!5175 ReadMe file normalize
Merge pull request !5175 from chenfei_mindspore/fix-readmepull/5175/MERGE
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
b178c0ccd1
File diff suppressed because it is too large
Load Diff
@ -1,218 +0,0 @@
|
||||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed 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.
|
||||
# ============================================================================
|
||||
"""MobileNetV2 Quant model define"""
|
||||
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
|
||||
__all__ = ['mobilenetV2_quant']
|
||||
|
||||
_quant_delay = 200
|
||||
_ema_decay = 0.999
|
||||
_symmetric = False
|
||||
_per_channel = False
|
||||
|
||||
|
||||
def _make_divisible(v, divisor, min_value=None):
|
||||
if min_value is None:
|
||||
min_value = divisor
|
||||
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
||||
# Make sure that round down does not go down by more than 10%.
|
||||
if new_v < 0.9 * v:
|
||||
new_v += divisor
|
||||
return new_v
|
||||
|
||||
|
||||
class GlobalAvgPooling(nn.Cell):
|
||||
"""
|
||||
Global avg pooling definition.
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> GlobalAvgPooling()
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super(GlobalAvgPooling, self).__init__()
|
||||
self.mean = P.ReduceMean(keep_dims=False)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.mean(x, (2, 3))
|
||||
return x
|
||||
|
||||
|
||||
class ConvBNReLU(nn.Cell):
|
||||
"""
|
||||
Convolution/Depthwise fused with Batchnorm and ReLU block definition.
|
||||
|
||||
Args:
|
||||
in_planes (int): Input channel.
|
||||
out_planes (int): Output channel.
|
||||
kernel_size (int): Input kernel size.
|
||||
stride (int): Stride size for the first convolutional layer. Default: 1.
|
||||
groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)
|
||||
"""
|
||||
|
||||
def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
|
||||
super(ConvBNReLU, self).__init__()
|
||||
padding = (kernel_size - 1) // 2
|
||||
conv = nn.Conv2dBnFoldQuant(in_planes, out_planes, kernel_size, stride,
|
||||
pad_mode='pad', padding=padding, quant_delay=_quant_delay, group=groups,
|
||||
per_channel=_per_channel, symmetric=_symmetric)
|
||||
layers = [conv, nn.ReLU()]
|
||||
self.features = nn.SequentialCell(layers)
|
||||
self.fake = nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay, min_init=0, quant_delay=_quant_delay)
|
||||
|
||||
def construct(self, x):
|
||||
output = self.features(x)
|
||||
output = self.fake(output)
|
||||
return output
|
||||
|
||||
|
||||
class InvertedResidual(nn.Cell):
|
||||
"""
|
||||
Mobilenetv2 residual block definition.
|
||||
|
||||
Args:
|
||||
inp (int): Input channel.
|
||||
oup (int): Output channel.
|
||||
stride (int): Stride size for the first convolutional layer. Default: 1.
|
||||
expand_ratio (int): expand ration of input channel
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> ResidualBlock(3, 256, 1, 1)
|
||||
"""
|
||||
|
||||
def __init__(self, inp, oup, stride, expand_ratio):
|
||||
super(InvertedResidual, self).__init__()
|
||||
assert stride in [1, 2]
|
||||
|
||||
hidden_dim = int(round(inp * expand_ratio))
|
||||
self.use_res_connect = stride == 1 and inp == oup
|
||||
|
||||
layers = []
|
||||
if expand_ratio != 1:
|
||||
layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
|
||||
layers.extend([
|
||||
# dw
|
||||
ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
|
||||
# pw-linear
|
||||
nn.Conv2dBnFoldQuant(hidden_dim, oup, kernel_size=1, stride=1, pad_mode='pad', padding=0, group=1,
|
||||
per_channel=_per_channel, symmetric=_symmetric, quant_delay=_quant_delay),
|
||||
nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay, quant_delay=_quant_delay)
|
||||
])
|
||||
self.conv = nn.SequentialCell(layers)
|
||||
self.add = P.TensorAdd()
|
||||
self.add_fake = nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay, quant_delay=_quant_delay)
|
||||
|
||||
def construct(self, x):
|
||||
identity = x
|
||||
x = self.conv(x)
|
||||
if self.use_res_connect:
|
||||
x = self.add(identity, x)
|
||||
x = self.add_fake(x)
|
||||
return x
|
||||
|
||||
|
||||
class MobileNetV2Quant(nn.Cell):
|
||||
"""
|
||||
MobileNetV2Quant architecture.
