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@ -39,7 +39,8 @@ __all__ = [
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'Conv2dBnAct',
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'DenseBnAct',
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'FakeQuantWithMinMax',
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'Conv2dBatchNormQuant',
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'Conv2dBnFoldQuant',
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'Conv2dBnWithoutFoldQuant',
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'Conv2dQuant',
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'DenseQuant',
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'ActQuant',
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@ -393,7 +394,7 @@ class FakeQuantWithMinMax(Cell):
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return out
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class Conv2dBatchNormQuant(Cell):
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class Conv2dBnFoldQuant(Cell):
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r"""
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2D convolution with BatchNormal op folded layer.
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@ -418,7 +419,7 @@ class Conv2dBatchNormQuant(Cell):
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mean vector. Default: 'zeros'.
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var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
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variance vector. Default: 'ones'.
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fake (bool): Conv2dBatchNormQuant Cell add FakeQuantWithMinMax op or not. Default: True.
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fake (bool): Conv2dBnFoldQuant Cell add FakeQuantWithMinMax op or not. Default: True.
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per_channel (bool): FakeQuantWithMinMax Parameters. Default: False.
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num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
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symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
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@ -433,7 +434,7 @@ class Conv2dBatchNormQuant(Cell):
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Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
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Examples:
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>>> batchnorm_quant = nn.Conv2dBatchNormQuant(1, 6, kernel_size= (2, 2), stride=(1, 1), pad_mode="valid",
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>>> batchnorm_quant = nn.Conv2dBnFoldQuant(1, 6, kernel_size= (2, 2), stride=(1, 1), pad_mode="valid",
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>>> dilation=(1, 1))
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>>> input_x = Tensor(np.random.randint(-2, 2, (2, 1, 1, 3)), mindspore.float32)
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>>> result = batchnorm_quant(input_x)
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@ -462,8 +463,8 @@ class Conv2dBatchNormQuant(Cell):
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narrow_range=False,
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quant_delay=0,
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freeze_bn=100000):
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"""init Conv2dBatchNormQuant layer"""
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super(Conv2dBatchNormQuant, self).__init__()
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"""init Conv2dBnFoldQuant layer"""
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super(Conv2dBnFoldQuant, self).__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = twice(kernel_size)
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@ -580,6 +581,132 @@ class Conv2dBatchNormQuant(Cell):
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return out
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class Conv2dBnWithoutFoldQuant(Cell):
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r"""
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2D convolution + batchnorm without fold with fake quant op 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]): Specifies the height and width of the 2D convolution window.
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stride (int): Specifies stride for all spatial dimensions with the same value. 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. 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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has_bn (bool): Specifies to used batchnorm or not. Default: False.
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eps (float): Parameters for BatchNormal. Default: 1e-5.
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momentum (float): Parameters for BatchNormal op. Default: 0.997.
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weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
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Default: 'normal'.
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bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the bias vector. Default: 'zeros'.
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per_channel (bool): FakeQuantWithMinMax Parameters. Default: False.
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num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
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symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
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narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
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quant_delay (int): Quantization delay parameters according by global step. Default: 0.
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Inputs:
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- **x** (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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>>> conv2d_quant = nn.Conv2dQuant(1, 6, kernel_size=(2, 2), stride=(1, 1), pad_mode="valid",
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>>> dilation=(1, 1))
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>>> input_x = Tensor(np.random.randint(-2, 2, (2, 1, 1, 3)), mstype.float32)
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>>> result = conv2d_quant(input_x)
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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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has_bn=True,
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eps=1e-5,
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momentum=0.997,
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weight_init='normal',
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bias_init='zeros',
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per_channel=False,
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num_bits=8,
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symmetric=False,
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narrow_range=False,
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quant_delay=0):
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super(Conv2dBnWithoutFoldQuant, self).__init__()
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if isinstance(kernel_size, int):
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self.kernel_size = (kernel_size, kernel_size)
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else:
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self.kernel_size = kernel_size
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self.in_channels = check_int_positive(in_channels)
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self.out_channels = check_int_positive(out_channels)
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self.has_bias = has_bias
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self.stride = twice(stride)
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self.dilation = twice(dilation)
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self.pad_mode = pad_mode
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self.padding = padding
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self.group = group
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self.quant_delay = quant_delay
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weight_shape = [out_channels, in_channels // group, *self.kernel_size]
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self.weight = Parameter(initializer(weight_init, weight_shape), name='weight')
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self.bias_add = P.BiasAdd()
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if check_bool(has_bias):
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self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
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else:
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self.bias = None
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self.conv = P.Conv2D(out_channel=self.out_channels,
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kernel_size=self.kernel_size,
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mode=1,
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pad_mode=self.pad_mode,
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pad=self.padding,
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stride=self.stride,
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dilation=self.dilation,
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group=self.group)
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self.fake_quant_weight = FakeQuantWithMinMax(min_init=-6,
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max_init=6,
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ema=False,
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per_channel=per_channel,
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channel_axis=0,
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num_channels=out_channels,
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num_bits=num_bits,
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symmetric=symmetric,
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narrow_range=narrow_range,
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quant_delay=quant_delay)
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self.has_bn = validator.check_bool("has_bn", has_bn)
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if has_bn:
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self.batchnorm = BatchNorm2d(out_channels)
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def construct(self, x):
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weight = self.fake_quant_weight(self.weight)
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out = self.conv(x, weight)
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if self.has_bias:
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out = self.bias_add(out, self.bias)
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if self.has_bn:
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out = self.batchnorm(out)
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return out
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def extend_repr(self):
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s = 'in_channels={}, out_channels={}, kernel_size={}, stride={}, ' \
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'pad_mode={}, padding={}, dilation={}, group={}, ' \
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'has_bias={}, quant_delay={}'.format(self.in_channels, self.out_channels, self.kernel_size, self.stride,
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self.pad_mode, self.padding, self.dilation, self.group,
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self.has_bias, self.quant_delay)
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return s
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class Conv2dQuant(Cell):
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r"""
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2D convolution with fake quant op layer.
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mindspore-ci-bot
5 years ago
committed by
Gitee