Use black for lib/models
This commit is contained in:
D-X-Y
@@ -7,164 +7,318 @@ from ..initialization import initialize_resnet
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class ConvBNReLU(nn.Module):
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num_conv = 1
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def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu):
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super(ConvBNReLU, self).__init__()
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if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
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else : self.avg = None
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self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
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if has_bn : self.bn = nn.BatchNorm2d(nOut)
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else : self.bn = None
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if has_relu: self.relu = nn.ReLU(inplace=True)
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else : self.relu = None
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def forward(self, inputs):
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if self.avg : out = self.avg( inputs )
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else : out = inputs
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conv = self.conv( out )
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if self.bn : out = self.bn( conv )
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else : out = conv
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if self.relu: out = self.relu( out )
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else : out = out
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num_conv = 1
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return out
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def __init__(
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self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu
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):
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super(ConvBNReLU, self).__init__()
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if has_avg:
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self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
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else:
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self.avg = None
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self.conv = nn.Conv2d(
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nIn,
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nOut,
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kernel_size=kernel,
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stride=stride,
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padding=padding,
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dilation=1,
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groups=1,
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bias=bias,
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)
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if has_bn:
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self.bn = nn.BatchNorm2d(nOut)
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else:
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self.bn = None
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if has_relu:
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self.relu = nn.ReLU(inplace=True)
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else:
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self.relu = None
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def forward(self, inputs):
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if self.avg:
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out = self.avg(inputs)
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else:
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out = inputs
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conv = self.conv(out)
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if self.bn:
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out = self.bn(conv)
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else:
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out = conv
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if self.relu:
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out = self.relu(out)
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else:
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out = out
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return out
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class ResNetBasicblock(nn.Module):
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num_conv = 2
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expansion = 1
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def __init__(self, iCs, stride):
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super(ResNetBasicblock, self).__init__()
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assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
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assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
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assert len(iCs) == 3,'invalid lengths of iCs : {:}'.format(iCs)
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self.conv_a = ConvBNReLU(iCs[0], iCs[1], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
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self.conv_b = ConvBNReLU(iCs[1], iCs[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False)
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residual_in = iCs[0]
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if stride == 2:
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self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=True, has_bn=True, has_relu=False)
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residual_in = iCs[2]
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elif iCs[0] != iCs[2]:
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self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
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else:
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self.downsample = None
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#self.out_dim = max(residual_in, iCs[2])
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self.out_dim = iCs[2]
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num_conv = 2
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expansion = 1
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def forward(self, inputs):
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basicblock = self.conv_a(inputs)
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basicblock = self.conv_b(basicblock)
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def __init__(self, iCs, stride):
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super(ResNetBasicblock, self).__init__()
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assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
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assert isinstance(iCs, tuple) or isinstance(
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iCs, list
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), "invalid type of iCs : {:}".format(iCs)
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assert len(iCs) == 3, "invalid lengths of iCs : {:}".format(iCs)
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if self.downsample is not None:
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residual = self.downsample(inputs)
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else:
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residual = inputs
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out = residual + basicblock
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return F.relu(out, inplace=True)
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self.conv_a = ConvBNReLU(
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iCs[0],
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iCs[1],
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3,
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stride,
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1,
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False,
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has_avg=False,
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has_bn=True,
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has_relu=True,
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)
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self.conv_b = ConvBNReLU(
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iCs[1], iCs[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False
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)
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residual_in = iCs[0]
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if stride == 2:
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self.downsample = ConvBNReLU(
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iCs[0],
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iCs[2],
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1,
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1,
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0,
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False,
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has_avg=True,
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has_bn=True,
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has_relu=False,
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)
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residual_in = iCs[2]
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elif iCs[0] != iCs[2]:
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self.downsample = ConvBNReLU(
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iCs[0],
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iCs[2],
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1,
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1,
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0,
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False,
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has_avg=False,
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has_bn=True,
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has_relu=False,
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)
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else:
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self.downsample = None
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# self.out_dim = max(residual_in, iCs[2])
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self.out_dim = iCs[2]
