Skip to content

Latest commit

 

History

History

youtube8m

README.md

Steps to run the YouTube-8M feature extraction graph

  1. Checkout the repository and follow the installation instructions to set up MediaPipe.

    git clone https://github.com/google/mediapipe.git
cd mediapipe

  2. Download the PCA and model data.

    mkdir /tmp/mediapipe
cd /tmp/mediapipe
curl -O http://data.yt8m.org/pca_matrix_data/inception3_mean_matrix_data.pb
curl -O http://data.yt8m.org/pca_matrix_data/inception3_projection_matrix_data.pb
curl -O http://data.yt8m.org/pca_matrix_data/vggish_mean_matrix_data.pb
curl -O http://data.yt8m.org/pca_matrix_data/vggish_projection_matrix_data.pb
curl -O http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
tar -xvf /tmp/mediapipe/inception-2015-12-05.tgz

  3. Get the VGGish frozen graph.

    Note: To run step 3 and step 4, you must have Python 2.7 or 3.5+ installed with the TensorFlow 1.14+ package installed.

    # cd to the root directory of the MediaPipe repo
cd -

pip3 install tf_slim
python -m mediapipe.examples.desktop.youtube8m.generate_vggish_frozen_graph

  4. Generate a MediaSequence metadata from the input video.

    Note: the output file is /tmp/mediapipe/metadata.pb

    # change clip_end_time_sec to match the length of your video.
python -m mediapipe.examples.desktop.youtube8m.generate_input_sequence_example \
  --path_to_input_video=/absolute/path/to/the/local/video/file \
  --clip_end_time_sec=120

  5. Run the MediaPipe binary to extract the features.

    bazel build -c opt --linkopt=-s \
  --define MEDIAPIPE_DISABLE_GPU=1 --define no_aws_support=true \
  mediapipe/examples/desktop/youtube8m:extract_yt8m_features

GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/extract_yt8m_features \
  --calculator_graph_config_file=mediapipe/graphs/youtube8m/feature_extraction.pbtxt \
  --input_side_packets=input_sequence_example=/tmp/mediapipe/metadata.pb  \
  --output_side_packets=output_sequence_example=/tmp/mediapipe/features.pb

  6. [Optional] Read the features.pb in Python.

    import tensorflow as tf

sequence_example = open('/tmp/mediapipe/features.pb', 'rb').read()
print(tf.train.SequenceExample.FromString(sequence_example))

Steps to run the YouTube-8M inference graph with the YT8M dataset

  1. Download the YT8M dataset

    For example, download one shard of the training data:

    curl http://us.data.yt8m.org/2/frame/train/trainpj.tfrecord --output /tmp/mediapipe/trainpj.tfrecord

  2. Copy the baseline model (model card) to local.

    curl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz

tar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe

  3. Build and run the inference binary.

    bazel build -c opt --define='MEDIAPIPE_DISABLE_GPU=1' --linkopt=-s \
mediapipe/examples/desktop/youtube8m:model_inference

GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/model_inference \
  --calculator_graph_config_file=mediapipe/graphs/youtube8m/yt8m_dataset_model_inference.pbtxt \
  --input_side_packets=tfrecord_path=/tmp/mediapipe/trainpj.tfrecord,record_index=0,desired_segment_size=5 \
  --output_stream=annotation_summary \
  --output_stream_file=/tmp/summary \
  --output_side_packets=yt8m_id \
  --output_side_packets_file=/tmp/yt8m_id

Steps to run the YouTube-8M model inference graph with Web Interface

  1. Copy the baseline model (model card) to local.

    curl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz

tar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe

  2. Build the inference binary.

    bazel build -c opt --define='MEDIAPIPE_DISABLE_GPU=1' --linkopt=-s \
  mediapipe/examples/desktop/youtube8m:model_inference

  3. Run the python web server.

    Note: pip3 install absl-py

    python mediapipe/examples/desktop/youtube8m/viewer/server.py --root `pwd`

    Navigate to localhost:8008 in a web browser.

Steps to run the YouTube-8M model inference graph with a local video

  1. Make sure you have the features.pb from the feature extraction pipeline.

  2. Copy the baseline model (model card) to local.

    curl -o /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz http://data.yt8m.org/models/baseline/saved_model.tar.gz

tar -xvf /tmp/mediapipe/yt8m_baseline_saved_model.tar.gz -C /tmp/mediapipe

  3. Build and run the inference binary.

    bazel build -c opt --define='MEDIAPIPE_DISABLE_GPU=1' --linkopt=-s \
  mediapipe/examples/desktop/youtube8m:model_inference

# segment_size is the number of seconds window of frames.
# overlap is the number of seconds adjacent segments share.
GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/model_inference \
  --calculator_graph_config_file=mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt \
  --input_side_packets=input_sequence_example_path=/tmp/mediapipe/features.pb,input_video_path=/absolute/path/to/the/local/video/file,output_video_path=/tmp/mediapipe/annotated_video.mp4,segment_size=5,overlap=4

  4. View the annotated video.