diff options
Diffstat (limited to 'text_recognizer/networks/vqvae')
-rw-r--r-- | text_recognizer/networks/vqvae/decoder.py | 20 | ||||
-rw-r--r-- | text_recognizer/networks/vqvae/encoder.py | 30 | ||||
-rw-r--r-- | text_recognizer/networks/vqvae/vqvae.py | 5 |
3 files changed, 38 insertions, 17 deletions
diff --git a/text_recognizer/networks/vqvae/decoder.py b/text_recognizer/networks/vqvae/decoder.py index 8847aba..93a1e43 100644 --- a/text_recognizer/networks/vqvae/decoder.py +++ b/text_recognizer/networks/vqvae/decoder.py @@ -44,7 +44,12 @@ class Decoder(nn.Module): # Configure encoder. self.decoder = self._build_decoder( - channels, kernel_sizes, strides, num_residual_layers, activation, dropout, + channels, + kernel_sizes, + strides, + num_residual_layers, + activation, + dropout, ) def _build_decompression_block( @@ -72,8 +77,10 @@ class Decoder(nn.Module): ) ) - if i < len(self.upsampling): - modules.append(nn.Upsample(size=self.upsampling[i]),) + if self.upsampling and i < len(self.upsampling): + modules.append( + nn.Upsample(size=self.upsampling[i]), + ) if dropout is not None: modules.append(dropout) @@ -102,7 +109,12 @@ class Decoder(nn.Module): ) -> nn.Sequential: self.res_block.append( - nn.Conv2d(self.embedding_dim, channels[0], kernel_size=1, stride=1,) + nn.Conv2d( + self.embedding_dim, + channels[0], + kernel_size=1, + stride=1, + ) ) # Bottleneck module. diff --git a/text_recognizer/networks/vqvae/encoder.py b/text_recognizer/networks/vqvae/encoder.py index d3adac5..b0cceed 100644 --- a/text_recognizer/networks/vqvae/encoder.py +++ b/text_recognizer/networks/vqvae/encoder.py @@ -1,5 +1,5 @@ """CNN encoder for the VQ-VAE.""" -from typing import List, Optional, Tuple, Type +from typing import Sequence, Optional, Tuple, Type import torch from torch import nn @@ -11,7 +11,10 @@ from text_recognizer.networks.vqvae.vector_quantizer import VectorQuantizer class _ResidualBlock(nn.Module): def __init__( - self, in_channels: int, out_channels: int, dropout: Optional[Type[nn.Module]], + self, + in_channels: int, + out_channels: int, + dropout: Optional[Type[nn.Module]], ) -> None: super().__init__() self.block = [ @@ -36,9 +39,9 @@ class Encoder(nn.Module): def __init__( self, in_channels: int, - channels: List[int], - kernel_sizes: List[int], - strides: List[int], + channels: Sequence[int], + kernel_sizes: Sequence[int], + strides: Sequence[int], num_residual_layers: int, embedding_dim: int, num_embeddings: int, @@ -77,12 +80,12 @@ class Encoder(nn.Module): self.num_embeddings, self.embedding_dim, self.beta ) + @staticmethod def _build_compression_block( - self, in_channels: int, channels: int, - kernel_sizes: List[int], - strides: List[int], + kernel_sizes: Sequence[int], + strides: Sequence[int], activation: Type[nn.Module], dropout: Optional[Type[nn.Module]], ) -> nn.ModuleList: @@ -109,8 +112,8 @@ class Encoder(nn.Module): self, in_channels: int, channels: int, - kernel_sizes: List[int], - strides: List[int], + kernel_sizes: Sequence[int], + strides: Sequence[int], num_residual_layers: int, activation: Type[nn.Module], dropout: Optional[Type[nn.Module]], @@ -135,7 +138,12 @@ class Encoder(nn.Module): ) encoder.append( - nn.Conv2d(channels[-1], self.embedding_dim, kernel_size=1, stride=1,) + nn.Conv2d( + channels[-1], + self.embedding_dim, + kernel_size=1, + stride=1, + ) ) return nn.Sequential(*encoder) diff --git a/text_recognizer/networks/vqvae/vqvae.py b/text_recognizer/networks/vqvae/vqvae.py index 50448b4..1f08e5e 100644 --- a/text_recognizer/networks/vqvae/vqvae.py +++ b/text_recognizer/networks/vqvae/vqvae.py @@ -1,8 +1,7 @@ """The VQ-VAE.""" -from typing import List, Optional, Tuple, Type +from typing import Any, Dict, List, Optional, Tuple -import torch from torch import nn from torch import Tensor @@ -25,6 +24,8 @@ class VQVAE(nn.Module): beta: float = 0.25, activation: str = "leaky_relu", dropout_rate: float = 0.0, + *args: Any, + **kwargs: Dict, ) -> None: super().__init__() |