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chores: refactor for the new ai research, add linter, gh action, etc (#27)
This commit is contained in:
Marina von Steinkirch, PhD
von-steinkirch
parent
fb4ab80dc3
commit
d5467e559f
40 changed files with 5177 additions and 2476 deletions
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@ -13,14 +13,11 @@
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# limitations under the License.
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# ==============================================================================
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"""Image pre-processing utilities.
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"""
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"""Image pre-processing utilities."""
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import tensorflow as tf
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IMAGE_DEPTH = 3 # color images
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IMAGE_DEPTH = 3 # color images
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import tensorflow as tf
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# _R_MEAN = 123.68
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# _G_MEAN = 116.78
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@ -35,303 +32,318 @@ _RESIZE_MIN = 128
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def _decode_crop_and_flip(image_buffer, bbox, num_channels):
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"""Crops the given image to a random part of the image, and randomly flips.
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"""Crops the given image to a random part of the image, and randomly flips.
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We use the fused decode_and_crop op, which performs better than the two ops
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used separately in series, but note that this requires that the image be
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passed in as an un-decoded string Tensor.
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We use the fused decode_and_crop op, which performs better than the two ops
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used separately in series, but note that this requires that the image be
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passed in as an un-decoded string Tensor.
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Args:
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image_buffer: scalar string Tensor representing the raw JPEG image buffer.
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bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
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where each coordinate is [0, 1) and the coordinates are arranged as
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[ymin, xmin, ymax, xmax].
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num_channels: Integer depth of the image buffer for decoding.
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Args:
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image_buffer: scalar string Tensor representing the raw JPEG image buffer.
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bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
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where each coordinate is [0, 1) and the coordinates are arranged as
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[ymin, xmin, ymax, xmax].
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num_channels: Integer depth of the image buffer for decoding.
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Returns:
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3-D tensor with cropped image.
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Returns:
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3-D tensor with cropped image.
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"""
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# A large fraction of image datasets contain a human-annotated bounding box
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# delineating the region of the image containing the object of interest. We
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# choose to create a new bounding box for the object which is a randomly
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# distorted version of the human-annotated bounding box that obeys an
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# allowed range of aspect ratios, sizes and overlap with the human-annotated
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# bounding box. If no box is supplied, then we assume the bounding box is
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# the entire image.
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sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
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tf.image.extract_jpeg_shape(image_buffer),
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bounding_boxes=bbox,
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min_object_covered=0.1,
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aspect_ratio_range=[0.75, 1.33],
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area_range=[0.05, 1.0],
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max_attempts=100,
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use_image_if_no_bounding_boxes=True)
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bbox_begin, bbox_size, _ = sample_distorted_bounding_box
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"""
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# A large fraction of image datasets contain a human-annotated bounding box
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# delineating the region of the image containing the object of interest. We
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# choose to create a new bounding box for the object which is a randomly
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# distorted version of the human-annotated bounding box that obeys an
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# allowed range of aspect ratios, sizes and overlap with the human-annotated
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# bounding box. If no box is supplied, then we assume the bounding box is
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# the entire image.
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sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
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tf.image.extract_jpeg_shape(image_buffer),
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bounding_boxes=bbox,
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min_object_covered=0.1,
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aspect_ratio_range=[0.75, 1.33],
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area_range=[0.05, 1.0],
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max_attempts=100,
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use_image_if_no_bounding_boxes=True,
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)
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bbox_begin, bbox_size, _ = sample_distorted_bounding_box
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# Reassemble the bounding box in the format the crop op requires.
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offset_y, offset_x, _ = tf.unstack(bbox_begin)
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target_height, target_width, _ = tf.unstack(bbox_size)
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crop_window = tf.stack([offset_y, offset_x, target_height, target_width])
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# Reassemble the bounding box in the format the crop op requires.
