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# Copyright 2017 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Post-process embeddings from VGGish."""
import numpy as np
import vggish_params
class Postprocessor(object):
"""Post-processes VGGish embeddings.
The initial release of AudioSet included 128-D VGGish embeddings for each
segment of AudioSet. These released embeddings were produced by applying
a PCA transformation (technically, a whitening transform is included as well)
and 8-bit quantization to the raw embedding output from VGGish, in order to
stay compatible with the YouTube-8M project which provides visual embeddings
in the same format for a large set of YouTube videos. This class implements
the same PCA (with whitening) and quantization transformations.
"""
def __init__(self, pca_params_npz_path):
"""Constructs a postprocessor.
Args:
pca_params_npz_path: Path to a NumPy-format .npz file that
contains the PCA parameters used in postprocessing.
"""
params = np.load(pca_params_npz_path)
self._pca_matrix = params[vggish_params.PCA_EIGEN_VECTORS_NAME]
# Load means into a column vector for easier broadcasting later.
self._pca_means = params[vggish_params.PCA_MEANS_NAME].reshape(-1, 1)
assert self._pca_matrix.shape == (
vggish_params.EMBEDDING_SIZE, vggish_params.EMBEDDING_SIZE), (
'Bad PCA matrix shape: %r' % (self._pca_matrix.shape,))
assert self._pca_means.shape == (vggish_params.EMBEDDING_SIZE, 1), (
'Bad PCA means shape: %r' % (self._pca_means.shape,))
def postprocess(self, embeddings_batch):
"""Applies postprocessing to a batch of embeddings.
Args:
embeddings_batch: An nparray of shape [batch_size, embedding_size]
containing output from the embedding layer of VGGish.
Returns:
An nparray of the same shape as the input but of type uint8,
containing the PCA-transformed and quantized version of the input.
"""
assert len(embeddings_batch.shape) == 2, (
'Expected 2-d batch, got %r' % (embeddings_batch.shape,))
assert embeddings_batch.shape[1] == vggish_params.EMBEDDING_SIZE, (
'Bad batch shape: %r' % (embeddings_batch.shape,))
# Apply PCA.
# - Embeddings come in as [batch_size, embedding_size].
# - Transpose to [embedding_size, batch_size].
# - Subtract pca_means column vector from each column.
# - Premultiply by PCA matrix of shape [output_dims, input_dims]
# where both are are equal to embedding_size in our case.
# - Transpose result back to [batch_size, embedding_size].
pca_applied = np.dot(self._pca_matrix,
(embeddings_batch.T - self._pca_means)).T
# Quantize by:
# - clipping to [min, max] range
clipped_embeddings = np.clip(
pca_applied, vggish_params.QUANTIZE_MIN_VAL,
vggish_params.QUANTIZE_MAX_VAL)
# - convert to 8-bit in range [0.0, 255.0]
quantized_embeddings = (
(clipped_embeddings - vggish_params.QUANTIZE_MIN_VAL) *
(255.0 /
(vggish_params.QUANTIZE_MAX_VAL - vggish_params.QUANTIZE_MIN_VAL)))
# - cast 8-bit float to uint8
quantized_embeddings = quantized_embeddings.astype(np.uint8)
return quantized_embeddings
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