File size: 15,305 Bytes
18ddfe2 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 |
# 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 object_detection.utils.shape_utils."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
from object_detection.utils import shape_utils
from object_detection.utils import test_case
class UtilTest(test_case.TestCase):
def test_pad_tensor_using_integer_input(self):
print('........pad tensor using interger input.')
def graph_fn():
t1 = tf.constant([1], dtype=tf.int32)
pad_t1 = shape_utils.pad_tensor(t1, 2)
t2 = tf.constant([[0.1, 0.2]], dtype=tf.float32)
pad_t2 = shape_utils.pad_tensor(t2, 2)
return pad_t1, pad_t2
pad_t1_result, pad_t2_result = self.execute(graph_fn, [])
self.assertAllEqual([1, 0], pad_t1_result)
self.assertAllClose([[0.1, 0.2], [0, 0]], pad_t2_result)
def test_pad_tensor_using_tensor_input(self):
def graph_fn():
t1 = tf.constant([1], dtype=tf.int32)
pad_t1 = shape_utils.pad_tensor(t1, tf.constant(2))
t2 = tf.constant([[0.1, 0.2]], dtype=tf.float32)
pad_t2 = shape_utils.pad_tensor(t2, tf.constant(2))
return pad_t1, pad_t2
pad_t1_result, pad_t2_result = self.execute(graph_fn, [])
self.assertAllEqual([1, 0], pad_t1_result)
self.assertAllClose([[0.1, 0.2], [0, 0]], pad_t2_result)
def test_clip_tensor_using_integer_input(self):
def graph_fn():
t1 = tf.constant([1, 2, 3], dtype=tf.int32)
clip_t1 = shape_utils.clip_tensor(t1, 2)
t2 = tf.constant([[0.1, 0.2], [0.2, 0.4], [0.5, 0.8]], dtype=tf.float32)
clip_t2 = shape_utils.clip_tensor(t2, 2)
self.assertEqual(2, clip_t1.get_shape()[0])
self.assertEqual(2, clip_t2.get_shape()[0])
return clip_t1, clip_t2
clip_t1_result, clip_t2_result = self.execute(graph_fn, [])
self.assertAllEqual([1, 2], clip_t1_result)
self.assertAllClose([[0.1, 0.2], [0.2, 0.4]], clip_t2_result)
def test_clip_tensor_using_tensor_input(self):
def graph_fn():
t1 = tf.constant([1, 2, 3], dtype=tf.int32)
clip_t1 = shape_utils.clip_tensor(t1, tf.constant(2))
t2 = tf.constant([[0.1, 0.2], [0.2, 0.4], [0.5, 0.8]], dtype=tf.float32)
clip_t2 = shape_utils.clip_tensor(t2, tf.constant(2))
return clip_t1, clip_t2
clip_t1_result, clip_t2_result = self.execute(graph_fn, [])
self.assertAllEqual([1, 2], clip_t1_result)
self.assertAllClose([[0.1, 0.2], [0.2, 0.4]], clip_t2_result)
def test_pad_or_clip_tensor_using_integer_input(self):
def graph_fn():
t1 = tf.constant([1], dtype=tf.int32)
tt1 = shape_utils.pad_or_clip_tensor(t1, 2)
t2 = tf.constant([[0.1, 0.2]], dtype=tf.float32)
tt2 = shape_utils.pad_or_clip_tensor(t2, 2)
t3 = tf.constant([1, 2, 3], dtype=tf.int32)
tt3 = shape_utils.clip_tensor(t3, 2)
t4 = tf.constant([[0.1, 0.2], [0.2, 0.4], [0.5, 0.8]], dtype=tf.float32)
tt4 = shape_utils.clip_tensor(t4, 2)
self.assertEqual(2, tt1.get_shape()[0])
self.assertEqual(2, tt2.get_shape()[0])
self.assertEqual(2, tt3.get_shape()[0])
self.assertEqual(2, tt4.get_shape()[0])
