def test_layer_norm_brew_wrapper(self, X, gc, dc):
axis = np.random.randint(0, len(X.shape))
scale_dim = [1] * np.ndim(X)
scale_dim[axis] = X.shape[axis]
self.ws.create_blob('input').feed(X)
model = ModelHelper(name='test_layer_norm_brew_wrapper')
brew.layer_norm(
model,
'input',
'output',
dim_in=X.shape[axis],
axis=axis,
epsilon=1e-4,
)
self.ws.create_net(model.param_init_net).run()
self.ws.create_net(model.net).run()
def test_layer_norm_brew_wrapper(self, X, gc, dc):
X = X[0]
if len(X.shape) == 1:
X = np.expand_dims(X, axis=0)
axis = np.random.randint(0, len(X.shape))
epsilon = 1e-4
workspace.FeedBlob('X', X)
model = ModelHelper(name='test_layer_norm_brew_wrapper')
brew.layer_norm(
model,
'X',
'Y',
axis=axis,
epsilon=epsilon,
)
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
def test_layer_norm_brew_wrapper(self, X, gc, dc):
X = X[0]
if len(X.shape) == 1:
X = np.expand_dims(X, axis=0)
axis = np.random.randint(0, len(X.shape))
epsilon = 1e-4
workspace.FeedBlob('X', X)
model = ModelHelper(name='test_layer_norm_brew_wrapper')
brew.layer_norm(
model,
'X',
'Y',
axis=axis,
epsilon=epsilon,
)
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
def test_layer_norm_brew_wrapper(self, X, gc, dc):
X = X[0]
if len(X.shape) == 1:
X = np.expand_dims(X, axis=0)
axis = np.random.randint(0, len(X.shape))
scale_dim = [1] * np.ndim(X)
scale_dim[axis] = X.shape[axis]
self.ws.create_blob('input').feed(X)
model = ModelHelper(name='test_layer_norm_brew_wrapper')
brew.layer_norm(
model,
'input',
'output',
dim_in=X.shape[axis],
axis=axis,
epsilon=1e-4,
)
self.ws.create_net(model.param_init_net).run()
self.ws.create_net(model.net).run()
