wehub-resource-sync
85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
|
|
# or more contributor license agreements. See the NOTICE file
|
|
# distributed with this work for additional information
|
|
# regarding copyright ownership. The ASF licenses this file
|
|
# to you under the Apache License, Version 2.0 (the
|
|
# "License"); you may not use this file except in compliance
|
|
# with the License. You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing,
|
|
# software distributed under the License is distributed on an
|
|
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
|
# KIND, either express or implied. See the License for the
|
|
# specific language governing permissions and limitations
|
|
# under the License.
|
|
# pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals
|
|
"""Group normalization in python"""
|
|
|
|
import numpy as np
|
|
|
|
|
|
def group_norm_python(data, gamma, beta, num_groups, channel_axis, axes, epsilon=1e-5):
|
|
"""Group normalization operator.
|
|
|
|
Parameters
|
|
----------
|
|
data : tvm.te.Tensor
|
|
N-D with shape (d_0, d_1, ..., d_{N-1})
|
|
|
|
gamma: tvm.te.Tensor
|
|
1-D with shape (r_0) where r_0 == d_{channel_axis}
|
|
|
|
beta: tvm.te.Tensor
|
|
Optional, 1-D with shape (r_0) where r_0 == d_{channel_axis}
|
|
|
|
num_groups : int
|
|
The number of groups
|
|
|
|
channel_axis : int
|
|
The channel axis
|
|
|
|
axes : list of int
|
|
Axis over the normalization applied, excluding the channel axis
|
|
|
|
epsilon : float
|
|
The epsilon value to avoid division by zero.
|
|
|
|
Returns
|
|
-------
|
|
result : tvm.te.Tensor
|
|
N-D with shape (d_0, d_1, ..., d_{N-1})
|
|
"""
|
|
old_shape = data.shape
|
|
old_dtype = data.dtype
|
|
new_shape = list(old_shape)
|
|
new_shape[channel_axis] = data.shape[channel_axis] // num_groups
|
|
new_shape.insert(channel_axis, num_groups)
|
|
data = np.reshape(data, new_shape).astype("float32")
|
|
new_axes = [channel_axis + 1]
|
|
for axis in axes:
|
|
if axis < channel_axis:
|
|
new_axes.append(axis)
|
|
else:
|
|
new_axes.append(axis + 1)
|
|
mean = np.mean(data, axis=tuple(new_axes), keepdims=True)
|
|
var = np.var(data, axis=tuple(new_axes), keepdims=True)
|
|
data = (data - mean) / np.sqrt(var + epsilon)
|
|
data = np.reshape(data, old_shape).astype(old_dtype)
|
|
|
|
gamma_broadcast_shape = [1 for _ in range(len(old_shape))]
|
|
gamma_broadcast_shape[channel_axis] = gamma.shape[0]
|
|
gamma = np.reshape(gamma, gamma_broadcast_shape)
|
|
|
|
beta_broadcast_shape = [1 for _ in range(len(old_shape))]
|
|
beta_broadcast_shape[channel_axis] = beta.shape[0]
|
|
if beta is not None:
|
|
beta = np.reshape(beta, beta_broadcast_shape)
|
|
|
|
data *= gamma
|
|
if beta is not None:
|
|
data += beta
|
|
|
|
return data
|