397 lines
16 KiB
Python
397 lines
16 KiB
Python
# Copyright 2015 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.
|
|
# ==============================================================================
|
|
"""Functional tests for SpacetoDepth op."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
|
|
from tensorflow.python.framework import constant_op
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.framework import test_util
|
|
from tensorflow.python.ops import array_ops
|
|
from tensorflow.python.ops import gen_array_ops
|
|
from tensorflow.python.ops import gradient_checker
|
|
from tensorflow.python.ops import math_ops
|
|
from tensorflow.python.platform import test
|
|
from tensorflow.python.platform import tf_logging
|
|
|
|
|
|
class SpaceToDepthTest(test.TestCase):
|
|
|
|
def _testOne(self, inputs, block_size, outputs, dtype=dtypes.float32):
|
|
input_nhwc = math_ops.cast(inputs, dtype)
|
|
with test_util.force_cpu():
|
|
# test NHWC (default) on CPU
|
|
x_tf = array_ops.space_to_depth(input_nhwc, block_size)
|
|
self.assertAllEqual(self.evaluate(x_tf), outputs)
|
|
|
|
if test_util.is_gpu_available():
|
|
with test_util.force_gpu():
|
|
# test NHWC (default) on GPU
|
|
x_tf = array_ops.space_to_depth(input_nhwc, block_size)
|
|
self.assertAllEqual(self.evaluate(x_tf), outputs)
|
|
# test NCHW on GPU
|
|
input_nchw = test_util.NHWCToNCHW(input_nhwc)
|
|
output_nchw = array_ops.space_to_depth(
|
|
input_nchw, block_size, data_format="NCHW")
|
|
output_nhwc = test_util.NCHWToNHWC(output_nchw)
|
|
self.assertAllEqual(self.evaluate(output_nhwc), outputs)
|
|
|
|
def testBasic(self):
|
|
x_np = [[[[1], [2]], [[3], [4]]]]
|
|
block_size = 2
|
|
x_out = [[[[1, 2, 3, 4]]]]
|
|
for dtype in [dtypes.float32, dtypes.float16, dtypes.uint8]:
|
|
self._testOne(x_np, block_size, x_out, dtype=dtype)
|
|
|
|
|
|
# Tests for larger input dimensions. To make sure elements are
|
|
# correctly ordered spatially.
|
|
def testLargerInput2x2(self):
|
|
x_np = [[[[1], [2], [5], [6]], [[3], [4], [7], [8]],
|
|
[[9], [10], [13], [14]], [[11], [12], [15], [16]]]]
|
|
block_size = 2
|
|
x_out = [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12],
|
|
[13, 14, 15, 16]]]]
|
|
self._testOne(x_np, block_size, x_out)
|
|
|
|
# Tests for larger input dimensions. To make sure elements are
|
|
# correctly ordered in depth. Here, larger block size.
|
|
def testLargerInput4x4(self):
|
|
x_np = [[[[1], [2], [5], [6]], [[3], [4], [7], [8]],
|
|
[[9], [10], [13], [14]], [[11], [12], [15], [16]]]]
|
|
block_size = 4
|
|
x_out = [[[[1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 13, 14, 11, 12, 15, 16]]]]
|
|
self._testOne(x_np, block_size, x_out)
|
|
|
|
# Tests for larger input depths.
|
|
# To make sure elements are properly interleaved in depth.
|
|
def testDepthInterleaved(self):
|
|
x_np = [[[[1, 10], [2, 20]], [[3, 30], [4, 40]]]]
|
|
block_size = 2
|
|
x_out = [[[[1, 10, 2, 20, 3, 30, 4, 40]]]]
|
|
self._testOne(x_np, block_size, x_out)