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offset_y, offset_x, _ = tf.unstack(bbox_begin)
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target_height, target_width, _ = tf.unstack(bbox_size)
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crop_window = tf.stack([offset_y, offset_x, target_height, target_width])
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# Use the fused decode and crop op here, which is faster than each in series.
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cropped = tf.image.decode_and_crop_jpeg(
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image_buffer, crop_window, channels=num_channels)
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# Use the fused decode and crop op here, which is faster than each in
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# series.
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cropped = tf.image.decode_and_crop_jpeg(
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image_buffer, crop_window, channels=num_channels
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)
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# Flip to add a little more random distortion in.
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cropped = tf.image.random_flip_left_right(cropped)
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return cropped
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# Flip to add a little more random distortion in.
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cropped = tf.image.random_flip_left_right(cropped)
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return cropped
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def _central_crop(image, crop_height, crop_width):
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"""Performs central crops of the given image list.
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"""Performs central crops of the given image list.
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Args:
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image: a 3-D image tensor
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crop_height: the height of the image following the crop.
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crop_width: the width of the image following the crop.
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Args:
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image: a 3-D image tensor
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crop_height: the height of the image following the crop.
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crop_width: the width of the image following the crop.
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Returns:
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3-D tensor with cropped image.
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"""
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shape = tf.shape(input=image)
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height, width = shape[0], shape[1]
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Returns:
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3-D tensor with cropped image.
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"""
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shape = tf.shape(input=image)
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height, width = shape[0], shape[1]
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amount_to_be_cropped_h = (height - crop_height)
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crop_top = amount_to_be_cropped_h // 2
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amount_to_be_cropped_w = (width - crop_width)
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crop_left = amount_to_be_cropped_w // 2
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return tf.slice(
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image, [crop_top, crop_left, 0], [crop_height, crop_width, -1])
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amount_to_be_cropped_h = height - crop_height
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crop_top = amount_to_be_cropped_h // 2
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amount_to_be_cropped_w = width - crop_width
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crop_left = amount_to_be_cropped_w // 2
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return tf.slice(image, [crop_top, crop_left, 0], [crop_height, crop_width, -1])
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def _mean_image_subtraction(image, means, num_channels):
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"""Subtracts the given means from each image channel.
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"""Subtracts the given means from each image channel.
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For example:
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means = [123.68, 116.779, 103.939]
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image = _mean_image_subtraction(image, means)
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For example:
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means = [123.68, 116.779, 103.939]
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image = _mean_image_subtraction(image, means)
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Note that the rank of `image` must be known.
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Note that the rank of `image` must be known.
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Args:
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image: a tensor of size [height, width, C].
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means: a C-vector of values to subtract from each channel.
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num_channels: number of color channels in the image that will be distorted.
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Args:
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image: a tensor of size [height, width, C].
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means: a C-vector of values to subtract from each channel.
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num_channels: number of color channels in the image that will be distorted.
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Returns:
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the centered image.
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Returns:
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the centered image.
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Raises:
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ValueError: If the rank of `image` is unknown, if `image` has a rank other
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than three or if the number of channels in `image` doesn't match the
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number of values in `means`.
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"""
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if image.get_shape().ndims != 3:
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raise ValueError('Input must be of size [height, width, C>0]')
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Raises:
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ValueError: If the rank of `image` is unknown, if `image` has a rank other
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than three or if the number of channels in `image` doesn't match the
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number of values in `means`.
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"""
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if image.get_shape().ndims != 3:
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raise ValueError("Input must be of size [height, width, C>0]")
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if len(means) != num_channels:
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raise ValueError('len(means) must match the number of channels')
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if len(means) != num_channels:
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raise ValueError("len(means) must match the number of channels")
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# We have a 1-D tensor of means; convert to 3-D.
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means = tf.expand_dims(tf.expand_dims(means, 0), 0)
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# We have a 1-D tensor of means; convert to 3-D.
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means = tf.expand_dims(tf.expand_dims(means, 0), 0)
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return image - means
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return image - means
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def _smallest_size_at_least(height, width, resize_min):
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"""Computes new shape with the smallest side equal to `smallest_side`.