return tt1, tt2, tt3, tt4
tt1_result, tt2_result, tt3_result, tt4_result = self.execute(graph_fn, [])
self.assertAllEqual([1, 0], tt1_result)
self.assertAllClose([[0.1, 0.2], [0, 0]], tt2_result)
self.assertAllEqual([1, 2], tt3_result)
self.assertAllClose([[0.1, 0.2], [0.2, 0.4]], tt4_result)
def test_pad_or_clip_tensor_using_tensor_input(self):
def graph_fn():
t1 = tf.constant([1], dtype=tf.int32)
tt1 = shape_utils.pad_or_clip_tensor(t1, tf.constant(2))
t2 = tf.constant([[0.1, 0.2]], dtype=tf.float32)
tt2 = shape_utils.pad_or_clip_tensor(t2, tf.constant(2))
t3 = tf.constant([1, 2, 3], dtype=tf.int32)
tt3 = shape_utils.clip_tensor(t3, tf.constant(2))
t4 = tf.constant([[0.1, 0.2], [0.2, 0.4], [0.5, 0.8]], dtype=tf.float32)
tt4 = shape_utils.clip_tensor(t4, tf.constant(2))
return tt1, tt2, tt3, tt4
tt1_result, tt2_result, tt3_result, tt4_result = self.execute(graph_fn, [])
self.assertAllEqual([1, 0], tt1_result)
self.assertAllClose([[0.1, 0.2], [0, 0]], tt2_result)
self.assertAllEqual([1, 2], tt3_result)
self.assertAllClose([[0.1, 0.2], [0.2, 0.4]], tt4_result)
def test_combined_static_dynamic_shape(self):
for n in [2, 3, 4]:
tensor = tf.zeros((n, 2, 3))
combined_shape = shape_utils.combined_static_and_dynamic_shape(
tensor)
self.assertListEqual(combined_shape[1:], [2, 3])
def test_pad_or_clip_nd_tensor(self):
def graph_fn(input_tensor):
output_tensor = shape_utils.pad_or_clip_nd(
input_tensor, [None, 3, 5, tf.constant(6)])
return output_tensor
for n in [2, 3, 4, 5]:
input_np = np.zeros((n, 5, 4, 7))
output_tensor_np = self.execute(graph_fn, [input_np])
self.assertAllEqual(output_tensor_np.shape[1:], [3, 5, 6])
class StaticOrDynamicMapFnTest(test_case.TestCase):
def test_with_dynamic_shape(self):
def fn(input_tensor):
return tf.reduce_sum(input_tensor)
def graph_fn(input_tensor):
return shape_utils.static_or_dynamic_map_fn(fn, input_tensor)
# The input has different shapes, but due to how self.execute()
# works, the shape is known at graph compile time.
result1 = self.execute(
graph_fn, [np.array([[1, 2], [3, 1], [0, 4]]),])
result2 = self.execute(
graph_fn, [np.array([[-1, 1], [0, 9]]),])
self.assertAllEqual(result1, [3, 4, 4])
self.assertAllEqual(result2, [0, 9])
def test_with_static_shape(self):
def fn(input_tensor):
return tf.reduce_sum(input_tensor)
def graph_fn():
input_tensor = tf.constant([[1, 2], [3, 1], [0, 4]], dtype=tf.float32)
return shape_utils.static_or_dynamic_map_fn(fn, input_tensor)
result = self.execute(graph_fn, [])
self.assertAllEqual(result, [3, 4, 4])
def test_with_multiple_dynamic_shapes(self):
def fn(elems):
input_tensor, scalar_index_tensor = elems
return tf.reshape(tf.slice(input_tensor, scalar_index_tensor, [1]), [])
def graph_fn(input_tensor, scalar_index_tensor):
map_fn_output = shape_utils.static_or_dynamic_map_fn(
fn, [input_tensor, scalar_index_tensor], dtype=tf.float32)
