Tried to implement Fuse-MBConv block, have not tested

efficientnet_pytorch/model.py

@@ -139,6 +139,100 @@ def set_swish(self, memory_efficient=True):
         """
         self._swish = MemoryEfficientSwish() if memory_efficient else Swish()
 
+# Basically a copy of MBConvBlock
+class FuseMBConvBlock(nn.Module):
+    """Mobile Inverted Residual Bottleneck Block.
+
+    Args:
+        block_args (namedtuple): BlockArgs, defined in utils.py.
+        global_params (namedtuple): GlobalParam, defined in utils.py.
+        image_size (tuple or list): [image_height, image_width].
+
+    References:
+        [1] https://arxiv.org/abs/1704.04861 (MobileNet v1)
+        [2] https://arxiv.org/abs/1801.04381 (MobileNet v2)
+        [3] https://arxiv.org/abs/1905.02244 (MobileNet v3)
+        [4] https://arxiv.org/pdf/2104.00298.pdf (EfficientNetV2)
+    """
+
+    def __init__(self, block_args, global_params, image_size=None):
+        super().__init__()
+        self._block_args = block_args
+        self._bn_mom = 1 - global_params.batch_norm_momentum  # pytorch's difference from tensorflow
+        self._bn_eps = global_params.batch_norm_epsilon
+        self.has_se = (self._block_args.se_ratio is not None) and (0 < self._block_args.se_ratio <= 1)
+        self.id_skip = block_args.id_skip  # whether to use skip connection and drop connect
+
+        # Expansion phase (Inverted Bottleneck)
+        inp = self._block_args.input_filters  # number of input channels
+        oup = self._block_args.input_filters * self._block_args.expand_ratio  # number of output channels
+        if self._block_args.expand_ratio != 1:
+            Conv2d = get_same_padding_conv2d(image_size=image_size)
+            self._expand_conv = Conv2d(in_channels=inp, out_channels=oup, kernel_size=3, bias=False)
+            self._bn0 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)
+            # image_size = calculate_output_image_size(image_size, 1) <-- this wouldn't modify image_size
+
+        # Squeeze and Excitation layer, if desired
+        if self.has_se:
+            Conv2d = get_same_padding_conv2d(image_size=(1, 1))
+            num_squeezed_channels = max(1, int(self._block_args.input_filters * self._block_args.se_ratio))
+            self._se_reduce = Conv2d(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1)
+            self._se_expand = Conv2d(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1)
+
+        # Pointwise convolution phase
+        final_oup = self._block_args.output_filters
+        Conv2d = get_same_padding_conv2d(image_size=image_size)
+        self._project_conv = Conv2d(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False)
+        self._bn2 = nn.BatchNorm2d(num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps)
+        self._swish = MemoryEfficientSwish()
+
+    def forward(self, inputs, drop_connect_rate=None):
+        """MBConvBlock's forward function.
+
+        Args:
+            inputs (tensor): Input tensor.
+            drop_connect_rate (bool): Drop connect rate (float, between 0 and 1).
+
+        Returns:
+            Output of this block after processing.
+        """
+
+        # Expansion and Depthwise Convolution
+        x = inputs
+        if self._block_args.expand_ratio != 1:
+            x = self._expand_conv(inputs)
+            x = self._bn0(x)
+            x = self._swish(x)
+
+        # Squeeze and Excitation
+        if self.has_se:
+            x_squeezed = F.adaptive_avg_pool2d(x, 1)
+            x_squeezed = self._se_reduce(x_squeezed)
+            x_squeezed = self._swish(x_squeezed)
+            x_squeezed = self._se_expand(x_squeezed)
+            x = torch.sigmoid(x_squeezed) * x
+
+        # Pointwise Convolution
+        x = self._project_conv(x) # Conv1x1
+        x = self._bn2(x)
+
+        # Skip connection and drop connect
+        input_filters, output_filters = self._block_args.input_filters, self._block_args.output_filters
+        if self.id_skip and self._block_args.stride == 1 and input_filters == output_filters:
+            # The combination of skip connection and drop connect brings about stochastic depth.
+            if drop_connect_rate:
+                x = drop_connect(x, p=drop_connect_rate, training=self.training)
+            x = x + inputs  # skip connection
+        return x
+
+    def set_swish(self, memory_efficient=True):
+        """Sets swish function as memory efficient (for training) or standard (for export).
+
+        Args:
+            memory_efficient (bool): Whether to use memory-efficient version of swish.
+        """
+        self._swish = MemoryEfficientSwish() if memory_efficient else Swish()
+
 
 class EfficientNet(nn.Module):
     """EfficientNet model.