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"""Computes new shape with the smallest side equal to `smallest_side`.
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Computes new shape with the smallest side equal to `smallest_side` while
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preserving the original aspect ratio.
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Computes new shape with the smallest side equal to `smallest_side` while
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preserving the original aspect ratio.
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Args:
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height: an int32 scalar tensor indicating the current height.
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width: an int32 scalar tensor indicating the current width.
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resize_min: A python integer or scalar `Tensor` indicating the size of
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the smallest side after resize.
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Args:
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height: an int32 scalar tensor indicating the current height.
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width: an int32 scalar tensor indicating the current width.
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resize_min: A python integer or scalar `Tensor` indicating the size of
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the smallest side after resize.
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Returns:
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new_height: an int32 scalar tensor indicating the new height.
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new_width: an int32 scalar tensor indicating the new width.
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"""
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resize_min = tf.cast(resize_min, tf.float32)
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Returns:
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new_height: an int32 scalar tensor indicating the new height.
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new_width: an int32 scalar tensor indicating the new width.
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"""
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resize_min = tf.cast(resize_min, tf.float32)
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# Convert to floats to make subsequent calculations go smoothly.
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height, width = tf.cast(height, tf.float32), tf.cast(width, tf.float32)
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# Convert to floats to make subsequent calculations go smoothly.
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height, width = tf.cast(height, tf.float32), tf.cast(width, tf.float32)
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smaller_dim = tf.minimum(height, width)
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scale_ratio = resize_min / smaller_dim
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smaller_dim = tf.minimum(height, width)
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scale_ratio = resize_min / smaller_dim
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# Convert back to ints to make heights and widths that TF ops will accept.
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new_height = tf.cast(tf.ceil(height * scale_ratio), tf.int32)
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new_width = tf.cast(tf.ceil(width * scale_ratio), tf.int32)
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# Convert back to ints to make heights and widths that TF ops will accept.
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new_height = tf.cast(tf.ceil(height * scale_ratio), tf.int32)
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new_width = tf.cast(tf.ceil(width * scale_ratio), tf.int32)
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return new_height, new_width
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return new_height, new_width
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def _aspect_preserving_resize(image, resize_min):
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"""Resize images preserving the original aspect ratio.
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"""Resize images preserving the original aspect ratio.
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Args:
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image: A 3-D image `Tensor`.
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resize_min: A python integer or scalar `Tensor` indicating the size of
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the smallest side after resize.
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Args:
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image: A 3-D image `Tensor`.
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resize_min: A python integer or scalar `Tensor` indicating the size of
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the smallest side after resize.
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Returns:
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resized_image: A 3-D tensor containing the resized image.
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"""
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shape = tf.shape(input=image)
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height, width = shape[0], shape[1]
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Returns:
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resized_image: A 3-D tensor containing the resized image.
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"""
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shape = tf.shape(input=image)
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height, width = shape[0], shape[1]
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new_height, new_width = _smallest_size_at_least(height, width, resize_min)
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new_height, new_width = _smallest_size_at_least(height, width, resize_min)
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return _resize_image(image, new_height, new_width)
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return _resize_image(image, new_height, new_width)
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def _resize_image(image, height, width):
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"""Simple wrapper around tf.resize_images.
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"""Simple wrapper around tf.resize_images.
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This is primarily to make sure we use the same `ResizeMethod` and other
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details each time.
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This is primarily to make sure we use the same `ResizeMethod` and other
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details each time.
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Args:
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image: A 3-D image `Tensor`.
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height: The target height for the resized image.
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width: The target width for the resized image.
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Args:
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image: A 3-D image `Tensor`.
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height: The target height for the resized image.
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width: The target width for the resized image.
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Returns:
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resized_image: A 3-D tensor containing the resized image. The first two
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dimensions have the shape [height, width].
