!1084 add new interface quant combined
Merge pull request !1084 from SanjayChan/04quantpull/1084/MERGE
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
4bb5c7b39a
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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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"""Use combination of Conv, Dense, Relu, Batchnorm."""
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from .normalization import BatchNorm2d
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from .activation import get_activation
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from ..cell import Cell
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from . import conv, basic
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from ..._checkparam import ParamValidator as validator
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__all__ = ['Conv2d', 'Dense']
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class Conv2d(Cell):
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r"""
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A combination of convolution, Batchnorm, activation layer.
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For a more Detailed overview of Conv2d op.
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Args:
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in_channels (int): The number of input channel :math:`C_{in}`.
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out_channels (int): The number of output channel :math:`C_{out}`.
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kernel_size (Union[int, tuple]): The data type is int or tuple with 2 integers. Specifies the height
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and width of the 2D convolution window. Single int means the value if for both height and width of
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the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
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width of the kernel.
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stride (int): Specifies stride for all spatial dimensions with the same value. Value of stride should be
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greater or equal to 1 but bounded by the height and width of the input. Default: 1.
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pad_mode (str): Specifies padding mode. The optional values are "same", "valid", "pad". Default: "same".
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padding (int): Implicit paddings on both sides of the input. Default: 0.
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dilation (int): Specifying the dilation rate to use for dilated convolution. If set to be :math:`k > 1`,
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there will be :math:`k - 1` pixels skipped for each sampling location. Its value should be greater
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or equal to 1 and bounded by the height and width of the input. Default: 1.
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group (int): Split filter into groups, `in_ channels` and `out_channels` should be
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divisible by the number of groups. Default: 1.
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has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
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weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
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It can be a Tensor, a string, an Initializer or a numbers.Number. When a string is specified,
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values from 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform' and 'XavierUniform' distributions as well
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as constant 'One' and 'Zero' distributions are possible. Alias 'xavier_uniform', 'he_uniform', 'ones'
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and 'zeros' are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of
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Initializer for more details. Default: 'normal'.
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bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the bias vector. Possible
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Initializer and string are the same as 'weight_init'. Refer to the values of
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Initializer for more details. Default: 'zeros'.
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batchnorm (bool): Specifies to used batchnorm or not. Default: None.
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activation (string): Specifies activation type. The optional values are as following:
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'softmax', 'logsoftmax', 'relu', 'relu6', 'tanh', 'gelu', 'sigmoid',
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'prelu', 'leakyrelu', 'hswish', 'hsigmoid'. Default: None.
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Inputs:
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- **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
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Outputs:
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Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
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Examples:
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>>> net = combined.Conv2d(120, 240, 4, batchnorm=True, activation='ReLU')
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>>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
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>>> net(input).shape()
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(1, 240, 1024, 640)
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"""
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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pad_mode='same',
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padding=0,
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dilation=1,
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group=1,
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has_bias=False,
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weight_init='normal',
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bias_init='zeros',
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batchnorm=None,
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activation=None):
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super(Conv2d, self).__init__()
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self.conv = conv.Conv2d(
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in_channels,
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out_channels,
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kernel_size,
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stride,
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pad_mode,
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padding,
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dilation,
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group,
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has_bias,
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weight_init,
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bias_init)
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self.has_bn = batchnorm is not None
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self.has_act = activation is not None
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self.batchnorm = batchnorm
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if batchnorm is True:
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self.batchnorm = BatchNorm2d(out_channels)
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elif batchnorm is not None:
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validator.check_isinstance('batchnorm', batchnorm, (BatchNorm2d,))
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self.activation = get_activation(activation)
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def construct(self, x):
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x = self.conv(x)
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if self.has_bn:
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x = self.batchnorm(x)
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if self.has_act:
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x = self.activation(x)
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return x
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class Dense(Cell):
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r"""
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A combination of Dense, Batchnorm, activation layer.
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For a more Detailed overview of Dense op.
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Args:
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in_channels (int): The number of channels in the input space.
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out_channels (int): The number of channels in the output space.
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weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
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is same as input x. The values of str refer to the function `initializer`. Default: 'normal'.
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bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
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same as input x. The values of str refer to the function `initializer`. Default: 'zeros'.
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has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
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activation (str): Regularizer function applied to the output of the layer, eg. 'relu'. Default: None.
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batchnorm (bool): Specifies to used batchnorm or not. Default: None.
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activation (string): Specifies activation type. The optional values are as following:
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'softmax', 'logsoftmax', 'relu', 'relu6', 'tanh', 'gelu', 'sigmoid',
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'prelu', 'leakyrelu', 'hswish', 'hsigmoid'. Default: None.
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Inputs:
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- **input** (Tensor) - Tensor of shape :math:`(N, in\_channels)`.
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Outputs:
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Tensor of shape :math:`(N, out\_channels)`.
