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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.init
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.backends.cudnn as cudnn
from torch.nn.utils.clip_grad import clip_grad_norm # clip_grad_norm_ for 0.4.0, clip_grad_norm for 0.3.1
import numpy as np
from collections import OrderedDict
import torch.nn.functional as F
from loss import TripletLoss, likelihoodBCEloss,unlikelihoodBCEloss
from util.bigfile import BigFile
def get_we_parameter(vocab, w2v_file):
w2v_reader = BigFile(w2v_file)
ndims = w2v_reader.ndims
we = []
# we.append([0]*ndims)
for i in range(len(vocab)):
try:
vec = w2v_reader.read_one(vocab.idx2word[i])
except:
vec = np.random.uniform(-1, 1, ndims)
we.append(vec)
print('getting pre-trained parameter for word embedding initialization', np.shape(we))
return np.array(we)
def l2norm(X):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()
X = torch.div(X, norm)
return X
def xavier_init_fc(fc):
"""Xavier initialization for the fully connected layer
"""
r = np.sqrt(6.) / np.sqrt(fc.in_features +
fc.out_features)
fc.weight.data.uniform_(-r, r)
fc.bias.data.fill_(0)
class MFC(nn.Module):
"""
Multi Fully Connected Layers
"""
def __init__(self, fc_layers, dropout, have_dp=True, have_bn=False, have_last_bn=False):
super(MFC, self).__init__()
# fc layers
self.n_fc = len(fc_layers)
if self.n_fc > 1:
if self.n_fc > 1:
self.fc1 = nn.Linear(fc_layers[0], fc_layers[1])
# dropout
self.have_dp = have_dp
if self.have_dp:
self.dropout = nn.Dropout(p=dropout)
# batch normalization
self.have_bn = have_bn
self.have_last_bn = have_last_bn
if self.have_bn:
if self.n_fc == 2 and self.have_last_bn:
self.bn_1 = nn.BatchNorm1d(fc_layers[1])
self.init_weights()
def init_weights(self):
"""Xavier initialization for the fully connected layer
"""
if self.n_fc > 1:
xavier_init_fc(self.fc1)
def forward(self, inputs):
if self.n_fc <= 1:
features = inputs
elif self.n_fc == 2:
features = self.fc1(inputs)
# batch noarmalization
if self.have_bn and self.have_last_bn:
features = self.bn_1(features)
if self.have_dp:
features = self.dropout(features)
return features
class Video_encoder(nn.Module):
"""
Section 3.1. Video-side Multi-level Encoding
"""
def __init__(self, opt):
super(Video_encoder, self).__init__()
self.rnn_output_size = opt.visual_rnn_size * 2
self.dropout = nn.Dropout(p=opt.dropout)
self.visual_norm = opt.visual_norm
self.concate = opt.vconcate
# visual bidirectional rnn encoder
self.rnn = nn.GRU(opt.visual_feat_dim, opt.visual_rnn_size, batch_first=True, bidirectional=True)
# visual 1-d convolutional network
self.convs1 = nn.ModuleList([
nn.Conv2d(1, opt.visual_kernel_num, (window_size, self.rnn_output_size), padding=(window_size - 1, 0))
for window_size in opt.visual_kernel_sizes
])
# visual mapping
self.visual_mapping = MFC(opt.visual_mapping_layers, opt.dropout, have_bn=True, have_last_bn=True)
def forward(self, videos):
"""Extract video feature vectors."""
