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import numpy as np
from hypothesis import given, assume, settings
import hypothesis.strategies as st
from caffe2.python import core, model_helper, brew, utils
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import unittest
class TestInstanceNorm(serial.SerializedTestCase):
def _get_inputs(self, N, C, H, W, order):
input_data = np.random.rand(N, C, H, W).astype(np.float32)
if order == 'NHWC':
# Allocate in the same order as NCHW and transpose to make sure
# the inputs are identical on freshly-seeded calls.
input_data = utils.NCHW2NHWC(input_data)
elif order != "NCHW":
raise Exception('unknown order type ({})'.format(order))
scale_data = np.random.rand(C).astype(np.float32)
bias_data = np.random.rand(C).astype(np.float32)
return input_data, scale_data, bias_data
def _get_op(self, device_option, store_mean, store_inv_stdev, epsilon,
order, inplace=False):
outputs = ['output' if not inplace else "input"]
if store_mean or store_inv_stdev:
outputs += ['mean']
if store_inv_stdev:
outputs += ['inv_stdev']
op = core.CreateOperator(
'InstanceNorm',
['input', 'scale', 'bias'],
outputs,
order=order,
epsilon=epsilon,
device_option=device_option)
return op
def _feed_inputs(self, input_blobs, device_option):
names = ['input', 'scale', 'bias']
for name, blob in zip(names, input_blobs):
self.ws.create_blob(name).feed(blob, device_option=device_option)
@given(gc=hu.gcs['gc'],
dc=hu.gcs['dc'],
N=st.integers(1, 4),
C=st.integers(1, 4),
H=st.integers(2, 4),
W=st.integers(2, 4),
order=st.sampled_from(['NCHW', 'NHWC']),
epsilon=st.floats(1e-6, 1e-4),
store_mean=st.booleans(),
seed=st.integers(0, 1000),
store_inv_stdev=st.booleans())
@settings(deadline=10000)
def test_instance_norm_gradients(
self, gc, dc, N, C, H, W, order, store_mean, store_inv_stdev,
epsilon, seed):
np.random.seed(seed)
# force store_inv_stdev if store_mean to match existing forward pass
# implementation
store_inv_stdev |= store_mean
op = self._get_op(
device_option=gc,
store_mean=store_mean,
store_inv_stdev=store_inv_stdev,
epsilon=epsilon,
order=order)
input_data = np.arange(N * C * H * W).astype(np.float32)
np.random.shuffle(input_data)
if order == "NCHW":
input_data = input_data.reshape(N, C, H, W)
else:
input_data = input_data.reshape(N, H, W, C)
scale_data = np.random.randn(C).astype(np.float32)
bias_data = np.random.randn(C).astype(np.float32)
input_blobs = (input_data, scale_data, bias_data)
output_indices = [0]
# if store_inv_stdev is turned on, store_mean must also be forced on
if store_mean or store_inv_stdev:
output_indices += [1]
if store_inv_stdev:
output_indices += [2]
self.assertDeviceChecks(dc, op, input_blobs, output_indices)
# The gradient only flows from output #0 since the other two only
# store the temporary mean and inv_stdev buffers.
# Check dl/dinput
self.assertGradientChecks(gc, op, input_blobs, 0, [0])
# Check dl/dscale
self.assertGradientChecks(gc, op, input_blobs, 1, [0])
# Check dl/dbias
self.assertGradientChecks(gc, op, input_blobs, 2, [0])
@given(gc=hu.gcs['gc'],
dc=hu.gcs['dc'],
N=st.integers(2, 10),
C=st.integers(3, 10),
H=st.integers(5, 10),
W=st.integers(7, 10),
seed=st.integers(0, 1000),
epsilon=st.floats(1e-6, 1e-4),
store_mean=st.booleans(),
store_inv_stdev=st.booleans())
def test_instance_norm_layout(self, gc, dc, N, C, H, W, store_mean,
store_inv_stdev, epsilon, seed):
# force store_inv_stdev if store_mean to match existing forward pass
# implementation
store_inv_stdev |= store_mean
outputs = {}
for order in ('NCHW', 'NHWC'):
np.random.seed(seed)
input_blobs = self._get_inputs(N, C, H, W, order)
self._feed_inputs(input_blobs, device_option=gc)
op = self._get_op(
device_option=gc,
store_mean=store_mean,
store_inv_stdev=store_inv_stdev,
epsilon=epsilon,
order=order)
self.ws.run(op)