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"""
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return tf.image.resize_images(
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image, [height, width], method=tf.image.ResizeMethod.BILINEAR,
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align_corners=False)
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Returns:
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resized_image: A 3-D tensor containing the resized image. The first two
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dimensions have the shape [height, width].
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"""
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return tf.image.resize_images(
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image,
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[height, width],
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method=tf.image.ResizeMethod.BILINEAR,
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align_corners=False,
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)
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def preprocess_image(image_buffer, bbox, output_height, output_width,
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num_channels, is_training=False):
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"""Preprocesses the given image.
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def preprocess_image(
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image_buffer, bbox, output_height, output_width, num_channels, is_training=False
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):
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"""Preprocesses the given image.
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Preprocessing includes decoding, cropping, and resizing for both training
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and eval images. Training preprocessing, however, introduces some random
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distortion of the image to improve accuracy.
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Preprocessing includes decoding, cropping, and resizing for both training
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and eval images. Training preprocessing, however, introduces some random
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distortion of the image to improve accuracy.
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Args:
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image_buffer: scalar string Tensor representing the raw JPEG image buffer.
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bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
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where each coordinate is [0, 1) and the coordinates are arranged as
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[ymin, xmin, ymax, xmax].
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output_height: The height of the image after preprocessing.
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output_width: The width of the image after preprocessing.
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num_channels: Integer depth of the image buffer for decoding.
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is_training: `True` if we're preprocessing the image for training and
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`False` otherwise.
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Args:
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image_buffer: scalar string Tensor representing the raw JPEG image buffer.
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bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
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where each coordinate is [0, 1) and the coordinates are arranged as
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[ymin, xmin, ymax, xmax].
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output_height: The height of the image after preprocessing.
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output_width: The width of the image after preprocessing.
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num_channels: Integer depth of the image buffer for decoding.
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is_training: `True` if we're preprocessing the image for training and
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`False` otherwise.
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Returns:
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A preprocessed image.
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"""
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if is_training:
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# For training, we want to randomize some of the distortions.
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image = _decode_crop_and_flip(image_buffer, bbox, num_channels)
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image = _resize_image(image, output_height, output_width)
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else:
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# For validation, we want to decode, resize, then just crop the middle.
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image = tf.image.decode_jpeg(image_buffer, channels=num_channels)
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image = _aspect_preserving_resize(image, _RESIZE_MIN)
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print(image)
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image = _central_crop(image, output_height, output_width)
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Returns:
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A preprocessed image.
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"""
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if is_training:
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# For training, we want to randomize some of the distortions.
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image = _decode_crop_and_flip(image_buffer, bbox, num_channels)
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image = _resize_image(image, output_height, output_width)
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else:
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# For validation, we want to decode, resize, then just crop the middle.
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image = tf.image.decode_jpeg(image_buffer, channels=num_channels)
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image = _aspect_preserving_resize(image, _RESIZE_MIN)
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print(image)
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image = _central_crop(image, output_height, output_width)
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image.set_shape([output_height, output_width, num_channels])
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image.set_shape([output_height, output_width, num_channels])
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return _mean_image_subtraction(image, _CHANNEL_MEANS, num_channels)
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return _mean_image_subtraction(image, _CHANNEL_MEANS, num_channels)
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def parse_example_proto(example_serialized):
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"""Parses an Example proto containing a training example of an image.
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"""Parses an Example proto containing a training example of an image.
