Text-to-Video retrieval with S3D MIL-NCE

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!pip install -q opencv-python

import os

import tensorflow.compat.v2 as tf
import tensorflow_hub as hub

import numpy as np
import cv2
from IPython import display
import math

导入 TF-Hub 模型

本教程演示了如何使用 TensorFlow Hub 中的 S3D MIL-NCE 模型执行文本到视频检索,以便找到与给定文本查询最相似的视频。

该模型有 2 个签名,一个用于生成视频嵌入向量,另一个用于生成文本嵌入向量,我们利用这些嵌入向量来查找嵌入向量空间中的最近邻。

# Load the model once from TF-Hub.
hub_handle = 'https://tfhub.dev/deepmind/mil-nce/s3d/1'
hub_model = hub.load(hub_handle)

def generate_embeddings(model, input_frames, input_words):
  """Generate embeddings from the model from video frames and input words."""
  # Input_frames must be normalized in [0, 1] and of the shape Batch x T x H x W x 3
  vision_output = model.signatures['video'](tf.constant(tf.cast(input_frames, dtype=tf.float32)))
  text_output = model.signatures['text'](tf.constant(input_words))
  return vision_output['video_embedding'], text_output['text_embedding']
2021-08-13 20:50:09.991128: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:50:09.999839: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:50:10.000833: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:50:10.002563: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-08-13 20:50:10.003428: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:50:10.004411: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:50:10.005323: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:50:10.653674: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:50:10.654788: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:50:10.655791: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:50:10.656798: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14648 MB memory:  -> device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0
2021-08-13 20:50:12.154242: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
# @title Define video loading and visualization functions  { display-mode: "form" }

# Utilities to open video files using CV2
def crop_center_square(frame):
  y, x = frame.shape[0:2]
  min_dim = min(y, x)
  start_x = (x // 2) - (min_dim // 2)
  start_y = (y // 2) - (min_dim // 2)
  return frame[start_y:start_y+min_dim,start_x:start_x+min_dim]


def load_video(video_url, max_frames=32, resize=(224, 224)):
  path = tf.keras.utils.get_file(os.path.basename(video_url)[-128:], video_url)
  cap = cv2.VideoCapture(path)
  frames = []
  try:
    while True:
      ret, frame = cap.read()
      if not ret:
        break
      frame = crop_center_square(frame)
      frame = cv2.resize(frame, resize)
      frame = frame[:, :, [2, 1, 0]]
      frames.append(frame)

      if len(frames) == max_frames:
        break
  finally:
    cap.release()
  frames = np.array(frames)
  if len(frames) < max_frames:
    n_repeat = int(math.ceil(max_frames / float(len(frames))))
    frames = frames.repeat(n_repeat, axis=0)
  frames = frames[:max_frames]
  return frames / 255.0

def display_video(urls):
    html = '<table>'
    html += '<tr><th>Video 1</th><th>Video 2</th><th>Video 3</th></tr><tr>'
    for url in urls:
        html += '<td>'
        html += '<img src="{}" height="224">'.format(url)
        html += '</td>'
    html += '</tr></table>'
    return display.HTML(html)

def display_query_and_results_video(query, urls, scores):
  """Display a text query and the top result videos and scores."""
  sorted_ix = np.argsort(-scores)
  html = ''
  html += '<h2>Input query: <i>{}</i> </h2><div>'.format(query)
  html += 'Results: <div>'
  html += '<table>'
  html += '<tr><th>Rank #1, Score:{:.2f}</th>'.format(scores[sorted_ix[0]])
  html += '<th>Rank #2, Score:{:.2f}</th>'.format(scores[sorted_ix[1]])
  html += '<th>Rank #3, Score:{:.2f}</th></tr><tr>'.format(scores[sorted_ix[2]])
  for i, idx in enumerate(sorted_ix):
    url = urls[sorted_ix[i]];
    html += '<td>'
    html += '<img src="{}" height="224">'.format(url)
    html += '</td>'
  html += '</tr></table>'
  return html
# @title Load example videos and define text queries  { display-mode: "form" }

video_1_url = 'https://upload.wikimedia.org/wikipedia/commons/b/b0/YosriAirTerjun.gif' # @param {type:"string"}
video_2_url = 'https://upload.wikimedia.org/wikipedia/commons/e/e6/Guitar_solo_gif.gif' # @param {type:"string"}
video_3_url = 'https://upload.wikimedia.org/wikipedia/commons/3/30/2009-08-16-autodrift-by-RalfR-gif-by-wau.gif' # @param {type:"string"}

video_1 = load_video(video_1_url)
video_2 = load_video(video_2_url)
video_3 = load_video(video_3_url)
all_videos = [video_1, video_2, video_3]

query_1_video = 'waterfall' # @param {type:"string"}
query_2_video = 'playing guitar' # @param {type:"string"}
query_3_video = 'car drifting' # @param {type:"string"}
all_queries_video = [query_1_video, query_2_video, query_3_video]
all_videos_urls = [video_1_url, video_2_url, video_3_url]
display_video(all_videos_urls)
Downloading data from https://upload.wikimedia.org/wikipedia/commons/b/b0/YosriAirTerjun.gif
1212416/1207385 [==============================] - 0s 0us/step
1220608/1207385 [==============================] - 0s 0us/step
Downloading data from https://upload.wikimedia.org/wikipedia/commons/e/e6/Guitar_solo_gif.gif
1024000/1021622 [==============================] - 0s 0us/step
1032192/1021622 [==============================] - 0s 0us/step
Downloading data from https://upload.wikimedia.org/wikipedia/commons/3/30/2009-08-16-autodrift-by-RalfR-gif-by-wau.gif
1507328/1506603 [==============================] - 0s 0us/step
1515520/1506603 [==============================] - 0s 0us/step

演示文本到视频检索

# Prepare video inputs.
videos_np = np.stack(all_videos, axis=0)

# Prepare text input.
words_np = np.array(all_queries_video)

# Generate the video and text embeddings.
video_embd, text_embd = generate_embeddings(hub_model, videos_np, words_np)

# Scores between video and text is computed by dot products.
all_scores = np.dot(text_embd, tf.transpose(video_embd))
2021-08-13 20:50:17.683271: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8100
2021-08-13 20:50:18.258332: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory
# Display results.
html = ''
for i, words in enumerate(words_np):
  html += display_query_and_results_video(words, all_videos_urls, all_scores[i, :])
  html += '<br>'
display.HTML(html)