Remove conv perceiver

1 files changed, 0 insertions, 66 deletions

diff --git a/text_recognizer/networks/conv_perceiver.py b/text_recognizer/networks/conv_perceiver.py
deleted file mode 100644
index 551f04f..0000000
--- a/text_recognizer/networks/conv_perceiver.py
+++ /dev/null
@@ -1,66 +0,0 @@
-"""Perceiver network module."""
-from typing import Optional, Tuple, Type
-
-from einops import repeat
-import torch
-from torch import nn, Tensor
-
-from text_recognizer.networks.perceiver.perceiver import PerceiverIO
-from text_recognizer.networks.transformer.embeddings.absolute import (
-    AbsolutePositionalEmbedding,
-)
-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_queries: int,
-        num_classes: int,
-        pad_index: Tensor,
-        encoder: Type[nn.Module],
-        decoder: PerceiverIO,
-        max_length: int,
-        pixel_embedding: AxialPositionalEmbedding,
-        query_pos_emb: AbsolutePositionalEmbedding,
-    ) -> 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.query_pos_emb = query_pos_emb
-        self.queries = nn.Parameter(torch.randn(num_queries, queries_dim))
-
-    def encode(self, x: Tensor) -> Tensor:
-        z = self.encoder(x)
-        z = torch.concat([z, self.pixel_embedding(z)], dim=1)
-        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:
-        b = z.shape[0]
-        queries = repeat(self.queries, "n d -> b n d", b=b)
-        pos_emb = repeat(self.query_pos_emb(queries), "n d -> b n d", b=b)
-        queries = torch.concat([queries, pos_emb], dim=-1)
-        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