Spaces:
Runtime error
Runtime error
File size: 7,959 Bytes
5672777 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
# Copyright 2023 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.
"""The panoptic quality evaluator.
The following snippet demonstrates the use of interfaces:
evaluator = PanopticQualityEvaluator(...)
for _ in range(num_evals):
for _ in range(num_batches_per_eval):
predictions, groundtruth = predictor.predict(...) # pop a batch.
evaluator.update_state(groundtruths, predictions)
evaluator.result() # finish one full eval and reset states.
See also: https://github.com/cocodataset/cocoapi/
"""
import numpy as np
import tensorflow as tf, tf_keras
from official.vision.evaluation import panoptic_quality
def _crop_padding(mask, image_info):
"""Crops padded masks to match original image shape.
Args:
mask: a padded mask tensor.
image_info: a tensor that holds information about original and preprocessed
images.
Returns:
cropped and padded masks: tf.Tensor
"""
image_shape = tf.cast(image_info[0, :], tf.int32)
mask = tf.image.crop_to_bounding_box(
tf.expand_dims(mask, axis=-1), 0, 0,
image_shape[0], image_shape[1])
return tf.expand_dims(mask[:, :, 0], axis=0)
class PanopticQualityEvaluator:
"""Panoptic Quality metric class."""
def __init__(self, num_categories, ignored_label, max_instances_per_category,
offset, is_thing=None, rescale_predictions=False):
"""Constructs Panoptic Quality evaluation class.
The class provides the interface to Panoptic Quality metrics_fn.
Args:
num_categories: The number of segmentation categories (or "classes" in the
dataset).
ignored_label: A category id that is ignored in evaluation, e.g. the void
label as defined in COCO panoptic segmentation dataset.
max_instances_per_category: The maximum number of instances for each
category. Used in ensuring unique instance labels.
offset: The maximum number of unique labels. This is used, by multiplying
the ground-truth labels, to generate unique ids for individual regions
of overlap between ground-truth and predicted segments.
is_thing: A boolean array of length `num_categories`. The entry
`is_thing[category_id]` is True iff that category is a "thing" category
instead of "stuff." Default to `None`, and it means categories are not
classified into these two categories.
rescale_predictions: `bool`, whether to scale back prediction to original
image sizes. If True, groundtruths['image_info'] is used to rescale
predictions.
"""
self._pq_metric_module = panoptic_quality.PanopticQuality(
num_categories, ignored_label, max_instances_per_category, offset)
self._is_thing = is_thing
self._rescale_predictions = rescale_predictions
self._required_prediction_fields = ['category_mask', 'instance_mask']
self._required_groundtruth_fields = ['category_mask', 'instance_mask']
self.reset_states()
@property
def name(self):
return 'panoptic_quality'
def reset_states(self):
"""Resets internal states for a fresh run."""
self._pq_metric_module.reset()
def result(self):
"""Evaluates detection results, and reset_states."""
results = self._pq_metric_module.result(self._is_thing)
self.reset_states()
return results
def _convert_to_numpy(self, groundtruths, predictions):
"""Converts tesnors to numpy arrays."""
if groundtruths:
labels = tf.nest.map_structure(lambda x: x.numpy(), groundtruths)
numpy_groundtruths = {}
for key, val in labels.items():
if isinstance(val, tuple):
val = np.concatenate(val)
numpy_groundtruths[key] = val
else:
numpy_groundtruths = groundtruths
if predictions:
outputs = tf.nest.map_structure(lambda x: x.numpy(), predictions)
numpy_predictions = {}
for key, val in outputs.items():
if isinstance(val, tuple):
val = np.concatenate(val)
numpy_predictions[key] = val
else:
numpy_predictions = predictions
return numpy_groundtruths, numpy_predictions
def update_state(self, groundtruths, predictions):
"""Update and aggregate detection results and ground-truth data.
Args:
groundtruths: a dictionary of Tensors including the fields below. See also
different parsers under `../dataloader` for more details.
Required fields:
- category_mask: a numpy array of uint16 of shape [batch_size, H, W].
- instance_mask: a numpy array of uint16 of shape [batch_size, H, W].
- image_info: [batch, 4, 2], a tensor that holds information about
original and preprocessed images. Each entry is in the format of
[[original_height, original_width], [input_height, input_width],
[y_scale, x_scale], [y_offset, x_offset]], where [desired_height,
desired_width] is the actual scaled image size, and [y_scale, x_scale]
is the scaling factor, which is the ratio of scaled dimension /
original dimension.
predictions: a dictionary of tensors including the fields below. See
different parsers under `../dataloader` for more details.
Required fields:
- category_mask: a numpy array of uint16 of shape [batch_size, H, W].
- instance_mask: a numpy array of uint16 of shape [batch_size, H, W].
Raises:
ValueError: if the required prediction or ground-truth fields are not
present in the incoming `predictions` or `groundtruths`.
"""
groundtruths, predictions = self._convert_to_numpy(groundtruths,
predictions)
for k in self._required_prediction_fields:
if k not in predictions:
raise ValueError(
'Missing the required key `{}` in predictions!'.format(k))
for k in self._required_groundtruth_fields:
if k not in groundtruths:
raise ValueError(
'Missing the required key `{}` in groundtruths!'.format(k))
if self._rescale_predictions:
for idx in range(len(groundtruths['category_mask'])):
image_info = groundtruths['image_info'][idx]
groundtruths_ = {
'category_mask':
_crop_padding(groundtruths['category_mask'][idx], image_info),
'instance_mask':
_crop_padding(groundtruths['instance_mask'][idx], image_info),
}
predictions_ = {
'category_mask':
_crop_padding(predictions['category_mask'][idx], image_info),
'instance_mask':
_crop_padding(predictions['instance_mask'][idx], image_info),
}
groundtruths_, predictions_ = self._convert_to_numpy(
groundtruths_, predictions_)
self._pq_metric_module.compare_and_accumulate(
groundtruths_, predictions_)
else:
for idx in range(len(groundtruths['category_mask'])):
groundtruths_ = {
'category_mask': groundtruths['category_mask'][idx],
'instance_mask': groundtruths['instance_mask'][idx]
}
predictions_ = {
'category_mask': predictions['category_mask'][idx],
'instance_mask': predictions['instance_mask'][idx]
}
self._pq_metric_module.compare_and_accumulate(groundtruths_,
predictions_)
|