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# Scene Text Recognition Model Hub
# Copyright 2022 Darwin Bautista
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Optional
import torch
from torch import nn as nn, Tensor
from torch.nn import functional as F
from torch.nn.modules import transformer
from timm.models.vision_transformer import VisionTransformer, PatchEmbed
class DecoderLayer(nn.Module):
"""A Transformer decoder layer supporting two-stream attention (XLNet)
This implements a pre-LN decoder, as opposed to the post-LN default in PyTorch."""
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation='gelu',
layer_norm_eps=1e-5):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=True)
self.cross_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=True)
# Implementation of Feedforward model
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.norm_q = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.norm_c = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = nn.Dropout(dropout)
self.activation = transformer._get_activation_fn(activation)
def __setstate__(self, state):
if 'activation' not in state:
state['activation'] = F.gelu
super().__setstate__(state)
def forward_stream(self, tgt: Tensor, tgt_norm: Tensor, tgt_kv: Tensor, memory: Tensor, tgt_mask: Optional[Tensor],
tgt_key_padding_mask: Optional[Tensor]):
"""Forward pass for a single stream (i.e. content or query)
tgt_norm is just a LayerNorm'd tgt. Added as a separate parameter for efficiency.
Both tgt_kv and memory are expected to be LayerNorm'd too.
memory is LayerNorm'd by ViT.
"""
tgt2, sa_weights = self.self_attn(tgt_norm, tgt_kv, tgt_kv, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask)
tgt = tgt + self.dropout1(tgt2)
tgt2, ca_weights = self.cross_attn(self.norm1(tgt), memory, memory)
tgt = tgt + self.dropout2(tgt2)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(self.norm2(tgt)))))
tgt = tgt + self.dropout3(tgt2)
return tgt, sa_weights, ca_weights
def forward(self, query, content, memory, query_mask: Optional[Tensor] = None, content_mask: Optional[Tensor] = None,
content_key_padding_mask: Optional[Tensor] = None, update_content: bool = True):
query_norm = self.norm_q(query)
content_norm = self.norm_c(content)
query = self.forward_stream(query, query_norm, content_norm, memory, query_mask, content_key_padding_mask)[0]
if update_content:
content = self.forward_stream(content, content_norm, content_norm, memory, content_mask,
content_key_padding_mask)[0]
return query, content
class Decoder(nn.Module):
__constants__ = ['norm']
def __init__(self, decoder_layer, num_layers, norm):
super().__init__()
self.layers = transformer._get_clones(decoder_layer, num_layers)
self.num_layers = num_layers
self.norm = norm
def forward(self, query, content, memory, query_mask: Optional[Tensor] = None, content_mask: Optional[Tensor] = None,
content_key_padding_mask: Optional[Tensor] = None):
for i, mod in enumerate(self.layers):
last = i == len(self.layers) - 1
query, content = mod(query, content, memory, query_mask, content_mask, content_key_padding_mask,
update_content=not last)
query = self.norm(query)
return query
class Encoder(VisionTransformer):
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.,
qkv_bias=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed):
super().__init__(img_size, patch_size, in_chans, embed_dim=embed_dim, depth=depth, num_heads=num_heads,
mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop_rate=drop_rate, attn_drop_rate=attn_drop_rate,
drop_path_rate=drop_path_rate, embed_layer=embed_layer,
num_classes=0, global_pool='', class_token=False) # these disable the classifier head
def forward(self, x):
# Return all tokens
return self.forward_features(x)
class TokenEmbedding(nn.Module):
def __init__(self, charset_size: int, embed_dim: int):
super().__init__()
self.embedding = nn.Embedding(charset_size, embed_dim)
self.embed_dim = embed_dim
def forward(self, tokens: torch.Tensor):
return math.sqrt(self.embed_dim) * self.embedding(tokens)
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