From d691b548cd0b6fc4ea184d64261f633789fee021 Mon Sep 17 00:00:00 2001
From: aktersnurra <gustaf.rydholm@gmail.com>
Date: Thu, 7 Jan 2021 23:35:42 +0100
Subject: working on vq-vae

---
 src/text_recognizer/networks/vqvae/encoder.py | 64 +++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 src/text_recognizer/networks/vqvae/encoder.py

(limited to 'src/text_recognizer/networks/vqvae/encoder.py')

diff --git a/src/text_recognizer/networks/vqvae/encoder.py b/src/text_recognizer/networks/vqvae/encoder.py
new file mode 100644
index 0000000..60c4c43
--- /dev/null
+++ b/src/text_recognizer/networks/vqvae/encoder.py
@@ -0,0 +1,64 @@
+"""CNN encoder for the VQ-VAE."""
+
+from typing import List, Optional, Type
+
+import torch
+from torch import nn
+from torch import Tensor
+
+from text_recognizer.networks.util import activation_function
+from text_recognizer.networks.vqvae.vector_quantizer import VectorQuantizer
+
+
+class _ResidualBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        activation: Type[nn.Module],
+        dropout: Optional[Type[nn.Module]],
+    ) -> None:
+        super().__init__()
+        self.block = [
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False),
+            activation,
+            nn.Conv2d(out_channels, out_channels, kernel_size=1, bias=False),
+        ]
+
+        if dropout is not None:
+            self.block.append(dropout)
+
+        self.block = nn.Sequential(*self.block)
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Apply the residual forward pass."""
+        return x + self.block(x)
+
+
+class Encoder(nn.Module):
+    """A CNN encoder network."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        channels: List[int],
+        num_residual_layers: int,
+        embedding_dim: int,
+        num_embeddings: int,
+        beta: float = 0.25,
+        activation: str = "elu",
+        dropout_rate: float = 0.0,
+    ) -> None:
+        super().__init__()
+        pass
+        # if dropout_rate:
+        #   if activation == "selu":
+        #      dropout = nn.AlphaDropout(p=dropout_rate)
+        # else:
+        #       dropout = nn.Dropout(p=dropout_rate)
+        # else:
+        #   dropout = None
+
+    def _build_encoder(self) -> nn.Sequential:
+        # TODO: Continue to implement encoder.
+        pass
-- 
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