ascend":
@@ -39,7 +40,7 @@ if args.device_target == "Ascend":
 
 if __name__ == '__main__':
 
-    net = AdvancedEast()
+    net = AdvancedEast(args)
 
     assert args.ckpt_file is not None, "checkpoint_path is None."
 

model_zoo/research/cv/advanced_east/src/model.py

@@ -22,7 +22,7 @@ from mindspore.ops import composite as C
 from mindspore.ops import functional as F
 from mindspore.ops import ResizeNearestNeighbor
 from mindspore import Tensor, ParameterTuple, Parameter
-from mindspore.common.initializer import initializer
+from mindspore.common.initializer import initializer, TruncatedNormal
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 import numpy as np
 
@@ -73,8 +73,9 @@ class AdvancedEast(nn.Cell):
         if self.device_target == 'GPU':
 
             self.vgg16 = vgg16()
-            param_dict = load_checkpoint(cfg.vgg_weights)
-            load_param_into_net(self.vgg16, param_dict)
+            if args.is_train:
+                param_dict = load_checkpoint(cfg.vgg_weights)
+                load_param_into_net(self.vgg16, param_dict)
 
             self.bn1 = nn.BatchNorm2d(1024, momentum=0.99, eps=1e-3)
             self.conv1 = nn.Conv2d(1024, 128, 1, weight_init='XavierUniform', has_bias=True)
@@ -110,7 +111,23 @@ class AdvancedEast(nn.Cell):
             self.conv9 = nn.Conv2d(32, 2, 1, weight_init='XavierUniform', has_bias=True)
             self.conv10 = nn.Conv2d(32, 4, 1, weight_init='XavierUniform', has_bias=True)
         else:
-            vgg_dict = np.load(cfg.vgg_npy, encoding='latin1', allow_pickle=True).item()
+            if args.is_train:
+                vgg_dict = np.load(cfg.vgg_npy, encoding='latin1', allow_pickle=True).item()
+            shape_dict = {
+                'conv1_1': [64, 3, 3, 3],
+                'conv1_2': [64, 64, 3, 3],
+                'conv2_1': [128, 64, 3, 3],
+                'conv2_2': [128, 128, 3, 3],
+                'conv3_1': [256, 128, 3, 3],
+                'conv3_2': [256, 256, 3, 3],
+                'conv3_3': [256, 256, 3, 3],
+                'conv4_1': [512, 256, 3, 3],
+                'conv4_2': [512, 512, 3, 3],
+                'conv4_3': [512, 512, 3, 3],
+                'conv5_1': [512, 512, 3, 3],
+                'conv5_2': [512, 512, 3, 3],
+                'conv5_3': [512, 512, 3, 3],
+            }
 
             def get_var(name, idx):
                 value = vgg_dict[name][idx]
@@ -131,13 +148,18 @@ class AdvancedEast(nn.Cell):
 
                 def __init__(self, name):
                     super(VGG_Conv, self).__init__()
-                    filters, conv_biases = get_conv_var(name)
-                    out_channels, in_channels, filter_size, _ = filters.shape
+                    if args.is_train:
+                        filters, conv_biases = get_conv_var(name)
+                        out_channels, in_channels, filter_size, _ = filters.shape
+                    else:
+                        out_channels, in_channels, filter_size, _ = shape_dict[name]
                     self.conv2d = P.Conv2D(out_channels, filter_size, pad_mode='same', mode=1)
                     self.bias_add = P.BiasAdd()
-                    self.weight = Parameter(initializer(filters, [out_channels, in_channels, filter_size, filter_size]),
+                    self.weight = Parameter(initializer(filters if args.is_train else TruncatedNormal(),
+                                                        [out_channels, in_channels, filter_size, filter_size]),
                                             name='weight')
-                    self.bias = Parameter(initializer(conv_biases, [out_channels]), name='bias')
+                    self.bias = Parameter(initializer(conv_biases if args.is_train else TruncatedNormal(),
+                                                      [out_channels]), name='bias')
                     self.relu = P.ReLU()
                     self.gn = nn.GroupNorm(32, out_channels)
 

model_zoo/research/cv/advanced_east/train.py

@@ -67,6 +67,7 @@ def parse_args():
     args_opt.last_model_name = cfg.last_model_name
     args_opt.saved_model_file_path = cfg.saved_model_file_path
     args_opt.ds_sink_mode = cfg.ds_sink_mode
+    args_opt.is_train = True
     return args_opt
 
 

model_zoo/research/cv/advanced_east/train_mindrecord.py

@@ -69,6 +69,7 @@ def parse_args(cloud_args=None):
     args_opt.results_dir = cfg.results_dir
     args_opt.last_model_name = cfg.last_model_name
     args_opt.saved_model_file_path = cfg.saved_model_file_path
+    args_opt.is_train = True
     return args_opt