# This file contains ShowAttendTell and AllImg model # ShowAttendTell is from Show, Attend and Tell: Neural Image Caption Generation with Visual Attention # https://arxiv.org/abs/1502.03044 # AllImg is a model where # img feature is concatenated with word embedding at every time step as the input of lstm from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import * from ..utils import misc as utils from . import utils as model_utils class CaptionModel(nn.Module): def __init__(self): super(CaptionModel, self).__init__() # implements beam search # calls beam_step and returns the final set of beams # augments log-probabilities with diversity terms when number of groups > 1 def forward(self, *args, **kwargs): mode = kwargs.get('mode', 'forward') if 'mode' in kwargs: del kwargs['mode'] return getattr(self, '_'+mode)(*args, **kwargs) def beam_search(self, init_state, init_logprobs, *args, **kwargs): # function computes the similarity score to be augmented def add_diversity(beam_seq_table, logprobs, t, divm, diversity_lambda, bdash): local_time = t - divm unaug_logprobs = logprobs.clone() batch_size = beam_seq_table[0].shape[0] if divm > 0: change = logprobs.new_zeros(batch_size, logprobs.shape[-1]) for prev_choice in range(divm): prev_decisions = beam_seq_table[prev_choice][:, :, local_time] # Nxb for prev_labels in range(bdash): change.scatter_add_(1, prev_decisions[:, prev_labels].unsqueeze(-1), change.new_ones(batch_size, 1)) if local_time == 0: logprobs = logprobs - change * diversity_lambda else: logprobs = logprobs - self.repeat_tensor(bdash, change) * diversity_lambda return logprobs, unaug_logprobs # does one step of classical beam search def beam_step(logprobs, unaug_logprobs, beam_size, t, beam_seq, beam_seq_logprobs, beam_logprobs_sum, state): #INPUTS: #logprobs: probabilities augmented after diversity N*bxV #beam_size: obvious #t : time instant #beam_seq : tensor contanining the beams #beam_seq_logprobs: tensor contanining the beam logprobs #beam_logprobs_sum: tensor contanining joint logprobs #OUPUTS: #beam_seq : tensor containing the word indices of the decoded captions Nxbxl #beam_seq_logprobs : log-probability of each decision made, NxbxlxV #beam_logprobs_sum : joint log-probability of each beam Nxb batch_size = beam_logprobs_sum.shape[0] vocab_size = logprobs.shape[-1] logprobs = logprobs.reshape(batch_size, -1, vocab_size) # NxbxV if t == 0: assert logprobs.shape[1] == 1 beam_logprobs_sum = beam_logprobs_sum[:, :1] candidate_logprobs = beam_logprobs_sum.unsqueeze(-1) + logprobs # beam_logprobs_sum Nxb logprobs is NxbxV ys, ix = torch.sort(candidate_logprobs.reshape(candidate_logprobs.shape[0], -1), -1, True) ys, ix = ys[:,:beam_size], ix[:,:beam_size] beam_ix = ix // vocab_size # Nxb which beam selected_ix = ix % vocab_size # Nxb # which world state_ix = (beam_ix + torch.arange(batch_size).type_as(beam_ix).unsqueeze(-1) * logprobs.shape[1]).reshape(-1) # N*b which in Nxb beams if t > 0: # gather according to beam_ix assert (beam_seq.gather(1, beam_ix.unsqueeze(-1).expand_as(beam_seq)) == beam_seq.reshape(-1, beam_seq.shape[-1])[state_ix].view_as(beam_seq)).all() beam_seq = beam_seq.gather(1, beam_ix.unsqueeze(-1).expand_as(beam_seq)) beam_seq_logprobs = beam_seq_logprobs.gather(1, beam_ix.unsqueeze(-1).unsqueeze(-1).expand_as(beam_seq_logprobs)) beam_seq = torch.cat([beam_seq, selected_ix.unsqueeze(-1)], -1) # beam_seq Nxbxl beam_logprobs_sum = beam_logprobs_sum.gather(1, beam_ix) + \ logprobs.reshape(batch_size, -1).gather(1, ix) assert (beam_logprobs_sum == ys).all() _tmp_beam_logprobs = unaug_logprobs[state_ix].reshape(batch_size, -1, vocab_size) beam_logprobs = unaug_logprobs.reshape(batch_size, -1, vocab_size).gather(1, beam_ix.unsqueeze(-1).expand(-1, -1, vocab_size)) # NxbxV assert (_tmp_beam_logprobs == beam_logprobs).all() beam_seq_logprobs = torch.cat([ beam_seq_logprobs, beam_logprobs.reshape(batch_size, -1, 1, vocab_size)], 2) new_state = [None for _ in state] for _ix in range(len(new_state)): # copy over state in previous beam q to new beam at vix new_state[_ix] = state[_ix][:, state_ix] state = new_state return beam_seq,beam_seq_logprobs,beam_logprobs_sum,state # Start diverse_beam_search opt = kwargs['opt'] temperature = opt.get('temperature', 1) # This should not affect beam search, but will affect dbs beam_size = opt.get('beam_size', 10) group_size = opt.get('group_size', 1) diversity_lambda = opt.get('diversity_lambda', 0.5) decoding_constraint = opt.get('decoding_constraint', 0) remove_bad_endings = opt.get('remove_bad_endings', 0) suppress_UNK = opt.get('suppress_UNK', 0) length_penalty = utils.penalty_builder(opt.get('length_penalty', '')) bdash = beam_size // group_size # beam per group batch_size = init_logprobs.shape[0] device = init_logprobs.device # INITIALIZATIONS beam_seq_table = [torch.LongTensor(batch_size, bdash, 0).to(device) for _ in range(group_size)] beam_seq_logprobs_table = [torch.FloatTensor(batch_size, bdash, 0, self.vocab_size + 1).to(device) for _ in range(group_size)] beam_logprobs_sum_table = [torch.zeros(batch_size, bdash).to(device) for _ in range(group_size)] # logprobs # logprobs predicted in last time step, shape (beam_size, vocab_size+1) done_beams_table = [[[] for __ in range(group_size)] for _ in range(batch_size)] # state_table = [list(torch.unbind(_)) for _ in torch.stack(init_state).chunk(group_size, 2)] # state_table = list(zip(*[_.reshape(-1, batch_size * bdash, group_size, *_.shape[2:]).chunk(group_size, 2) for _ in init_state])) state_table = [[_.clone() for _ in init_state] for _ in range(group_size)] # logprobs_table = list(init_logprobs.reshape(batch_size * bdash, group_size, -1).chunk(group_size, 0)) logprobs_table = [init_logprobs.clone() for _ in range(group_size)] # END INIT # Chunk elements in the args args = list(args) args = model_utils.split_tensors(group_size, args) # For each arg, turn (Bbg)x... to (Bb)x(g)x... if self.__class__.__name__ == 'AttEnsemble': args = [[[args[j][i][k] for i in range(len(self.models))] for j in range(len(args))] for k in range(group_size)] # group_name, arg_name, model_name else: args = [[args[i][j] for i in range(len(args))] for j in range(group_size)] for t in range(self.seq_length + group_size - 1): for divm in range(group_size): if t >= divm and t <= self.seq_length + divm - 1: # add diversity logprobs = logprobs_table[divm] # suppress previous word if decoding_constraint and t-divm > 0: logprobs.scatter_(1, beam_seq_table[divm][:, :, t-divm-1].reshape(-1, 1).to(device), float('-inf')) if remove_bad_endings and t-divm > 0: logprobs[torch.from_numpy(np.isin(beam_seq_table[divm][:, :, t-divm-1].cpu().numpy(), self.bad_endings_ix)).reshape(-1), 0] = float('-inf') # suppress UNK tokens in the decoding if suppress_UNK and hasattr(self, 'vocab') and self.vocab[str(logprobs.size(1)-1)] == 'UNK': logprobs[:,logprobs.size(1)-1] = logprobs[:, logprobs.size(1)-1] - 1000 # diversity is added here # the function directly modifies the logprobs values