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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).to(x.device)
indecies[:, 0] = self.start_index
for Sy in range(1, self.max_output_len):
tokens = indecies[:, :Sy] # (B, Sy)
logits = self.network.decode(tokens, img_features) # (B, C, Sy)
indecies_ = torch.argmax(logits, dim=1) # (B, Sy)
indecies[:, Sy : Sy + 1] = indecies_[:, -1:]
# Early stopping of prediction loop if token is end or padding token.
if (
(indecies[:, Sy - 1] == self.end_index)
| (indecies[:, Sy - 1] == self.pad_index)
).all():
break
# Set all tokens after end token to pad token.
for Sy in range(1, self.max_output_len):
idx = (indecies[:, Sy - 1] == self.end_index) | (
indecies[:, Sy - 1] == self.pad_index
)
indecies[idx, Sy] = self.pad_index
return indecies
|