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The output of the build_image_data.py image preprocessing script is a dataset
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containing serialized Example protocol buffers. Each Example proto contains
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the following fields:
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The output of the build_image_data.py image preprocessing script is a dataset
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containing serialized Example protocol buffers. Each Example proto contains
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the following fields:
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image/height: 462
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image/width: 581
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image/colorspace: 'RGB'
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image/channels: 3
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image/class/label: 615
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image/class/synset: 'n03623198'
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image/class/text: 'knee pad'
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image/object/bbox/xmin: 0.1
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image/object/bbox/xmax: 0.9
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image/object/bbox/ymin: 0.2
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image/object/bbox/ymax: 0.6
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image/object/bbox/label: 615
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image/format: 'JPEG'
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image/filename: 'ILSVRC2012_val_00041207.JPEG'
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image/encoded: <JPEG encoded string>
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image/height: 462
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image/width: 581
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image/colorspace: 'RGB'
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image/channels: 3
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image/class/label: 615
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image/class/synset: 'n03623198'
|
||||
image/class/text: 'knee pad'
|
||||
image/object/bbox/xmin: 0.1
|
||||
image/object/bbox/xmax: 0.9
|
||||
image/object/bbox/ymin: 0.2
|
||||
image/object/bbox/ymax: 0.6
|
||||
image/object/bbox/label: 615
|
||||
image/format: 'JPEG'
|
||||
image/filename: 'ILSVRC2012_val_00041207.JPEG'
|
||||
image/encoded: <JPEG encoded string>
|
||||
|
||||
Args:
|
||||
example_serialized: scalar Tensor tf.string containing a serialized
|
||||
Example protocol buffer.
|
||||
Args:
|
||||
example_serialized: scalar Tensor tf.string containing a serialized
|
||||
Example protocol buffer.
|
||||
|
||||
Returns:
|
||||
image_buffer: Tensor tf.string containing the contents of a JPEG file.
|
||||
label: Tensor tf.int32 containing the label.
|
||||
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
|
||||
where each coordinate is [0, 1) and the coordinates are arranged as
|
||||
[ymin, xmin, ymax, xmax].
|
||||
text: Tensor tf.string containing the human-readable label.
|
||||
"""
|
||||
# Dense features in Example proto.
|
||||
feature_map = {
|
||||
'image/encoded': tf.FixedLenFeature([], dtype=tf.string,
|
||||
default_value=''),
|
||||
'image/class/label': tf.FixedLenFeature([1], dtype=tf.int64,
|
||||
default_value=-1),
|
||||
'image/class/text': tf.FixedLenFeature([], dtype=tf.string,
|
||||
default_value=''),
|
||||
}
|
||||
sparse_float32 = tf.VarLenFeature(dtype=tf.float32)
|
||||
# Sparse features in Example proto.
|
||||
feature_map.update(
|
||||
{k: sparse_float32 for k in ['image/object/bbox/xmin',
|
||||
'image/object/bbox/ymin',
|
||||
'image/object/bbox/xmax',
|
||||
'image/object/bbox/ymax']})
|
||||
Returns:
|
||||
image_buffer: Tensor tf.string containing the contents of a JPEG file.
|
||||
label: Tensor tf.int32 containing the label.
|
||||
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
|
||||
where each coordinate is [0, 1) and the coordinates are arranged as
|
||||
[ymin, xmin, ymax, xmax].
|
||||
text: Tensor tf.string containing the human-readable label.
|
||||
"""
|
||||
# Dense features in Example proto.
|
||||
feature_map = {
|
||||
"image/encoded": tf.FixedLenFeature([], dtype=tf.string, default_value=""),
|
||||
"image/class/label": tf.FixedLenFeature([1], dtype=tf.int64, default_value=-1),
|
||||
"image/class/text": tf.FixedLenFeature([], dtype=tf.string, default_value=""),
|
||||
}
|
||||
sparse_float32 = tf.VarLenFeature(dtype=tf.float32)