outputs[order] = self.ws.blobs['output'].fetch()
np.testing.assert_allclose(
outputs['NCHW'],
utils.NHWC2NCHW(outputs["NHWC"]),
atol=1e-4,
rtol=1e-4)
@serial.given(gc=hu.gcs['gc'],
dc=hu.gcs['dc'],
N=st.integers(2, 10),
C=st.integers(3, 10),
H=st.integers(5, 10),
W=st.integers(7, 10),
order=st.sampled_from(['NCHW', 'NHWC']),
epsilon=st.floats(1e-6, 1e-4),
store_mean=st.booleans(),
seed=st.integers(0, 1000),
store_inv_stdev=st.booleans(),
inplace=st.booleans())
def test_instance_norm_reference_check(
self, gc, dc, N, C, H, W, order, store_mean, store_inv_stdev,
epsilon, seed, inplace):
np.random.seed(seed)
# force store_inv_stdev if store_mean to match existing forward pass
# implementation
store_inv_stdev |= store_mean
if order != "NCHW":
assume(not inplace)
inputs = self._get_inputs(N, C, H, W, order)
op = self._get_op(
device_option=gc,
store_mean=store_mean,
store_inv_stdev=store_inv_stdev,
epsilon=epsilon,
order=order,
inplace=inplace)
def ref(input_blob, scale_blob, bias_blob):
if order == 'NHWC':
input_blob = utils.NHWC2NCHW(input_blob)
mean_blob = input_blob.reshape((N, C, -1)).mean(axis=2)
inv_stdev_blob = 1.0 / \
np.sqrt(input_blob.reshape((N, C, -1)).var(axis=2) + epsilon)
# _bc indicates blobs that are reshaped for broadcast
scale_bc = scale_blob[np.newaxis, :, np.newaxis, np.newaxis]
mean_bc = mean_blob[:, :, np.newaxis, np.newaxis]
inv_stdev_bc = inv_stdev_blob[:, :, np.newaxis, np.newaxis]
bias_bc = bias_blob[np.newaxis, :, np.newaxis, np.newaxis]
normalized_blob = scale_bc * (input_blob - mean_bc) * inv_stdev_bc \
+ bias_bc
if order == 'NHWC':
normalized_blob = utils.NCHW2NHWC(normalized_blob)
if not store_mean and not store_inv_stdev:
return normalized_blob,
elif not store_inv_stdev:
return normalized_blob, mean_blob
else:
return normalized_blob, mean_blob, inv_stdev_blob
self.assertReferenceChecks(gc, op, inputs, ref)
@given(gc=hu.gcs['gc'],
dc=hu.gcs['dc'],
N=st.integers(2, 10),
C=st.integers(3, 10),
H=st.integers(5, 10),
W=st.integers(7, 10),
order=st.sampled_from(['NCHW', 'NHWC']),
epsilon=st.floats(1e-6, 1e-4),
store_mean=st.booleans(),
seed=st.integers(0, 1000),
store_inv_stdev=st.booleans())
def test_instance_norm_device_check(
self, gc, dc, N, C, H, W, order, store_mean, store_inv_stdev,
epsilon, seed):
np.random.seed(seed)
# force store_inv_stdev if store_mean to match existing forward pass
# implementation
store_inv_stdev |= store_mean
inputs = self._get_inputs(N, C, H, W, order)
op = self._get_op(
device_option=gc,
store_mean=store_mean,
store_inv_stdev=store_inv_stdev,
epsilon=epsilon,
order=order)
self.assertDeviceChecks(dc, op, inputs, [0])
@given(is_test=st.booleans(),
N=st.integers(2, 10),
C=st.integers(3, 10),
H=st.integers(5, 10),
W=st.integers(7, 10),
order=st.sampled_from(['NCHW', 'NHWC']),
epsilon=st.floats(1e-6, 1e-4),
seed=st.integers(0, 1000))
def test_instance_norm_model_helper(
self, N, C, H, W, order, epsilon, seed, is_test):
np.random.seed(seed)
model = model_helper.ModelHelper(name="test_model")
brew.instance_norm(
model,
'input',
'output',
C,
epsilon=epsilon,
order=order,
is_test=is_test)
input_blob = np.random.rand(N, C, H, W).astype(np.float32)
if order == 'NHWC':
input_blob = utils.NCHW2NHWC(input_blob)
self.ws.create_blob('input').feed(input_blob)
self.ws.create_net(model.param_init_net).run()
self.ws.create_net(model.net).run()
if is_test:
scale = self.ws.blobs['output_s'].fetch()
assert scale is not None
assert scale.shape == (C, )
bias = self.ws.blobs['output_b'].fetch()
assert bias is not None
assert bias.shape == (C, )
output_blob = self.ws.blobs['output'].fetch()
if order == 'NHWC':
output_blob = utils.NHWC2NCHW(output_blob)
assert output_blob.shape == (N, C, H, W)
if __name__ == '__main__':
unittest.main()
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