You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
627 lines
23 KiB
627 lines
23 KiB
# 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.
|
|
# ==============================================================================
|
|
|
|
"""Tests for preprocessor_builder."""
|
|
|
|
import tensorflow as tf
|
|
|
|
from google.protobuf import text_format
|
|
|
|
from object_detection.builders import preprocessor_builder
|
|
from object_detection.core import preprocessor
|
|
from object_detection.protos import preprocessor_pb2
|
|
|
|
|
|
class PreprocessorBuilderTest(tf.test.TestCase):
|
|
|
|
def assert_dictionary_close(self, dict1, dict2):
|
|
"""Helper to check if two dicts with floatst or integers are close."""
|
|
self.assertEqual(sorted(dict1.keys()), sorted(dict2.keys()))
|
|
for key in dict1:
|
|
value = dict1[key]
|
|
if isinstance(value, float):
|
|
self.assertAlmostEqual(value, dict2[key])
|
|
else:
|
|
self.assertEqual(value, dict2[key])
|
|
|
|
def test_build_normalize_image(self):
|
|
preprocessor_text_proto = """
|
|
normalize_image {
|
|
original_minval: 0.0
|
|
original_maxval: 255.0
|
|
target_minval: -1.0
|
|
target_maxval: 1.0
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.normalize_image)
|
|
self.assertEqual(args, {
|
|
'original_minval': 0.0,
|
|
'original_maxval': 255.0,
|
|
'target_minval': -1.0,
|
|
'target_maxval': 1.0,
|
|
})
|
|
|
|
def test_build_random_horizontal_flip(self):
|
|
preprocessor_text_proto = """
|
|
random_horizontal_flip {
|
|
keypoint_flip_permutation: 1
|
|
keypoint_flip_permutation: 0
|
|
keypoint_flip_permutation: 2
|
|
keypoint_flip_permutation: 3
|
|
keypoint_flip_permutation: 5
|
|
keypoint_flip_permutation: 4
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_horizontal_flip)
|
|
self.assertEqual(args, {'keypoint_flip_permutation': (1, 0, 2, 3, 5, 4)})
|
|
|
|
def test_build_random_vertical_flip(self):
|
|
preprocessor_text_proto = """
|
|
random_vertical_flip {
|
|
keypoint_flip_permutation: 1
|
|
keypoint_flip_permutation: 0
|
|
keypoint_flip_permutation: 2
|
|
keypoint_flip_permutation: 3
|
|
keypoint_flip_permutation: 5
|
|
keypoint_flip_permutation: 4
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_vertical_flip)
|
|
self.assertEqual(args, {'keypoint_flip_permutation': (1, 0, 2, 3, 5, 4)})
|
|
|
|
def test_build_random_rotation90(self):
|
|
preprocessor_text_proto = """
|
|
random_rotation90 {}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_rotation90)
|
|
self.assertEqual(args, {})
|
|
|
|
def test_build_random_pixel_value_scale(self):
|
|
preprocessor_text_proto = """
|
|
random_pixel_value_scale {
|
|
minval: 0.8
|
|
maxval: 1.2
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_pixel_value_scale)
|
|
self.assert_dictionary_close(args, {'minval': 0.8, 'maxval': 1.2})
|
|
|
|
def test_build_random_image_scale(self):
|
|
preprocessor_text_proto = """
|
|
random_image_scale {
|
|
min_scale_ratio: 0.8
|
|
max_scale_ratio: 2.2
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_image_scale)
|
|
self.assert_dictionary_close(args, {'min_scale_ratio': 0.8,
|
|
'max_scale_ratio': 2.2})
|
|
|
|
def test_build_random_rgb_to_gray(self):
|
|
preprocessor_text_proto = """
|
|
random_rgb_to_gray {
|
|
probability: 0.8
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_rgb_to_gray)
|
|
self.assert_dictionary_close(args, {'probability': 0.8})
|
|
|
|
def test_build_random_adjust_brightness(self):
|
|
preprocessor_text_proto = """
|
|
random_adjust_brightness {
|
|
max_delta: 0.2
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_adjust_brightness)
|
|
self.assert_dictionary_close(args, {'max_delta': 0.2})
|
|
|
|
def test_build_random_adjust_contrast(self):
|
|
preprocessor_text_proto = """
|
|
random_adjust_contrast {
|
|
min_delta: 0.7
|
|
