inference_pb.py
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#-*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf
from tensorflow import logging
from tensorflow import gfile
import operator
import pb_util as pbutil
import video_recommender as recommender
import video_util as videoutil
# Define file paths.
MODEL_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/model/inference_model/segment_inference_model"
VOCAB_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/vocabulary.csv"
VIDEO_TAGS_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/segment_tags.csv"
VIDEO_IDS_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/videoIds.csv"
TAG_VECTOR_MODEL_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/tag_vectors.model"
VIDEO_VECTOR_MODEL_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/video_vectors.model"
VIDEO_ID_MODEL_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/esot3ria/videoId_vectors.model"
SEGMENT_LABEL_PATH = "/mnt/e/khuhub/2015104192/web/backend/yt8m/segment_label_ids.csv"
# Define parameters.
TAG_TOP_K = 5
VIDEO_TOP_K = 30
def get_segments(batch_video_mtx, batch_num_frames, segment_size):
"""Get segment-level inputs from frame-level features."""
video_batch_size = batch_video_mtx.shape[0]
max_frame = batch_video_mtx.shape[1]
feature_dim = batch_video_mtx.shape[-1]
padded_segment_sizes = (batch_num_frames + segment_size - 1) // segment_size
padded_segment_sizes *= segment_size
segment_mask = (
0 < (padded_segment_sizes[:, np.newaxis] - np.arange(0, max_frame)))
# Segment bags.
frame_bags = batch_video_mtx.reshape((-1, feature_dim))
segment_frames = frame_bags[segment_mask.reshape(-1)].reshape(
(-1, segment_size, feature_dim))
# Segment num frames.
segment_start_times = np.arange(0, max_frame, segment_size)
num_segments = batch_num_frames[:, np.newaxis] - segment_start_times
num_segment_bags = num_segments.reshape((-1))
valid_segment_mask = num_segment_bags > 0
segment_num_frames = num_segment_bags[valid_segment_mask]
segment_num_frames[segment_num_frames > segment_size] = segment_size
max_segment_num = (max_frame + segment_size - 1) // segment_size
video_idxs = np.tile(
np.arange(0, video_batch_size)[:, np.newaxis], [1, max_segment_num])
segment_idxs = np.tile(segment_start_times, [video_batch_size, 1])
idx_bags = np.stack([video_idxs, segment_idxs], axis=-1).reshape((-1, 2))
video_segment_ids = idx_bags[valid_segment_mask]
return {
"video_batch": segment_frames,
"num_frames_batch": segment_num_frames,
"video_segment_ids": video_segment_ids
}
def format_predictions(video_ids, predictions, top_k, whitelisted_cls_mask=None):
batch_size = len(video_ids)
for video_index in range(batch_size):
video_prediction = predictions[video_index]
if whitelisted_cls_mask is not None:
# Whitelist classes.
video_prediction *= whitelisted_cls_mask
top_indices = np.argpartition(video_prediction, -top_k)[-top_k:]
line = [(class_index, predictions[video_index][class_index])
for class_index in top_indices]
line = sorted(line, key=lambda p: -p[1])
yield (video_ids[video_index] + "," +
" ".join("%i %g" % (label, score) for (label, score) in line) +
"\n").encode("utf8")
def normalize_tag(tag):
if isinstance(tag, str):
new_tag = tag.lower().replace('[^a-zA-Z]', ' ')
if new_tag.find(" (") != -1:
new_tag = new_tag[:new_tag.find(" (")]
new_tag = new_tag.replace(" ", "-")
return new_tag
else:
return tag
def inference_pb(file_path, threshold):
print("started step 1")
VIDEO_TOP_K = int(threshold)
inference_result = {}
print("STEP ==== 1")
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
print("STEP ==== 1.5 " + file_path)
# 0. Import SequenceExample type target from pb.
target_video = pbutil.convert_pb(file_path)
print("STEP ==== 2")
# 1. Load video features from pb.
video_id_batch_val = np.array([b'video'])
n_frames = len(target_video.feature_lists.feature_list['rgb'].feature)
# Restrict frame size to 300
if n_frames > 300:
n_frames = 300
video_batch_val = np.zeros((300, 1152))
for i in range(n_frames):
video_batch_rgb_raw = target_video.feature_lists.feature_list['rgb'].feature[i].bytes_list.value[0]
video_batch_rgb = np.array(tf.cast(tf.decode_raw(video_batch_rgb_raw, tf.float32), tf.float32).eval())
video_batch_audio_raw = target_video.feature_lists.feature_list['audio'].feature[i].bytes_list.value[0]
video_batch_audio = np.array(tf.cast(tf.decode_raw(video_batch_audio_raw, tf.float32), tf.float32).eval())
video_batch_val[i] = np.concatenate([video_batch_rgb, video_batch_audio], axis=0)
video_batch_val = np.array([video_batch_val])
num_frames_batch_val = np.array([n_frames])
print("STEP ==== 3")
# Restore checkpoint and meta-graph file.
if not gfile.Exists(MODEL_PATH + ".meta"):
raise IOError("Cannot find %s. Did you run eval.py?" % MODEL_PATH)
meta_graph_location = MODEL_PATH + ".meta"
logging.info("loading meta-graph: " + meta_graph_location)
print("STEP ==== 4")
with tf.device("/cpu:0"):
saver = tf.train.import_meta_graph(meta_graph_location, clear_devices=True)
logging.info("restoring variables from " + MODEL_PATH)
saver.restore(sess, MODEL_PATH)
input_tensor = tf.get_collection("input_batch_raw")[0]
num_frames_tensor = tf.get_collection("num_frames")[0]
predictions_tensor = tf.get_collection("predictions")[0]
print("STEP ==== 5")
