diff options
Diffstat (limited to 'src/text_recognizer/networks/unet.py')
| -rw-r--r-- | src/text_recognizer/networks/unet.py | 159 |
1 files changed, 128 insertions, 31 deletions
diff --git a/src/text_recognizer/networks/unet.py b/src/text_recognizer/networks/unet.py index 51f242a..510910f 100644 --- a/src/text_recognizer/networks/unet.py +++ b/src/text_recognizer/networks/unet.py @@ -8,64 +8,118 @@ from torch import Tensor from text_recognizer.networks.util import activation_function -class ConvBlock(nn.Module): - """Basic UNet convolutional block.""" +class _ConvBlock(nn.Module): + """Modified UNet convolutional block with dilation.""" - def __init__(self, channels: List[int], activation: str) -> None: + def __init__( + self, + channels: List[int], + activation: str, + num_groups: int, + dropout_rate: float = 0.1, + kernel_size: int = 3, + dilation: int = 1, + padding: int = 0, + ) -> None: super().__init__() self.channels = channels + self.dropout_rate = dropout_rate + self.kernel_size = kernel_size + self.dilation = dilation + self.padding = padding + self.num_groups = num_groups self.activation = activation_function(activation) self.block = self._configure_block() + self.residual_conv = nn.Sequential( + nn.Conv2d( + self.channels[0], self.channels[-1], kernel_size=3, stride=1, padding=1 + ), + self.activation, + ) def _configure_block(self) -> nn.Sequential: block = [] for i in range(len(self.channels) - 1): block += [ + nn.Dropout(p=self.dropout_rate), + nn.GroupNorm(self.num_groups, self.channels[i]), + self.activation, nn.Conv2d( - self.channels[i], self.channels[i + 1], kernel_size=3, padding=1 + self.channels[i], + self.channels[i + 1], + kernel_size=self.kernel_size, + padding=self.padding, + stride=1, + dilation=self.dilation, ), - nn.BatchNorm2d(self.channels[i + 1]), - self.activation, ] return nn.Sequential(*block) def forward(self, x: Tensor) -> Tensor: """Apply the convolutional block.""" - return self.block(x) + residual = self.residual_conv(x) + return self.block(x) + residual -class DownSamplingBlock(nn.Module): +class _DownSamplingBlock(nn.Module): """Basic down sampling block.""" def __init__( self, channels: List[int], activation: str, + num_groups: int, pooling_kernel: Union[int, bool] = 2, + dropout_rate: float = 0.1, + kernel_size: int = 3, + dilation: int = 1, + padding: int = 0, ) -> None: super().__init__() - self.conv_block = ConvBlock(channels, activation) + self.conv_block = _ConvBlock( + channels, + activation, + num_groups, + dropout_rate, + kernel_size, + dilation, + padding, + ) self.down_sampling = nn.MaxPool2d(pooling_kernel) if pooling_kernel else None def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]: """Return the convolutional block output and a down sampled tensor.""" x = self.conv_block(x) - if self.down_sampling is not None: - x_down = self.down_sampling(x) - else: - x_down = None + x_down = self.down_sampling(x) if self.down_sampling is not None else x + return x_down, x -class UpSamplingBlock(nn.Module): +class _UpSamplingBlock(nn.Module): """The upsampling block of the UNet.""" def __init__( - self, channels: List[int], activation: str, scale_factor: int = 2 + self, + channels: List[int], + activation: str, + num_groups: int, + scale_factor: int = 2, + dropout_rate: float = 0.1, + kernel_size: int = 3, + dilation: int = 1, + padding: int = 0, ) -> None: super().__init__() - self.conv_block = ConvBlock(channels, activation) + self.conv_block = _ConvBlock( + channels, + activation, + num_groups, + dropout_rate, + kernel_size, + dilation, + padding, + ) self.up_sampling = nn.Upsample( scale_factor=scale_factor, mode="bilinear", align_corners=True ) @@ -87,14 +141,43 @@ class UNet(nn.Module): base_channels: int = 64, num_classes: int = 3, depth: int = 4, - out_channels: int = 3, activation: str = "relu", + num_groups: int = 8, + dropout_rate: float = 0.1, pooling_kernel: int = 2, scale_factor: int = 2, + kernel_size: Optional[List[int]] = None, + dilation: Optional[List[int]] = None, + padding: Optional[List[int]] = None, ) -> None: super().__init__() self.depth = depth - channels = [1] + [base_channels * 2 ** i for i in range(depth)] + self.num_groups = num_groups + + if kernel_size is not None and dilation is not None and padding is not None: + if ( + len(kernel_size) != depth + and len(dilation) != depth + and len(padding) != depth + ): + raise RuntimeError( + "Length of convolutional parameters does not match the depth." + ) + self.kernel_size = kernel_size + self.padding = padding + self.dilation = dilation + + else: + self.kernel_size = [3] * depth + self.padding = [1] * depth + self.dilation = [1] * depth + + self.dropout_rate = dropout_rate + self.conv = nn.Conv2d( + in_channels, base_channels, kernel_size=3, stride=1, padding=1 + ) + + channels = [base_channels] + [base_channels * 2 ** i for i in range(depth)] self.encoder_blocks = self._configure_down_sampling_blocks( channels, activation, pooling_kernel ) @@ -110,49 +193,63 @@ class UNet(nn.Module): blocks = nn.ModuleList([]) for i in range(len(channels) - 1): pooling_kernel = pooling_kernel if i < self.depth - 1 else False + dropout_rate = self.dropout_rate if i < 0 else 0 blocks += [ - DownSamplingBlock( + _DownSamplingBlock( [channels[i], channels[i + 1], channels[i + 1]], activation, + self.num_groups, pooling_kernel, + dropout_rate, + self.kernel_size[i], + self.dilation[i], + self.padding[i], ) ] return blocks def _configure_up_sampling_blocks( - self, - channels: List[int], - activation: str, - scale_factor: int, + self, channels: List[int], activation: str, scale_factor: int, ) -> nn.ModuleList: channels.reverse() + self.kernel_size.reverse() + self.dilation.reverse() + self.padding.reverse() return nn.ModuleList( [ - UpSamplingBlock( + _UpSamplingBlock( [channels[i] + channels[i + 1], channels[i + 1], channels[i + 1]], activation, + self.num_groups, scale_factor, + self.dropout_rate, + self.kernel_size[i], + self.dilation[i], + self.padding[i], ) for i in range(len(channels) - 2) ] ) - def encode(self, x: Tensor) -> Tuple[Tensor, List[Tensor]]: + def _encode(self, x: Tensor) -> List[Tensor]: x_skips = [] for block in self.encoder_blocks: x, x_skip = block(x) - if x_skip is not None: - x_skips.append(x_skip) - return x, x_skips + x_skips.append(x_skip) + return x_skips - def decode(self, x: Tensor, x_skips: List[Tensor]) -> Tensor: + def _decode(self, x_skips: List[Tensor]) -> Tensor: x = x_skips[-1] for i, block in enumerate(self.decoder_blocks): x = block(x, x_skips[-(i + 2)]) return x def forward(self, x: Tensor) -> Tensor: - x, x_skips = self.encode(x) - x = self.decode(x, x_skips) + """Forward pass with the UNet model.""" + if len(x.shape) < 4: + x = x[(None,) * (4 - len(x.shape))] + x = self.conv(x) + x_skips = self._encode(x) + x = self._decode(x_skips) return self.head(x) |