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from typing import Dict, Optional, Tuple
from einops import rearrange
from einops.layers.torch import Rearrange
import torch
from torch import nn
from torch import Tensor
from text_recognizer.networks.util import configure_backbone
class Encoder(nn.Module):
def __init__(self, embedding_dim: int, encoder_dim: int, decoder_dim: int, dropout_rate: float = 0.1) -> None:
super().__init__()
self.rnn = nn.GRU(input_size=embedding_dim, hidden_size=encoder_dim, bidirectional=True)
self.fc = nn.Sequential(nn.Linear(in_features=2*encoder_dim, out_features=decoder_dim), nn.Tanh())
self.dropout = nn.Dropout(p=dropout_rate)
def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
"""Encodes a sequence of tensors with a bidirectional GRU.
Args:
x (Tensor): A input sequence.
Shape:
- x: :math:`(T, N, E)`.
- output[0]: :math:`(T, N, 2 * E)`.
- output[1]: :math:`(T, N, D)`.
where T is the sequence length, N is the batch size, E is the
embedding/encoder dimension, and D is the decoder dimension.
Returns:
Tuple[Tensor, Tensor]: The encoder output and the hidden state of the
encoder.
"""
output, hidden = self.rnn(x)
# Get the hidden state from the forward and backward rnn.
hidden_state = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1))
# Apply fully connected layer and tanh activation.
hidden_state = self.fc(hidden_state)
return output, hidden_state
class Attention(nn.Module):
def __init__(self, encoder_dim: int, decoder_dim: int) -> None:
super().__init__()
self.atten = nn.Linear(in_features=2*encoder_dim + decoder_dim, out_features=decoder_dim)
self.value = nn.Linear(in_features=decoder_dim, out_features=1, bias=False)
def forward(self, hidden_state: Tensor, encoder_outputs: Tensor) -> Tensor:
"""Short summary.
Args:
hidden_state (Tensor): Description of parameter `h`.
encoder_outputs (Tensor): Description of parameter `enc_out`.
Shape:
- x: :math:`(T, N, E)`.
- output[0]: :math:`(T, N, 2 * E)`.
- output[1]: :math:`(T, N, D)`.
where T is the sequence length, N is the batch size, E is the
embedding/encoder dimension, and D is the decoder dimension.
Returns:
Tensor: Description of returned object.
"""
t, b = enc_out.shape[:2]
#repeat decoder hidden state src_len times
hidden_state = hidden_state.unsqueeze(1).repeat(1, t, 1)
encoder_outputs = rearrange(encoder_outputs, "t b e2 -> b t e2")
# Calculate the energy between the decoders previous hidden state and the
# encoders hidden states.
energy = torch.tanh(self.attn(torch.cat((hidden_state, encoder_outputs), dim = 2)))
attention = self.value(energy).squeeze(2)
# Apply softmax on the attention to squeeze it between 0 and 1.
attention = F.softmax(attention, dim=1)
return attention
class Decoder(nn.Module):
def __init__(self, embedding_dim: int, encoder_dim: int, decoder_dim: int, output_dim: int, dropout_rate: float = 0.1) -> None:
super().__init__()
self.output_dim = output_dim
self.embedding = nn.Embedding(output_dim, embedding_dim)
self.attention = Attention(encoder_dim, decoder_dim)
self.rnn = nn.GRU(input_size=2*encoder_dim + embedding_dim, hidden_size=decoder_dim)
self.head = nn.Linear(in_features=2*encoder_dim+embedding_dim+decoder_dim, out_features=output_dim)
self.dropout = nn.Dropout(p=dropout_rate)
def forward(self, trg: Tensor, hidden_state: Tensor, encoder_outputs: Tensor) -> Tensor:
#input = [batch size]
#hidden = [batch size, dec hid dim]
#encoder_outputs = [src len, batch size, enc hid dim * 2]
trg = trg.unsqueeze(0)
trg_embedded = self.dropout(self.embedding(trg))
a = self.attention(hidden_state, encoder_outputs)
weighted = torch.bmm(a, encoder_outputs)
# Permutate the tensor.
weighted = rearrange(weighted, "b a e2 -> a b e2")
rnn_input = torch.cat((trg_embedded, weighted), dim = 2)
output, hidden = self.rnn(rnn_input, hidden.unsqueeze(0))
#seq len, n layers and n directions will always be 1 in this decoder, therefore:
#output = [1, batch size, dec hid dim]
#hidden = [1, batch size, dec hid dim]
#this also means that output == hidden
assert (output == hidden).all()
trg_embedded = trg_embedded.squeeze(0)
output = output.squeeze(0)
weighted = weighted.squeeze(0)
logits = self.fc_out(torch.cat((output, weighted, trg_embedded), dim = 1))
#prediction = [batch size, output dim]
return logits, hidden.squeeze(0)
class NeuralMachineReader(nn.Module):
def __init__(self, embedding_dim: int, encoder_dim: int, decoder_dim: int, output_dim: int, backbone: Optional[str] = None,
backbone_args: Optional[Dict] = None, patch_size: Tuple[int, int] = (28, 28),
stride: Tuple[int, int] = (1, 14), dropout_rate: float = 0.1, teacher_forcing_ratio: float = 0.5) -> None:
super().__init__()
self.patch_size = patch_size
self.stride = stride
self.sliding_window = self._configure_sliding_window()
self.backbone =
self.encoder = Encoder(embedding_dim, encoder_dim, decoder_dim, dropout_rate)
self.decoder = Decoder(embedding_dim, encoder_dim, decoder_dim, output_dim, dropout_rate)
self.teacher_forcing_ratio = teacher_forcing_ratio
def _configure_sliding_window(self) -> nn.Sequential:
return nn.Sequential(
nn.Unfold(kernel_size=self.patch_size, stride=self.stride),
Rearrange(
"b (c h w) t -> b t c h w",
h=self.patch_size[0],
w=self.patch_size[1],
c=1,
),
)
def forward(self, x: Tensor, trg: Tensor) -> Tensor:
#x = [batch size, height, width]
#trg = [trg len, batch size]
# Create image patches with a sliding window kernel.
x = self.sliding_window(x)
# Rearrange from a sequence of patches for feedforward network.
b, t = x.shape[:2]
x = rearrange(x, "b t c h w -> (b t) c h w", b=b, t=t)
x = self.backbone(x)
x = rearrange(x, "(b t) h -> t b h", b=b, t=t)
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