Merge pull request #1208 from weisy11/fix_bug_of_theseus

dbg theseus

ppcls/arch/__init__.py

@@ -23,6 +23,7 @@ from . import backbone, gears
 from .backbone import *
 from .gears import build_gear
 from .utils import *
+from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
 from ppcls.utils import logger
 from ppcls.utils.save_load import load_dygraph_pretrain
 

ppcls/arch/backbone/base/theseus_layer.py

@@ -38,24 +38,31 @@ class TheseusLayer(nn.Layer):
         for layer_i in self._sub_layers:
             layer_name = self._sub_layers[layer_i].full_name()
             if isinstance(self._sub_layers[layer_i], (nn.Sequential, nn.LayerList)):
-                self._sub_layers[layer_i] = wrap_theseus(self._sub_layers[layer_i])
-                self._sub_layers[layer_i].res_dict = self.res_dict
+                self._sub_layers[layer_i] = wrap_theseus(self._sub_layers[layer_i], self.res_dict)
                 self._sub_layers[layer_i].update_res(return_patterns)
             else:
                 for return_pattern in return_patterns:
                     if re.match(return_pattern, layer_name):
                         if not isinstance(self._sub_layers[layer_i], TheseusLayer):
-                            self._sub_layers[layer_i] = wrap_theseus(self._sub_layers[layer_i])
+                            self._sub_layers[layer_i] = wrap_theseus(self._sub_layers[layer_i], self.res_dict)
+                        else:
+                            self._sub_layers[layer_i].res_dict = self.res_dict
+
                         self._sub_layers[layer_i].register_forward_post_hook(
                             self._sub_layers[layer_i]._save_sub_res_hook)
-                        self._sub_layers[layer_i].res_dict = self.res_dict
-                if isinstance(self._sub_layers[layer_i], TheseusLayer):
-                    self._sub_layers[layer_i].res_dict = self.res_dict
-                    self._sub_layers[layer_i].update_res(return_patterns)
+            if isinstance(self._sub_layers[layer_i], TheseusLayer):
+                self._sub_layers[layer_i].res_dict = self.res_dict
+                self._sub_layers[layer_i].update_res(return_patterns)
 
     def _save_sub_res_hook(self, layer, input, output):
         self.res_dict[layer.full_name()] = output
 
+    def _return_dict_hook(self, layer, input, output):
+        res_dict = {"output": output}
+        for res_key in list(self.res_dict):
+            res_dict[res_key] = self.res_dict.pop(res_key)
+        return res_dict
+
     def replace_sub(self, layer_name_pattern, replace_function, recursive=True):
         for layer_i in self._sub_layers:
             layer_name = self._sub_layers[layer_i].full_name()
@@ -85,10 +92,12 @@ class TheseusLayer(nn.Layer):
 
 
 class WrapLayer(TheseusLayer):
-    def __init__(self, sub_layer):
+    def __init__(self, sub_layer, res_dict=None):
         super(WrapLayer, self).__init__()
         self.sub_layer = sub_layer
         self.name = sub_layer.full_name()
+        if res_dict is not None:
+            self.res_dict = res_dict
 
     def full_name(self):
         return self.name
@@ -101,14 +110,14 @@ class WrapLayer(TheseusLayer):
             return
         for layer_i in self.sub_layer._sub_layers:
             if isinstance(self.sub_layer._sub_layers[layer_i], (nn.Sequential, nn.LayerList)):
-                self.sub_layer._sub_layers[layer_i] = wrap_theseus(self.sub_layer._sub_layers[layer_i])
-                self.sub_layer._sub_layers[layer_i].res_dict = self.res_dict
+                self.sub_layer._sub_layers[layer_i] = wrap_theseus(self.sub_layer._sub_layers[layer_i], self.res_dict)
                 self.sub_layer._sub_layers[layer_i].update_res(return_patterns)
+            elif isinstance(self.sub_layer._sub_layers[layer_i], TheseusLayer):
+                self.sub_layer._sub_layers[layer_i].res_dict = self.res_dict
 
             layer_name = self.sub_layer._sub_layers[layer_i].full_name()
             for return_pattern in return_patterns:
                 if re.match(return_pattern, layer_name):
-                    self.sub_layer._sub_layers[layer_i].res_dict = self.res_dict
                     self.sub_layer._sub_layers[layer_i].register_forward_post_hook(
                         self._sub_layers[layer_i]._save_sub_res_hook)
 
@@ -116,6 +125,6 @@ class WrapLayer(TheseusLayer):
                 self.sub_layer._sub_layers[layer_i].update_res(return_patterns)
 
 
-def wrap_theseus(sub_layer):
-    wrapped_layer = WrapLayer(sub_layer)
+def wrap_theseus(sub_layer, res_dict=None):
+    wrapped_layer = WrapLayer(sub_layer, res_dict)
     return wrapped_layer

ppcls/arch/backbone/legendary_models/hrnet.py

@@ -367,7 +367,7 @@ class HRNet(TheseusLayer):
         model: nn.Layer. Specific HRNet model depends on args.
     """
 
