Merge pull request #746 from FredHuang16/patch-1

add inception_v3.py

ppcls/arch/backbone/legendary_models/inception_v3.py

+# 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.
+# 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, division, print_function
+
+import paddle
+from paddle import ParamAttr
+import paddle.nn as nn
+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, load_dygraph_pretrain_from_url
+
+
+MODEL_URLS = {
+    "InceptionV3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV3_pretrained.pdparams",
+}
+
+
+__all__ = MODEL_URLS.keys()
+
+'''
+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],   
+    'inception_c':[[768, 768, 768, 768], [128, 160, 160, 192]],
+    'inception_d':[768],   
+    'inception_e':[1280,2048]
+}
+
+class ConvBNLayer(TheseusLayer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 padding=0,
+                 groups=1,
+                 act="relu"):
+        super(ConvBNLayer, self).__init__()
+        self.act = act
+        self.conv = Conv2D(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            bias_attr=False)
+        self.batch_norm = BatchNorm(
+            num_filters)
+        self.relu = nn.ReLU()
+
+    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):
+        super(InceptionStem, self).__init__()
+        self.conv_1a_3x3 = ConvBNLayer(num_channels=3,
+                                       num_filters=32,
+                                       filter_size=3,
+                                       stride=2,
+                                       act="relu")
+        self.conv_2a_3x3 = ConvBNLayer(num_channels=32,
+                                       num_filters=32,
+                                       filter_size=3,
+                                       stride=1,
+                                       act="relu")
+        self.conv_2b_3x3 = ConvBNLayer(num_channels=32,
+                                       num_filters=64,
+                                       filter_size=3,
+                                       padding=1,
+                                       act="relu")
+
+        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")        
+        self.conv_4a_3x3 = ConvBNLayer(num_channels=80,
+                                       num_filters=192,
+                                       filter_size=3,
+                                       act="relu")
+    def forward(self, x):
+        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):
+    def __init__(self, num_channels, pool_features):
+        super(InceptionA, self).__init__()
+        self.branch1x1 = ConvBNLayer(num_channels=num_channels,
+                                     num_filters=64,
+                                     filter_size=1,
+                                     act="relu") 
+        self.branch5x5_1 = ConvBNLayer(num_channels=num_channels,
+                                       num_filters=48, 
+                                       filter_size=1, 
+                                       act="relu")
+        self.branch5x5_2 = ConvBNLayer(num_channels=48, 
+                                       num_filters=64, 
+                                       filter_size=5, 
+                                       padding=2, 
+                                       act="relu")
+
+        self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels,
+                                       num_filters=64, 
+                                       filter_size=1, 
+                                       act="relu")
+        self.branch3x3dbl_2 = ConvBNLayer(num_channels=64,
+                                       num_filters=96, 
+                                       filter_size=3, 
+                                       padding=1,
+                                       act="relu")
+        self.branch3x3dbl_3 = ConvBNLayer(num_channels=96,
+                               num_filters=96, 
+                               filter_size=3, 
+                               padding=1,
+                               act="relu")
+        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")
+
+    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)
+        x = paddle.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
+        return x
+
+    
+class InceptionB(TheseusLayer):
+    def __init__(self, num_channels):
+        super(InceptionB, self).__init__()
+        self.branch3x3 = ConvBNLayer(num_channels=num_channels,
+                                     num_filters=384,
+                                     filter_size=3,
+                                     stride=2,
+                                     act="relu") 
+        self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=64, 
+                                       filter_size=1, 
+                                       act="relu")
+        self.branch3x3dbl_2 = ConvBNLayer(num_channels=64, 
+                                       num_filters=96, 
+                                       filter_size=3, 
+                                       padding=1,
+                                       act="relu")
+        self.branch3x3dbl_3 = ConvBNLayer(num_channels=96, 
+                                       num_filters=96, 
+                                       filter_size=3,
+                                       stride=2,
+                                       act="relu")
+        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)
+
+        x = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
+
+        return x
+
+class InceptionC(TheseusLayer):
+    def __init__(self, num_channels, channels_7x7):
+        super(InceptionC, self).__init__()
+        self.branch1x1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=192, 
+                                       filter_size=1, 
+                                       act="relu")
+
+
+        self.branch7x7_1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=channels_7x7, 
+                                       filter_size=1, 
+                                       stride=1,
+                                       act="relu")
+        self.branch7x7_2 = ConvBNLayer(num_channels=channels_7x7,
+                                       num_filters=channels_7x7, 
+                                       filter_size=(1, 7), 
+                                       stride=1,
+                                       padding=(0, 3),
+                                       act="relu")
+        self.branch7x7_3 = ConvBNLayer(num_channels=channels_7x7,
+                                       num_filters=192, 
+                                       filter_size=(7, 1), 
+                                       stride=1,
+                                       padding=(3, 0),
+                                       act="relu")
+        
+        self.branch7x7dbl_1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=channels_7x7, 
+                                       filter_size=1, 
+                                       act="relu")
+        self.branch7x7dbl_2 = ConvBNLayer(num_channels=channels_7x7,  
+                                       num_filters=channels_7x7, 
+                                       filter_size=(7, 1), 
+                                       padding = (3, 0),
+                                       act="relu")
+        self.branch7x7dbl_3 = ConvBNLayer(num_channels=channels_7x7, 
+                                       num_filters=channels_7x7, 
