155 lines
4.2 KiB
Python
155 lines
4.2 KiB
Python
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import numpy as np
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import tensorflow as tf
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from tensorboard import util
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from tensorboard.plugins.beholder import colormaps
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# pylint: disable=not-context-manager
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def global_extrema(arrays):
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return min([x.min() for x in arrays]), max([x.max() for x in arrays])
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def scale_sections(sections, scaling_scope):
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'''
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input: unscaled sections.
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returns: sections scaled to [0, 255]
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'''
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new_sections = []
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if scaling_scope == 'layer':
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for section in sections:
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new_sections.append(scale_image_for_display(section))
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elif scaling_scope == 'network':
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global_min, global_max = global_extrema(sections)
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for section in sections:
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new_sections.append(scale_image_for_display(section,
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global_min,
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global_max))
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return new_sections
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def scale_image_for_display(image, minimum=None, maximum=None):
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image = image.astype(float)
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minimum = image.min() if minimum is None else minimum
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image -= minimum
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maximum = image.max() if maximum is None else maximum
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if maximum == 0:
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return image
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else:
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image *= 255 / maximum
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return image.astype(np.uint8)
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def pad_to_shape(array, shape, constant=245):
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padding = []
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for actual_dim, target_dim in zip(array.shape, shape):
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start_padding = 0
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end_padding = target_dim - actual_dim
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padding.append((start_padding, end_padding))
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return np.pad(array, padding, mode='constant', constant_values=constant)
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def apply_colormap(image, colormap='magma'):
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if colormap == 'grayscale':
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return image
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cm = getattr(colormaps, colormap)
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return image if cm is None else cm[image]
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class PNGDecoder(util.PersistentOpEvaluator):
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def __init__(self):
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super(PNGDecoder, self).__init__()
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self._image_placeholder = None
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self._decode_op = None
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def initialize_graph(self):
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self._image_placeholder = tf.placeholder(dtype=tf.string)
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self._decode_op = tf.image.decode_png(self._image_placeholder)
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# pylint: disable=arguments-differ
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def run(self, image):
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return self._decode_op.eval(feed_dict={
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self._image_placeholder: image,
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})
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class Resizer(util.PersistentOpEvaluator):
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def __init__(self):
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super(Resizer, self).__init__()
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self._image_placeholder = None
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self._size_placeholder = None
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self._resize_op = None
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def initialize_graph(self):
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self._image_placeholder = tf.placeholder(dtype=tf.float32)
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self._size_placeholder = tf.placeholder(dtype=tf.int32)
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self._resize_op = tf.image.resize_nearest_neighbor(self._image_placeholder,
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self._size_placeholder)
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# pylint: disable=arguments-differ
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def run(self, image, height, width):
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if len(image.shape) == 2:
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image = image.reshape([image.shape[0], image.shape[1], 1])
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resized = np.squeeze(self._resize_op.eval(feed_dict={
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self._image_placeholder: [image],
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self._size_placeholder: [height, width]
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}))
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return resized
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decode_png = PNGDecoder()
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resize = Resizer()
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def read_image(filename):
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with tf.gfile.Open(filename, 'rb') as image_file:
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return np.array(decode_png(image_file.read()))
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def write_image(array, filename):
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with tf.gfile.Open(filename, 'w') as image_file:
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image_file.write(util.encode_png(array))
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def get_image_relative_to_script(filename):
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script_directory = os.path.dirname(__file__)
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filename = os.path.join(script_directory, 'resources', filename)
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return read_image(filename)
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