videos, motions,videos_origin, lengths, vidoes_mask = videos
# Level 1. Global Encoding by Mean Pooling According
org_out = videos_origin
# Level 2. Temporal-Aware Encoding by biGRU
gru_init_out, _ = self.rnn(videos)
mean_gru = Variable(torch.zeros(gru_init_out.size(0), self.rnn_output_size)).cuda()
for i, batch in enumerate(gru_init_out):
mean_gru[i] = torch.mean(batch[:lengths[i]], 0)
gru_out = mean_gru
gru_out = self.dropout(gru_out)
# Level 3. Local-Enhanced Encoding by biGRU-CNN
vidoes_mask = vidoes_mask.unsqueeze(2).expand(-1, -1, gru_init_out.size(2)) # (N,C,F1)
gru_init_out = gru_init_out * vidoes_mask
con_out = gru_init_out.unsqueeze(1)
con_out = [F.relu(conv(con_out)).squeeze(3) for conv in self.convs1]
con_out = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in con_out]
con_out = torch.cat(con_out, 1)
con_out = self.dropout(con_out)
##level 4 motion feature (e.g., slowfast)
motion_out = motions
# concatenation
if self.concate == 'full': # level 1+2+3
features = torch.cat((gru_out, con_out, org_out,motion_out), 1)
elif self.concate == 'reduced': # level 2+3
features = torch.cat((gru_out, con_out), 1)
# mapping to common space
features = self.visual_mapping(features)
if self.visual_norm:
features = l2norm(features)
return features
def load_state_dict(self, state_dict):
"""Copies parameters. overwritting the default one to
accept state_dict from Full model
"""
own_state = self.state_dict()
new_state = OrderedDict()
for name, param in state_dict.items():
if name in own_state:
new_state[name] = param
super(Video_encoder, self).load_state_dict(new_state)
class Text_encoder(nn.Module):
"""
Section 3.2. Text-side Multi-level Encoding
"""
def __init__(self, opt):
super(Text_encoder, self).__init__()
self.text_norm = opt.text_norm
self.dropout = nn.Dropout(p=opt.dropout)
self.tconcate = opt.tconcate
# multi fc layers
self.text_mapping = MFC(opt.text_mapping_layers, opt.dropout, have_bn=True, have_last_bn=True)
self.word_dim = opt.word_dim
self.we_parameter = opt.we_parameter
self.rnn_output_size = opt.text_rnn_size * 2
# visual bidirectional rnn encoder
self.embed = nn.Embedding(opt.vocab_size, opt.word_dim)
self.rnn = nn.GRU(opt.word_dim, opt.text_rnn_size, batch_first=True, bidirectional=True)
# visual 1-d convolutional network
self.convs1 = nn.ModuleList([
nn.Conv2d(1, opt.text_kernel_num, (window_size, self.rnn_output_size), padding=(window_size - 1, 0))
for window_size in opt.text_kernel_sizes
])
self.init_weights()
def init_weights(self):
if self.word_dim == 500 and self.we_parameter is not None:
self.embed.weight.data.copy_(torch.from_numpy(self.we_parameter))
else:
self.embed.weight.data.uniform_(-0.1, 0.1)
def forward(self, text, *args):
# Embed word ids to vectors
# cap_wids, cap_w2vs, cap_bows, cap_mask = x
cap_wids, cap_bows, lengths, cap_mask = text
org_out = cap_bows
# Level 2. Temporal-Aware Encoding by biGRU
cap_wids = self.embed(cap_wids)
packed = pack_padded_sequence(cap_wids, lengths, batch_first=True)
gru_init_out, _ = self.rnn(packed)
# Reshape *final* output to (batch_size, hidden_size)
padded = pad_packed_sequence(gru_init_out, batch_first=True)
gru_init_out = padded[0]
gru_out = Variable(torch.zeros(padded[0].size(0), self.rnn_output_size)).cuda()
for i, batch in enumerate(padded[0]):
gru_out[i] = torch.mean(batch[:lengths[i]], 0)