+                                       filter_size=(1, 7), 
+                                       padding = (0, 3),
+                                       act="relu")
+        self.branch7x7dbl_4 = ConvBNLayer(num_channels=channels_7x7,  
+                                       num_filters=channels_7x7, 
+                                       filter_size=(7, 1), 
+                                       padding = (3, 0),
+                                       act="relu")
+        self.branch7x7dbl_5 = ConvBNLayer(num_channels=channels_7x7, 
+                                       num_filters=192, 
+                                       filter_size=(1, 7), 
+                                       padding = (0, 3),
+                                       act="relu")
+       
+        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")
+        
+    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)
+
+        x = paddle.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
+        
+        return x
+    
+class InceptionD(TheseusLayer):
+    def __init__(self, num_channels):
+        super(InceptionD, self).__init__()
+        self.branch3x3_1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=192, 
+                                       filter_size=1, 
+                                       act="relu")
+        self.branch3x3_2 = ConvBNLayer(num_channels=192, 
+                                       num_filters=320, 
+                                       filter_size=3, 
+                                       stride=2,
+                                       act="relu")
+        self.branch7x7x3_1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=192, 
+                                       filter_size=1, 
+                                       act="relu")
+        self.branch7x7x3_2 = ConvBNLayer(num_channels=192,
+                                       num_filters=192, 
+                                       filter_size=(1, 7), 
+                                       padding=(0, 3),
+                                       act="relu")
+        self.branch7x7x3_3 = ConvBNLayer(num_channels=192, 
+                                       num_filters=192, 
+                                       filter_size=(7, 1), 
+                                       padding=(3, 0),
+                                       act="relu")
+        self.branch7x7x3_4 = ConvBNLayer(num_channels=192,  
+                                       num_filters=192, 
+                                       filter_size=3, 
+                                       stride=2,
+                                       act="relu")
+        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)
+        
+        x = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
+        return x
+    
+class InceptionE(TheseusLayer):
+    def __init__(self, num_channels):
+        super(InceptionE, self).__init__()
+        self.branch1x1 = ConvBNLayer(num_channels=num_channels,
+                                       num_filters=320, 
+                                       filter_size=1, 
+                                       act="relu")
+        self.branch3x3_1 = ConvBNLayer(num_channels=num_channels,
+                                       num_filters=384, 
+                                       filter_size=1, 
+                                       act="relu")
+        self.branch3x3_2a = ConvBNLayer(num_channels=384, 
+                                       num_filters=384, 
+                                       filter_size=(1, 3), 
+                                       padding=(0, 1),
+                                       act="relu")
+        self.branch3x3_2b = ConvBNLayer(num_channels=384, 
+                                       num_filters=384, 
+                                       filter_size=(3, 1), 
+                                       padding=(1, 0),
+                                       act="relu")
+        
+        self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels, 
+                                       num_filters=448, 
+                                       filter_size=1, 
+                                       act="relu")
+        self.branch3x3dbl_2 = ConvBNLayer(num_channels=448, 
+                                       num_filters=384, 
+                                       filter_size=3, 
+                                       padding=1,
+                                       act="relu")
+        self.branch3x3dbl_3a = ConvBNLayer(num_channels=384,
+                                       num_filters=384, 
+                                       filter_size=(1, 3), 
+                                       padding=(0, 1),
+                                       act="relu")
+        self.branch3x3dbl_3b = ConvBNLayer(num_channels=384,
+                                       num_filters=384, 
+                                       filter_size=(3, 1), 
+                                       padding=(1, 0),
+                                       act="relu")
+        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")
+    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)
+
+        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, 
+                 pretrained=False,
+                 **kwargs):
+        super(Inception_V3, self).__init__()
+
+        self.inception_a_list = config['inception_a']
+        self.inception_c_list = config['inception_c']
+        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()
+        for i in range(len(self.inception_a_list[0])):
+            inception_a = InceptionA(self.inception_a_list[0][i], 
+                                     self.inception_a_list[1][i])
+            self.inception_block_list.append(inception_a)
+
+        for i in range(len(self.inception_b_list)):
+            inception_b = InceptionB(self.inception_b_list[i])
+            self.inception_block_list.append(inception_b)
+
+        for i in range(len(self.inception_c_list[0])):
+            inception_c = InceptionC(self.inception_c_list[0][i], 
+                                     self.inception_c_list[1][i])
+            self.inception_block_list.append(inception_c)
+
+        for i in range(len(self.inception_d_list)):
+            inception_d = InceptionD(self.inception_d_list[i])
+            self.inception_block_list.append(inception_d)
+
+        for i in range(len(self.inception_e_list)):
+            inception_e = InceptionE(self.inception_e_list[i])
+            self.inception_block_list.append(inception_e)
+ 
+        self.avg_pool = AdaptiveAvgPool2D(1)
+        self.dropout = Dropout(p=0.2, mode="downscale_in_infer")
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+        self.fc = Linear(
+            2048,
+            class_num,
+            weight_attr=ParamAttr(
+                initializer=Uniform(-stdv, stdv)),
+            bias_attr=ParamAttr())
+
+    def forward(self, x):
+        x = self.inception_stem(x)
+        for inception_block in self.inception_block_list:
+           x = inception_block(x)
+        x = self.avg_pool(x)
+        x = paddle.reshape(x, shape=[-1, 2048])
+        x = self.dropout(x)
+        x = self.fc(x)
+        return x
+
+
+def InceptionV3(**kwargs):
+    """
+    InceptionV3
+    Args:
+        kwargs: 
+            class_num: int=1000. Output dim of last fc layer.
+            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
+