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.cached_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 = self.evaluate(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 _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 _testBadInputSize(self,
tin=None,
tfilter=None,
min_input=None,
max_input=None,
min_filter=None,
max_filter=None,
error_regex=""):
strides = [1, 1, 1, 1]
padding = "SAME"
if tin is None:
tin = math_ops.cast(constant_op.constant(1, shape=[1, 2, 3, 3]),
dtype=dtypes.quint8)
if tfilter is None:
tfilter = math_ops.cast(constant_op.constant(1, shape=[1, 2, 3,
3]),
dtype=dtypes.quint8)
if min_input is None:
min_input = constant_op.constant(0, shape=[], dtype=dtypes.float32)
if max_input is None:
max_input = constant_op.constant(0, shape=[], dtype=dtypes.float32)
if min_filter is None:
min_filter = constant_op.constant(0,
shape=[],
dtype=dtypes.float32)
if max_filter is None:
max_filter = constant_op.constant(0,
shape=[],
dtype=dtypes.float32)
with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
error_regex):
self.evaluate(
nn_ops.quantized_conv2d(tin,
tfilter,
out_type=dtypes.qint32,
strides=strides,
padding=padding,
min_input=min_input,
max_input=max_input,
min_filter=min_filter,
max_filter=max_filter))