gru_out = self.dropout(gru_out)
# Level 3. Local-Enhanced Encoding by biGRU-CNN
con_out = gru_init_out.unsqueeze(1)
con_out = [F.relu(conv(con_out)).squeeze(3) for conv in self.convs1]
con_out = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in con_out]
con_out = torch.cat(con_out, 1)
con_out = self.dropout(con_out)
# concatenation
features = torch.cat((gru_out, con_out, org_out), 1)
# mapping to common space
features = self.text_mapping(features)
if self.text_norm:
features = l2norm(features)
if np.sum(np.isnan(features.data.cpu().numpy())) > 0:
print('features is nan')
return features
class BaseModel(object):
def state_dict(self):
state_dict = [self.vid_encoder.state_dict(), self.text_encoder.state_dict() ,self.unify_decoder.state_dict()]
return state_dict
def load_state_dict(self, state_dict):
self.vid_encoder.load_state_dict(state_dict[0])
self.text_encoder.load_state_dict(state_dict[1])
self.unify_decoder.load_state_dict(state_dict[2])
def forward_loss(self, cap_emb, vid_emb ,pred_vid_class ,pred_text_class ,class_label ,*agrs, **kwargs):
# 1. Compute the triplet loss given pairs of video and caption embeddings
matching_loss = self.matching_loss(cap_emb, vid_emb)
# 2. Compute the likelihood loss of concept decoding loss
labels = Variable(class_label, requires_grad=False) ##cap_bow may have value larger than 1
if torch.cuda.is_available():
labels = labels.cuda()
likelihoodLoss_vid = self.likelihoodLoss(pred_vid_class ,labels)
likelihoodLoss_text = self.likelihoodLoss(pred_text_class, labels)
likelihoodLoss = likelihoodLoss_vid +likelihoodLoss_text
loss = matching_loss + likelihoodLoss
# 3. Compute the unlikelihood loss of concept decoding loss
if self.unlikelihood and not self.contradicted_matrix_sp is None :
unlikelihoodLoss_vid = self.unlikelihoodLoss(pred_vid_class ,labels)
unlikelihoodLoss_text = self.unlikelihoodLoss(pred_text_class ,labels)
unlikelihoodLoss = unlikelihoodLoss_vid +unlikelihoodLoss_text
loss = loss +self.ul_alpha *unlikelihoodLoss
self.logger.update('Le', loss.item(), vid_emb.size(0))
if self.unlikelihood and not self.contradicted_matrix_sp is None :
return loss, matching_loss, likelihoodLoss,likelihoodLoss_vid, likelihoodLoss_text,unlikelihoodLoss,unlikelihoodLoss_vid,unlikelihoodLoss_text
return loss, matching_loss, likelihoodLoss,likelihoodLoss_vid, likelihoodLoss_text
def train_dualtask(self, videos, captions, class_label ,captions_text ,lengths, *args):
"""One training step given videos and captions.
"""
self.Eiters += 1
self.logger.update('Eit', self.Eiters)
self.logger.update('lr', self.optimizer.param_groups[0]['lr'])
# compute the embeddings
vid_emb, cap_emb = self.forward_matching(videos, captions, False)
pred_vid_class ,pred_text_class = self.forward_classification(vid_emb ,cap_emb ,False)
# measure accuracy and record loss
self.optimizer.zero_grad()
if self.unlikelihood:
loss, loss_matching, likelihoodLoss,likelihoodLoss_vid, likelihoodLoss_text,unlikelihoodLoss, unlikelihoodLoss_vid,unlikelihoodLoss_text = self.forward_loss \
(cap_emb, vid_emb, pred_vid_class ,pred_text_class, class_label)
else:
loss, loss_matching, likelihoodLoss,likelihoodLoss_vid, likelihoodLoss_text= self.forward_loss(cap_emb, vid_emb,pred_vid_class,pred_text_class,class_label)
loss_value = loss.item()
loss_matching_value = loss_matching.item()