# Workaround for num_epochs issue.
def set_up_init_ops(variables):
init_op_list = []
for variable in list(variables):
if "train_input" in variable.name:
init_op_list.append(tf.assign(variable, 1))
variables.remove(variable)
init_op_list.append(tf.variables_initializer(variables))
return init_op_list
print("STEP ==== 6")
sess.run(
set_up_init_ops(tf.get_collection_ref(tf.GraphKeys.LOCAL_VARIABLES)))
print("STEP ==== 7")
whitelisted_cls_mask = np.zeros((predictions_tensor.get_shape()[-1],),
dtype=np.float32)
with tf.io.gfile.GFile(SEGMENT_LABEL_PATH) as fobj:
for line in fobj:
try:
cls_id = int(line)
whitelisted_cls_mask[cls_id] = 1.
except ValueError:
# Simply skip the non-integer line.
continue
# 2. Make segment features.
results = get_segments(video_batch_val, num_frames_batch_val, 5)
video_segment_ids = results["video_segment_ids"]
video_id_batch_val = video_id_batch_val[video_segment_ids[:, 0]]
video_id_batch_val = np.array([
"%s:%d" % (x.decode("utf8"), y)
for x, y in zip(video_id_batch_val, video_segment_ids[:, 1])
])
video_batch_val = results["video_batch"]
num_frames_batch_val = results["num_frames_batch"]
if input_tensor.get_shape()[1] != video_batch_val.shape[1]:
raise ValueError("max_frames mismatch. Please re-run the eval.py "
"with correct segment_labels settings.")
predictions_val, = sess.run([predictions_tensor],
feed_dict={
input_tensor: video_batch_val,
num_frames_tensor: num_frames_batch_val
})
# 3. Make vocabularies.
voca_dict = {}
vocabs = open(VOCAB_PATH, 'r', encoding='UTF8')
while True:
line = vocabs.readline()
if not line: break
vocab_dict_item = line.split(",")
if vocab_dict_item[0] != "Index":
voca_dict[vocab_dict_item[0]] = vocab_dict_item[3]
vocabs.close()
print("made vocabs")
# 4. Make combined scores.
combined_scores = {}
inputVideoSegmentResult = []
inputVideoTagResults = []
# 입력영상의 각 세그먼트에 대해 반복
for line in format_predictions(video_id_batch_val, predictions_val, TAG_TOP_K, whitelisted_cls_mask):
segment_id, preds = line.decode("utf8").split(",")
#각 세그먼트마다 일단 넣어둠
inputVideoSegmentResult.append(preds)
preds = preds.split(" ")
pred_cls_ids = [int(preds[idx]) for idx in range(0, len(preds), 2)]
pred_cls_scores = [float(preds[idx]) for idx in range(1, len(preds), 2)]
for i in range(len(pred_cls_ids)):
if pred_cls_ids[i] in combined_scores:
combined_scores[pred_cls_ids[i]] += pred_cls_scores[i]
else:
combined_scores[pred_cls_ids[i]] = pred_cls_scores[i]
combined_scores = sorted(combined_scores.items(), key=operator.itemgetter(1), reverse=True)
demoninator = float(combined_scores[0][1] + combined_scores[1][1]
+ combined_scores[2][1] + combined_scores[3][1] + combined_scores[4][1])
#각 세그먼트별 태그확률 정규화
for i in range(0, len(inputVideoSegmentResult)):
predictForSegment = inputVideoSegmentResult[i].split(" ")
denom = 0
for j in range(0,int(len(predictForSegment)/2)):
denom = denom + float(predictForSegment[j*2 + 1])
for j in range(0,int(len(predictForSegment)/2)):
predictForSegment[j*2 + 1] = str(float(predictForSegment[j*2 + 1]) / denom)
predictForSegment[j*2] = normalize_tag(str(voca_dict[str(predictForSegment[j*2])]))
#클래스 확률 클래스 확률... 일케 저장되어 있음
inputVideoTagResults.append(predictForSegment)
#이 다음문단은 이전거 원래 이게 recomment videos의 첫 파라미터임
tag_result = []
for itemIndex in range(TAG_TOP_K):
segment_tag = str(voca_dict[str(combined_scores[itemIndex][0])])
normalized_tag = normalize_tag(segment_tag)
tag_percentage = format(combined_scores[itemIndex][1] / demoninator, ".4f")
tag_result.append((normalized_tag, tag_percentage))
# 5. Create recommend videos info, Combine results.
recommend_video_ids = recommender.recommend_videos(tag_result, inputVideoTagResults, TAG_VECTOR_MODEL_PATH,
VIDEO_VECTOR_MODEL_PATH, VIDEO_ID_MODEL_PATH, VIDEO_TOP_K,True)
video_result = [videoutil.getVideoInfo(ids, VIDEO_TAGS_PATH, TAG_TOP_K,False) for ids in recommend_video_ids]
inference_result = {
"tag_result": tag_result,
"video_result": video_result
}
# 6. Dispose instances.
sess.close()
return inference_result
if __name__ == '__main__':
filepath = "/tmp/mediapipe/features.pb"
result = inference_pb(filepath,5)
print(result)