1 files changed, 0 insertions, 83 deletions
diff --git a/text_recognizer/networks/beam.py b/text_recognizer/networks/beam.py
deleted file mode 100644
index dccccdb..0000000
--- a/text_recognizer/networks/beam.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""Implementation of beam search decoder for a sequence to sequence network.
-
-Stolen from: https://github.com/budzianowski/PyTorch-Beam-Search-Decoding/blob/master/decode_beam.py
-
-"""
-# from typing import List
-# from Queue import PriorityQueue
-
-# from loguru import logger
-# import torch
-# from torch import nn
-# from torch import Tensor
-# import torch.nn.functional as F
-
-
-# class Node:
-#     def __init__(
-#         self, parent: Node, target_index: int, log_prob: Tensor, length: int
-#     ) -> None:
-#         self.parent = parent
-#         self.target_index = target_index
-#         self.log_prob = log_prob
-#         self.length = length
-#         self.reward = 0.0
-
-#     def eval(self, alpha: float = 1.0) -> Tensor:
-#         return self.log_prob / (self.length - 1 + 1e-6) + alpha * self.reward
-
-
-# @torch.no_grad()
-# def beam_decoder(
-#     network, mapper, device, memory: Tensor = None, max_len: int = 97,
-# ) -> Tensor:
-#     beam_width = 10
-#     topk = 1  # How many sentences to generate.
-
-#     trg_indices = [mapper(mapper.init_token)]
-
-#     end_nodes = []
-
-#     node = Node(None, trg_indices, 0, 1)
-#     nodes = PriorityQueue()
-
-#     nodes.put((node.eval(), node))
-#     q_size = 1
-
-#     # Beam search
-#     for _ in range(max_len):
-#         if q_size > 2000:
-#             logger.warning("Could not decoder input")
-#             break
-
-#         # Fetch the best node.
-#         score, n = nodes.get()
-#         decoder_input = n.target_index
-
-#         if n.target_index == mapper(mapper.eos_token) and n.parent is not None:
-#             end_nodes.append((score, n))
-
-#             # If we reached the maximum number of sentences required.
-#             if len(end_nodes) >= 1:
-#                 break
-#             else:
-#                 continue
-
-#         # Forward pass with transformer.
-#         trg = torch.tensor(trg_indices, device=device)[None, :].long()
-#         trg = network.target_embedding(trg)
-#         logits = network.decoder(trg=trg, memory=memory, trg_mask=None)
-#         log_prob = F.log_softmax(logits, dim=2)
-
-#         log_prob, indices = torch.topk(log_prob, beam_width)
-
-#         for new_k in range(beam_width):
-#             # TODO: continue from here
-#             token_index = indices[0][new_k].view(1, -1)
-#             log_p = log_prob[0][new_k].item()
-
-#             node = Node()
-
-#             pass
-
-#     pass