!5175 ReadMe file normalize
Merge pull request !5175 from chenfei_mindspore/fix-readmepull/5175/MERGE
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""MobileNetV2 Quant model define"""
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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__all__ = ['mobilenetV2_quant']
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_quant_delay = 200
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_ema_decay = 0.999
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_symmetric = False
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_per_channel = False
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def _make_divisible(v, divisor, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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class GlobalAvgPooling(nn.Cell):
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"""
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Global avg pooling definition.
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Args:
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Returns:
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Tensor, output tensor.
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Examples:
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>>> GlobalAvgPooling()
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"""
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def __init__(self):
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super(GlobalAvgPooling, self).__init__()
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self.mean = P.ReduceMean(keep_dims=False)
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def construct(self, x):
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x = self.mean(x, (2, 3))
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return x
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class ConvBNReLU(nn.Cell):
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"""
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Convolution/Depthwise fused with Batchnorm and ReLU block definition.
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Args:
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in_planes (int): Input channel.
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out_planes (int): Output channel.
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kernel_size (int): Input kernel size.
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stride (int): Stride size for the first convolutional layer. Default: 1.
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groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.
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Returns:
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Tensor, output tensor.
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Examples:
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>>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)
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"""
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def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
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super(ConvBNReLU, self).__init__()
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padding = (kernel_size - 1) // 2
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conv = nn.Conv2dBnFoldQuant(in_planes, out_planes, kernel_size, stride,
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pad_mode='pad', padding=padding, quant_delay=_quant_delay, group=groups,
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per_channel=_per_channel, symmetric=_symmetric)
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layers = [conv, nn.ReLU()]
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self.features = nn.SequentialCell(layers)
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self.fake = nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay, min_init=0, quant_delay=_quant_delay)
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def construct(self, x):
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output = self.features(x)
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output = self.fake(output)
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return output
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class InvertedResidual(nn.Cell):
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"""
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Mobilenetv2 residual block definition.
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Args:
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inp (int): Input channel.
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oup (int): Output channel.
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stride (int): Stride size for the first convolutional layer. Default: 1.
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expand_ratio (int): expand ration of input channel
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Returns:
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Tensor, output tensor.
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Examples:
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>>> ResidualBlock(3, 256, 1, 1)
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"""
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def __init__(self, inp, oup, stride, expand_ratio):
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super(InvertedResidual, self).__init__()
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assert stride in [1, 2]
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hidden_dim = int(round(inp * expand_ratio))
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self.use_res_connect = stride == 1 and inp == oup
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layers = []
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if expand_ratio != 1:
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layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
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layers.extend([
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# dw
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ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
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# pw-linear
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nn.Conv2dBnFoldQuant(hidden_dim, oup, kernel_size=1, stride=1, pad_mode='pad', padding=0, group=1,
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per_channel=_per_channel, symmetric=_symmetric, quant_delay=_quant_delay),
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nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay, quant_delay=_quant_delay)
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])
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self.conv = nn.SequentialCell(layers)
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self.add = P.TensorAdd()
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self.add_fake = nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay, quant_delay=_quant_delay)
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def construct(self, x):
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identity = x
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x = self.conv(x)
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if self.use_res_connect:
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x = self.add(identity, x)
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x = self.add_fake(x)
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return x
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class MobileNetV2Quant(nn.Cell):
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"""
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MobileNetV2Quant architecture.
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Args:
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class_num (Cell): number of classes.
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width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
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has_dropout (bool): Is dropout used. Default is false
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inverted_residual_setting (list): Inverted residual settings. Default is None
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round_nearest (list): Channel round to . Default is 8
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Returns:
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Tensor, output tensor.
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Examples:
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>>> MobileNetV2Quant(num_classes=1000)
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"""
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def __init__(self, num_classes=1000, width_mult=1.,
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has_dropout=False, inverted_residual_setting=None, round_nearest=8):
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super(MobileNetV2Quant, self).__init__()
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block = InvertedResidual
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input_channel = 32
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last_channel = 1280
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# setting of inverted residual blocks
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self.cfgs = inverted_residual_setting
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if inverted_residual_setting is None:
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self.cfgs = [
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# t, c, n, s
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[1, 16, 1, 1],
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[6, 24, 2, 2],
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[6, 32, 3, 2],
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[6, 64, 4, 2],
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[6, 96, 3, 1],
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[6, 160, 3, 2],
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[6, 320, 1, 1],
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]
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# building first layer
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input_channel = _make_divisible(input_channel * width_mult, round_nearest)
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self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
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self.input_fake = nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay, quant_delay=_quant_delay)
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features = [ConvBNReLU(3, input_channel, stride=2)]
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# building inverted residual blocks
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for t, c, n, s in self.cfgs:
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output_channel = _make_divisible(c * width_mult, round_nearest)
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for i in range(n):
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stride = s if i == 0 else 1
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features.append(block(input_channel, output_channel, stride, expand_ratio=t))
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input_channel = output_channel
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# building last several layers
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features.append(ConvBNReLU(input_channel, self.out_channels, kernel_size=1))
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# make it nn.CellList
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self.features = nn.SequentialCell(features)
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# mobilenet head
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head = ([GlobalAvgPooling(),
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nn.DenseQuant(self.out_channels, num_classes, has_bias=True, per_channel=_per_channel,
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symmetric=_symmetric, quant_delay=_quant_delay),
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nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay)] if not has_dropout else
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[GlobalAvgPooling(),
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nn.Dropout(0.2),
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nn.DenseQuant(self.out_channels, num_classes, has_bias=True, per_channel=_per_channel,
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symmetric=_symmetric, quant_delay=_quant_delay),
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nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay, quant_delay=_quant_delay)])
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self.head = nn.SequentialCell(head)
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def construct(self, x):
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x = self.input_fake(x)
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x = self.features(x)
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x = self.head(x)
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return x
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def mobilenetV2_quant(**kwargs):
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"""
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Constructs a MobileNet V2 model
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"""
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return MobileNetV2Quant(**kwargs)
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