return map_fn_output
# The input has different shapes, but due to how self.execute()
# works, the shape is known at graph compile time.
result1 = self.execute(
graph_fn, [
np.array([[1, 2, 3], [4, 5, -1], [0, 6, 9]]),
np.array([[0], [2], [1]]),
])
result2 = self.execute(
graph_fn, [
np.array([[-1, 1, 0], [3, 9, 30]]),
np.array([[1], [0]])
])
self.assertAllEqual(result1, [1, -1, 6])
self.assertAllEqual(result2, [1, 3])
def test_with_multiple_static_shapes(self):
def fn(elems):
input_tensor, scalar_index_tensor = elems
return tf.reshape(tf.slice(input_tensor, scalar_index_tensor, [1]), [])
def graph_fn():
input_tensor = tf.constant([[1, 2, 3], [4, 5, -1], [0, 6, 9]],
dtype=tf.float32)
scalar_index_tensor = tf.constant([[0], [2], [1]], dtype=tf.int32)
map_fn_output = shape_utils.static_or_dynamic_map_fn(
fn, [input_tensor, scalar_index_tensor], dtype=tf.float32)
return map_fn_output
result = self.execute(graph_fn, [])
self.assertAllEqual(result, [1, -1, 6])
def test_fails_with_nested_input(self):
def fn(input_tensor):
return input_tensor
input_tensor1 = tf.constant([1])
input_tensor2 = tf.constant([2])
with self.assertRaisesRegexp(
ValueError, '`elems` must be a Tensor or list of Tensors.'):
shape_utils.static_or_dynamic_map_fn(
fn, [input_tensor1, [input_tensor2]], dtype=tf.float32)
class CheckMinImageShapeTest(test_case.TestCase):
def test_check_min_image_dim_static_shape(self):
input_tensor = tf.constant(np.zeros([1, 42, 42, 3]))
_ = shape_utils.check_min_image_dim(33, input_tensor)
with self.assertRaisesRegexp(
ValueError, 'image size must be >= 64 in both height and width.'):
_ = shape_utils.check_min_image_dim(64, input_tensor)
def test_check_min_image_dim_dynamic_shape(self):
def graph_fn(input_tensor):
return shape_utils.check_min_image_dim(33, input_tensor)
self.execute(graph_fn,
[np.zeros([1, 42, 42, 3])])
self.assertRaises(
ValueError, self.execute,
graph_fn, np.zeros([1, 32, 32, 3])
)
class AssertShapeEqualTest(test_case.TestCase):
def test_unequal_static_shape_raises_exception(self):
shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
shape_b = tf.constant(np.zeros([4, 2, 3, 1]))
self.assertRaisesRegex(
ValueError, 'Unequal shapes',
shape_utils.assert_shape_equal,
shape_utils.combined_static_and_dynamic_shape(shape_a),
shape_utils.combined_static_and_dynamic_shape(shape_b)
)
def test_equal_static_shape_succeeds(self):
def graph_fn():
shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
shape_b = tf.constant(np.zeros([4, 2, 2, 1]))
shape_utils.assert_shape_equal(
shape_utils.combined_static_and_dynamic_shape(shape_a),
shape_utils.combined_static_and_dynamic_shape(shape_b))
return tf.constant(0)
self.execute(graph_fn, [])
def test_unequal_dynamic_shape_raises_tf_assert(self):
def graph_fn(tensor_a, tensor_b):
shape_utils.assert_shape_equal(
shape_utils.combined_static_and_dynamic_shape(tensor_a),
shape_utils.combined_static_and_dynamic_shape(tensor_b))
return tf.constant(0)
self.assertRaises(ValueError,
self.execute, graph_fn,
[np.zeros([1, 2, 2, 3]), np.zeros([1, 4, 4, 3])])
def test_equal_dynamic_shape_succeeds(self):
def graph_fn(tensor_a, tensor_b):
shape_utils.assert_shape_equal(
shape_utils.combined_static_and_dynamic_shape(tensor_a),
shape_utils.combined_static_and_dynamic_shape(tensor_b)
)
return tf.constant(0)
self.execute(graph_fn, [np.zeros([1, 2, 2, 3]),
np.zeros([1, 2, 2, 3])])