-    def __init__(self, width=18, has_se=False, class_num=1000):
+    def __init__(self, width=18, has_se=False, class_num=1000, return_patterns=None):
         super().__init__()
 
         self.width = width
@@ -456,8 +456,11 @@ class HRNet(TheseusLayer):
             2048,
             class_num,
             weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
+        if return_patterns is not None:
+            self.update_res(return_patterns)
+            self.register_forward_post_hook(self._return_dict_hook)
 
-    def forward(self, x, res_dict=None):
+    def forward(self, x):
         x = self.conv_layer1_1(x)
         x = self.conv_layer1_2(x)
 

ppcls/arch/backbone/legendary_models/inception_v3.py

@@ -454,7 +454,7 @@ class Inception_V3(TheseusLayer):
         model: nn.Layer. Specific Inception_V3 model depends on args.
     """
 
-    def __init__(self, config, class_num=1000):
+    def __init__(self, config, class_num=1000, return_patterns=None):
         super().__init__()
 
         self.inception_a_list = config["inception_a"]
@@ -496,6 +496,9 @@ class Inception_V3(TheseusLayer):
             class_num,
             weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
             bias_attr=ParamAttr())
+        if return_patterns is not None:
+            self.update_res(return_patterns)
+            self.register_forward_post_hook(self._return_dict_hook)
 
     def forward(self, x):
         x = self.inception_stem(x)

ppcls/arch/backbone/legendary_models/mobilenet_v1.py

@@ -102,7 +102,7 @@ class MobileNet(TheseusLayer):
         model: nn.Layer. Specific MobileNet model depends on args.
     """
 
-    def __init__(self, scale=1.0, class_num=1000):
+    def __init__(self, scale=1.0, class_num=1000, return_patterns=None):
         super().__init__()
         self.scale = scale
 
@@ -145,6 +145,9 @@ class MobileNet(TheseusLayer):
             int(1024 * scale),
             class_num,
             weight_attr=ParamAttr(initializer=KaimingNormal()))
+        if return_patterns is not None:
+            self.update_res(return_patterns)
+            self.register_forward_post_hook(self._return_dict_hook)
 
     def forward(self, x):
         x = self.conv(x)

ppcls/arch/backbone/legendary_models/mobilenet_v3.py

@@ -142,7 +142,8 @@ class MobileNetV3(TheseusLayer):
                  inplanes=STEM_CONV_NUMBER,
                  class_squeeze=LAST_SECOND_CONV_LARGE,
                  class_expand=LAST_CONV,
-                 dropout_prob=0.2):
+                 dropout_prob=0.2,
+                 return_patterns=None):
         super().__init__()
 
         self.cfg = config
@@ -199,6 +200,9 @@ class MobileNetV3(TheseusLayer):
         self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
 
         self.fc = Linear(self.class_expand, class_num)
+        if return_patterns is not None:
+            self.update_res(return_patterns)
+            self.register_forward_post_hook(self._return_dict_hook)
 
     def forward(self, x):
         x = self.conv(x)

ppcls/arch/backbone/legendary_models/resnet.py

@@ -269,7 +269,8 @@ class ResNet(TheseusLayer):
                  class_num=1000,
                  lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
                  data_format="NCHW",
-                 input_image_channel=3):
+                 input_image_channel=3,
+                 return_patterns=None):
         super().__init__()
 
         self.cfg = config
@@ -337,6 +338,9 @@ class ResNet(TheseusLayer):
             weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
 
         self.data_format = data_format
+        if return_patterns is not None:
+            self.update_res(return_patterns)
+            self.register_forward_post_hook(self._return_dict_hook)
 
     def forward(self, x):
         with paddle.static.amp.fp16_guard():

ppcls/arch/backbone/legendary_models/vgg.py

@@ -137,8 +137,11 @@ class VGGNet(TheseusLayer):
         self.fc1 = Linear(7 * 7 * 512, 4096)
         self.fc2 = Linear(4096, 4096)
         self.fc3 = Linear(4096, class_num)
+        if return_patterns is not None:
+            self.update_res(return_patterns)
+            self.register_forward_post_hook(self._return_dict_hook)
 
-    def forward(self, inputs, res_dict=None):
+    def forward(self, inputs):
         x = self.conv_block_1(inputs)
         x = self.conv_block_2(x)
         x = self.conv_block_3(x)
@@ -152,9 +155,6 @@ class VGGNet(TheseusLayer):
         x = self.relu(x)
         x = self.drop(x)
         x = self.fc3(x)
-        if self.res_dict and res_dict is not None:
-            for res_key in list(self.res_dict):
-                res_dict[res_key] = self.res_dict.pop(res_key)
         return x
 
 

ppcls/engine/engine.py

@@ -346,6 +346,8 @@ class Engine(object):
                 out = self.model(batch_tensor)
                 if isinstance(out, list):
                     out = out[0]
+                if isinstance(out, dict):
+                    out = out["output"]
                 result = self.postprocess_func(out, image_file_list)
                 print(result)
                 batch_data.clear()