From 1ca8b0b9e0613c1e02f6a5d8b49e20c4d6916412 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Thu, 22 Apr 2021 08:15:58 +0200
Subject: Fixed training script, able to train vqvae

---
 text_recognizer/networks/vqvae/decoder.py | 20 ++++++++++++++++----
 text_recognizer/networks/vqvae/encoder.py | 30 +++++++++++++++++++-----------
 text_recognizer/networks/vqvae/vqvae.py   |  5 +++--
 3 files changed, 38 insertions(+), 17 deletions(-)

(limited to 'text_recognizer/networks/vqvae')

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__()
 
-- 
cgit v1.2.3-70-g09d2