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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import PackedSequence, pack_padded_sequence, pad_packed_sequence
import numpy as np
from KMVE_RG.modules.Caption import MyCaption
def sort_pack_padded_sequence(input, lengths):
sorted_lengths, indices = torch.sort(lengths, descending=True)
tmp = pack_padded_sequence(input[indices], sorted_lengths, batch_first=True)
inv_ix = indices.clone()
inv_ix[indices] = torch.arange(0, len(indices)).type_as(inv_ix)
return tmp, inv_ix
def pad_unsort_packed_sequence(input, inv_ix):
tmp, _ = pad_packed_sequence(input, batch_first=True)
tmp = tmp[inv_ix]
return tmp
def pack_wrapper(module, att_feats, att_masks):
if att_masks is not None:
packed, inv_ix = sort_pack_padded_sequence(att_feats, att_masks.data.long().sum(1))
return pad_unsort_packed_sequence(PackedSequence(module(packed[0]), packed[1]), inv_ix)
else:
return module(att_feats)
class GenModel(MyCaption):
def __init__(self, args, tokenizer):
super(GenModel, self).__init__()
self.args = args
self.tokenizer = tokenizer
self.vocab_size = len(tokenizer.idx2token)
self.input_encoding_size = args.d_model
self.rnn_size = args.d_ff
self.num_layers = args.num_layers
self.drop_prob_lm = args.drop_prob_lm
self.max_seq_length = args.max_seq_length
self.att_feat_size = args.d_vf
self.att_hid_size = args.d_model
self.bos_idx = args.bos_idx
self.eos_idx = args.eos_idx
self.pad_idx = args.pad_idx
self.use_bn = args.use_bn
self.embed = lambda x: x
self.fc_embed = lambda x: x
self.att_embed = nn.Sequential(*(
((nn.BatchNorm1d(self.att_feat_size),) if self.use_bn else ()) +
(nn.Linear(self.att_feat_size, self.input_encoding_size),
nn.ReLU(),
nn.Dropout(self.drop_prob_lm)) +
((nn.BatchNorm1d(self.input_encoding_size),) if self.use_bn == 2 else ())))
def clip_att(self, att_feats, att_masks):
if att_masks is not None:
max_len = att_masks.data.long().sum(1).max()
att_feats = att_feats[:, :max_len].contiguous()
att_masks = att_masks[:, :max_len].contiguous()
return att_feats, att_masks
def _prepare_feature(self, fc_feats, att_feats, att_masks):
att_feats, att_masks = self.clip_att(att_feats, att_masks)
# embed fc and att feats
fc_feats = self.fc_embed(fc_feats)
att_feats = pack_wrapper(self.att_embed, att_feats, att_masks)
p_att_feats = self.ctx2att(att_feats)
return fc_feats, att_feats, p_att_feats, att_masks
def get_logprobs_state(self, it, fc_feats, att_feats, p_att_feats, att_masks, state, output_logsoftmax=1):
xt = self.embed(it)
output, state = self.core(xt, p_att_feats, state, att_masks)
if output_logsoftmax:
logprobs = F.log_softmax(self.logit(output), dim=1)
else:
logprobs = self.logit(output)
output_weight = output.unsqueeze(-1)
attn_map = torch.matmul(p_att_feats, output_weight)
return logprobs, state, attn_map
def _sample(self, fc_feats, att_feats, att_masks=None):
opt = self.args.__dict__
sample_n = int(opt.get('sample_n', 1))
output_logsoftmax = opt.get('output_logsoftmax', 1)
decoding_constraint = opt.get('decoding_constraint', 0)
# import pdb
# pdb.set_trace()
batch_size = fc_feats.size(0)
state = []
p_fc_feats, p_att_feats, pp_att_feats, p_att_masks = self._prepare_feature(fc_feats, att_feats, att_masks)
seq = fc_feats.new_full((batch_size * sample_n, self.max_seq_length), self.pad_idx, dtype=torch.long)
seqLogprobs = fc_feats.new_zeros(batch_size * sample_n, self.max_seq_length, self.vocab_size + 1)
for t in range(self.max_seq_length + 1):
if t == 0: # input <bos>
it = fc_feats.new_full([batch_size * sample_n], self.bos_idx, dtype=torch.long)
logprobs, state, attn_map = self.get_logprobs_state(it, p_fc_feats, p_att_feats, pp_att_feats, p_att_masks, state,
output_logsoftmax=output_logsoftmax)
if decoding_constraint and t > 0:
tmp = logprobs.new_zeros(logprobs.size())
tmp.scatter_(1, seq[:, t - 1].data.unsqueeze(1), float('-inf'))
logprobs = logprobs + tmp
if t == self.max_seq_length:
break
it, sampleLogprobs = self.sample_next_word(logprobs)
if t == 0:
unfinished = it != self.eos_idx
else:
it[~unfinished] = self.pad_idx
logprobs = logprobs * unfinished.unsqueeze(1).float()
unfinished = unfinished * (it != self.eos_idx)
seq[:, t] = it
seqLogprobs[:, t] = logprobs
return seq, seqLogprobs
def _evaluate(self, fc_feats, att_feats, att_masks=None):
opt = self.args.__dict__
sample_n = int(opt.get('sample_n', 1))
output_logsoftmax = opt.get('output_logsoftmax', 1)
decoding_constraint = opt.get('decoding_constraint', 0)
batch_size = fc_feats.size(0)
state = []
p_fc_feats, p_att_feats, pp_att_feats, p_att_masks = self._prepare_feature(fc_feats, att_feats, att_masks)
seq = fc_feats.new_full((batch_size * sample_n, self.max_seq_length), self.pad_idx, dtype=torch.long)
seqLogprobs = fc_feats.new_zeros(batch_size * sample_n, self.max_seq_length, self.vocab_size + 1)
first_sentence = []
first_attmap = []
first_sentence_probs = []
for t in range(self.max_seq_length + 1):
if t == 0: # input <bos>
it = fc_feats.new_full([batch_size * sample_n], self.bos_idx, dtype=torch.long)
logprobs, state, attn_map = self.get_logprobs_state(it, p_fc_feats, p_att_feats, pp_att_feats, p_att_masks,
state,
output_logsoftmax=output_logsoftmax)
if decoding_constraint and t > 0:
tmp = logprobs.new_zeros(logprobs.size())
tmp.scatter_(1, seq[:, t - 1].data.unsqueeze(1), float('-inf'))
logprobs = logprobs + tmp
if t == self.max_seq_length:
break
it, sampleLogprobs = self.sample_next_word(logprobs)
if t == 0:
unfinished = it != self.eos_idx
else:
it[~unfinished] = self.pad_idx
logprobs = logprobs * unfinished.unsqueeze(1).float()
unfinished = unfinished * (it != self.eos_idx)
seq[:, t] = it
seqLogprobs[:, t] = logprobs
log_probs = logprobs[0].cpu()
probabilities = np.exp(log_probs)
index = int(it[0].cpu())
prob = probabilities[index]
first_attmap.append(attn_map[0])
first_sentence.append(index)
first_sentence_probs.append(prob)
if unfinished.sum() == 0:
break
return seq, first_sentence, first_attmap, first_sentence_probs
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