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Examples:
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>>> net = nn.Dense(3, 4)
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>>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
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>>> net(input)
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"""
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def __init__(self,
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in_channels,
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out_channels,
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weight_init='normal',
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bias_init='zeros',
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has_bias=True,
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batchnorm=None,
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activation=None):
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super(Dense, self).__init__()
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self.dense = basic.Dense(
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in_channels,
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out_channels,
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weight_init,
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bias_init,
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has_bias)
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self.has_bn = batchnorm is not None
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self.has_act = activation is not None
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if batchnorm is True:
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self.batchnorm = BatchNorm2d(out_channels)
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elif batchnorm is not None:
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validator.check_isinstance('batchnorm', batchnorm, (BatchNorm2d,))
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self.activation = get_activation(activation)
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def construct(self, x):
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x = self.dense(x)
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if self.has_bn:
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x = self.batchnorm(x)
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if self.has_act:
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x = self.activation(x)
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return x
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@ -0,0 +1,26 @@
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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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"""
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quantization.
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User can use aware quantization to train a model. Mindspore supports quantization aware training,
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which models quantization errors in both the forward and backward passes using fake-quantization
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ops. Note that the entire computation is carried out in floating point. At the end of quantization
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aware training, Mindspore provides conversion functions to convert the trained model into lower precision.
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"""
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from .quant import convert_quant_network
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__all__ = ["convert_quant_network"]
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File diff suppressed because it is too large
Load Diff
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"""MobileNetV2"""
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from mindspore import nn
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from mindspore.ops import operations as P
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def make_divisible(input_x, div_by=8):
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return int((input_x + div_by) // div_by)
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def _conv_bn(in_channel,
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out_channel,
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ksize,
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stride=1):
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"""Get a conv2d batchnorm and relu layer."""
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return nn.SequentialCell(
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[nn.Conv2d(in_channel,
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out_channel,
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kernel_size=ksize,
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stride=stride),
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nn.BatchNorm2d(out_channel)])
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class InvertedResidual(nn.Cell):
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def __init__(self, inp, oup, stride, expend_ratio):
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super(InvertedResidual, self).__init__()
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self.stride = stride
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assert stride in [1, 2]
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hidden_dim = int(inp * expend_ratio)
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self.use_res_connect = self.stride == 1 and inp == oup
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if expend_ratio == 1:
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self.conv = nn.SequentialCell([
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nn.Conv2d(hidden_dim, hidden_dim, 3, stride, group=hidden_dim),
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nn.BatchNorm2d(hidden_dim),
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nn.ReLU6(),
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nn.Conv2d(hidden_dim, oup, 1, 1),
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nn.BatchNorm2d(oup)
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])
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else:
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self.conv = nn.SequentialCell([
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nn.Conv2d(inp, hidden_dim, 1, 1),
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nn.BatchNorm2d(hidden_dim),
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nn.ReLU6(),
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nn.Conv2d(hidden_dim, hidden_dim, 3, stride, group=hidden_dim),
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nn.BatchNorm2d(hidden_dim),
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nn.ReLU6(),
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nn.Conv2d(hidden_dim, oup, 1, 1),
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nn.BatchNorm2d(oup)
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])
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def construct(self, input_x):
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out = self.conv(input_x)
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if self.use_res_connect:
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out = input_x + out
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return out
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class MobileNetV2(nn.Cell):
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def __init__(self, num_class=1000, input_size=224, width_mul=1.):
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super(MobileNetV2, 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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inverted_residual_setting = [
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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, 230, 1, 1],
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]
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if width_mul > 1.0:
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last_channel = make_divisible(last_channel * width_mul)
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self.last_channel = last_channel
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features = [_conv_bn(3, input_channel, 3, 2)]
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for t, c, n, s in inverted_residual_setting:
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out_channel = make_divisible(c * width_mul) if t > 1 else c
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for i in range(n):
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if i == 0:
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features.append(block(input_channel, out_channel, s, t))
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else:
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features.append(block(input_channel, out_channel, 1, t))
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input_channel = out_channel
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features.append(_conv_bn(input_channel, self.last_channel, 1))
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self.features = nn.SequentialCell(features)
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self.mean = P.ReduceMean(keep_dims=False)
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self.classifier = nn.Dense(self.last_channel, num_class)
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def construct(self, input_x):
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out = input_x
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out = self.features(out)
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out = self.mean(out, (2, 3))
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out = self.classifier(out)
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return out
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@ -0,0 +1,108 @@
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"""mobile net v2"""
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from mindspore import nn
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from mindspore.ops import operations as P
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from mindspore.nn.layer import combined
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def make_divisible(input_x, div_by=8):
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return int((input_x + div_by) // div_by)
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def _conv_bn(in_channel,
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out_channel,
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ksize,
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stride=1):
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"""Get a conv2d batchnorm and relu layer."""