|
|
|
|
# Tests for larger input depths. Here an odd depth.
|
|
# To make sure elements are properly interleaved in depth.
|
|
def testDepthInterleavedDepth3(self):
|
|
x_np = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]]
|
|
block_size = 2
|
|
x_out = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
|
|
self._testOne(x_np, block_size, x_out)
|
|
|
|
# Tests for larger input dimensions AND for larger input depths.
|
|
# To make sure elements are properly interleaved in depth and ordered
|
|
# spatially.
|
|
def testDepthInterleavedLarge(self):
|
|
x_np = [[[[1, 10], [2, 20], [5, 50], [6, 60]],
|
|
[[3, 30], [4, 40], [7, 70], [8, 80]],
|
|
[[9, 90], [10, 100], [13, 130], [14, 140]],
|
|
[[11, 110], [12, 120], [15, 150], [16, 160]]]]
|
|
block_size = 2
|
|
x_out = [[[[1, 10, 2, 20, 3, 30, 4, 40], [5, 50, 6, 60, 7, 70, 8, 80]],
|
|
[[9, 90, 10, 100, 11, 110, 12, 120],
|
|
[13, 130, 14, 140, 15, 150, 16, 160]]]]
|
|
self._testOne(x_np, block_size, x_out)
|
|
|
|
def testBlockSize2Batch10(self):
|
|
block_size = 2
|
|
|
|
def batch_input_elt(i):
|
|
return [[[1 * i], [2 * i], [5 * i], [6 * i]],
|
|
[[3 * i], [4 * i], [7 * i], [8 * i]],
|
|
[[9 * i], [10 * i], [13 * i], [14 * i]],
|
|
[[11 * i], [12 * i], [15 * i], [16 * i]]]
|
|
|
|
def batch_output_elt(i):
|
|
return [[[1 * i, 2 * i, 3 * i, 4 * i], [5 * i, 6 * i, 7 * i, 8 * i]],
|
|
[[9 * i, 10 * i, 11 * i, 12 * i],
|
|
[13 * i, 14 * i, 15 * i, 16 * i]]]
|
|
|
|
batch_size = 10
|
|
x_np = [batch_input_elt(i) for i in range(batch_size)]
|
|
x_out = [batch_output_elt(i) for i in range(batch_size)]
|
|
self._testOne(x_np, block_size, x_out)
|
|
|
|
def testBatchSize0(self):
|
|
block_size = 2
|
|
batch_size = 0
|
|
input_nhwc = array_ops.ones([batch_size, 4, 6, 3])
|
|
x_out = array_ops.ones([batch_size, 2, 3, 12])
|
|
|
|
with test_util.force_cpu():
|
|
# test NHWC (default) on CPU
|
|
x_tf = array_ops.space_to_depth(input_nhwc, block_size)
|
|
self.assertAllEqual(x_tf.shape, x_out.shape)
|
|
self.evaluate(x_tf)
|
|
|
|
if test.is_gpu_available():
|
|
with test_util.use_gpu():
|
|
# test NHWC (default) on GPU
|
|
x_tf = array_ops.space_to_depth(input_nhwc, block_size)
|
|
self.assertAllEqual(x_tf.shape, x_out.shape)
|
|
self.evaluate(x_tf)
|
|
|
|
# Tests for different width and height.
|
|
def testNonSquare(self):
|
|
x_np = [[[[1, 10], [2, 20]], [[3, 30], [4, 40]], [[5, 50], [6, 60]],
|
|
[[7, 70], [8, 80]], [[9, 90], [10, 100]], [[11, 110], [12, 120]]]]
|
|
block_size = 2
|
|
x_out = [[[[1, 10, 2, 20, 3, 30, 4, 40]], [[5, 50, 6, 60, 7, 70, 8, 80]],
|
|
[[9, 90, 10, 100, 11, 110, 12, 120]]]]
|
|
self._testOne(x_np, block_size, x_out)
|
|
|
|
# Error handling:
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testInputWrongDimMissingDepth(self):
|
|
# The input is missing the last dimension ("depth")
|
|
x_np = [[[1, 2], [3, 4]]]
|
|
block_size = 2
|
|
with self.assertRaises(ValueError):
|
|
out_tf = array_ops.space_to_depth(x_np, block_size)
|
|
self.evaluate(out_tf)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testInputWrongDimMissingBatch(self):
|
|
# The input is missing the first dimension ("batch")
|
|
x_np = [[[1], [2]], [[3], [4]]]
|
|
block_size = 2
|
|
with self.assertRaises(ValueError):
|
|
_ = array_ops.space_to_depth(x_np, block_size)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testBlockSize0(self):
