def testDirectNotUseOverlapping(self):
for num_batches in [1, 3]:
for row_window_size in [2, 5]:
for col_window_size in [2, 4]:
num_rows = row_window_size * 5
num_cols = col_window_size * 7
for num_channels in [1, 2]:
input_shape = (num_batches, num_rows, num_cols, num_channels)
with self.cached_session() as _:
input_tensor = constant_op.constant(
self._GenerateUniqueRandomInputTensor(input_shape))
window_size = [1, row_window_size, col_window_size, 1]
stride_size = [1, row_window_size, col_window_size, 1]
padding = "VALID"
output_tensor = nn_ops.max_pool(input_tensor, window_size,
stride_size, padding)
output_data = self.evaluate(output_tensor)
output_backprop = self._PRNG.randint(100, size=output_data.shape)
input_backprop_tensor = gen_nn_ops.max_pool_grad(
input_tensor, output_tensor, output_backprop, window_size,
stride_size, padding)
input_backprop = self.evaluate(input_backprop_tensor)
row_seq = list(range(0, num_rows + 1, row_window_size))
col_seq = list(range(0, num_cols + 1, col_window_size))
fmp_input_backprop_tensor = gen_nn_ops.fractional_max_pool_grad(
input_tensor,
output_tensor,
output_backprop,
row_seq,
col_seq,
overlapping=False)
fmp_input_backprop = self.evaluate(fmp_input_backprop_tensor)
self.assertShapeEqual(input_backprop, fmp_input_backprop_tensor)
self.assertAllClose(input_backprop, fmp_input_backprop)
def testDirectUseOverlapping(self):
for num_batches in [1, 3]:
for row_window_size in [2, 5]:
for col_window_size in [2, 4]:
num_rows = (row_window_size - 1) * 5 + 1
num_cols = (col_window_size - 1) * 7 + 1
for num_channels in [1, 2]:
input_shape = (num_batches, num_rows, num_cols,
num_channels)
with self.test_session() as _:
input_tensor = constant_op.constant(
self._GenerateUniqueRandomInputTensor(
input_shape))
window_size = [
1, row_window_size, col_window_size, 1
]
stride_size = [
1, row_window_size - 1, col_window_size - 1, 1
]
padding = "VALID"
output_tensor = nn_ops.max_pool(
input_tensor, window_size, stride_size,
padding)
output_data = output_tensor.eval()
output_backprop = self._PRNG.randint(
100, size=output_data.shape)
input_backprop_tensor = gen_nn_ops.max_pool_grad(
input_tensor, output_tensor, output_backprop,
window_size, stride_size, padding)
input_backprop = input_backprop_tensor.eval()
row_seq = list(
range(0, num_rows, row_window_size - 1))
col_seq = list(
range(0, num_cols, col_window_size - 1))
row_seq[-1] += 1
col_seq[-1] += 1
fmp_input_backprop_tensor = gen_nn_ops.fractional_max_pool_grad(
input_tensor,
output_tensor,
output_backprop,
row_seq,
col_seq,
overlapping=True)
fmp_input_backprop = fmp_input_backprop_tensor.eval(
)
self.assertShapeEqual(input_backprop,
fmp_input_backprop_tensor)
self.assertAllClose(input_backprop,
fmp_input_backprop)
def _FractionalMaxPoolGrad(op, grad_0, unused_grad_1, unused_grad_2):
"""Returns gradient for FractionalMaxPool.
Since FractionalMaxPool has three outputs, there are three gradients passed in
for each of the outputs. Only the first one is useful, the other two gradients
are empty.
Args:
op: The FractionalMaxPoolOp.
grad_0: Gradient with respect to op.outputs[0]
unused_grad_1: Gradient with respect to op.outputs[1]/row_seq. It is empty.
unused_grad_2: Gradient with respect to op.outputs[2]/col_seq. It is empty.
Returns:
Input backprop for FractionalMaxPool op.
"""
return gen_nn_ops.fractional_max_pool_grad(
op.inputs[0], op.outputs[0], grad_0, op.outputs[1], op.outputs[2],
op.get_attr("overlapping"))
def _FractionalMaxPoolGrad(op, grad_0, unused_grad_1, unused_grad_2):
"""Returns gradient for FractionalMaxPool.
