Update inception_v3.py

ppcls/arch/backbone/legendary_models/inception_v3.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,28 +12,32 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
 import paddle
 from paddle import ParamAttr
 import paddle.nn as nn
-import paddle.nn.functional as F
 from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
 from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.nn.initializer import Uniform
 import math
 
 from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
-from ppcls.utils.save_load import load_dygraph_pretrain
+from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
-__all__ = ["InceptionV3"]
 
+MODEL_URLS = {
+    "InceptionV3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV3_pretrained.pdparams",
+}
+
+
+__all__ = MODEL_URLS.keys()
 
-# InceptionV3 config
-# key: inception blocks 
-# value: conv num in different blocks
+'''
+InceptionV3 config: dict.
+    key: inception blocks of InceptionV3.
+    values: conv num in different blocks.
+'''
 NET_CONFIG = {
     'inception_a':[[192, 256, 288], [32, 64, 64]], 
     'inception_b':[288],   
@@ -42,7 +46,6 @@ NET_CONFIG = {
     'inception_e':[1280,2048]
 }
 
-
 class ConvBNLayer(TheseusLayer):
     def __init__(self,
                  num_channels,
@@ -53,7 +56,7 @@ class ConvBNLayer(TheseusLayer):
                  groups=1,
                  act="relu"):
         super(ConvBNLayer, self).__init__()
-
+        self.act = act
         self.conv = Conv2D(
             in_channels=num_channels,
             out_channels=num_filters,
@@ -63,13 +66,15 @@ class ConvBNLayer(TheseusLayer):
             groups=groups,
             bias_attr=False)
         self.batch_norm = BatchNorm(
-            num_filters,
-            act=act)
+            num_filters)
+        self.relu = nn.ReLU()
 
-    def forward(self, inputs):
-        y = self.conv(inputs)
-        y = self.batch_norm(y)
-        return y
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.batch_norm(x)
+        if self.act:
+            x = self.relu(x)
+        return x
 
 class InceptionStem(TheseusLayer):
     def __init__(self):
@@ -100,14 +105,14 @@ class InceptionStem(TheseusLayer):
                                        filter_size=3,
                                        act="relu")
     def forward(self, x):
-        y = self.conv_1a_3x3(x)
-        y = self.conv_2a_3x3(y)
-        y = self.conv_2b_3x3(y)
-        y = self.maxpool(y)
-        y = self.conv_3b_1x1(y)
-        y = self.conv_4a_3x3(y)
-        y = self.maxpool(y)
-        return y
+        x = self.conv_1a_3x3(x)
+        x = self.conv_2a_3x3(x)
+        x = self.conv_2b_3x3(x)
+        x = self.maxpool(x)
+        x = self.conv_3b_1x1(x)
+        x = self.conv_4a_3x3(x)
+        x = self.maxpool(x)
+        return x
 
                          
 class InceptionA(TheseusLayer):
@@ -158,8 +163,8 @@ class InceptionA(TheseusLayer):
 
         branch_pool = self.branch_pool(x)
         branch_pool = self.branch_pool_conv(branch_pool)
-        outputs = paddle.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
-        return outputs
+        x = paddle.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
+        return x
 
     
 class InceptionB(TheseusLayer):
@@ -195,9 +200,9 @@ class InceptionB(TheseusLayer):
 
         branch_pool = self.branch_pool(x)
 
-        outputs = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
+        x = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
 
-        return outputs
+        return x
 
 class InceptionC(TheseusLayer):
     def __init__(self, num_channels, channels_7x7):
@@ -273,9 +278,9 @@ class InceptionC(TheseusLayer):
         branch_pool = self.branch_pool(x)
         branch_pool = self.branch_pool_conv(branch_pool)
 
-        outputs = paddle.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
+        x = paddle.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
         
-        return outputs
+        return x
     
 class InceptionD(TheseusLayer):
     def __init__(self, num_channels):
@@ -321,8 +326,8 @@ class InceptionD(TheseusLayer):
 
         branch_pool = self.branch_pool(x)
         
-        outputs = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
-        return outputs
+        x = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
+        return x
     
 class InceptionE(TheseusLayer):
     def __init__(self, num_channels):
@@ -391,12 +396,20 @@ class InceptionE(TheseusLayer):
         branch_pool = self.branch_pool(x)
         branch_pool = self.branch_pool_conv(branch_pool)
 
-        outputs = paddle.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
-        return outputs   
+        x = paddle.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
+        return x   
 
 
 class Inception_V3(TheseusLayer):
-
+    """
+    Inception_V3
+    Args:
+        config: dict. config of Inception_V3.
+        class_num: int=1000. The number of classes.
+        pretrained: (True or False) or path of pretrained_model. Whether to load the pretrained model.
+    Returns:
+        model: nn.Layer. Specific Inception_V3 model depends on args.
+    """
     def __init__(self, 
                  config, 
                  class_num=1000, 
@@ -409,7 +422,8 @@ class Inception_V3(TheseusLayer):
         self.inception_b_list = config['inception_b']
         self.inception_d_list = config['inception_d']
         self.inception_e_list = config ['inception_e']
-        
+        self.pretrained = pretrained   
+
         self.inception_stem = InceptionStem()
 
         self.inception_block_list = nn.LayerList()
@@ -445,20 +459,15 @@ class Inception_V3(TheseusLayer):
                 initializer=Uniform(-stdv, stdv)),
             bias_attr=ParamAttr())
 
-        if pretrained is not None:
-            load_dygraph_pretrain(self, pretrained)
-
     def forward(self, x):
-        y = self.inception_stem(x)
+        x = self.inception_stem(x)
         for inception_block in self.inception_block_list:
-           y = inception_block(y)
-        y = self.gap(y)
-        y = paddle.reshape(y, shape=[-1, 2048])
-        y = self.drop(y)
-        y = self.out(y)
-        return y
-
-
+           x = inception_block(x)
+        x = self.gap(x)
+        x = paddle.reshape(x, shape=[-1, 2048])
+        x = self.drop(x)
+        x = self.out(x)
+        return x
 
 
 def InceptionV3(**kwargs):
@@ -467,10 +476,19 @@ def InceptionV3(**kwargs):
     Args:
         kwargs: 
             class_num: int=1000. Output dim of last fc layer.
-            pretrained: str, pretrained model file
+            pretrained: bool or str, default: bool=False. Whether to load the pretrained model.
     Returns:
         model: nn.Layer. Specific `InceptionV3` model 
     """
     model = Inception_V3(NET_CONFIG, **kwargs)
+    
+    if isinstance(model.pretrained, bool):
+        if model.pretrained is True:
+            load_dygraph_pretrain_from_url(model, MODEL_URLS["InceptionV3"])
+    elif isinstance(model.pretrained, str):
+        load_dygraph_pretrain(model, model.pretrained)
+    else:
+        raise RuntimeError(
+            "pretrained type is not available. Please use `string` or `boolean` type")
     return model