|
|
# The block size is 0.
|
|
x_np = [[[[1], [2]], [[3], [4]]]]
|
|
block_size = 0
|
|
with self.assertRaises(ValueError):
|
|
out_tf = array_ops.space_to_depth(x_np, block_size)
|
|
self.evaluate(out_tf)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testBlockSizeOne(self):
|
|
# The block size is 1. The block size needs to be > 1.
|
|
x_np = [[[[1], [2]], [[3], [4]]]]
|
|
block_size = 1
|
|
with self.assertRaises(ValueError):
|
|
out_tf = array_ops.space_to_depth(x_np, block_size)
|
|
self.evaluate(out_tf)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testBlockSizeLarger(self):
|
|
# The block size is too large for this input.
|
|
x_np = [[[[1], [2]], [[3], [4]]]]
|
|
block_size = 10
|
|
with self.assertRaises(ValueError):
|
|
out_tf = array_ops.space_to_depth(x_np, block_size)
|
|
self.evaluate(out_tf)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testBlockSizeNotDivisibleWidth(self):
|
|
# The block size divides width but not height.
|
|
x_np = [[[[1], [2], [3]], [[3], [4], [7]]]]
|
|
block_size = 3
|
|
with self.assertRaises(ValueError):
|
|
_ = array_ops.space_to_depth(x_np, block_size)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testBlockSizeNotDivisibleHeight(self):
|
|
# The block size divides height but not width.
|
|
x_np = [[[[1], [2]], [[3], [4]], [[5], [6]]]]
|
|
block_size = 3
|
|
with self.assertRaises(ValueError):
|
|
_ = array_ops.space_to_depth(x_np, block_size)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testBlockSizeNotDivisibleBoth(self):
|
|
# The block size does not divide neither width or height.
|
|
x_np = [[[[1], [2]], [[3], [4]]]]
|
|
block_size = 3
|
|
with self.assertRaises(ValueError):
|
|
_ = array_ops.space_to_depth(x_np, block_size)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testUnknownShape(self):
|
|
t = array_ops.space_to_depth(
|
|
array_ops.placeholder(dtypes.float32), block_size=4)
|
|
self.assertEqual(4, t.get_shape().ndims)
|
|
|
|
def spaceToDepthUsingTranspose(self, tensor, block_size, data_format):
|
|
block_size_sq = block_size * block_size
|
|
|
|
dtype = tensor.dtype
|
|
if dtype == dtypes.qint8:
|
|
tensor = array_ops.bitcast(tensor, dtypes.int8)
|
|
|
|
if data_format == "NHWC":
|
|
b, ih, iw, ic = tensor.shape.as_list()
|
|
assert ih % block_size == 0, (ih, block_size)
|
|
assert iw % block_size == 0, (iw, block_size)
|
|
ow, oh, oc = iw // block_size, ih // block_size, ic * block_size_sq
|
|
tensor = array_ops.reshape(tensor,
|
|
[b, oh, block_size, ow, block_size, ic])
|
|
tensor = array_ops.transpose(tensor, [0, 1, 3, 2, 4, 5])
|
|
tensor = array_ops.reshape(tensor, [b, oh, ow, oc])
|
|
elif data_format == "NCHW":
|
|
b, ic, ih, iw = tensor.shape.as_list()
|
|
assert ih % block_size == 0, (ih, block_size)
|
|
assert iw % block_size == 0, (iw, block_size)
|
|
ow, oh, oc = iw // block_size, ih // block_size, ic * block_size_sq
|
|
tensor = array_ops.reshape(tensor,
|
|
[b, ic, oh, block_size, ow, block_size])
|
|
tensor = array_ops.transpose(tensor, [0, 3, 5, 1, 2, 4])
|
|
tensor = array_ops.reshape(tensor, [b, oc, oh, ow])
|
|
|
|
if dtype == dtypes.qint8:
|
|
tensor = array_ops.bitcast(tensor, dtype)
|
|
return tensor
|
|
|
|
def compareToTranspose(self, batch_size, out_height, out_width, in_channels,
|
|
block_size, data_format, data_type, use_gpu):
|
|
in_height = out_height * block_size
|
|
in_width = out_width * block_size
|
|
nhwc_input_shape = [batch_size, in_height, in_width, in_channels]
|
|
nchw_input_shape = [batch_size, in_channels, in_height, in_width]
|
|
total_size = np.prod(nhwc_input_shape)