|
||||
|
||||
Args:
|
||||
class_num (Cell): number of classes.
|
||||
width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
|
||||
has_dropout (bool): Is dropout used. Default is false
|
||||
inverted_residual_setting (list): Inverted residual settings. Default is None
|
||||
round_nearest (list): Channel round to . Default is 8
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> MobileNetV2Quant(num_classes=1000)
|
||||
"""
|
||||
|
||||
def __init__(self, num_classes=1000, width_mult=1.,
|
||||
has_dropout=False, inverted_residual_setting=None, round_nearest=8):
|
||||
super(MobileNetV2Quant, self).__init__()
|
||||
block = InvertedResidual
|
||||
input_channel = 32
|
||||
last_channel = 1280
|
||||
# setting of inverted residual blocks
|
||||
self.cfgs = inverted_residual_setting
|
||||
if inverted_residual_setting is None:
|
||||
self.cfgs = [
|
||||
# t, c, n, s
|
||||
[1, 16, 1, 1],
|
||||
[6, 24, 2, 2],
|
||||
[6, 32, 3, 2],
|
||||
[6, 64, 4, 2],
|
||||
[6, 96, 3, 1],
|
||||
[6, 160, 3, 2],
|
||||
[6, 320, 1, 1],
|
||||
]
|
||||
|
||||
# building first layer
|
||||
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
|
||||
self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
|
||||
self.input_fake = nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay, quant_delay=_quant_delay)
|
||||
features = [ConvBNReLU(3, input_channel, stride=2)]
|
||||
# building inverted residual blocks
|
||||
for t, c, n, s in self.cfgs:
|
||||
output_channel = _make_divisible(c * width_mult, round_nearest)
|
||||
for i in range(n):
|
||||
stride = s if i == 0 else 1
|
||||
features.append(block(input_channel, output_channel, stride, expand_ratio=t))
|
||||
input_channel = output_channel
|
||||
# building last several layers
|
||||
features.append(ConvBNReLU(input_channel, self.out_channels, kernel_size=1))
|
||||
# make it nn.CellList
|
||||
self.features = nn.SequentialCell(features)
|
||||
# mobilenet head
|
||||
head = ([GlobalAvgPooling(),
|
||||
nn.DenseQuant(self.out_channels, num_classes, has_bias=True, per_channel=_per_channel,
|
||||
symmetric=_symmetric, quant_delay=_quant_delay),
|
||||
nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay)] if not has_dropout else
|
||||
[GlobalAvgPooling(),
|
||||
nn.Dropout(0.2),
|
||||
nn.DenseQuant(self.out_channels, num_classes, has_bias=True, per_channel=_per_channel,
|
||||
symmetric=_symmetric, quant_delay=_quant_delay),
|
||||
nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay, quant_delay=_quant_delay)])
|
||||
self.head = nn.SequentialCell(head)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.input_fake(x)
|
||||
x = self.features(x)
|
||||
x = self.head(x)
|
||||
return x
|
||||
|
||||
|
||||
def mobilenetV2_quant(**kwargs):
|
||||
"""
|
||||
Constructs a MobileNet V2 model
|
||||
"""
|
||||
return MobileNetV2Quant(**kwargs)
|
||||
File diff suppressed because it is too large
Load Diff
Loading…
Reference in new issue