max_delta: 1.1
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_adjust_contrast)
|
|
self.assert_dictionary_close(args, {'min_delta': 0.7, 'max_delta': 1.1})
|
|
|
|
def test_build_random_adjust_hue(self):
|
|
preprocessor_text_proto = """
|
|
random_adjust_hue {
|
|
max_delta: 0.01
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_adjust_hue)
|
|
self.assert_dictionary_close(args, {'max_delta': 0.01})
|
|
|
|
def test_build_random_adjust_saturation(self):
|
|
preprocessor_text_proto = """
|
|
random_adjust_saturation {
|
|
min_delta: 0.75
|
|
max_delta: 1.15
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_adjust_saturation)
|
|
self.assert_dictionary_close(args, {'min_delta': 0.75, 'max_delta': 1.15})
|
|
|
|
def test_build_random_distort_color(self):
|
|
preprocessor_text_proto = """
|
|
random_distort_color {
|
|
color_ordering: 1
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_distort_color)
|
|
self.assertEqual(args, {'color_ordering': 1})
|
|
|
|
def test_build_random_jitter_boxes(self):
|
|
preprocessor_text_proto = """
|
|
random_jitter_boxes {
|
|
ratio: 0.1
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_jitter_boxes)
|
|
self.assert_dictionary_close(args, {'ratio': 0.1})
|
|
|
|
def test_build_random_crop_image(self):
|
|
preprocessor_text_proto = """
|
|
random_crop_image {
|
|
min_object_covered: 0.75
|
|
min_aspect_ratio: 0.75
|
|
max_aspect_ratio: 1.5
|
|
min_area: 0.25
|
|
max_area: 0.875
|
|
overlap_thresh: 0.5
|
|
clip_boxes: False
|
|
random_coef: 0.125
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_crop_image)
|
|
self.assertEqual(args, {
|
|
'min_object_covered': 0.75,
|
|
'aspect_ratio_range': (0.75, 1.5),
|
|
'area_range': (0.25, 0.875),
|
|
'overlap_thresh': 0.5,
|
|
'clip_boxes': False,
|
|
'random_coef': 0.125,
|
|
})
|
|
|
|
def test_build_random_pad_image(self):
|
|
preprocessor_text_proto = """
|
|
random_pad_image {
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_pad_image)
|
|
self.assertEqual(args, {
|
|
'min_image_size': None,
|
|
'max_image_size': None,
|
|
'pad_color': None,
|
|
})
|
|
|
|
def test_build_random_absolute_pad_image(self):
|
|
preprocessor_text_proto = """
|
|
random_absolute_pad_image {
|
|
max_height_padding: 50
|
|
max_width_padding: 100
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_absolute_pad_image)
|
|
self.assertEqual(args, {
|
|
'max_height_padding': 50,
|
|
'max_width_padding': 100,
|
|
'pad_color': None,
|
|
})
|
|
|
|
def test_build_random_crop_pad_image(self):
|
|
preprocessor_text_proto = """
|
|
random_crop_pad_image {
|
|
min_object_covered: 0.75
|
|
min_aspect_ratio: 0.75
|
|
max_aspect_ratio: 1.5
|
|
min_area: 0.25
|
|
max_area: 0.875
|
|
overlap_thresh: 0.5
|
|
clip_boxes: False
|
|
random_coef: 0.125
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_crop_pad_image)
|
|
self.assertEqual(args, {
|
|
'min_object_covered': 0.75,
|
|
'aspect_ratio_range': (0.75, 1.5),
|
|
'area_range': (0.25, 0.875),
|
|
'overlap_thresh': 0.5,
|
|
'clip_boxes': False,
|
|
'random_coef': 0.125,
|
|
'pad_color': None,
|
|
})
|
|
|
|
def test_build_random_crop_pad_image_with_optional_parameters(self):
|
|
preprocessor_text_proto = """
|
|
random_crop_pad_image {
|
|
min_object_covered: 0.75
|
|
min_aspect_ratio: 0.75
|
|
max_aspect_ratio: 1.5
|
|
min_area: 0.25
|
|
max_area: 0.875
|
|
overlap_thresh: 0.5
|
|
clip_boxes: False
|
|
random_coef: 0.125
|
|
min_padded_size_ratio: 0.5
|
|
min_padded_size_ratio: 0.75
|
|
max_padded_size_ratio: 0.5
|
|
max_padded_size_ratio: 0.75
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_crop_pad_image)
|
|
self.assertEqual(args, {
|
|
'min_object_covered': 0.75,
|
|
'aspect_ratio_range': (0.75, 1.5),
|
|
'area_range': (0.25, 0.875),
|
|
'overlap_thresh': 0.5,
|
|
'clip_boxes': False,
|
|
