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tmbed_viterbi.py
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tmbed_viterbi.py
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# Copyright 2022 Rostlab
#
# 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
#
# http://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 torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self):
super().__init__()
self._init_transitions()
def _init_transitions(self):
num_tags = 27
end_transitions = torch.full((num_tags,), -100)
start_transitions = torch.full((num_tags,), -100)
transitions = torch.full((num_tags, num_tags), -100)
for i in [0, 5, 10, 15, 20, -2, -1]:
start_transitions[i] = 0 # B1a, B1b, H1a, H1b, S1, i, o
for i in range(4):
transitions[0+i, 1+i] = 0 # Bxa -> Bya
transitions[5+i, 6+i] = 0 # Bxb -> Byb
transitions[10+i, 11+i] = 0 # Hxa -> Hya
transitions[15+i, 16+i] = 0 # Hxb -> Hyb
transitions[20+i, 21+i] = 0 # Sx -> Sy
for i in [4, 9, 14, 19, 24]:
transitions[i, i] = 0 # X5 -> X5
transitions[4, -1] = 0 # B5a -> o
transitions[9, -2] = 0 # B5b -> i
transitions[14, -1] = 0 # H5a -> o
transitions[19, -2] = 0 # H5b -> i
transitions[24, -2:] = 0 # S5 -> (i, o)
transitions[-2, 0] = 0 # i -> B1a
transitions[-2, 10] = 0 # i -> H1a
transitions[-2, -2:] = 0 # i -> (i, o)
transitions[-1, 5] = 0 # o -> B1b
transitions[-1, 15] = 0 # o -> H1b
transitions[-1, -2:] = 0 # o -> (i, o)
for i in [4, 9, 14, 19, 24, -2, -1]:
end_transitions[i] = 0 # B5a, B5b, H5a, H5b, S5, i, o
repeats = torch.tensor([10, 10, 5, 1, 1], dtype=torch.int32)
mapping = torch.arange(7, dtype=torch.int32)
mapping = mapping.repeat_interleave(torch.tensor([5, 5, # B
5, 5, # H
5, # S
1, # i
1])) # o
assert repeats.sum() == num_tags
assert mapping.shape == (num_tags,)
self.register_buffer('transitions', tensor=transitions)
self.register_buffer('end_transitions', tensor=end_transitions)
self.register_buffer('start_transitions', tensor=start_transitions)
self.register_buffer('repeats', tensor=repeats)
self.register_buffer('mapping', tensor=mapping)
def forward(self, emissions, mask):
mask = mask.transpose(0, 1).bool()
emissions = emissions.permute(2, 0, 1)
emissions = emissions.repeat_interleave(self.repeats, dim=2)
decoded = self._viterbi_decode(emissions, mask)
decoded = self.mapping[decoded]
return decoded
def _viterbi_decode(self, emissions, mask):
device = emissions.device
seq_length, batch_size, num_tags = emissions.shape
score = self.start_transitions + emissions[0]
history = torch.zeros((seq_length, batch_size, num_tags),
dtype=torch.long, device=device)
for i in range(1, seq_length):
next_score = (self.transitions
+ score.unsqueeze(2)
+ emissions[i].unsqueeze(1))
next_score, indices = next_score.max(dim=1)
score = torch.where(mask[i].unsqueeze(-1), next_score, score)
history[i - 1] = indices
score = score + self.end_transitions
_, end_tag = score.max(dim=1)
seq_ends = mask.long().sum(dim=0) - 1
history = history.transpose(1, 0)
history.scatter_(1,
seq_ends.view(-1, 1, 1).expand(-1, 1, num_tags),
end_tag.view(-1, 1, 1).expand(-1, 1, num_tags))
history = history.transpose(1, 0)
best_tags = torch.zeros((batch_size, 1), dtype=torch.long,
device=device)
best_tags_arr = torch.zeros((seq_length, batch_size), dtype=torch.long,
device=device)
for idx in range(seq_length - 1, -1, -1):
best_tags = torch.gather(history[idx], 1, best_tags)
best_tags_arr[idx] = best_tags.view(batch_size)
return best_tags_arr.transpose(0, 1)