Add InceptionV3 architecture (#360)

Add InceptionV3 model

ppcls/modeling/architectures/__init__.py

@@ -34,6 +34,7 @@ from .mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75
 from .mobilenet_v3 import MobileNetV3_small_x0_35, MobileNetV3_small_x0_5, MobileNetV3_small_x0_75, MobileNetV3_small_x1_0, MobileNetV3_small_x1_25, MobileNetV3_large_x0_35, MobileNetV3_large_x0_5, MobileNetV3_large_x0_75, MobileNetV3_large_x1_0, MobileNetV3_large_x1_25
 from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish
 from .alexnet import AlexNet
+from .inception_v3 import InceptionV3
 from .inception_v4 import InceptionV4
 from .xception import Xception41, Xception65, Xception71
 from .xception_deeplab import Xception41_deeplab, Xception65_deeplab, Xception71_deeplab

ppcls/modeling/architectures/inception_v3.py

+# copyright (c) 2020 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.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# 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
+
+
+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
+
+__all__ = ["InceptionV3"]
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 padding=0,
+                 groups=1,
+                 act="relu",
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+
+        self.conv = Conv2D(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            weight_attr=ParamAttr(name=name+"_weights"),
+            bias_attr=False)
+        self.batch_norm = BatchNorm(
+            num_filters,
+            act=act,
+            param_attr=ParamAttr(name=name+"_bn_scale"),
+            bias_attr=ParamAttr(name=name+"_bn_offset"),
+            moving_mean_name=name+"_bn_mean",
+            moving_variance_name=name+"_bn_variance")
+
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        y = self.batch_norm(y)
+        return y
+
+class InceptionStem(nn.Layer):
+    def __init__(self):
+        super(InceptionStem, self).__init__()
+        self.conv_1a_3x3 = ConvBNLayer(num_channels=3,
+                                       num_filters=32,
+                                       filter_size=3,
+                                       stride=2,
+                                       act="relu",
+                                       name="conv_1a_3x3")
+        self.conv_2a_3x3 = ConvBNLayer(num_channels=32,
+                                       num_filters=32,
+                                       filter_size=3,
+                                       stride=1,
+                                       act="relu",
+                                       name="conv_2a_3x3")
+        self.conv_2b_3x3 = ConvBNLayer(num_channels=32,
+                                       num_filters=64,
+                                       filter_size=3,
+                                       padding=1,
+                                       act="relu",
+                                       name="conv_2b_3x3")
+        self.maxpool = MaxPool2D(kernel_size=3, stride=2, padding=0)
+        self.conv_3b_1x1 = ConvBNLayer(num_channels=64,
+                                       num_filters=80,
+                                       filter_size=1,
+                                       act="relu",
+                                       name="conv_3b_1x1")        
+        self.conv_4a_3x3 = ConvBNLayer(num_channels=80,
+                                       num_filters=192,
+                                       filter_size=3,
+                                       act="relu",
+                                       name="conv_4a_3x3")
+    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
+
+                         
+class InceptionA(nn.Layer):
+    def __init__(self, num_channels, pool_features, name=None):
+        super(InceptionA, self).__init__()
+        self.branch1x1 = ConvBNLayer(num_channels=num_channels,
+                                     num_filters=64,
+                                     filter_size=1,
+                                     act="relu",
+                                     name="inception_a_branch1x1_"+name) 
+        self.branch5x5_1 = ConvBNLayer(num_channels=num_channels,
+                                       num_filters=48, 
+                                       filter_size=1, 
+                                       act="relu",
+                                       name="inception_a_branch5x5_1_"+name)
+        self.branch5x5_2 = ConvBNLayer(num_channels=48, 
+                                       num_filters=64, 
+                                       filter_size=5, 
+                                       padding=2, 
+                                       act="relu",
+                                       name="inception_a_branch5x5_2_"+name)
+
+        self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels,
+                                       num_filters=64, 
+                                       filter_size=1, 
+                                       act="relu",
+                                       name="inception_a_branch3x3dbl_1_"+name)
+        self.branch3x3dbl_2 = ConvBNLayer(num_channels=64,
+                                       num_filters=96, 
+                                       filter_size=3, 