and hence, we need to return # the unaugmented ones for sorting the candidates in the end. # for historical # reasons :-) logprobs, unaug_logprobs = add_diversity(beam_seq_table,logprobs,t,divm,diversity_lambda,bdash) # infer new beams beam_seq_table[divm],\ beam_seq_logprobs_table[divm],\ beam_logprobs_sum_table[divm],\ state_table[divm] = beam_step(logprobs, unaug_logprobs, bdash, t-divm, beam_seq_table[divm], beam_seq_logprobs_table[divm], beam_logprobs_sum_table[divm], state_table[divm]) # if time's up... or if end token is reached then copy beams for b in range(batch_size): is_end = beam_seq_table[divm][b, :, t-divm] == self.eos_idx assert beam_seq_table[divm].shape[-1] == t-divm+1 if t == self.seq_length + divm - 1: is_end.fill_(1) for vix in range(bdash): if is_end[vix]: final_beam = { 'seq': beam_seq_table[divm][b, vix].clone(), 'logps': beam_seq_logprobs_table[divm][b, vix].clone(), 'unaug_p': beam_seq_logprobs_table[divm][b, vix].sum().item(), 'p': beam_logprobs_sum_table[divm][b, vix].item() } final_beam['p'] = length_penalty(t-divm+1, final_beam['p']) done_beams_table[b][divm].append(final_beam) beam_logprobs_sum_table[divm][b, is_end] -= 1000 # move the current group one step forward in time it = beam_seq_table[divm][:, :, t-divm].reshape(-1).to(logprobs.device) logprobs_table[divm], state_table[divm] = self.get_logprobs_state(it, *(args[divm] + [state_table[divm]])) logprobs_table[divm] = F.log_softmax(logprobs_table[divm] / temperature, dim=-1) # all beams are sorted by their log-probabilities done_beams_table = [[sorted(done_beams_table[b][i], key=lambda x: -x['p'])[:bdash] for i in range(group_size)] for b in range(batch_size)] done_beams = [sum(_, []) for _ in done_beams_table] return done_beams def old_beam_search(self, init_state, init_logprobs, *args, **kwargs): # function computes the similarity score to be augmented def add_diversity(beam_seq_table, logprobsf, t, divm, diversity_lambda, bdash): local_time = t - divm unaug_logprobsf = logprobsf.clone() for prev_choice in range(divm): prev_decisions = beam_seq_table[prev_choice][local_time] for sub_beam in range(bdash): for prev_labels in range(bdash): logprobsf[sub_beam][prev_decisions[prev_labels]] = logprobsf[sub_beam][prev_decisions[prev_labels]] - diversity_lambda return unaug_logprobsf # does one step of classical beam search def beam_step(logprobsf, unaug_logprobsf, beam_size, t, beam_seq, beam_seq_logprobs, beam_logprobs_sum, state): #INPUTS: #logprobsf: probabilities augmented after diversity #beam_size: obvious #t : time instant #beam_seq : tensor contanining the beams #beam_seq_logprobs: tensor contanining the beam logprobs #beam_logprobs_sum: tensor contanining joint logprobs #OUPUTS: #beam_seq : tensor containing the word indices of the decoded captions #beam_seq_logprobs : log-probability of each decision made, same size as beam_seq #beam_logprobs_sum : joint log-probability of each beam ys,ix = torch.sort(logprobsf,1,True) candidates = [] cols = min(beam_size, ys.size(1)) rows = beam_size if t == 0: rows = 1 for c in range(cols): # for each column (word, essentially) for q in range(rows): # for each beam expansion #compute logprob of expanding beam q with word in (sorted) position c local_logprob = ys[q,c].item() candidate_logprob = beam_logprobs_sum[q] + local_logprob # local_unaug_logprob = unaug_logprobsf[q,ix[q,c]] candidates.append({'c':ix[q,c], 'q':q, 'p':candidate_logprob, 'r':unaug_logprobsf[q]}) candidates = sorted(candidates, key=lambda