'random_coef': 0.125,
|
|
'min_padded_size_ratio': (0.5, 0.75),
|
|
'max_padded_size_ratio': (0.5, 0.75),
|
|
'pad_color': None,
|
|
})
|
|
|
|
def test_build_random_crop_to_aspect_ratio(self):
|
|
preprocessor_text_proto = """
|
|
random_crop_to_aspect_ratio {
|
|
aspect_ratio: 0.85
|
|
overlap_thresh: 0.35
|
|
clip_boxes: False
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_crop_to_aspect_ratio)
|
|
self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
|
|
'overlap_thresh': 0.35,
|
|
'clip_boxes': False})
|
|
|
|
def test_build_random_black_patches(self):
|
|
preprocessor_text_proto = """
|
|
random_black_patches {
|
|
max_black_patches: 20
|
|
probability: 0.95
|
|
size_to_image_ratio: 0.12
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_black_patches)
|
|
self.assert_dictionary_close(args, {'max_black_patches': 20,
|
|
'probability': 0.95,
|
|
'size_to_image_ratio': 0.12})
|
|
|
|
def test_build_random_resize_method(self):
|
|
preprocessor_text_proto = """
|
|
random_resize_method {
|
|
target_height: 75
|
|
target_width: 100
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_resize_method)
|
|
self.assert_dictionary_close(args, {'target_size': [75, 100]})
|
|
|
|
def test_build_scale_boxes_to_pixel_coordinates(self):
|
|
preprocessor_text_proto = """
|
|
scale_boxes_to_pixel_coordinates {}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.scale_boxes_to_pixel_coordinates)
|
|
self.assertEqual(args, {})
|
|
|
|
def test_build_resize_image(self):
|
|
preprocessor_text_proto = """
|
|
resize_image {
|
|
new_height: 75
|
|
new_width: 100
|
|
method: BICUBIC
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.resize_image)
|
|
self.assertEqual(args, {'new_height': 75,
|
|
'new_width': 100,
|
|
'method': tf.image.ResizeMethod.BICUBIC})
|
|
|
|
def test_build_rgb_to_gray(self):
|
|
preprocessor_text_proto = """
|
|
rgb_to_gray {}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.rgb_to_gray)
|
|
self.assertEqual(args, {})
|
|
|
|
def test_build_subtract_channel_mean(self):
|
|
preprocessor_text_proto = """
|
|
subtract_channel_mean {
|
|
means: [1.0, 2.0, 3.0]
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.subtract_channel_mean)
|
|
self.assertEqual(args, {'means': [1.0, 2.0, 3.0]})
|
|
|
|
def test_random_self_concat_image(self):
|
|
preprocessor_text_proto = """
|
|
random_self_concat_image {
|
|
concat_vertical_probability: 0.5
|
|
concat_horizontal_probability: 0.25
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.random_self_concat_image)
|
|
self.assertEqual(args, {'concat_vertical_probability': 0.5,
|
|
'concat_horizontal_probability': 0.25})
|
|
|
|
def test_build_ssd_random_crop(self):
|
|
preprocessor_text_proto = """
|
|
ssd_random_crop {
|
|
operations {
|
|
min_object_covered: 0.0
|
|
min_aspect_ratio: 0.875
|
|
max_aspect_ratio: 1.125
|
|
min_area: 0.5
|
|
max_area: 1.0
|
|
overlap_thresh: 0.0
|
|
clip_boxes: False
|
|
random_coef: 0.375
|
|
}
|
|
operations {
|
|
min_object_covered: 0.25
|
|
min_aspect_ratio: 0.75
|
|
max_aspect_ratio: 1.5
|
|
min_area: 0.5
|
|
max_area: 1.0
|
|
overlap_thresh: 0.25
|
|
clip_boxes: True
|
|
random_coef: 0.375
|
|
}
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.ssd_random_crop)
|
|
self.assertEqual(args, {'min_object_covered': [0.0, 0.25],
|
|
'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)],
|
|
'area_range': [(0.5, 1.0), (0.5, 1.0)],
|
|
'overlap_thresh': [0.0, 0.25],
|
|
'clip_boxes': [False, True],
|
|
'random_coef': [0.375, 0.375]})
|
|
|
|
def test_build_ssd_random_crop_empty_operations(self):
|
|
preprocessor_text_proto = """
|
|
ssd_random_crop {
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.ssd_random_crop)
|
|
self.assertEqual(args, {})
|
|
|
|
def test_build_ssd_random_crop_pad(self):
|
|
preprocessor_text_proto = """