likelihoodLoss_value = likelihoodLoss.item()
likelihoodloss_vid_value = likelihoodLoss_vid.item()
likelihoodloss_text_value = likelihoodLoss_text.item()
if self.unlikelihood:
unlikelihoodLoss_value = unlikelihoodLoss.item()
unlikelihoodLoss_vid_value = unlikelihoodLoss_vid.item()
unlikelihoodLoss_text_value = unlikelihoodLoss_text.item()
# compute gradient and do SGD step
loss.backward()
if self.grad_clip > 0:
clip_grad_norm(self.params, self.grad_clip)
self.optimizer.step()
if self.unlikelihood:
return vid_emb.size(0), loss_value, loss_matching_value, likelihoodLoss_value,likelihoodloss_vid_value ,likelihoodloss_text_value ,unlikelihoodLoss_value ,unlikelihoodLoss_vid_value ,unlikelihoodLoss_text_value
else:
return vid_emb.size(0), loss_value ,loss_matching_value,likelihoodLoss_value ,likelihoodloss_vid_value ,likelihoodloss_text_value
class ITV(BaseModel):
"""
ITV network
"""
def __init__(self, opt):
# Build Models
self.modelname = opt.postfix
self.grad_clip = opt.grad_clip
self.vid_encoder = Video_encoder(opt)
self.text_encoder = Text_encoder(opt)
self.decoder_num_layer=len(opt.decoder_mapping_layers)
if 'ul_type' in opt:
self.ul_type = opt.ul_type
if len(opt.decoder_mapping_layers)==2:
self.unify_decoder =MFC(opt.decoder_mapping_layers, opt.dropout, have_bn=True, have_last_bn=True)
elif len(opt.decoder_mapping_layers)==3:
mapping_layer1 = [opt.decoder_mapping_layers[0],opt.decoder_mapping_layers[1]]
mapping_layer2 = [opt.decoder_mapping_layers[1],opt.decoder_mapping_layers[2]]
self.unify_decoder=nn.ModuleList([MFC(mapping_layer1, opt.dropout, have_bn=False, have_last_bn=False),
MFC(mapping_layer2, opt.dropout, have_bn=True,
have_last_bn=True)])
else:
NotImplemented
self.sigmod = nn.Sigmoid()
self.loss_type = opt.loss_type
self.unlikelihood=opt.unlikelihood
self.ul_alpha = opt.ul_alpha
self.concept = opt.concept
if self.unlikelihood:
self.contradicted_matrix_sp = opt.contradicted_matrix_sp
print(self.vid_encoder)
print(self.text_encoder)
print(self.unify_decoder)
print(self.sigmod)
if torch.cuda.is_available():
self.vid_encoder.cuda()
self.text_encoder.cuda()
self.unify_decoder.cuda()
cudnn.benchmark = True
# Loss and Optimizer
self.matching_loss = TripletLoss(margin=opt.margin,
measure=opt.measure,
max_violation=opt.max_violation,
cost_style=opt.cost_style,
direction=opt.direction)
self.likelihoodLoss = likelihoodBCEloss(opt.multiclass_loss_lamda,opt.cost_style)
if self.unlikelihood:
self.unlikelihoodLoss = unlikelihoodBCEloss(self.contradicted_matrix_sp,opt.cost_style)
params_end_text = list(self.text_encoder.parameters())
params_end_vid = list(self.vid_encoder.parameters())
params_unify_dec = list(self.unify_decoder.parameters())
self.params_end_text = params_end_text
self.params_end_vid = params_end_vid
self.params_unify_dec=params_unify_dec
params= params_end_text+params_end_vid+params_unify_dec
self.params = params
if opt.optimizer == 'adam':
self.optimizer = torch.optim.Adam(params, lr=opt.learning_rate)
elif opt.optimizer == 'rmsprop':
self.optimizer = torch.optim.RMSprop(params, lr=opt.learning_rate)
self.Eiters = 0
def forward_matching(self, videos, targets, volatile=False, *args):
"""Compute the video and caption embeddings
"""
# video data
frames,motions,mean_origin, video_lengths, vidoes_mask = videos
frames = Variable(frames, requires_grad=True)