+                                       padding=1,
+                                       act="relu",
+                                       name="inception_a_branch3x3dbl_2_"+name)
+        self.branch3x3dbl_3 = ConvBNLayer(num_channels=96,
+                               num_filters=96, 
+                               filter_size=3, 
+                               padding=1,
+                               act="relu",
+                               name="inception_a_branch3x3dbl_3_"+name)
+        self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
+        self.branch_pool_conv = ConvBNLayer(num_channels=num_channels,
+                               num_filters=pool_features, 
+                               filter_size=1, 
+                               act="relu",
+                               name="inception_a_branch_pool_"+name)
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+        branch5x5 = self.branch5x5_1(x)
+        branch5x5 = self.branch5x5_2(branch5x5)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
+
+        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
+
+    
+class InceptionB(nn.Layer):
+    def __init__(self, num_channels, name=None):
+        super(InceptionB, self).__init__()
+        self.branch3x3 = ConvBNLayer(num_channels=num_channels,
+                                     num_filters=384,
+                                     filter_size=3,
+                                     stride=2,
+                                     act="relu",
+                                     name="inception_b_branch3x3_"+name) 
+        self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=64, 
+                                       filter_size=1, 
+                                       act="relu",
+                                       name="inception_b_branch3x3dbl_1_"+name)
+        self.branch3x3dbl_2 = ConvBNLayer(num_channels=64, 
+                                       num_filters=96, 
+                                       filter_size=3, 
+                                       padding=1,
+                                       act="relu",
+                                       name="inception_b_branch3x3dbl_2_"+name)
+        self.branch3x3dbl_3 = ConvBNLayer(num_channels=96, 
+                                       num_filters=96, 
+                                       filter_size=3,
+                                       stride=2,
+                                       act="relu",
+                                       name="inception_b_branch3x3dbl_3_"+name)
+        self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
+        
+    def forward(self, x):
+        branch3x3 = self.branch3x3(x)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
+
+        branch_pool = self.branch_pool(x)
+
+        outputs = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
+
+        return outputs
+
+class InceptionC(nn.Layer):
+    def __init__(self, num_channels, channels_7x7, name=None):
+        super(InceptionC, self).__init__()
+        self.branch1x1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=192, 
+                                       filter_size=1, 
+                                       act="relu",
+                                       name="inception_c_branch1x1_"+name)
+        self.branch7x7_1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=channels_7x7, 
+                                       filter_size=1, 
+                                       stride=1,
+                                       act="relu",
+                                       name="inception_c_branch7x7_1_"+name)
+        self.branch7x7_2 = ConvBNLayer(num_channels=channels_7x7,
+                                       num_filters=channels_7x7, 
+                                       filter_size=(1, 7), 
+                                       stride=1,
+                                       padding=(0, 3),
+                                       act="relu",
+                                       name="inception_c_branch7x7_2_"+name)
+        self.branch7x7_3 = ConvBNLayer(num_channels=channels_7x7,
+                                       num_filters=192, 
+                                       filter_size=(7, 1), 
+                                       stride=1,
+                                       padding=(3, 0),
+                                       act="relu",
+                                       name="inception_c_branch7x7_3_"+name)
+        
+        self.branch7x7dbl_1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=channels_7x7, 
+                                       filter_size=1, 
+                                       act="relu",
+                                       name="inception_c_branch7x7dbl_1_"+name)
+        self.branch7x7dbl_2 = ConvBNLayer(num_channels=channels_7x7,  
+                                       num_filters=channels_7x7, 
+                                       filter_size=(7, 1), 
+                                       padding = (3, 0),
+                                       act="relu",
+                                       name="inception_c_branch7x7dbl_2_"+name)
+        self.branch7x7dbl_3 = ConvBNLayer(num_channels=channels_7x7, 
