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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.autograd import *
from . import utils
from .CaptionModel import CaptionModel
class LSTMCore(nn.Module):
def __init__(self, opt):
super(LSTMCore, self).__init__()
self.input_encoding_size = opt.input_encoding_size
self.rnn_size = opt.rnn_size
self.drop_prob_lm = opt.drop_prob_lm
# Build a LSTM
self.i2h = nn.Linear(self.input_encoding_size, 5 * self.rnn_size)
self.h2h = nn.Linear(self.rnn_size, 5 * self.rnn_size)
self.dropout = nn.Dropout(self.drop_prob_lm)
def forward(self, xt, state):
all_input_sums = self.i2h(xt) + self.h2h(state[0][-1])
sigmoid_chunk = all_input_sums.narrow(1, 0, 3 * self.rnn_size)
sigmoid_chunk = torch.sigmoid(sigmoid_chunk)
in_gate = sigmoid_chunk.narrow(1, 0, self.rnn_size)
forget_gate = sigmoid_chunk.narrow(1, self.rnn_size, self.rnn_size)
out_gate = sigmoid_chunk.narrow(1, self.rnn_size * 2, self.rnn_size)
in_transform = torch.max(\
all_input_sums.narrow(1, 3 * self.rnn_size, self.rnn_size),
all_input_sums.narrow(1, 4 * self.rnn_size, self.rnn_size))
next_c = forget_gate * state[1][-1] + in_gate * in_transform
next_h = out_gate * torch.tanh(next_c)
output = self.dropout(next_h)
state = (next_h.unsqueeze(0), next_c.unsqueeze(0))
return output, state
class FCModel(CaptionModel):
def __init__(self, opt):
super(FCModel, self).__init__()
self.vocab_size = opt.vocab_size
self.input_encoding_size = opt.input_encoding_size
self.rnn_type = opt.rnn_type
self.rnn_size = opt.rnn_size
self.num_layers = opt.num_layers
self.drop_prob_lm = opt.drop_prob_lm
self.seq_length = opt.seq_length
self.fc_feat_size = opt.fc_feat_size
self.ss_prob = 0.0 # Schedule sampling probability
self.img_embed = nn.Linear(self.fc_feat_size, self.input_encoding_size)
self.core = LSTMCore(opt)
self.embed = nn.Embedding(self.vocab_size + 1, self.input_encoding_size)
self.logit = nn.Linear(self.rnn_size, self.vocab_size + 1)
self.init_weights()
def init_weights(self):
initrange = 0.1
self.embed.weight.data.uniform_(-initrange, initrange)
self.logit.bias.data.fill_(0)
self.logit.weight.data.uniform_(-initrange, initrange)
def init_hidden(self, bsz):
weight = self.logit.weight
if self.rnn_type == 'lstm':
return (weight.new_zeros(self.num_layers, bsz, self.rnn_size),
weight.new_zeros(self.num_layers, bsz, self.rnn_size))
else:
return weight.new_zeros(self.num_layers, bsz, self.rnn_size)
def _forward(self, fc_feats, att_feats, seq, att_masks=None):
batch_size = fc_feats.size(0)
seq_per_img = seq.shape[0] // batch_size
state = self.init_hidden(batch_size*seq_per_img)
outputs = []
if seq_per_img > 1:
fc_feats = utils.repeat_tensors(seq_per_img, fc_feats)
for i in range(seq.size(1) + 1):
if i == 0:
xt = self.img_embed(fc_feats)
else:
if self.training and i >= 2 and self.ss_prob > 0.0: # otherwiste no need to sample
sample_prob = fc_feats.data.new(batch_size*seq_per_img).uniform_(0, 1)
sample_mask = sample_prob < self.ss_prob
if sample_mask.sum() == 0:
it = seq[:, i-1].clone()
else:
sample_ind = sample_mask.nonzero().view(-1)
it = seq[:, i-1].data.clone()
#prob_prev = torch.exp(outputs[-1].data.index_select(0, sample_ind)) # fetch prev distribution: shape Nx(M+1)
#it.index_copy_(0, sample_ind, torch.multinomial(prob_prev, 1).view(-1))
prob_prev = torch.exp(outputs[-1].data) # fetch prev distribution: shape Nx(M+1)
it.index_copy_(0, sample_ind, torch.multinomial(prob_prev, 1).view(-1).index_select(0, sample_ind))
else:
it = seq[:, i-1].clone()