x: -x['p']) new_state = [_.clone() for _ in state] #beam_seq_prev, beam_seq_logprobs_prev if t >= 1: #we''ll need these as reference when we fork beams around beam_seq_prev = beam_seq[:t].clone() beam_seq_logprobs_prev = beam_seq_logprobs[:t].clone() for vix in range(beam_size): v = candidates[vix] #fork beam index q into index vix if t >= 1: beam_seq[:t, vix] = beam_seq_prev[:, v['q']] beam_seq_logprobs[:t, vix] = beam_seq_logprobs_prev[:, v['q']] #rearrange recurrent states for state_ix in range(len(new_state)): # copy over state in previous beam q to new beam at vix new_state[state_ix][:, vix] = state[state_ix][:, v['q']] # dimension one is time step #append new end terminal at the end of this beam beam_seq[t, vix] = v['c'] # c'th word is the continuation beam_seq_logprobs[t, vix] = v['r'] # the raw logprob here beam_logprobs_sum[vix] = v['p'] # the new (sum) logprob along this beam state = new_state return beam_seq,beam_seq_logprobs,beam_logprobs_sum,state,candidates # Start diverse_beam_search opt = kwargs['opt'] temperature = opt.get('temperature', 1) # This should not affect beam search, but will affect dbs beam_size = opt.get('beam_size', 10) group_size = opt.get('group_size', 1) diversity_lambda = opt.get('diversity_lambda', 0.5) decoding_constraint = opt.get('decoding_constraint', 0) remove_bad_endings = opt.get('remove_bad_endings', 0) suppress_UNK = opt.get('suppress_UNK', 0) length_penalty = utils.penalty_builder(opt.get('length_penalty', '')) bdash = beam_size // group_size # beam per group # INITIALIZATIONS beam_seq_table = [torch.LongTensor(self.seq_length, bdash).zero_() for _ in range(group_size)] beam_seq_logprobs_table = [torch.FloatTensor(self.seq_length, bdash, self.vocab_size + 1).zero_() for _ in range(group_size)] beam_logprobs_sum_table = [torch.zeros(bdash) for _ in range(group_size)] # logprobs # logprobs predicted in last time step, shape (beam_size, vocab_size+1) done_beams_table = [[] for _ in range(group_size)] # state_table = [list(torch.unbind(_)) for _ in torch.stack(init_state).chunk(group_size, 2)] state_table = list(zip(*[_.chunk(group_size, 1) for _ in init_state])) logprobs_table = list(init_logprobs.chunk(group_size, 0)) # END INIT # Chunk elements in the args args = list(args) if self.__class__.__name__ == 'AttEnsemble': args = [[_.chunk(group_size) if _ is not None else [None]*group_size for _ in args_] for args_ in args] # arg_name, model_name, group_name args = [[[args[j][i][k] for i in range(len(self.models))] for j in range(len(args))] for k in range(group_size)] # group_name, arg_name, model_name else: args = [_.chunk(group_size) if _ is not None else [None]*group_size for _ in args] args = [[args[i][j] for i in range(len(args))] for j in range(group_size)] for t in range(self.seq_length + group_size - 1): for divm in range(group_size): if t >= divm and t <= self.seq_length + divm - 1: # add diversity logprobsf = logprobs_table[divm] # suppress previous word if decoding_constraint and t-divm > 0: logprobsf.scatter_(1, beam_seq_table[divm][t-divm-1].unsqueeze(1).to(logprobsf.device), float('-inf')) if remove_bad_endings and t-divm > 0: logprobsf[torch.from_numpy(np.isin(beam_seq_table[divm][t-divm-1].cpu().numpy(), self.bad_endings_ix)), 0] = float('-inf') # suppress UNK tokens in the decoding if suppress_UNK and hasattr(self, 'vocab') and self.vocab[str(logprobsf.size(1)-1)] == 'UNK': logprobsf[:,logprobsf.size(1)-1] = logprobsf[:, logprobsf.size(1)-1] - 1000 # diversity