def exe_train(sess,
data,
cate_info,
batch_size,
v2i,
hf1,
hf2,
feature_shape1,
feature_shape2,
train,
loss,
input_video1,
input_video2,
input_captions,
input_categories,
y,
capl=16):
np.random.shuffle(data)
total_data = len(data)
num_batch = int(round(total_data * 1.0 / batch_size))
total_loss = 0.0
for batch_idx in xrange(num_batch):
# for batch_idx in xrange(500):
# if batch_idx < 100:
batch_caption = data[batch_idx *
batch_size:min((batch_idx + 1) *
batch_size, total_data)]
data_v1 = MsrDataUtil.getBatchVideoFeature(batch_caption, hf1,
feature_shape1)
data_v2 = MsrDataUtil.getBatchC3DVideoFeature(batch_caption, hf2,
feature_shape2)
flag = np.random.randint(0, 2)
if flag == 1:
data_v1 = data_v1[:, ::-1, :]
data_v2 = data_v2[:, ::-1, :]
data_c, data_y = MsrDataUtil.getBatchTrainCaptionWithSparseLabel(
batch_caption, v2i, capl=capl)
data_cate = MsrDataUtil.getBatchVideoCategoriesInfo(
batch_caption, cate_info, feature_shape1)
_, l = sess.run(
[train, loss],
feed_dict={
input_video1: data_v1,
input_video2: data_v2,
input_captions: data_c,
input_categories: data_cate,
y: data_y
})
total_loss += l
print(' batch_idx:%d/%d, loss:%.5f' % (batch_idx + 1, num_batch, l))
total_loss = total_loss / num_batch
return total_loss
def exe_train(sess, data, batch_size, v2i, hf1, hf2, feature_shape,
train, loss, input_video, input_captions, y, capl=16):
np.random.shuffle(data)
total_data = len(data)
num_batch = int(round(total_data*1.0/batch_size))
total_loss = 0.0
for batch_idx in xrange(num_batch):
# for batch_idx in xrange(500):
# if batch_idx < 100:
batch_caption = data[batch_idx*batch_size:min((batch_idx+1)*batch_size,total_data)]
data_v1 = MsrDataUtil.getBatchVideoFeature(batch_caption,hf1,(feature_shape[0],2048))
data_v2 = MsrDataUtil.getBatchC3DVideoFeature(batch_caption,hf2,(feature_shape[0],4096))
# data_v1 = data_v1/(np.linalg.norm(data_v1, ord=None, axis=-1, keepdims=True)+sys.float_info.epsilon)
# data_v2 = data_v2/(np.linalg.norm(data_v2, ord=None, axis=-1, keepdims=True)+sys.float_info.epsilon)
data_v = np.concatenate((data_v1,data_v2),axis=-1)
data_c, data_y = MsrDataUtil.getBatchTrainCaptionWithSparseLabel(batch_caption, v2i, capl=capl)
_, l = sess.run([train,loss],feed_dict={input_video:data_v, input_captions:data_c, y:data_y})
total_loss += l
print(' batch_idx:%d/%d, loss:%.5f' %(batch_idx+1,num_batch,l))
total_loss = total_loss/num_batch
return total_loss
def exe_train(sess,
data,
audio_info,
cate_info,
batch_size,
v2i,
hf,
feature_shape,
train,
loss,
input_video,
input_captions,
input_categories,
input_audio,
y,
capl=16):
np.random.shuffle(data)
total_data = len(data)
num_batch = int(round(total_data * 1.0 / batch_size))
total_loss = 0.0
for batch_idx in xrange(num_batch):
# for batch_idx in xrange(500):
# if batch_idx < 100:
batch_caption = data[batch_idx *
batch_size:min((batch_idx + 1) *
batch_size, total_data)]
data_v = MsrDataUtil.getBatchVideoFeature(batch_caption, hf,
feature_shape)
data_c, data_y = MsrDataUtil.getBatchTrainCaptionWithSparseLabel(
batch_caption, v2i, capl=capl)
data_cate = MsrDataUtil.getBatchVideoCategoriesInfo(
batch_caption, cate_info, feature_shape)
data_audio = MsrDataUtil.getBatchVideoAudioInfo(
batch_caption, audio_info, feature_shape)
_, l = sess.run(
[train, loss],
feed_dict={
input_video: data_v,
input_captions: data_c,
input_categories: data_cate,
input_audio: data_audio,
y: data_y
})
total_loss += l
print(' batch_idx:%d/%d, loss:%.5f' % (batch_idx + 1, num_batch, l))
total_loss = total_loss / num_batch
return total_loss
def exe_train(sess,
data,
batch_size,
v2i,
hf,
feature_shape,
train,
loss,
input_video,
input_captions,
y,
capl=16):
np.random.shuffle(data)
total_data = len(data)
num_batch = int(round(total_data * 1.0 / batch_size))
total_loss = 0.0
for batch_idx in xrange(num_batch):
# for batch_idx in xrange(500):
# if batch_idx < 100:
batch_caption = data[batch_idx *
batch_size:min((batch_idx + 1) *
batch_size, total_data)]
data_v = MsrDataUtil.getBatchVideoFeature(batch_caption, hf,
feature_shape)
flag = np.random.randint(0, 2)
if flag == 1:
data_v = data_v[:, ::-1, :]
data_c, data_y = MsrDataUtil.getBatchTrainCaptionWithSparseLabel(
batch_caption, v2i, capl=capl)
_, l = sess.run([train, loss],
feed_dict={
input_video: data_v,
input_captions: data_c,
y: data_y
})
total_loss += l
print(' batch_idx:%d/%d, loss:%.5f' % (batch_idx + 1, num_batch, l))
total_loss = total_loss / num_batch
return total_loss