|
|
|
|
# Construct the input tensor in data_type and NHWC.
|
|
# force_cpu is needed because quantize_v2 runs on only CPU.
|
|
with test_util.force_cpu():
|
|
if data_type == dtypes.qint8:
|
|
# Initialize the input tensor with qint8 values that circle -127..127.
|
|
x = [((f + 128) % 255) - 127 for f in range(total_size)]
|
|
t = constant_op.constant(
|
|
x, shape=nhwc_input_shape, dtype=dtypes.float32)
|
|
t, _, _ = gen_array_ops.quantize_v2(t, -128.0, 127.0, dtypes.qint8)
|
|
else:
|
|
assert data_type == dtypes.float32
|
|
# Initialize the input tensor with ascending whole numbers as floats.
|
|
x = [f * 1.0 for f in range(total_size)]
|
|
shape = nchw_input_shape if data_format == "NCHW" else nhwc_input_shape
|
|
t = constant_op.constant(x, shape=shape, dtype=dtypes.float32)
|
|
|
|
with test_util.device(use_gpu):
|
|
if data_format == "NCHW_VECT_C":
|
|
assert data_type == dtypes.qint8
|
|
|
|
# Convert to int8, then NHWCToNCHW_VECT_C, and then back to qint8.
|
|
actual = array_ops.bitcast(t, dtypes.int8)
|
|
actual = test_util.NHWCToNCHW_VECT_C(actual)
|
|
actual = array_ops.bitcast(actual, dtypes.qint8)
|
|
actual = array_ops.space_to_depth(
|
|
actual, block_size, data_format=data_format)
|
|
actual = array_ops.bitcast(actual, dtypes.int8)
|
|
actual = test_util.NCHW_VECT_CToNHWC(actual)
|
|
actual = array_ops.bitcast(actual, dtypes.qint8)
|
|
|
|
expected = array_ops.bitcast(t, dtypes.int8)
|
|
expected = math_ops.cast(expected, dtypes.float32)
|
|
expected = self.spaceToDepthUsingTranspose(expected, block_size, "NHWC")
|
|
expected = math_ops.cast(expected, dtypes.int8)
|
|
expected = array_ops.bitcast(expected, dtypes.qint8)
|
|
else:
|
|
# Initialize the input tensor with ascending whole numbers as floats.
|
|
actual = array_ops.space_to_depth(
|
|
t, block_size, data_format=data_format)
|
|
expected = self.spaceToDepthUsingTranspose(t, block_size, data_format)
|
|
|
|
actual_vals, expected_vals = self.evaluate([actual, expected])
|
|
self.assertTrue(np.array_equal(actual_vals, expected_vals))
