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# 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 quantized convolutional operations."""
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.ops import nn_ops
from tensorflow.python.platform import test
class Conv2DTest(test.TestCase):
def __init__(self, method_name="runTest"):
super(Conv2DTest, self).__init__(method_name)
def _VerifyValues(self, tensor_in_sizes, filter_in_sizes, stride, padding,
expected):
"""Verifies the output values of the convolution function.
Args:
tensor_in_sizes: Input tensor dimensions in
[batch, input_rows, input_cols, input_depth].
filter_in_sizes: Filter tensor dimensions in
[kernel_rows, kernel_cols, input_depth, output_depth].
stride: Stride.
padding: Padding type.
expected: An array containing the expected operation outputs.
"""
total_size_1 = 1
total_size_2 = 1
for s in tensor_in_sizes:
total_size_1 *= s
for s in filter_in_sizes:
total_size_2 *= s
# Initializes the input tensor with array containing incrementing
# numbers from 1.
x1 = np.array([f for f in range(1, total_size_1 + 1)])
x1 = x1.astype(np.uint8).reshape(tensor_in_sizes)
x1_min = 0.0
x1_max = 255.0
x2 = np.array([f for f in range(1, total_size_2 + 1)]).astype(np.uint8)
x2 = x2.astype(np.uint8).reshape(filter_in_sizes)
x2_min = 0.0
x2_max = 255.0
with self.test_session(use_gpu=False) as sess:
t1 = constant_op.constant(x1, shape=tensor_in_sizes, dtype=dtypes.quint8)
t2 = constant_op.constant(x2, shape=filter_in_sizes, dtype=dtypes.quint8)
conv = nn_ops.quantized_conv2d(
t1,
t2,
out_type=dtypes.qint32,
strides=[1, stride, stride, 1],
padding=padding,
min_input=x1_min,
max_input=x1_max,
min_filter=x2_min,
max_filter=x2_max)
value = sess.run(conv)
quantized_output = value[0]
output_min = value[1]
output_max = value[2]
float_output = self._QuantizedOutputToFloat(quantized_output, output_min,
output_max)
self.assertArrayNear(expected, float_output.flatten(), 1.0)
self.assertEqual(value[0].shape, conv[0].get_shape())
def _assertQuantizedArrayEquals(self, iarray1, iarray2):
for i1, i2 in zip(iarray1, iarray2):
self.assertTrue(i1 == i2)
def _QuantizedOutputToFloat(self, quantized, quantized_min, quantized_max):
number_of_bits = 32
number_of_steps = 1 << number_of_bits
range_adjust = (number_of_steps / (number_of_steps - 1.0))
quantized_range = ((quantized_max - quantized_min) * range_adjust)
range_scale = (quantized_range / number_of_steps)
lowest_quantized = -(1 << (number_of_bits - 1))
result = np.array([(quantized_min +
((float(x) - lowest_quantized) * range_scale))
for x in quantized.flatten()])
return result
def testConv2D1x1Filter(self):
# Our generated input is [batch, rows, cols, depth], and looks like this:
# (1,2,3) (4,5,6) (7,8,9)
# (10,11,12) (13,14,15) (16,17,18)
# The filter data is:
# (1,4,7) (2,5,8) (3,6,9)
# That means the calculations are:
# 1*1+2*4+3*7=30
# 1*2+2*5+3*8=36
# 1*3+2*6+3*9=42
# 4*1+5*4+6*7=66
# 4*2+5*5+6*8=81
# 4*3+5*6+6*9=96
# 7*1+5*8+6*9=102
# 7*2+8*5+9*8=126
# 7*3+8*6+9*9=150
# 10*1+11*4+12*7=138
# 10*2+11*5+12*8=171
# 10*3+11*6+12*9=204
# 13*1+14*4+15*7=174
# 13*2+14*5+15*8=216
# 13*3+14*6+15*9=258, clamped to 255
# 16*1+17*4+18*7=210
# 16*2+17*5+18*8=261, clamped to 255
# 16*3+17*6+18*9=312, clamped to 255
# Because the output shift is zero, we call the non-optimized reference
# path for the convolution.
expected_output = [
30, 36, 42, 66, 81, 96, 102, 126, 150, 138, 171, 204, 174, 216, 258,
210, 261, 312
]
self._VerifyValues(
tensor_in_sizes=[1, 2, 3, 3],
filter_in_sizes=[1, 1, 3, 3],
stride=1,
padding="VALID",
expected=expected_output)
def testConv2D2x2Filter(self):
# Our generated input is [batch, rows, cols, depth], and looks like this:
# (1,2,3) (4,5,6) (7,8,9)
# (10,11,12) (13,14,15) (16,17,18)
# The filter data is [filter_height, filter_width, depth, filter_count]:
# ( 1, 4, 7) (10, 13, 16)
# (19,22,25) (28, 31, 34)
# -
# ( 2, 5, 8) (11, 14, 17)
# (20,23,26) (29, 32, 35)
# -
# ( 3, 6, 9) (12, 15, 18)
# (21,24,27) (30, 33, 36)
# The raw accumulated totals are:
# 1*1+2*4+3*7+4*10+5*13+6*16+10*19+11*22+12*25+13*28+14*31+15*34=2271
# 1*2+2*5+3*8+4*11+5*14+6*17+10*20+11*23+12*26+13*29+14*32+15*35=2367
# 1*3+2*6+3*9+4*12+5*15+6*18+10*21+11*24+12*27+13*30+14*33+15*36=2463
# 4*1+5*4+6*7+7*10+8*13+9*16+13*19+14*22+15*25+16*28+17*31+18*34=2901
# 4*2+5*5+6*8+7*11+8*14+9*17+13*20+14*23+15*26+16*29+17*32+18*35=3033
# 4*3+5*6+6*9+7*12+8*15+9*18+13*21+14*24+15*27+16*30+17*33+18*36=3165
# The expected values are taken from the raw totals and rescaled to fit into
# eight bits.
expected_output = [2271.0, 2367.0, 2463.0, 2901.0, 3033.0, 3165.0]
self._VerifyValues(
tensor_in_sizes=[1, 2, 3, 3],
filter_in_sizes=[2, 2, 3, 3],
stride=1,
padding="VALID",
expected=expected_output)
def testConv2D1x2Filter(self):
# The outputs are computed using third_party/py/IPython/notebook.
# With a shift of 21, we should execute the optimized path here.
expected_output = [
231.0, 252.0, 273.0, 384.0, 423.0, 462.0, 690.0, 765.0, 840.0, 843.0,
936.0, 1029.0
]
self._VerifyValues(
tensor_in_sizes=[1, 2, 3, 3],
filter_in_sizes=[1, 2, 3, 3],
stride=1,
padding="VALID",
expected=expected_output)
def testConv2D2x2FilterStride2(self):
# With a shift of 21, we should execute the optimized path here.
expected_output = [2271.0, 2367.0, 2463.0]
self._VerifyValues(
tensor_in_sizes=[1, 2, 3, 3],
filter_in_sizes=[2, 2, 3, 3],
stride=2,
padding="VALID",
expected=expected_output)
def testConv2D2x2FilterStride2Same(self):
# With a shift of 21, we should execute the optimized path here.
expected_output = [2271.0, 2367.0, 2463.0, 1230.0, 1305.0, 1380.0]
self._VerifyValues(
tensor_in_sizes=[1, 2, 3, 3],
filter_in_sizes=[2, 2, 3, 3],
stride=2,
padding="SAME",
expected=expected_output)
if __name__ == "__main__":
test.main()
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