if volatile:
with torch.no_grad():
frames = Variable(frames)
if torch.cuda.is_available():
frames = frames.cuda()
motions = Variable(motions, requires_grad=True)
if volatile:
with torch.no_grad():
motions = Variable(motions)
if torch.cuda.is_available():
motions = motions.cuda()
mean_origin = Variable(mean_origin, requires_grad=True)
if volatile:
with torch.no_grad():
mean_origin = Variable(mean_origin)
if torch.cuda.is_available():
mean_origin = mean_origin.cuda()
vidoes_mask = Variable(vidoes_mask, requires_grad=True)
if volatile:
with torch.no_grad():
vidoes_mask = Variable(vidoes_mask)
if torch.cuda.is_available():
vidoes_mask = vidoes_mask.cuda()
videos_data = (frames,motions, mean_origin, video_lengths, vidoes_mask)
# text data
captions, cap_bows, lengths, cap_masks = targets
if captions is not None:
captions = Variable(captions)
if volatile:
with torch.no_grad():
captions = Variable(captions)
if torch.cuda.is_available():
captions = captions.cuda()
if cap_bows is not None:
cap_bows = Variable(cap_bows)
if volatile:
with torch.no_grad():
cap_bows = Variable(cap_bows)
if torch.cuda.is_available():
cap_bows = cap_bows.cuda()
if cap_masks is not None:
cap_masks = Variable(cap_masks)
if volatile:
with torch.no_grad():
cap_masks = Variable(cap_masks)
if torch.cuda.is_available():
cap_masks = cap_masks.cuda()
text_data = (captions, cap_bows, lengths, cap_masks)
vid_emb = self.vid_encoder(videos_data)
cap_emb = self.text_encoder(text_data)
return vid_emb, cap_emb
def forward_classification(self, vid_embs,text_embs, volatile=False, *args):
"""Compute the video and caption embeddings
"""
if self.decoder_num_layer>2:
for decod in self.unify_decoder:
vid_embs = decod(vid_embs)
text_embs = decod(text_embs)
else:
text_embs=self.unify_decoder(text_embs)
vid_embs = self.unify_decoder(vid_embs)
pred_vid=self.sigmod(vid_embs)
pred_text=self.sigmod(text_embs)
return pred_vid,pred_text
def embed_vis(self, vis_data, volatile=True,sigmoid_output=True):
# video data
frames, motions,mean_origin, video_lengths, vidoes_mask = vis_data
if volatile:
with torch.no_grad():
frames = Variable(frames)
else:
frames = Variable(frames, requires_grad=True)
if torch.cuda.is_available():
frames = frames.cuda()
if volatile:
with torch.no_grad():
motions = Variable(motions)
else:
motions = Variable(motions, requires_grad=True)
if torch.cuda.is_available():
motions = motions.cuda()
if volatile:
with torch.no_grad():
mean_origin = Variable(mean_origin)
else:
mean_origin = Variable(mean_origin, requires_grad=True)
if torch.cuda.is_available():
mean_origin = mean_origin.cuda()
if volatile:
with torch.no_grad():
vidoes_mask = Variable(vidoes_mask)
else:
vidoes_mask = Variable(vidoes_mask, requires_grad=True)
if torch.cuda.is_available():
vidoes_mask = vidoes_mask.cuda()
vis_data = (frames, motions,mean_origin, video_lengths, vidoes_mask)
embs = self.vid_encoder(vis_data)
pred= self.vid_encoder(vis_data)
if self.decoder_num_layer > 2:
for decod in self.unify_decoder:
pred = decod(pred)
else:
pred=self.unify_decoder(pred)
sigmoid_out=self.sigmod(pred)
if sigmoid_output:
return embs,sigmoid_out
else:
return embs
def embed_vis_emb_only(self, vis_data, volatile=True):
# video data
frames, motions,mean_origin, video_lengths, vidoes_mask = vis_data
frames = Variable(frames, volatile=volatile)