|
|
|
|
# TODO(jingyue): figure out why this test failed in eager mode.
|
|
@test_util.run_deprecated_v1
|
|
def testAgainstTranspose(self):
|
|
self.compareToTranspose(3, 2, 3, 1, 2, "NHWC", dtypes.float32, False)
|
|
self.compareToTranspose(1, 2, 3, 2, 2, "NHWC", dtypes.float32, False)
|
|
self.compareToTranspose(1, 2, 3, 2, 3, "NHWC", dtypes.float32, False)
|
|
|
|
self.compareToTranspose(3, 2, 3, 1, 2, "NHWC", dtypes.qint8, False)
|
|
self.compareToTranspose(1, 2, 3, 2, 2, "NHWC", dtypes.qint8, False)
|
|
self.compareToTranspose(1, 2, 3, 2, 3, "NHWC", dtypes.qint8, False)
|
|
|
|
if not test.is_gpu_available():
|
|
tf_logging.info("skipping gpu tests since gpu not available")
|
|
return
|
|
|
|
self.compareToTranspose(3, 2, 3, 1, 2, "NHWC", dtypes.float32, True)
|
|
self.compareToTranspose(3, 2, 3, 2, 2, "NHWC", dtypes.float32, True)
|
|
self.compareToTranspose(3, 2, 3, 1, 2, "NCHW", dtypes.float32, True)
|
|
self.compareToTranspose(3, 2, 3, 2, 3, "NCHW", dtypes.float32, True)
|
|
self.compareToTranspose(5, 7, 11, 3, 2, "NCHW", dtypes.float32, True)
|
|
|
|
self.compareToTranspose(3, 2, 3, 4, 2, "NCHW_VECT_C", dtypes.qint8, True)
|
|
self.compareToTranspose(3, 2, 3, 8, 3, "NCHW_VECT_C", dtypes.qint8, True)
|
|
self.compareToTranspose(5, 7, 11, 12, 2, "NCHW_VECT_C", dtypes.qint8, True)
|
|
|
|
|
|
class SpaceToDepthGradientTest(test.TestCase):
|
|
|
|
# Check the gradients.
|
|
def _checkGrad(self, x, block_size, data_format):
|
|
# NCHW is implemented for only GPU.
|
|
if data_format == "NCHW" and not test.is_gpu_available():
|
|
return
|
|
|
|
assert 4 == x.ndim
|
|
with self.cached_session(use_gpu=True):
|
|
tf_x = ops.convert_to_tensor(x)
|
|
tf_y = array_ops.space_to_depth(tf_x, block_size, data_format=data_format)
|
|
epsilon = 1e-2
|
|
((x_jacob_t, x_jacob_n)) = gradient_checker.compute_gradient(
|
|
tf_x,
|
|
x.shape,
|
|
tf_y,
|
|
tf_y.get_shape().as_list(),
|
|
x_init_value=x,
|
|
delta=epsilon)
|
|
|
|
self.assertAllClose(x_jacob_t, x_jacob_n, rtol=1e-2, atol=epsilon)
|
|
|
|
# Tests a gradient for space_to_depth of x which is a four dimensional
|
|
# tensor of shape [b, h * block_size, w * block_size, d].
|
|
def _compare(self, b, h, w, d, block_size, data_format):
|
|
block_size_sq = block_size * block_size
|
|
data = np.random.normal(0, 1, b * h * w * d * block_size_sq).astype(
|
|
np.float32)
|
|
if data_format == "NHWC":
|
|
x = data.reshape([b, h * block_size, w * block_size, d])
|
|
else:
|
|
x = data.reshape([b, d, h * block_size, w * block_size])
|
|
|
|
self._checkGrad(x, block_size, data_format)
|
|
|
|
# Don't use very large numbers as dimensions here as the result is tensor
|
|
# with cartesian product of the dimensions.
|
|
@test_util.run_deprecated_v1
|
|
def testSmall(self):
|
|
block_size = 2
|
|
self._compare(1, 2, 3, 5, block_size, "NHWC")
|
|
self._compare(1, 2, 3, 5, block_size, "NCHW")
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testSmall2(self):
|
|
block_size = 2
|
|
self._compare(2, 4, 3, 2, block_size, "NHWC")
|
|
self._compare(2, 4, 3, 2, block_size, "NCHW")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|