# break if all the sequences end
if i >= 2 and seq[:, i-1].sum() == 0:
break
xt = self.embed(it)
output, state = self.core(xt, state)
output = F.log_softmax(self.logit(output), dim=1)
outputs.append(output)
return torch.cat([_.unsqueeze(1) for _ in outputs[1:]], 1).contiguous()
def get_logprobs_state(self, it, state):
# 'it' is contains a word index
xt = self.embed(it)
output, state = self.core(xt, state)
logprobs = F.log_softmax(self.logit(output), dim=1)
return logprobs, state
def _sample_beam(self, fc_feats, att_feats, att_masks=None, opt={}):
beam_size = opt.get('beam_size', 10)
batch_size = fc_feats.size(0)
assert beam_size <= self.vocab_size + 1, 'lets assume this for now, otherwise this corner case causes a few headaches down the road. can be dealt with in future if needed'
seq = torch.LongTensor(self.seq_length, batch_size).zero_()
seqLogprobs = torch.FloatTensor(self.seq_length, batch_size, self.vocab_size + 1)
# lets process every image independently for now, for simplicity
self.done_beams = [[] for _ in range(batch_size)]
for k in range(batch_size):
state = self.init_hidden(beam_size)
for t in range(2):
if t == 0:
xt = self.img_embed(fc_feats[k:k+1]).expand(beam_size, self.input_encoding_size)
elif t == 1: # input <bos>
it = fc_feats.data.new(beam_size).long().zero_()
xt = self.embed(it)
output, state = self.core(xt, state)
logprobs = F.log_softmax(self.logit(output), dim=1)
self.done_beams[k] = self.beam_search(state, logprobs, opt=opt)
seq[:, k] = self.done_beams[k][0]['seq'] # the first beam has highest cumulative score
seqLogprobs[:, k] = self.done_beams[k][0]['logps']
# return the samples and their log likelihoods
return seq.transpose(0, 1), seqLogprobs.transpose(0, 1)
def _sample(self, fc_feats, att_feats, att_masks=None, opt={}):
sample_method = opt.get('sample_method', 'greedy')
beam_size = opt.get('beam_size', 1)
temperature = opt.get('temperature', 1.0)
if beam_size > 1 and sample_method in ['greedy', 'beam_search']:
return self._sample_beam(fc_feats, att_feats, opt)
batch_size = fc_feats.size(0)
state = self.init_hidden(batch_size)
seq = fc_feats.new_zeros(batch_size, self.seq_length, dtype=torch.long)
seqLogprobs = fc_feats.new_zeros(batch_size, self.seq_length, self.vocab_size + 1)
for t in range(self.seq_length + 2):
if t == 0:
xt = self.img_embed(fc_feats)
else:
if t == 1: # input <bos>
it = fc_feats.data.new(batch_size).long().zero_()
xt = self.embed(it)
output, state = self.core(xt, state)
logprobs = F.log_softmax(self.logit(output), dim=1)
# sample the next_word
if t == self.seq_length + 1: # skip if we achieve maximum length
break
if sample_method == 'greedy':
sampleLogprobs, it = torch.max(logprobs.data, 1)
it = it.view(-1).long()
else:
if temperature == 1.0:
prob_prev = torch.exp(logprobs.data).cpu() # fetch prev distribution: shape Nx(M+1)
else:
# scale logprobs by temperature
prob_prev = torch.exp(torch.div(logprobs.data, temperature)).cpu()
it = torch.multinomial(prob_prev, 1).to(logprobs.device)
sampleLogprobs = logprobs.gather(1, it) # gather the logprobs at sampled positions
it = it.view(-1).long() # and flatten indices for downstream processing
if t >= 1:
# stop when all finished
if t == 1:
unfinished = it > 0
else:
unfinished = unfinished & (it > 0)
it = it * unfinished.type_as(it)
seq[:,t-1] = it #seq[t] the input of t+2 time step
seqLogprobs[:,t-1] = sampleLogprobs.view(-1)
if unfinished.sum() == 0:
break
return seq, seqLogprobs
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