|
|
ssd_random_crop_pad {
|
|
operations {
|
|
min_object_covered: 0.0
|
|
min_aspect_ratio: 0.875
|
|
max_aspect_ratio: 1.125
|
|
min_area: 0.5
|
|
max_area: 1.0
|
|
overlap_thresh: 0.0
|
|
clip_boxes: False
|
|
random_coef: 0.375
|
|
min_padded_size_ratio: [1.0, 1.0]
|
|
max_padded_size_ratio: [2.0, 2.0]
|
|
pad_color_r: 0.5
|
|
pad_color_g: 0.5
|
|
pad_color_b: 0.5
|
|
}
|
|
operations {
|
|
min_object_covered: 0.25
|
|
min_aspect_ratio: 0.75
|
|
max_aspect_ratio: 1.5
|
|
min_area: 0.5
|
|
max_area: 1.0
|
|
overlap_thresh: 0.25
|
|
clip_boxes: True
|
|
random_coef: 0.375
|
|
min_padded_size_ratio: [1.0, 1.0]
|
|
max_padded_size_ratio: [2.0, 2.0]
|
|
pad_color_r: 0.5
|
|
pad_color_g: 0.5
|
|
pad_color_b: 0.5
|
|
}
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.ssd_random_crop_pad)
|
|
self.assertEqual(args, {'min_object_covered': [0.0, 0.25],
|
|
'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)],
|
|
'area_range': [(0.5, 1.0), (0.5, 1.0)],
|
|
'overlap_thresh': [0.0, 0.25],
|
|
'clip_boxes': [False, True],
|
|
'random_coef': [0.375, 0.375],
|
|
'min_padded_size_ratio': [(1.0, 1.0), (1.0, 1.0)],
|
|
'max_padded_size_ratio': [(2.0, 2.0), (2.0, 2.0)],
|
|
'pad_color': [(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)]})
|
|
|
|
def test_build_ssd_random_crop_fixed_aspect_ratio(self):
|
|
preprocessor_text_proto = """
|
|
ssd_random_crop_fixed_aspect_ratio {
|
|
operations {
|
|
min_object_covered: 0.0
|
|
min_area: 0.5
|
|
max_area: 1.0
|
|
overlap_thresh: 0.0
|
|
clip_boxes: False
|
|
random_coef: 0.375
|
|
}
|
|
operations {
|
|
min_object_covered: 0.25
|
|
min_area: 0.5
|
|
max_area: 1.0
|
|
overlap_thresh: 0.25
|
|
clip_boxes: True
|
|
random_coef: 0.375
|
|
}
|
|
aspect_ratio: 0.875
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.ssd_random_crop_fixed_aspect_ratio)
|
|
self.assertEqual(args, {'min_object_covered': [0.0, 0.25],
|
|
'aspect_ratio': 0.875,
|
|
'area_range': [(0.5, 1.0), (0.5, 1.0)],
|
|
'overlap_thresh': [0.0, 0.25],
|
|
'clip_boxes': [False, True],
|
|
'random_coef': [0.375, 0.375]})
|
|
|
|
def test_build_ssd_random_crop_pad_fixed_aspect_ratio(self):
|
|
preprocessor_text_proto = """
|
|
ssd_random_crop_pad_fixed_aspect_ratio {
|
|
operations {
|
|
min_object_covered: 0.0
|
|
min_aspect_ratio: 0.875
|
|
max_aspect_ratio: 1.125
|
|
min_area: 0.5
|
|
max_area: 1.0
|
|
overlap_thresh: 0.0
|
|
clip_boxes: False
|
|
random_coef: 0.375
|
|
}
|
|
operations {
|
|
min_object_covered: 0.25
|
|
min_aspect_ratio: 0.75
|
|
max_aspect_ratio: 1.5
|
|
min_area: 0.5
|
|
max_area: 1.0
|
|
overlap_thresh: 0.25
|
|
clip_boxes: True
|
|
random_coef: 0.375
|
|
}
|
|
aspect_ratio: 0.875
|
|
min_padded_size_ratio: [1.0, 1.0]
|
|
max_padded_size_ratio: [2.0, 2.0]
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function,
|
|
preprocessor.ssd_random_crop_pad_fixed_aspect_ratio)
|
|
self.assertEqual(args, {'min_object_covered': [0.0, 0.25],
|
|
'aspect_ratio': 0.875,
|
|
'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)],
|
|
'area_range': [(0.5, 1.0), (0.5, 1.0)],
|
|
'overlap_thresh': [0.0, 0.25],
|
|
'clip_boxes': [False, True],
|
|
'random_coef': [0.375, 0.375],
|
|
'min_padded_size_ratio': (1.0, 1.0),
|
|
'max_padded_size_ratio': (2.0, 2.0)})
|
|
|
|
def test_build_normalize_image_convert_class_logits_to_softmax(self):
|
|
preprocessor_text_proto = """
|
|
convert_class_logits_to_softmax {
|
|
temperature: 2
|
|
}
|
|
"""
|
|
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
|
|
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
|
|
function, args = preprocessor_builder.build(preprocessor_proto)
|
|
self.assertEqual(function, preprocessor.convert_class_logits_to_softmax)
|
|
self.assertEqual(args, {'temperature': 2})
|
|
|
|
|
|
if __name__ == '__main__':
|
|
tf.test.main()
|