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def forward(self, inputs):
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basicblock = self.conv_a(inputs)
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basicblock = self.conv_b(basicblock)
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if self.downsample is not None:
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residual = self.downsample(inputs)
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else:
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residual = inputs
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out = residual + basicblock
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return F.relu(out, inplace=True)
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class ResNetBottleneck(nn.Module):
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expansion = 4
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num_conv = 3
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def __init__(self, iCs, stride):
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super(ResNetBottleneck, self).__init__()
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assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
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assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
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assert len(iCs) == 4,'invalid lengths of iCs : {:}'.format(iCs)
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self.conv_1x1 = ConvBNReLU(iCs[0], iCs[1], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True)
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self.conv_3x3 = ConvBNReLU(iCs[1], iCs[2], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
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self.conv_1x4 = ConvBNReLU(iCs[2], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
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residual_in = iCs[0]
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if stride == 2:
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self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=True , has_bn=True, has_relu=False)
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residual_in = iCs[3]
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elif iCs[0] != iCs[3]:
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self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
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residual_in = iCs[3]
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else:
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self.downsample = None
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#self.out_dim = max(residual_in, iCs[3])
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self.out_dim = iCs[3]
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expansion = 4
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num_conv = 3
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def forward(self, inputs):
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def __init__(self, iCs, stride):
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super(ResNetBottleneck, self).__init__()
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assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
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assert isinstance(iCs, tuple) or isinstance(
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iCs, list
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), "invalid type of iCs : {:}".format(iCs)
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assert len(iCs) == 4, "invalid lengths of iCs : {:}".format(iCs)
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self.conv_1x1 = ConvBNReLU(
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iCs[0], iCs[1], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True
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)
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self.conv_3x3 = ConvBNReLU(
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iCs[1],
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iCs[2],
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3,
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stride,
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1,
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False,
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has_avg=False,
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has_bn=True,
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has_relu=True,
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)
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self.conv_1x4 = ConvBNReLU(
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iCs[2], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False
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)
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residual_in = iCs[0]
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if stride == 2:
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self.downsample = ConvBNReLU(
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iCs[0],
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iCs[3],
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1,
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1,
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0,
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False,
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has_avg=True,
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has_bn=True,
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has_relu=False,
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)
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residual_in = iCs[3]
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elif iCs[0] != iCs[3]:
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self.downsample = ConvBNReLU(
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iCs[0],
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iCs[3],
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1,
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1,
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0,
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False,
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has_avg=False,
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has_bn=True,
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has_relu=False,
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)
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residual_in = iCs[3]
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else:
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self.downsample = None
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# self.out_dim = max(residual_in, iCs[3])
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self.out_dim = iCs[3]
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bottleneck = self.conv_1x1(inputs)
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bottleneck = self.conv_3x3(bottleneck)
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bottleneck = self.conv_1x4(bottleneck)
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def forward(self, inputs):
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if self.downsample is not None:
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residual = self.downsample(inputs)
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else:
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residual = inputs
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out = residual + bottleneck
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return F.relu(out, inplace=True)
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bottleneck = self.conv_1x1(inputs)
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bottleneck = self.conv_3x3(bottleneck)
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bottleneck = self.conv_1x4(bottleneck)
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if self.downsample is not None:
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residual = self.downsample(inputs)
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else:
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residual = inputs
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out = residual + bottleneck
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return F.relu(out, inplace=True)
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class InferImagenetResNet(nn.Module):
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def __init__(
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self,
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block_name,
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layers,
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xblocks,
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xchannels,
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deep_stem,
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num_classes,
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zero_init_residual,
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):
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super(InferImagenetResNet, self).__init__()
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def __init__(self, block_name, layers, xblocks, xchannels, deep_stem, num_classes, zero_init_residual):
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super(InferImagenetResNet, self).__init__()
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# Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
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if block_name == "BasicBlock":
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block = ResNetBasicblock
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elif block_name == "Bottleneck":
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block = ResNetBottleneck
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else:
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raise ValueError("invalid block : {:}".format(block_name))
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assert len(xblocks) == len(
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layers
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), "invalid layers : {:} vs xblocks : {:}".format(layers, xblocks)
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#Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
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if block_name == 'BasicBlock':
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block = ResNetBasicblock
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elif block_name == 'Bottleneck':
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block = ResNetBottleneck
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else:
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raise ValueError('invalid block : {:}'.format(block_name))
