Remove perceiver net

1 files changed, 0 insertions, 91 deletions

diff --git a/text_recognizer/networks/perceiver/perceiver.py b/text_recognizer/networks/perceiver/perceiver.py
deleted file mode 100644
index 5b4ab26..0000000
--- a/text_recognizer/networks/perceiver/perceiver.py
+++ /dev/null
@@ -1,91 +0,0 @@
-"""Perceiver IO.
-
-A copy from lucidrains.
-"""
-from typing import Optional
-
-from einops import repeat, rearrange
-import torch
-from torch import nn, Tensor
-
-from text_recognizer.networks.perceiver.attention import Attention
-from text_recognizer.networks.transformer.ff import FeedForward
-from text_recognizer.networks.transformer.norm import PreNorm
-
-
-class PerceiverIO(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        cross_heads: int,
-        cross_head_dim: int,
-        num_latents: int,
-        latent_dim: int,
-        latent_heads: int,
-        depth: int,
-        queries_dim: int,
-        logits_dim: int,
-    ) -> None:
-        super().__init__()
-        self.latents = nn.Parameter(torch.randn(num_latents, latent_dim))
-
-        self.cross_attn_block = nn.ModuleList(
-            [
-                PreNorm(
-                    latent_dim,
-                    Attention(
-                        latent_dim, dim, heads=cross_heads, dim_head=cross_head_dim
-                    ),
-                    context_dim=dim,
-                ),
-                PreNorm(latent_dim, FeedForward(latent_dim)),
-            ]
-        )
-
-        self.layers = nn.ModuleList(
-            [
-                nn.ModuleList(
-                    [
-                        PreNorm(
-                            latent_dim,
-                            Attention(
-                                latent_dim, heads=latent_heads, dim_head=latent_dim
-                            ),
-                        ),
-                        PreNorm(latent_dim, FeedForward(latent_dim)),
-                    ]
-                )
-                for _ in range(depth)
-            ]
-        )
-
-        self.decoder_cross_attn = PreNorm(
-            queries_dim,
-            Attention(
-                queries_dim, latent_dim, heads=cross_heads, dim_head=cross_head_dim
-            ),
-            context_dim=latent_dim,
-        )
-        self.decoder_ff = PreNorm(queries_dim, FeedForward(queries_dim))
-        self.to_logits = nn.Linear(queries_dim, logits_dim)
-
-    def forward(self, data: Tensor, queries: Tensor) -> Tensor:
-        b = data.shape[0]
-        x = repeat(self.latents, "n d -> b n d", b=b)
-
-        cross_attn, cross_ff = self.cross_attn_block
-
-        x = cross_attn(x, context=data) + x
-        x = cross_ff(x) + x
-
-        for attn, ff in self.layers:
-            x = attn(x) + x
-            x = ff(x) + x
-
-        if queries.ndim == 2:
-            queries = repeat(queries, "nd->bnd", b=b)
-
-        latents = self.decoder_cross_attn(queries, context=x)
-        latents = latents + self.decoder_ff(latents)
-
-        return self.to_logits(latents)