is added here # the function directly modifies the logprobsf values and hence, we need to return # the unaugmented ones for sorting the candidates in the end. # for historical # reasons :-) unaug_logprobsf = add_diversity(beam_seq_table,logprobsf,t,divm,diversity_lambda,bdash) # infer new beams beam_seq_table[divm],\ beam_seq_logprobs_table[divm],\ beam_logprobs_sum_table[divm],\ state_table[divm],\ candidates_divm = beam_step(logprobsf, unaug_logprobsf, bdash, t-divm, beam_seq_table[divm], beam_seq_logprobs_table[divm], beam_logprobs_sum_table[divm], state_table[divm]) # if time's up... or if end token is reached then copy beams for vix in range(bdash): if beam_seq_table[divm][t-divm,vix] == self.eos_idx or t == self.seq_length + divm - 1: final_beam = { 'seq': beam_seq_table[divm][:, vix].clone(), 'logps': beam_seq_logprobs_table[divm][:, vix].clone(), 'unaug_p': beam_seq_logprobs_table[divm][:, vix].sum().item(), 'p': beam_logprobs_sum_table[divm][vix].item() } final_beam['p'] = length_penalty(t-divm+1, final_beam['p']) done_beams_table[divm].append(final_beam) # don't continue beams from finished sequences beam_logprobs_sum_table[divm][vix] = -1000 # move the current group one step forward in time it = beam_seq_table[divm][t-divm].to(logprobsf.device) logprobs_table[divm], state_table[divm] = self.get_logprobs_state(it, *(args[divm] + [state_table[divm]])) logprobs_table[divm] = F.log_softmax(logprobs_table[divm] / temperature, dim=-1) # all beams are sorted by their log-probabilities done_beams_table = [sorted(done_beams_table[i], key=lambda x: -x['p'])[:bdash] for i in range(group_size)] done_beams = sum(done_beams_table, []) return done_beams def sample_next_word(self, logprobs, sample_method, temperature): if sample_method == 'greedy': sampleLogprobs, it = torch.max(logprobs.data, 1) it = it.view(-1).long() elif sample_method == 'gumbel': # gumbel softmax # ref: https://gist.github.com/yzh119/fd2146d2aeb329d067568a493b20172f def sample_gumbel(shape, eps=1e-20): U = torch.rand(shape).to(logprobs.device) return -torch.log(-torch.log(U + eps) + eps) def gumbel_softmax_sample(logits, temperature): y = logits + sample_gumbel(logits.size()) return F.log_softmax(y / temperature, dim=-1) _logprobs = gumbel_softmax_sample(logprobs, temperature) _, it = torch.max(_logprobs.data, 1) sampleLogprobs = logprobs.gather(1, it.unsqueeze(1)) # gather the logprobs at sampled positions else: logprobs = logprobs / temperature if sample_method.startswith('top'): # topk sampling top_num = float(sample_method[3:]) if 0 < top_num < 1: # nucleus sampling from # The Curious Case of Neural Text Degeneration probs = F.softmax(logprobs, dim=1) sorted_probs, sorted_indices = torch.sort(probs, descending=True, dim=1) _cumsum = sorted_probs.cumsum(1) mask = _cumsum < top_num mask = torch.cat([torch.ones_like(mask[:,:1]), mask[:,:-1]], 1) sorted_probs = sorted_probs * mask.to(sorted_probs) sorted_probs = sorted_probs / sorted_probs.sum(1, keepdim=True) logprobs.scatter_(1, sorted_indices, sorted_probs.log()) else: the_k = int(top_num) tmp = torch.empty_like(logprobs).fill_(float('-inf')) topk, indices = torch.topk(logprobs, the_k, dim=1) tmp = tmp.scatter(1, indices, topk) logprobs = tmp it = torch.distributions.Categorical(logits=logprobs.detach()).sample() sampleLogprobs = logprobs.gather(1, it.unsqueeze(1)) # gather the logprobs at sampled positions return it, sampleLogprobs def decode_sequence(self, seq): return utils.decode_sequence(self.vocab, seq)