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assert len(xblocks) == len(layers), 'invalid layers : {:} vs xblocks : {:}'.format(layers, xblocks)
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self.message = "InferImagenetResNet : Depth : {:} -> {:}, Layers for each block : {:}".format(
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sum(layers) * block.num_conv, sum(xblocks) * block.num_conv, xblocks
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)
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self.num_classes = num_classes
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self.xchannels = xchannels
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if not deep_stem:
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self.layers = nn.ModuleList(
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[
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ConvBNReLU(
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xchannels[0],
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xchannels[1],
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7,
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2,
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3,
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False,
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has_avg=False,
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has_bn=True,
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has_relu=True,
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)
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]
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)
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last_channel_idx = 1
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else:
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self.layers = nn.ModuleList(
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[
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ConvBNReLU(
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xchannels[0],
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xchannels[1],
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3,
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2,
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1,
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False,
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has_avg=False,
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has_bn=True,
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has_relu=True,
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),
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ConvBNReLU(
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xchannels[1],
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xchannels[2],
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3,
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1,
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1,
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False,
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has_avg=False,
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has_bn=True,
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has_relu=True,
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),
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]
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)
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last_channel_idx = 2
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self.layers.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
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for stage, layer_blocks in enumerate(layers):
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for iL in range(layer_blocks):
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num_conv = block.num_conv
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iCs = self.xchannels[last_channel_idx : last_channel_idx + num_conv + 1]
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stride = 2 if stage > 0 and iL == 0 else 1
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module = block(iCs, stride)
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last_channel_idx += num_conv
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self.xchannels[last_channel_idx] = module.out_dim
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self.layers.append(module)
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self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format(
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stage,
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iL,
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layer_blocks,
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len(self.layers) - 1,
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iCs,
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module.out_dim,
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stride,
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)
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if iL + 1 == xblocks[stage]: # reach the maximum depth
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out_channel = module.out_dim
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for iiL in range(iL + 1, layer_blocks):
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last_channel_idx += num_conv
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self.xchannels[last_channel_idx] = module.out_dim
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break
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assert last_channel_idx + 1 == len(self.xchannels), "{:} vs {:}".format(
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last_channel_idx, len(self.xchannels)
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)
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.classifier = nn.Linear(self.xchannels[-1], num_classes)
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self.message = 'InferImagenetResNet : Depth : {:} -> {:}, Layers for each block : {:}'.format(sum(layers)*block.num_conv, sum(xblocks)*block.num_conv, xblocks)
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self.num_classes = num_classes
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self.xchannels = xchannels
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if not deep_stem:
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self.layers = nn.ModuleList( [ ConvBNReLU(xchannels[0], xchannels[1], 7, 2, 3, False, has_avg=False, has_bn=True, has_relu=True) ] )
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last_channel_idx = 1
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else:
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self.layers = nn.ModuleList( [ ConvBNReLU(xchannels[0], xchannels[1], 3, 2, 1, False, has_avg=False, has_bn=True, has_relu=True)
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,ConvBNReLU(xchannels[1], xchannels[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
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last_channel_idx = 2
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self.layers.append( nn.MaxPool2d(kernel_size=3, stride=2, padding=1) )
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for stage, layer_blocks in enumerate(layers):
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for iL in range(layer_blocks):
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num_conv = block.num_conv
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iCs = self.xchannels[last_channel_idx:last_channel_idx+num_conv+1]
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stride = 2 if stage > 0 and iL == 0 else 1
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module = block(iCs, stride)
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last_channel_idx += num_conv
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self.xchannels[last_channel_idx] = module.out_dim
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self.layers.append ( module )
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self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iCs, module.out_dim, stride)
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if iL + 1 == xblocks[stage]: # reach the maximum depth
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out_channel = module.out_dim
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for iiL in range(iL+1, layer_blocks):
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last_channel_idx += num_conv
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self.xchannels[last_channel_idx] = module.out_dim
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break
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assert last_channel_idx + 1 == len(self.xchannels), '{:} vs {:}'.format(last_channel_idx, len(self.xchannels))
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self.avgpool = nn.AdaptiveAvgPool2d((1,1))
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self.classifier = nn.Linear(self.xchannels[-1], num_classes)
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self.apply(initialize_resnet)
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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, ResNetBasicblock):
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nn.init.constant_(m.conv_b.bn.weight, 0)
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elif isinstance(m, ResNetBottleneck):
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nn.init.constant_(m.conv_1x4.bn.weight, 0)
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self.apply(initialize_resnet)
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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, ResNetBasicblock):
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nn.init.constant_(m.conv_b.bn.weight, 0)
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elif isinstance(m, ResNetBottleneck):
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nn.init.constant_(m.conv_1x4.bn.weight, 0)
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def get_message(self):
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return self.message
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def get_message(self):
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return self.message
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|
||||
def forward(self, inputs):
|
||||
x = inputs
|
||||
for i, layer in enumerate(self.layers):
|
||||
x = layer( x )
|
||||
features = self.avgpool(x)
|
||||
features = features.view(features.size(0), -1)
|
||||
logits = self.classifier(features)
|
||||
return features, logits
|
||||
def forward(self, inputs):
|
||||
x = inputs
|
||||
for i, layer in enumerate(self.layers):
|
||||
x = layer(x)
|
||||
features = self.avgpool(x)
|
||||
features = features.view(features.size(0), -1)
|
||||
logits = self.classifier(features)
|
||||
return features, logits
|
||||
|
||||
Reference in New Issue
Block a user