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133 lines (104 loc) · 4.72 KB
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import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as functional
from layers.wordspretrained import PretrainedEmbeddings
from layers.videocaptiondecoder import VideoCaptionDecoder
from layers.videoframeencoder import VideoFrameEncoder
import layers.utils as utils
class STAL(nn.Module):
def __init__(self, dict_args):
super(STAL, self).__init__()
self.word_embeddings = dict_args["word_embeddings"]
self.pretrained_embdim = dict_args["word_embdim"]
self.vocabulary_size = dict_args["vocabulary_size"]
self.encoder_configuration = dict_args["encoder_configuration"]
self.decoder_rnn_word_dim = dict_args["decoder_rnn_word_dim"]
self.decoder_rnn_input_dim = dict_args["decoder_rnn_input_dim"]
self.decoder_rnn_hidden_dim = dict_args["decoder_rnn_hidden_dim"]
self.decoder_rnn_type = dict_args["decoder_rnn_type"]
self.every_step = dict_args["every_step"]
self.decoder_top_dropout_rate = dict_args["decoder_top_dropout_rate"]
self.decoder_bottom_dropout_rate = dict_args["decoder_bottom_dropout_rate"]
self.decoder_residual_connection = dict_args["residual_connection"]
#PretrainedWordsLayer
pretrained_words_layer_args = dict_args
self.pretrained_words_layer = PretrainedEmbeddings(pretrained_words_layer_args)
frame_encoder_layer_args = dict_args
#FrameEncoderLayer
self.frame_encoder_layer = VideoFrameEncoder(frame_encoder_layer_args)
#SentenceDecoderLayer
sentence_decoder_layer_args = {
'word_dim' : self.decoder_rnn_word_dim,
'input_dim' : self.decoder_rnn_input_dim,
'rnn_hdim' : self.decoder_rnn_hidden_dim,
'rnn_type' : self.decoder_rnn_type,
'vocabulary_size' : self.vocabulary_size,
'every_step': self.every_step,
'top_dropout_rate' : self.decoder_top_dropout_rate,
'bottom_dropout_rate' : self.decoder_bottom_dropout_rate,
'residual_connection' : self.decoder_residual_connection
}
self.sentence_decoder_layer = VideoCaptionDecoder(sentence_decoder_layer_args)
def forward(self, videoframes, videoframes_lengths, inputwords=None, captionwords_lengths=None):
#videoframes : batch_size*num_frames*256*3*3
#videoframes_lengths : batch_size
#inputwords : batch_size*num_words
#outputwords : batch_size*num_words
#captionwords_lengths : batch_size
#Remove additional padding after truncation
videoframes = videoframes[:,0:videoframes_lengths.data.max()].contiguous()
if self.training: inputword_vectors = self.pretrained_words_layer(inputwords)
#inputword_vectors: batch_size*num_words*wembed_dim
if not self.training:
return self.sentence_decoder_layer.inference(
self.frame_encoder_layer,
videoframes,
videoframes_lengths,
self.pretrained_words_layer
)
outputword_log_probabilities = self.sentence_decoder_layer(
inputword_vectors,
self.frame_encoder_layer,
videoframes,
videoframes_lengths
)
return outputword_log_probabilities #batch_size*num_words*vocab_size
if __name__=='__main__':
pretrained_wordvecs = torch.randn(10,3)
glove_embdim = 3
hidden_dim = 12
dict_args = {
"word_embeddings" : pretrained_wordvecs,
"word_embdim" : glove_embdim,
"use_pretrained_emb" : True,
"backprop_embeddings" : False,
"vocabulary_size" : len(pretrained_wordvecs),
"encoder_configuration" : 'LSTMTrackSpatialTemporal',
"frame_channel_dim" : 256,
"frame_spatial_dim" : 2,
"encoder_rnn_type" : 'LSTM',
"encoder_rnn_hdim" : hidden_dim,
"encoder_dropout_rate" : 0.2,
"encoderattn_projection_dim" : hidden_dim/2,
"encoderattn_query_dim" : hidden_dim,
"decoder_rnn_word_dim" : glove_embdim,
"decoder_rnn_input_dim" : hidden_dim + hidden_dim, #channel_dim
"decoder_rnn_hidden_dim" : hidden_dim,
"decoder_rnn_type" : 'LSTM',
"every_step" : True,
"decoder_top_dropout_rate" : 0.2,
"decoder_bottom_dropout_rate" : 0.2,
"residual_connection" : True
}
stal = STAL(dict_args)
videoframes = Variable(torch.randn(1, 4, 256, 2, 2))
videoframes_lengths = Variable(torch.LongTensor([3]))
inputwords = Variable(torch.LongTensor([[2,3,5]]))
#outputwords = Variable(torch.LongTensor([[3,5,2], [9,7,1]]))
captionwords_lengths = Variable(torch.LongTensor([3]))
outputword_log_probabilities = stal(videoframes, videoframes_lengths, inputwords, captionwords_lengths)
#print(outputword_log_probabilities)
stal.eval()
outputword_log_probabilities = stal(videoframes, videoframes_lengths, inputwords, captionwords_lengths)
print(outputword_log_probabilities)