From 73ccaaa24936faed36fcc467532baa5386d402ae Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sat, 3 Sep 2022 12:13:02 +0200
Subject: Update perceiver

---
 text_recognizer/networks/conv_perceiver.py | 62 ++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)
 create mode 100644 text_recognizer/networks/conv_perceiver.py

(limited to 'text_recognizer/networks/conv_perceiver.py')

diff --git a/text_recognizer/networks/conv_perceiver.py b/text_recognizer/networks/conv_perceiver.py
new file mode 100644
index 0000000..cda5e0a
--- /dev/null
+++ b/text_recognizer/networks/conv_perceiver.py
@@ -0,0 +1,62 @@
+"""Perceiver network module."""
+from typing import Optional, Tuple, Type
+
+from torch import nn, Tensor
+
+from text_recognizer.networks.perceiver.perceiver import PerceiverIO
+from text_recognizer.networks.transformer.embeddings.axial import (
+    AxialPositionalEmbedding,
+)
+
+
+class ConvPerceiver(nn.Module):
+    """Base transformer network."""
+
+    def __init__(
+        self,
+        input_dims: Tuple[int, int, int],
+        hidden_dim: int,
+        queries_dim: int,
+        num_classes: int,
+        pad_index: Tensor,
+        encoder: Type[nn.Module],
+        decoder: PerceiverIO,
+        max_length: int,
+        pixel_embedding: AxialPositionalEmbedding,
+    ) -> None:
+        super().__init__()
+        self.input_dims = input_dims
+        self.hidden_dim = hidden_dim
+        self.num_classes = num_classes
+        self.pad_index = pad_index
+        self.max_length = max_length
+        self.encoder = encoder
+        self.decoder = decoder
+        self.pixel_embedding = pixel_embedding
+        self.to_queries = nn.Linear(self.hidden_dim, queries_dim)
+        self.conv = nn.Conv2d(
+            in_channels=self.encoder.out_channels,
+            out_channels=self.hidden_dim,
+            kernel_size=1,
+        )
+
+    def encode(self, x: Tensor) -> Tensor:
+        z = self.encoder(x)
+        z = self.conv(z)
+        z = self.pixel_embedding(z)
+        z = z.flatten(start_dim=2)
+
+        # Permute tensor from [B, E, Ho * Wo] to [B, Sx, E]
+        z = z.permute(0, 2, 1)
+        return z
+
+    def decode(self, z: Tensor) -> Tensor:
+        queries = self.to_queries(z[:, : self.max_length, :])
+        logits = self.decoder(data=z, queries=queries)
+        logits = logits.permute(0, 2, 1)  # [B, C, Sy]
+        return logits
+
+    def forward(self, x: Tensor) -> Tensor:
+        z = self.encode(x)
+        logits = self.decode(z)
+        return logits
-- 
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