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return nn.SequentialCell(
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[combined.Conv2d(in_channel,
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out_channel,
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kernel_size=ksize,
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stride=stride,
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batchnorm=True)])
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class InvertedResidual(nn.Cell):
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def __init__(self, inp, oup, stride, expend_ratio):
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super(InvertedResidual, self).__init__()
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self.stride = stride
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assert stride in [1, 2]
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hidden_dim = int(inp * expend_ratio)
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self.use_res_connect = self.stride == 1 and inp == oup
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if expend_ratio == 1:
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self.conv = nn.SequentialCell([
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combined.Conv2d(hidden_dim,
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hidden_dim,
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3,
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stride,
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group=hidden_dim,
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batchnorm=True,
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activation='relu6'),
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combined.Conv2d(hidden_dim, oup, 1, 1,
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batchnorm=True)
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])
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else:
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self.conv = nn.SequentialCell([
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combined.Conv2d(inp, hidden_dim, 1, 1,
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batchnorm=True,
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activation='relu6'),
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combined.Conv2d(hidden_dim,
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hidden_dim,
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3,
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stride,
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group=hidden_dim,
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batchnorm=True,
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activation='relu6'),
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combined.Conv2d(hidden_dim, oup, 1, 1,
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batchnorm=True)
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])
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self.add = P.TensorAdd()
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def construct(self, input_x):
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out = self.conv(input_x)
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if self.use_res_connect:
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out = self.add(input_x, out)
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return out
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class MobileNetV2(nn.Cell):
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def __init__(self, num_class=1000, input_size=224, width_mul=1.):
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super(MobileNetV2, 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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inverted_residual_setting = [
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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, 230, 1, 1],
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]
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if width_mul > 1.0:
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last_channel = make_divisible(last_channel * width_mul)
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self.last_channel = last_channel
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features = [_conv_bn(3, input_channel, 3, 2)]
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for t, c, n, s in inverted_residual_setting:
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out_channel = make_divisible(c * width_mul) if t > 1 else c
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for i in range(n):
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if i == 0:
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features.append(block(input_channel, out_channel, s, t))
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else:
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features.append(block(input_channel, out_channel, 1, t))
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input_channel = out_channel
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features.append(_conv_bn(input_channel, self.last_channel, 1))
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self.features = nn.SequentialCell(features)
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self.mean = P.ReduceMean(keep_dims=False)
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self.classifier = combined.Dense(self.last_channel, num_class)
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def construct(self, input_x):
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out = input_x
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out = self.features(out)
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out = self.mean(out, (2, 3))
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out = self.classifier(out)
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return out
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@ -0,0 +1,94 @@
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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.
|
||||
# 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.
|
||||
# ============================================================================
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""" tests for quant """
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import numpy as np
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from mindspore import Tensor
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from mindspore.train.quant import quant as qat
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from mindspore import nn
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import mindspore.ops.operations as P
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from mindspore.nn.layer import combined
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import mindspore.context as context
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from mobilenetv2_combined import MobileNetV2
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context.set_context(mode=context.GRAPH_MODE)
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class LeNet5(nn.Cell):
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"""
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Lenet network
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Args:
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num_class (int): Num classes. Default: 10.
|
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|
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Returns:
|
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Tensor, output tensor
|
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Examples:
|
||||
>>> LeNet(num_class=10)
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, num_class=10):
|
||||
super(LeNet5, self).__init__()
|
||||
self.num_class = num_class
|
||||
self.conv1 = combined.Conv2d(
|
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1, 6, kernel_size=5, batchnorm=True, activation='relu6')
|
||||
self.conv2 = combined.Conv2d(6, 16, kernel_size=5, activation='relu')
|
||||
self.fc1 = combined.Dense(16 * 5 * 5, 120, activation='relu')
|
||||
self.fc2 = combined.Dense(120, 84, activation='relu')
|
||||
self.fc3 = combined.Dense(84, self.num_class)
|
||||
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
|
||||
self.flattern = nn.Flatten()
|
||||
|
||||
def construct(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.bn(x)
|
||||
x = self.relu(x)
|
||||
x = self.max_pool2d(x)
|
||||
x = self.conv2(x)
|
||||
x = self.max_pool2d(x)
|
||||
x = self.flattern(x)
|
||||
x = self.fc1(x)
|
||||
x = self.fc2(x)
|
||||
x = self.fc3(x)
|
||||
return x
|
||||
|
||||
|
||||
def test_qat_lenet():
|
||||
net = LeNet5()
|
||||
net = qat.convert_quant_network(
|
||||
net, quant_delay=0, bn_fold=False, freeze_bn=10000, weight_bits=8, act_bits=8)
|
||||
|
||||
|
||||
def test_qat_mobile():
|
||||
net = MobileNetV2()
|
||||
img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
|
||||
net = qat.convert_quant_network(
|
||||
net, quant_delay=0, bn_fold=False, freeze_bn=10000, weight_bits=8, act_bits=8)
|
||||
net(img)
|
||||
|
||||
|
||||
def test_qat_mobile_train():
|
||||
net = MobileNetV2(num_class=10)
|
||||
img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
|
||||
label = Tensor(np.ones((1, 10)).astype(np.float32))
|
||||
net = qat.convert_quant_network(
|
||||
net, quant_delay=0, bn_fold=False, freeze_bn=10000, weight_bits=8, act_bits=8)
|
||||
|
||||
loss = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
|
||||
optimizer = nn.Momentum(net.trainable_params(),
|
||||
learning_rate=0.1, momentum=0.9)
|
||||
net = nn.WithLossCell(net, loss)
|
||||
net = nn.TrainOneStepCell(net, optimizer)
|
||||
net(img, label)
|
||||
Loading…
Reference in new issue