+                                       num_filters=channels_7x7, 
+                                       filter_size=(1, 7), 
+                                       padding = (0, 3),
+                                       act="relu",
+                                       name="inception_c_branch7x7dbl_3_"+name)
+        self.branch7x7dbl_4 = ConvBNLayer(num_channels=channels_7x7,  
+                                       num_filters=channels_7x7, 
+                                       filter_size=(7, 1), 
+                                       padding = (3, 0),
+                                       act="relu",
+                                       name="inception_c_branch7x7dbl_4_"+name)
+        self.branch7x7dbl_5 = ConvBNLayer(num_channels=channels_7x7, 
+                                       num_filters=192, 
+                                       filter_size=(1, 7), 
+                                       padding = (0, 3),
+                                       act="relu",
+                                       name="inception_c_branch7x7dbl_5_"+name)
+       
+        self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
+        self.branch_pool_conv = ConvBNLayer(num_channels=num_channels,
+                                       num_filters=192, 
+                                       filter_size=1, 
+                                       act="relu",
+                                       name="inception_c_branch_pool_"+name)
+        
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch7x7 = self.branch7x7_1(x)
+        branch7x7 = self.branch7x7_2(branch7x7)
+        branch7x7 = self.branch7x7_3(branch7x7)
+
+        branch7x7dbl = self.branch7x7dbl_1(x)
+        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
+
+        branch_pool = self.branch_pool(x)
+        branch_pool = self.branch_pool_conv(branch_pool)
+
+        outputs = paddle.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
+        
+        return outputs
+    
+class InceptionD(nn.Layer):
+    def __init__(self, num_channels, name=None):
+        super(InceptionD, self).__init__()
+        self.branch3x3_1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=192, 
+                                       filter_size=1, 
+                                       act="relu",
+                                       name="inception_d_branch3x3_1_"+name)
+        self.branch3x3_2 = ConvBNLayer(num_channels=192, 
+                                       num_filters=320, 
+                                       filter_size=3, 
+                                       stride=2,
+                                       act="relu",
+                                       name="inception_d_branch3x3_2_"+name)
+        self.branch7x7x3_1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=192, 
+                                       filter_size=1, 
+                                       act="relu",
+                                       name="inception_d_branch7x7x3_1_"+name)
+        self.branch7x7x3_2 = ConvBNLayer(num_channels=192,
+                                       num_filters=192, 
+                                       filter_size=(1, 7), 
+                                       padding=(0, 3),
+                                       act="relu",
+                                       name="inception_d_branch7x7x3_2_"+name)
+        self.branch7x7x3_3 = ConvBNLayer(num_channels=192, 
+                                       num_filters=192, 
+                                       filter_size=(7, 1), 
+                                       padding=(3, 0),
+                                       act="relu",
+                                       name="inception_d_branch7x7x3_3_"+name)
+        self.branch7x7x3_4 = ConvBNLayer(num_channels=192,  
+                                       num_filters=192, 
+                                       filter_size=3, 
+                                       stride=2,
+                                       act="relu",
+                                       name="inception_d_branch7x7x3_4_"+name)
+        self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
+
+    def forward(self, x):
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = self.branch3x3_2(branch3x3)
+
+        branch7x7x3 = self.branch7x7x3_1(x)
+        branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
+        branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
+        branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
+
+        branch_pool = self.branch_pool(x)
+        
+        outputs = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
+        return outputs
+    
+class InceptionE(nn.Layer):
+    def __init__(self, num_channels, name=None):
+        super(InceptionE, self).__init__()
+        self.branch1x1 = ConvBNLayer(num_channels=num_channels,
+                                       num_filters=320, 
+                                       filter_size=1, 
+                                       act="relu",
+                                       name="inception_e_branch1x1_"+name)
+        self.branch3x3_1 = ConvBNLayer(num_channels=num_channels,
+                                       num_filters=384, 
+                                       filter_size=1, 