Since FractionalMaxPool has three outputs, there are three gradients passed in
for each of the outputs. Only the first one is useful, the other two gradients
are empty.
Args:
op: The FractionalMaxPoolOp.
grad_0: Gradient with respect to op.outputs[0]
unused_grad_1: Gradient with respect to op.outputs[1]/row_seq. It is empty.
unused_grad_2: Gradient with respect to op.outputs[2]/col_seq. It is empty.
Returns:
Input backprop for FractionalMaxPool op.
"""
return gen_nn_ops.fractional_max_pool_grad(
op.inputs[0], op.outputs[0], grad_0, op.outputs[1], op.outputs[2],
op.get_attr("overlapping"))
def testDirectUseOverlapping(self):
for num_batches in [1, 3]:
for row_window_size in [2, 5]:
for col_window_size in [2, 4]:
num_rows = (row_window_size - 1) * 5 + 1
num_cols = (col_window_size - 1) * 7 + 1
for num_channels in [1, 2]:
input_shape = (num_batches, num_rows, num_cols, num_channels)
with self.test_session() as _:
input_tensor = constant_op.constant(
self._GenerateUniqueRandomInputTensor(input_shape))
window_size = [1, row_window_size, col_window_size, 1]
stride_size = [1, row_window_size - 1, col_window_size - 1, 1]
padding = "VALID"
output_tensor = nn_ops.max_pool(input_tensor, window_size,
stride_size, padding)
output_data = output_tensor.eval()
output_backprop = self._PRNG.randint(100, size=output_data.shape)
input_backprop_tensor = gen_nn_ops.max_pool_grad(
input_tensor, output_tensor, output_backprop, window_size,
stride_size, padding)
input_backprop = input_backprop_tensor.eval()
row_seq = list(range(0, num_rows, row_window_size - 1))
col_seq = list(range(0, num_cols, col_window_size - 1))
row_seq[-1] += 1
col_seq[-1] += 1
fmp_input_backprop_tensor = gen_nn_ops.fractional_max_pool_grad(
input_tensor,
output_tensor,
output_backprop,
row_seq,
col_seq,
overlapping=True)
fmp_input_backprop = fmp_input_backprop_tensor.eval()
self.assertShapeEqual(input_backprop, fmp_input_backprop_tensor)
self.assertAllClose(input_backprop, fmp_input_backprop)
def testWhenRepeatedMaxValueInPoolingRegion(self):
"""Test when there's repeating value in pooling region.
There's no formal definition for what the gradient should be when there're
multiple max value within a pooling cell. Such as
| 1 5 |
| 5 3 |
The expected result depends heavily on implementation, if someone swap the
order of a nested for loop when walking through the tensor, result would be
very different.
The goal of this test is to alert when someone else change the
implementation. Current implementation scans row-by-row.