def test_unequal_static_shape_along_first_dim_raises_exception(self):
shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
shape_b = tf.constant(np.zeros([6, 2, 3, 1]))
self.assertRaisesRegexp(
ValueError, 'Unequal first dimension',
shape_utils.assert_shape_equal_along_first_dimension,
shape_utils.combined_static_and_dynamic_shape(shape_a),
shape_utils.combined_static_and_dynamic_shape(shape_b)
)
def test_equal_static_shape_along_first_dim_succeeds(self):
def graph_fn():
shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
shape_b = tf.constant(np.zeros([4, 7, 2]))
shape_utils.assert_shape_equal_along_first_dimension(
shape_utils.combined_static_and_dynamic_shape(shape_a),
shape_utils.combined_static_and_dynamic_shape(shape_b))
return tf.constant(0)
self.execute(graph_fn, [])
def test_unequal_dynamic_shape_along_first_dim_raises_tf_assert(self):
def graph_fn(tensor_a, tensor_b):
shape_utils.assert_shape_equal_along_first_dimension(
shape_utils.combined_static_and_dynamic_shape(tensor_a),
shape_utils.combined_static_and_dynamic_shape(tensor_b))
return tf.constant(0)
self.assertRaises(ValueError,
self.execute, graph_fn,
[np.zeros([1, 2, 2, 3]), np.zeros([2, 4, 3])])
def test_equal_dynamic_shape_along_first_dim_succeeds(self):
def graph_fn(tensor_a, tensor_b):
shape_utils.assert_shape_equal_along_first_dimension(
shape_utils.combined_static_and_dynamic_shape(tensor_a),
shape_utils.combined_static_and_dynamic_shape(tensor_b))
return tf.constant(0)
self.execute(graph_fn, [np.zeros([5, 2, 2, 3]), np.zeros([5])])
class FlattenExpandDimensionTest(test_case.TestCase):
def test_flatten_given_dims(self):
def graph_fn():
inputs = tf.random_uniform([5, 2, 10, 10, 3])
actual_flattened = shape_utils.flatten_dimensions(inputs, first=1, last=3)
expected_flattened = tf.reshape(inputs, [5, 20, 10, 3])
return actual_flattened, expected_flattened
(actual_flattened_np,
expected_flattened_np) = self.execute(graph_fn, [])
self.assertAllClose(expected_flattened_np, actual_flattened_np)
def test_raises_value_error_incorrect_dimensions(self):
inputs = tf.random_uniform([5, 2, 10, 10, 3])
self.assertRaises(ValueError,
shape_utils.flatten_dimensions, inputs,
first=0, last=6)
def test_flatten_first_two_dimensions(self):
def graph_fn():
inputs = tf.constant(
[
[[1, 2], [3, 4]],
[[5, 6], [7, 8]],
[[9, 10], [11, 12]]
], dtype=tf.int32)
flattened_tensor = shape_utils.flatten_first_n_dimensions(
inputs, 2)
return flattened_tensor
flattened_tensor_out = self.execute(graph_fn, [])
expected_output = [[1, 2],
[3, 4],
[5, 6],
[7, 8],
[9, 10],
[11, 12]]
self.assertAllEqual(expected_output, flattened_tensor_out)
def test_expand_first_dimension(self):
def graph_fn():
inputs = tf.constant(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
[9, 10],
[11, 12]
], dtype=tf.int32)
dims = [3, 2]
expanded_tensor = shape_utils.expand_first_dimension(
inputs, dims)
return expanded_tensor
expanded_tensor_out = self.execute(graph_fn, [])
expected_output = [
[[1, 2], [3, 4]],
[[5, 6], [7, 8]],
[[9, 10], [11, 12]]]
self.assertAllEqual(expected_output, expanded_tensor_out)
def test_expand_first_dimension_with_incompatible_dims(self):
def graph_fn():
inputs = tf.constant(
[
[[1, 2]],
[[3, 4]],
[[5, 6]],
], dtype=tf.int32)
dims = [3, 2]
expanded_tensor = shape_utils.expand_first_dimension(
inputs, dims)
return expanded_tensor
self.assertRaises(ValueError, self.execute, graph_fn, [])
if __name__ == '__main__':
tf.test.main()
|