def exe_test(sess,
data,
batch_size,
v2i,
i2v,
hf,
feature_shape,
predict_words,
input_video,
input_captions,
y,
capl=16):
caption_output = []
total_data = len(data)
num_batch = int(round(total_data * 1.0 / batch_size)) + 1
for batch_idx in xrange(num_batch):
batch_caption = data[batch_idx *
batch_size:min((batch_idx + 1) *
batch_size, total_data)]
data_v = MsrDataUtil.getBatchStepVideoFeature(batch_caption, hf,
feature_shape)
data_c, data_y = MsrDataUtil.getBatchTestCaption(batch_caption,
v2i,
capl=capl)
[gw] = sess.run([predict_words],
feed_dict={
input_video: data_v,
input_captions: data_c,
y: data_y
})
generated_captions = MsrDataUtil.convertCaptionI2V(
batch_caption, gw, i2v)
for idx, sen in enumerate(generated_captions):
print('%s : %s' % (batch_caption[idx].keys()[0], sen))
caption_output.append({
'image_id': batch_caption[idx].keys()[0],
'caption': sen
})
js = {}
js['val_predictions'] = caption_output
return js
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.getBatchStepVideoFeature(batch_caption, hf,
feature_shape)
data_c, data_y = MsrDataUtil.getNewBatchTrainCaption(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