"""
input_data = [
5.0, 4.0, 6.0, 7.0, 3.0, 5.0, 9.0, 6.0, 8.0, 8.0, 9.0, 5.0, 7.0,
4.0, 0.0, 0.0
] # pyformat: disable
input_size = [1, 4, 4, 1]
output_backprop = [12.0, 15.0, 17.0, -5.0, 6.0,
21.0] # pyformat: disable
row_seq = [0, 1, 3, 4]
col_seq = [0, 2, 4]
output_data_not_overlapping = [5.0, 7.0, 8.0, 9.0, 7.0,
0.0] # pyformat: disable
output_data_overlapping = [9.0, 9.0, 9.0, 9.0, 7.0,
0.0] # pyformat: disable
output_size = [1, 3, 2, 1]
expected_input_backprop_not_overlapping = np.reshape([
12.0, 0.0, 0.0, 15.0, 0.0, 0.0, -5.0, 0.0, 17.0, 0.0, 0.0, 0.0,
6.0, 0.0, 21.0, 0.0
], input_size) # pyformat: disable
expected_input_backprop_overlapping = np.reshape([
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 39.0, 0.0, 0.0, 0.0, 0.0, 0.0, 6.0,
0.0, 21.0, 0.0
], input_size) # pyformat: disable
with self.test_session() as _:
# Test when overlapping is False
input_tensor = constant_op.constant(input_data, shape=input_size)
output_tensor = constant_op.constant(output_data_not_overlapping,
shape=output_size)
grad = constant_op.constant(output_backprop, shape=output_size)
r = gen_nn_ops.fractional_max_pool_grad(input_tensor,
output_tensor,
grad,
row_seq,
col_seq,
overlapping=False)
input_backprop_not_overlapping = r.eval()
self.assertShapeEqual(
np.reshape(expected_input_backprop_not_overlapping,
input_size), r)
self.assertAllClose(expected_input_backprop_not_overlapping,
input_backprop_not_overlapping)
# Test when overlapping is True
output_tensor = constant_op.constant(output_data_overlapping,
shape=output_size)
r = gen_nn_ops.fractional_max_pool_grad(input_tensor,
output_tensor,
grad,
row_seq,
col_seq,
overlapping=True)
input_backprop_overlapping = r.eval()
self.assertShapeEqual(
np.reshape(expected_input_backprop_overlapping, input_size), r)
self.assertAllClose(expected_input_backprop_overlapping,
input_backprop_overlapping)
def testWhenRepeatedMaxValueInPoolingRegion(self):
"""Test when there's repeating value in pooling region.
There's no formal definition for what the gradient should be when there're
multiple max value within a pooling cell. Such as
| 1 5 |
| 5 3 |
The expected result depends heavily on implementation, if someone swap the
order of a nested for loop when walking through the tensor, result would be
very different.
The goal of this test is to alert when someone else change the
implementation. Current implementation scans row-by-row.
"""
input_data = [5.0, 4.0, 6.0, 7.0,
3.0, 5.0, 9.0, 6.0,
8.0, 8.0, 9.0, 5.0,
7.0, 4.0, 0.0, 0.0] # pyformat: disable
input_size = [1, 4, 4, 1]
output_backprop = [12.0, 15.0,
17.0, -5.0,
6.0, 21.0] # pyformat: disable
row_seq = [0, 1, 3, 4]
col_seq = [0, 2, 4]
output_data_not_overlapping = [5.0, 7.0,
8.0, 9.0,
7.0, 0.0] # pyformat: disable
output_data_overlapping = [9.0, 9.0,
9.0, 9.0,
7.0, 0.0] # pyformat: disable
output_size = [1, 3, 2, 1]
expected_input_backprop_not_overlapping = np.reshape(
[12.0, 0.0, 0.0, 15.0,
0.0, 0.0, -5.0, 0.0,
17.0, 0.0, 0.0, 0.0,
6.0, 0.0, 21.0, 0.0],
input_size) # pyformat: disable
expected_input_backprop_overlapping = np.reshape(
[0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 39.0, 0.0,
0.0, 0.0, 0.0, 0.0,
6.0, 0.0, 21.0, 0.0],
input_size) # pyformat: disable
with self.cached_session() as _:
# Test when overlapping is False
input_tensor = constant_op.constant(input_data, shape=input_size)
output_tensor = constant_op.constant(
output_data_not_overlapping, shape=output_size)
grad = constant_op.constant(output_backprop, shape=output_size)
r = gen_nn_ops.fractional_max_pool_grad(
input_tensor,
output_tensor,
grad,
row_seq,
col_seq,
overlapping=False)
input_backprop_not_overlapping = self.evaluate(r)
self.assertShapeEqual(
np.reshape(expected_input_backprop_not_overlapping, input_size), r)
self.assertAllClose(expected_input_backprop_not_overlapping,
input_backprop_not_overlapping)
# Test when overlapping is True
output_tensor = constant_op.constant(
output_data_overlapping, shape=output_size)
r = gen_nn_ops.fractional_max_pool_grad(
input_tensor, output_tensor, grad, row_seq, col_seq, overlapping=True)
input_backprop_overlapping = self.evaluate(r)
self.assertShapeEqual(
np.reshape(expected_input_backprop_overlapping, input_size), r)
self.assertAllClose(expected_input_backprop_overlapping,
input_backprop_overlapping)