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|
"""Greedy decoder."""
from typing import Type
from text_recognizer.data.tokenizer import Tokenizer
import torch
from torch import nn, Tensor
class GreedyDecoder:
def __init__(
self,
network: Type[nn.Module],
tokenizer: Tokenizer,
max_output_len: int = 682,
) -> None:
self.network = network
self.start_index = tokenizer.start_index
self.end_index = tokenizer.end_index
self.pad_index = tokenizer.pad_index
self.max_output_len = max_output_len
def __call__(self, x: Tensor) -> Tensor:
bsz = x.shape[0]
# Encode image(s) to latent vectors.
img_features = self.network.encode(x)
# Create a placeholder matrix for storing outputs from the network
indecies = (
torch.ones((bsz, self.max_output_len), dtype=torch.long, device=x.device)
* self.pad_index
)
indecies[:, 0] = self.start_index
for i in range(1, self.max_output_len):
tokens = indecies[:, :i] # (B, Sy)
logits = self.network.decode(tokens, img_features) # [ B, N, C ]
indecies_ = logits.argmax(dim=2) # [ B, N ]
indecies[:, i] = indecies_[:, -1]
# Early stopping of prediction loop if token is end or padding token.
if (
(indecies[:, i] == self.end_index) | (indecies[:, i] == self.pad_index)
).all():
break
# Set all tokens after end token to pad token.
for i in range(1, self.max_output_len):
idx = (indecies[:, i - 1] == self.end_index) | (
indecies[:, i - 1] == self.pad_index
)
indecies[idx, i] = self.pad_index
return indecies
|