if torch.cuda.is_available():
frames = frames.cuda()
motions = Variable(motions, volatile=volatile)
if torch.cuda.is_available():
motions = motions.cuda()
mean_origin = Variable(mean_origin, volatile=volatile)
if torch.cuda.is_available():
mean_origin = mean_origin.cuda()
vidoes_mask = Variable(vidoes_mask, volatile=volatile)
if torch.cuda.is_available():
vidoes_mask = vidoes_mask.cuda()
vis_data = (frames,motions, mean_origin, video_lengths, vidoes_mask)
embs = self.vid_encoder(vis_data)
return embs
def embed_vis_concept_only(self, vis_data, volatile=True):
# video data
frames, motions,mean_origin, video_lengths, vidoes_mask = vis_data
if volatile:
with torch.no_grad:
frames = Variable(frames)
else:
frames = Variable(frames, requires_grad=True)
if torch.cuda.is_available():
frames = frames.cuda()
if volatile:
with torch.no_grad:
motions = Variable(motions)
else:
motions = Variable(motions, requires_grad=True)
if torch.cuda.is_available():
motions = motions.cuda()
if volatile:
with torch.no_grad:
mean_origin = Variable(mean_origin)
else:
mean_origin = Variable(mean_origin, requires_grad=True)
if torch.cuda.is_available():
mean_origin = mean_origin.cuda()
if volatile:
with torch.no_grad:
vidoes_mask = Variable(vidoes_mask)
else:
vidoes_mask = Variable(vidoes_mask, requires_grad=True)
if torch.cuda.is_available():
vidoes_mask = vidoes_mask.cuda()
vis_data = (frames,motions, mean_origin, video_lengths, vidoes_mask)
vid_embs = self.vid_encoder(vis_data)
if self.decoder_num_layer > 2:
for decod in self.unify_decoder:
vid_embs = decod(vid_embs)
else:
vid_embs=self.unify_decoder(vid_embs)
sigmoid_out=self.sigmod(vid_embs)
return sigmoid_out
def embed_txt(self, txt_data, volatile=True,sigmoid_output=False):
# text data
captions, cap_bows, lengths, cap_masks = txt_data
if captions is not None:
captions = Variable(captions, volatile=volatile)
if torch.cuda.is_available():
captions = captions.cuda()
if cap_bows is not None:
cap_bows = Variable(cap_bows, volatile=volatile)
if torch.cuda.is_available():
cap_bows = cap_bows.cuda()
if cap_masks is not None:
cap_masks = Variable(cap_masks, volatile=volatile)
if torch.cuda.is_available():
cap_masks = cap_masks.cuda()
txt_data = (captions, cap_bows, lengths, cap_masks)
text_emb = self.text_encoder(txt_data)
if sigmoid_output:
pred = self.text_encoder(txt_data)
if self.decoder_num_layer > 2:
for decod in self.unify_decoder:
pred = decod(pred)
else:
pred=self.unify_decoder(pred)
sigmoid_out=self.sigmod(pred)
return text_emb, sigmoid_out
else:
return text_emb
def embed_txt_concept_only(self, txt_data, volatile=True):
# text data
captions, cap_bows, lengths, cap_masks = txt_data
if captions is not None:
captions = Variable(captions, volatile=volatile)
if torch.cuda.is_available():
captions = captions.cuda()
if cap_bows is not None:
cap_bows = Variable(cap_bows, volatile=volatile)
if torch.cuda.is_available():
cap_bows = cap_bows.cuda()
if cap_masks is not None:
cap_masks = Variable(cap_masks, volatile=volatile)
if torch.cuda.is_available():
cap_masks = cap_masks.cuda()
txt_data = (captions, cap_bows, lengths, cap_masks)
text_emb = self.text_encoder(txt_data)
if self.decoder_num_layer > 2:
for decod in self.unify_decoder:
text_emb = decod(text_emb)
else:
text_emb=self.unify_decoder(text_emb)
sigmoid_out=self.sigmod(text_emb)
return sigmoid_out