Fix VQVAE into en/decoder, bug in wandb artifact code uploading

1 files changed, 45 insertions, 38 deletions

diff --git a/text_recognizer/networks/vqvae/decoder.py b/text_recognizer/networks/vqvae/decoder.py
index fcf768b..f51e0a3 100644
--- a/text_recognizer/networks/vqvae/decoder.py
+++ b/text_recognizer/networks/vqvae/decoder.py
@@ -1,62 +1,69 @@
 """CNN decoder for the VQ-VAE."""
-import attr
+from typing import Sequence
+
 from torch import nn
 from torch import Tensor
 
 from text_recognizer.networks.util import activation_function
+from text_recognizer.networks.vqvae.norm import Normalize
 from text_recognizer.networks.vqvae.residual import Residual
 
 
-@attr.s(eq=False)
 class Decoder(nn.Module):
     """A CNN encoder network."""
 
-    in_channels: int = attr.ib()
-    embedding_dim: int = attr.ib()
-    out_channels: int = attr.ib()
-    res_channels: int = attr.ib()
-    num_residual_layers: int = attr.ib()
-    activation: str = attr.ib()
-    decoder: nn.Sequential = attr.ib(init=False)
-
-    def __attrs_post_init__(self) -> None:
-        """Post init configuration."""
+    def __init__(self, out_channels: int, hidden_dim: int, channels_multipliers: Sequence[int], dropout_rate: float, activation: str = "mish") -> None:
         super().__init__()
+        self.out_channels = out_channels
+        self.hidden_dim = hidden_dim
+        self.channels_multipliers = tuple(channels_multipliers)
+        self.activation = activation
+        self.dropout_rate = dropout_rate
         self.decoder = self._build_decompression_block()
 
     def _build_decompression_block(self,) -> nn.Sequential:
+        in_channels = self.hidden_dim * self.channels_multipliers[0]
+        decoder = []
+        for _ in range(2):
+            decoder += [
+                Residual(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    dropout_rate=self.dropout_rate,
+                    use_norm=True,
+                ),
+            ]
+        
         activation_fn = activation_function(self.activation)
-        blocks = [
+        out_channels_multipliers = self.channels_multipliers + (1, )
+        num_blocks = len(self.channels_multipliers)
+
+        for i in range(num_blocks):
+            in_channels = self.hidden_dim * self.channels_multipliers[i]
+            out_channels = self.hidden_dim * out_channels_multipliers[i + 1]
+            decoder += [
+                nn.ConvTranspose2d(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    kernel_size=4,
+                    stride=2,
+                    padding=1,
+                ),
+                activation_fn,
+            ]
+
+        decoder += [
+            Normalize(num_channels=self.hidden_dim * out_channels_multipliers[-1]),
+            nn.Mish(),
             nn.Conv2d(
-                in_channels=self.in_channels,
-                out_channels=self.embedding_dim,
-                kernel_size=3,
-                padding=1,
-            )
-        ]
-        for _ in range(self.num_residual_layers):
-            blocks.append(
-                Residual(in_channels=self.embedding_dim, out_channels=self.res_channels)
-            )
-        blocks.append(activation_fn)
-        blocks += [
-            nn.ConvTranspose2d(
-                in_channels=self.embedding_dim,
-                out_channels=self.embedding_dim // 2,
-                kernel_size=4,
-                stride=2,
-                padding=1,
-            ),
-            activation_fn,
-            nn.ConvTranspose2d(
-                in_channels=self.embedding_dim // 2,
+                in_channels=self.hidden_dim * out_channels_multipliers[-1],
                 out_channels=self.out_channels,
-                kernel_size=4,
-                stride=2,
+                kernel_size=3,
+                stride=1,
                 padding=1,
             ),
         ]
-        return nn.Sequential(*blocks)
+        return nn.Sequential(*decoder)
 
     def forward(self, z_q: Tensor) -> Tensor:
         """Reconstruct input from given codes."""