+                                       act="relu",
+                                       name="inception_e_branch3x3_1_"+name)
+        self.branch3x3_2a = ConvBNLayer(num_channels=384, 
+                                       num_filters=384, 
+                                       filter_size=(1, 3), 
+                                       padding=(0, 1),
+                                       act="relu",
+                                       name="inception_e_branch3x3_2a_"+name)
+        self.branch3x3_2b = ConvBNLayer(num_channels=384, 
+                                       num_filters=384, 
+                                       filter_size=(3, 1), 
+                                       padding=(1, 0),
+                                       act="relu",
+                                       name="inception_e_branch3x3_2b_"+name)
+        
+        self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=448, 
+                                       filter_size=1, 
+                                       act="relu",
+                                       name="inception_e_branch3x3dbl_1_"+name)
+        self.branch3x3dbl_2 = ConvBNLayer(num_channels=448, 
+                                       num_filters=384, 
+                                       filter_size=3, 
+                                       padding=1,
+                                       act="relu",
+                                       name="inception_e_branch3x3dbl_2_"+name)
+        self.branch3x3dbl_3a = ConvBNLayer(num_channels=384,
+                                       num_filters=384, 
+                                       filter_size=(1, 3), 
+                                       padding=(0, 1),
+                                       act="relu",
+                                       name="inception_e_branch3x3dbl_3a_"+name)
+        self.branch3x3dbl_3b = ConvBNLayer(num_channels=384,
+                                       num_filters=384, 
+                                       filter_size=(3, 1), 
+                                       padding=(1, 0),
+                                       act="relu",
+                                       name="inception_e_branch3x3dbl_3b_"+name)
+        self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
+        self.branch_pool_conv = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=192, 
+                                       filter_size=1, 
+                                       act="relu",
+                                       name="inception_e_branch_pool_"+name)
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = paddle.concat(branch3x3, axis=1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = paddle.concat(branch3x3dbl, axis=1)
+
+        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   
+
+    
+class InceptionV3(nn.Layer):
+    def __init__(self, class_dim=1000):
+        super(InceptionV3, self).__init__()
+        self.inception_a_list = [[192, 256, 288], [32, 64, 64]]
+        self.inception_c_list = [[768, 768, 768, 768], [128, 160, 160, 192]]
+        
+        self.inception_stem = InceptionStem()
+        self.inception_block_list = []
+        for i in range(len(self.inception_a_list[0])):
+            inception_a = self.add_sublayer("inception_a_"+str(i+1),
+                                           InceptionA(self.inception_a_list[0][i], 
+                                                      self.inception_a_list[1][i], 
+                                                      name=str(i+1)))
+            self.inception_block_list.append(inception_a)
+        inception_b = self.add_sublayer("nception_b_1",
+                                        InceptionB(288, name="1"))
+        self.inception_block_list.append(inception_b)
+        
+        for i in range(len(self.inception_c_list[0])):
+            inception_c = self.add_sublayer("inception_c_"+str(i+1),
+                                           InceptionC(self.inception_c_list[0][i], 
+                                                      self.inception_c_list[1][i], 
+                                                      name=str(i+1)))
+            self.inception_block_list.append(inception_c)
+        inception_d = self.add_sublayer("inception_d_1",
+                                        InceptionD(768, name="1"))
+        self.inception_block_list.append(inception_d)
+        inception_e = self.add_sublayer("inception_e_1", 
+                                        InceptionE(1280, name="1"))
+        self.inception_block_list.append(inception_e)
+        inception_e = self.add_sublayer("inception_e_2",
+                                        InceptionE(2048, name="2"))
+        self.inception_block_list.append(inception_e)
+ 
+        self.gap = AdaptiveAvgPool2D(1)
+        self.drop = Dropout(p=0.2, mode="downscale_in_infer")
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+        self.out = Linear(
+            2048,
+            class_dim,
+            weight_attr=ParamAttr(
+                initializer=Uniform(-stdv, stdv), name="fc_weights"),
+            bias_attr=ParamAttr(name="fc_offset"))
+
+    def forward(self, x):
+        y = 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
+