def test_instance_norm_model_helper_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,
is_test=is_test)
input_blob = np.random.rand(N, C, H, W).astype(np.float32)
if order == 'NHWC':
input_blob = np.transpose(input_blob, axes=(0, 2, 3, 1))
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 = np.transpose(output_blob, axes=(0, 3, 1, 2))
assert output_blob.shape == (N, C, H, W)
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)
def InstanceNorm(self, *args, **kwargs):
return brew.instance_norm(self, *args, order=self.order, **kwargs)
def InstanceNorm(self, *args, **kwargs):
return brew.instance_norm(self, *args, order=self.order, **kwargs)