|
||||
# Sparse features in Example proto.
|
||||
feature_map.update(
|
||||
{
|
||||
k: sparse_float32
|
||||
for k in [
|
||||
"image/object/bbox/xmin",
|
||||
"image/object/bbox/ymin",
|
||||
"image/object/bbox/xmax",
|
||||
"image/object/bbox/ymax",
|
||||
]
|
||||
}
|
||||
)
|
||||
|
||||
features = tf.parse_single_example(example_serialized, feature_map)
|
||||
label = tf.cast(features['image/class/label'], dtype=tf.int32)
|
||||
features = tf.parse_single_example(example_serialized, feature_map)
|
||||
label = tf.cast(features["image/class/label"], dtype=tf.int32)
|
||||
|
||||
xmin = tf.expand_dims(features['image/object/bbox/xmin'].values, 0)
|
||||
ymin = tf.expand_dims(features['image/object/bbox/ymin'].values, 0)
|
||||
xmax = tf.expand_dims(features['image/object/bbox/xmax'].values, 0)
|
||||
ymax = tf.expand_dims(features['image/object/bbox/ymax'].values, 0)
|
||||
xmin = tf.expand_dims(features["image/object/bbox/xmin"].values, 0)
|
||||
ymin = tf.expand_dims(features["image/object/bbox/ymin"].values, 0)
|
||||
xmax = tf.expand_dims(features["image/object/bbox/xmax"].values, 0)
|
||||
ymax = tf.expand_dims(features["image/object/bbox/ymax"].values, 0)
|
||||
|
||||
# Note that we impose an ordering of (y, x) just to make life difficult.
|
||||
bbox = tf.concat([ymin, xmin, ymax, xmax], 0)
|
||||
# Note that we impose an ordering of (y, x) just to make life difficult.
|
||||
bbox = tf.concat([ymin, xmin, ymax, xmax], 0)
|
||||
|
||||
# Force the variable number of bounding boxes into the shape
|
||||
# [1, num_boxes, coords].
|
||||
bbox = tf.expand_dims(bbox, 0)
|
||||
bbox = tf.transpose(bbox, [0, 2, 1])
|
||||
# Force the variable number of bounding boxes into the shape
|
||||
# [1, num_boxes, coords].
|
||||
bbox = tf.expand_dims(bbox, 0)
|
||||
bbox = tf.transpose(bbox, [0, 2, 1])
|
||||
|
||||
return features['image/encoded'], label, bbox, features['image/class/text']
|
||||
return features["image/encoded"], label, bbox, features["image/class/text"]
|
||||
|
||||
|
||||
class ImagenetPreprocessor:
|
||||
def __init__(self, image_size, dtype, train):
|
||||
self.image_size = image_size
|
||||
self.dtype = dtype
|
||||
self.train = train
|
||||
def __init__(self, image_size, dtype, train):
|
||||
self.image_size = image_size
|
||||
self.dtype = dtype
|
||||
self.train = train
|
||||
|
||||
def preprocess(self, image_buffer, bbox):
|
||||
# pylint: disable=g-import-not-at-top
|
||||
image = preprocess_image(image_buffer, bbox, self.image_size, self.image_size, IMAGE_DEPTH, is_training=self.train)
|
||||
return tf.cast(image, self.dtype)
|
||||
|
||||
def parse_and_preprocess(self, value):
|
||||
image_buffer, label_index, bbox, _ = parse_example_proto(value)
|
||||
image = self.preprocess(image_buffer, bbox)
|
||||
image = tf.reshape(image, [self.image_size, self.image_size, IMAGE_DEPTH])
|
||||
return label_index, image
|
||||
def preprocess(self, image_buffer, bbox):
|
||||
# pylint: disable=g-import-not-at-top
|
||||
image = preprocess_image(
|
||||
image_buffer,
|
||||
bbox,
|
||||
self.image_size,
|
||||
self.image_size,
|
||||
IMAGE_DEPTH,
|
||||
is_training=self.train,
|
||||
)
|
||||
return tf.cast(image, self.dtype)
|
||||
|
||||
def parse_and_preprocess(self, value):
|
||||
image_buffer, label_index, bbox, _ = parse_example_proto(value)
|
||||
image = self.preprocess(image_buffer, bbox)
|
||||
image = tf.reshape(image, [self.image_size, self.image_size, IMAGE_DEPTH])
|
||||
return label_index, image
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue