rebuild

model/mobilenet_v1.py

@@ -0,0 +1,148 @@
+# Copyright 2022 Dakewe Biotech Corporation. All Rights Reserved.
+# 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 typing import Callable, Any, Optional
+
+import torch
+from torch import Tensor
+from torch import nn
+from torchvision.ops.misc import Conv2dNormActivation
+from config import config as conf
+
+__all__ = [
+    "MobileNetV1",
+    "DepthWiseSeparableConv2d",
+    "mobilenet_v1",
+]
+
+
+class MobileNetV1(nn.Module):
+
+    def __init__(
+            self,
+            num_classes: int = conf.embedding_size,
+    ) -> None:
+        super(MobileNetV1, self).__init__()
+        self.features = nn.Sequential(
+            Conv2dNormActivation(3,
+                                 32,
+                                 kernel_size=3,
+                                 stride=2,
+                                 padding=1,
+                                 norm_layer=nn.BatchNorm2d,
+                                 activation_layer=nn.ReLU,
+                                 inplace=True,
+                                 bias=False,
+                                 ),
+
+            DepthWiseSeparableConv2d(32, 64, 1),
+            DepthWiseSeparableConv2d(64, 128, 2),
+            DepthWiseSeparableConv2d(128, 128, 1),
+            DepthWiseSeparableConv2d(128, 256, 2),
+            DepthWiseSeparableConv2d(256, 256, 1),
+            DepthWiseSeparableConv2d(256, 512, 2),
+            DepthWiseSeparableConv2d(512, 512, 1),
+            DepthWiseSeparableConv2d(512, 512, 1),
+            DepthWiseSeparableConv2d(512, 512, 1),
+            DepthWiseSeparableConv2d(512, 512, 1),
+            DepthWiseSeparableConv2d(512, 512, 1),
+            DepthWiseSeparableConv2d(512, 1024, 2),
+            DepthWiseSeparableConv2d(1024, 1024, 1),
+        )
+
+        self.avgpool = nn.AvgPool2d((7, 7))
+
+        self.classifier = nn.Linear(1024, num_classes)
+
+        # Initialize neural network weights
+        self._initialize_weights()
+
+    def forward(self, x: Tensor) -> Tensor:
+        out = self._forward_impl(x)
+
+        return out
+
+    # Support torch.script function
+    def _forward_impl(self, x: Tensor) -> Tensor:
+        out = self.features(x)
+        out = self.avgpool(out)
+        out = torch.flatten(out, 1)
+        out = self.classifier(out)
+
+        return out
+
+    def _initialize_weights(self) -> None:
+        for module in self.modules():
+            if isinstance(module, nn.Conv2d):
+                nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+            elif isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.ones_(module.weight)
+                nn.init.zeros_(module.bias)
+            elif isinstance(module, nn.Linear):
+                nn.init.normal_(module.weight, 0, 0.01)
+                nn.init.zeros_(module.bias)
+
+
+class DepthWiseSeparableConv2d(nn.Module):
+    def __init__(
+            self,
+            in_channels: int,
+            out_channels: int,
+            stride: int,
+            norm_layer: Optional[Callable[..., nn.Module]] = None
+    ) -> None:
+        super(DepthWiseSeparableConv2d, self).__init__()
+        self.stride = stride
+        if stride not in [1, 2]:
+            raise ValueError(f"stride should be 1 or 2 instead of {stride}")
+
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+
+        self.conv = nn.Sequential(
+            Conv2dNormActivation(in_channels,
+                                 in_channels,
+                                 kernel_size=3,
+                                 stride=stride,
+                                 padding=1,
+                                 groups=in_channels,
+                                 norm_layer=norm_layer,
+                                 activation_layer=nn.ReLU,
+                                 inplace=True,
+                                 bias=False,
+                                 ),
+            Conv2dNormActivation(in_channels,
+                                 out_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0,
+                                 norm_layer=norm_layer,
+                                 activation_layer=nn.ReLU,
+                                 inplace=True,
+                                 bias=False,
+                                 ),
+
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        out = self.conv(x)
+
+        return out
+
+
+def mobilenet_v1(**kwargs: Any) -> MobileNetV1:
+    model = MobileNetV1(**kwargs)
+
+    return model