def beam_search_exe_test(sess, data, cate_info, batch_size, v2i, i2v, hf, feature_shape, predict_words, input_video, input_captions, input_categories, y, finished_beam, logprobs_finished_beams, past_logprobs, capl=16): caption_output = [] total_data = len(data) num_batch = int(round(total_data * 1.0 / batch_size)) 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.getBatchVideoFeature(batch_caption, hf, feature_shape) data_c, data_y = MsrDataUtil.getBatchTestCaptionWithSparseLabel( batch_caption, v2i, capl=capl) data_cate = MsrDataUtil.getBatchVideoCategoriesInfo( batch_caption, cate_info, feature_shape) [gw, tw, gp, gl] = sess.run( [ finished_beam, predict_words, logprobs_finished_beams, past_logprobs ], feed_dict={ input_video: data_v, input_captions: data_c, input_categories: data_cate, 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_test(sess, data, batch_size, v2i, i2v, hf, feature_shape, predict_words, input_video, input_captions, y, finished_beam, logprobs_finished_beams, past_logprobs, beam_hidden_state, past_symbols_states, finished_beams_states, capl=16): caption_output = [] total_data = len(data) num_batch = int(round(total_data*1.0/batch_size)) 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.getBatchVideoFeature(batch_caption,hf,feature_shape) data_c, data_y = MsrDataUtil.getBatchTestCaption(batch_caption, v2i, capl=capl) [gw,tw,gp, gl, pp, pss, fbs] = sess.run([finished_beam, predict_words, logprobs_finished_beams, past_logprobs, beam_hidden_state, past_symbols_states, finished_beams_states],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(gw[idx]) print('%s : %s' %(batch_caption[idx].keys()[0],sen)) # print(pss) # print(fbs) # print(gp) # print(gl) # print(pp) caption_output.append({'image_id':batch_caption[idx].keys()[0],'caption':sen}) js = {} js['val_predictions'] = caption_output return js
def exe_test(sess, data, batch_size, v2i, i2v, hf1, hf2, hf3, feature_shape1, feature_shape2, predict_words, input_video1, input_video2, input_video3, 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_v1 = MsrDataUtil.getBatchVideoFeature(batch_caption, hf1, feature_shape1) data_v2 = MsrDataUtil.getBatchVideoFeature(batch_caption, hf2, feature_shape2) data_v3 = MsrDataUtil.getBatchVideoFeature(batch_caption, hf3, feature_shape3) data_c, data_y = MsrDataUtil.getBatchTestCaptionWithSparseLabel( batch_caption, v2i, capl=capl) [gw] = sess.run( [predict_words], feed_dict={ input_video1: data_v1, input_video2: data_v2, input_video3: data_v3, 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_test(sess, data, batch_size, v2i, i2v, hf, feature_shape, predict_words, input_video, input_captions, y, predict_words2, capl=16): caption_output = [] total_data = len(data) num_batch = int(round(total_data * 1.0 / batch_size)) 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.getBatchVideoFeature(batch_caption, hf, feature_shape) data_c, data_y = MsrDataUtil.getBatchTestCaption(batch_caption, v2i, capl=capl) [gw, tw] = sess.run([predict_words, predict_words2], feed_dict={ input_video: data_v, input_captions: data_c, y: data_y }) # print(gw) # print(tp) # print(atp) 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_test(sess, data, batch_size, v2i, i2v, hf1, hf2, feature_shape, predict_words, input_video, input_captions, y, finished_beam, logprobs_finished_beams, past_logprobs, beam_hidden_state, past_symbols_states, finished_beams_states, capl=16): caption_output = [] total_data = len(data) num_batch = int(math.ceil((total_data*1.0/batch_size))) print(num_batch) for batch_idx in xrange(num_batch): 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.getBatchTestCaptionWithSparseLabel(batch_caption, v2i, capl=capl) # [gw,tw,gp, gl, pp, pss, fbs] = sess.run([finished_beam, predict_words, logprobs_finished_beams, past_logprobs, beam_hidden_state, past_symbols_states, finished_beams_states],feed_dict={input_video:data_v, input_captions:data_c, y:data_y}) [tw] = sess.run([predict_words],feed_dict={input_video:data_v, input_captions:data_c, y:data_y}) generated_captions = MsrDataUtil.convertCaptionI2V(batch_caption, tw, 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 beamsearch_exe_test(sess, data, cate_info, batch_size, v2i, i2v, hf1, hf2, feature_shape1, feature_shape2, predict_words, input_video1, input_video2, input_captions, input_categories, y, finished_beam, logprobs_finished_beams, capl=16): caption_output = [] total_data = len(data) num_batch = int(round(total_data * 1.0 / batch_size)) for batch_idx in xrange(num_batch): batch_caption = data[batch_idx * batch_size:min((batch_idx + 1) * batch_size, total_data)] data_v1 = MsrFinalDataUtil.getBatchVideoFeature( batch_caption, hf1, feature_shape1) data_v2 = MsrFinalDataUtil.getBatchC3DVideoFeature( batch_caption, hf2, feature_shape2) data_c, data_y = MsrDataUtil.getBatchTestCaptionWithSparseLabel( batch_caption, v2i, capl=capl) data_cate = MsrDataUtil.getBatchVideoCategoriesInfo( batch_caption, cate_info, feature_shape1) [tw, gw, gp] = sess.run( [predict_words, finished_beam, logprobs_finished_beams], feed_dict={ input_video1: data_v1, input_video2: data_v2, input_captions: data_c, input_categories: data_cate, 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({ 'video_id': batch_caption[idx].keys()[0], 'caption': sen.strip() }) js = {} js['result'] = caption_output js['version'] = '3' js['external_data'] = { 'used': 'true', 'details': 'the features of video frames are extracted by ResNet152 and C3D' } return js