340 lines
9.7 KiB
Python
340 lines
9.7 KiB
Python
import paddle.nn as nn
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import paddle
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import numpy as np
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__all__ = [
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'RepVGG',
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'RepVGG_A0',
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'RepVGG_A1',
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'RepVGG_A2',
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'RepVGG_B0',
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'RepVGG_B1',
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'RepVGG_B2',
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'RepVGG_B3',
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'RepVGG_B1g2',
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'RepVGG_B1g4',
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'RepVGG_B2g2',
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'RepVGG_B2g4',
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'RepVGG_B3g2',
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'RepVGG_B3g4',
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]
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class ConvBN(nn.Layer):
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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,
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padding,
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groups=1):
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super(ConvBN, self).__init__()
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self.conv = nn.Conv2D(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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groups=groups,
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bias_attr=False)
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self.bn = nn.BatchNorm2D(num_features=out_channels)
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def forward(self, x):
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y = self.conv(x)
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y = self.bn(y)
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return y
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class RepVGGBlock(nn.Layer):
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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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padding=0,
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dilation=1,
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groups=1,
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padding_mode='zeros'):
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super(RepVGGBlock, 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 = kernel_size
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self.stride = stride
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self.padding = padding
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self.dilation = dilation
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self.groups = groups
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self.padding_mode = padding_mode
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assert kernel_size == 3
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assert padding == 1
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padding_11 = padding - kernel_size // 2
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self.nonlinearity = nn.ReLU()
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self.rbr_identity = nn.BatchNorm2D(
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num_features=in_channels
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) if out_channels == in_channels and stride == 1 else None
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self.rbr_dense = ConvBN(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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groups=groups)
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self.rbr_1x1 = ConvBN(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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stride=stride,
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padding=padding_11,
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groups=groups)
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def forward(self, inputs):
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if not self.training:
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return self.nonlinearity(self.rbr_reparam(inputs))
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if self.rbr_identity is None:
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id_out = 0
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else:
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id_out = self.rbr_identity(inputs)
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return self.nonlinearity(
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self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)
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def eval(self):
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if not hasattr(self, 'rbr_reparam'):
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self.rbr_reparam = nn.Conv2D(
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in_channels=self.in_channels,
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out_channels=self.out_channels,
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kernel_size=self.kernel_size,
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stride=self.stride,
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padding=self.padding,
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dilation=self.dilation,
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groups=self.groups,
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padding_mode=self.padding_mode)
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self.training = False
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kernel, bias = self.get_equivalent_kernel_bias()
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self.rbr_reparam.weight.set_value(kernel)
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self.rbr_reparam.bias.set_value(bias)
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for layer in self.sublayers():
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layer.eval()
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def get_equivalent_kernel_bias(self):
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kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
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kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
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kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
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return kernel3x3 + self._pad_1x1_to_3x3_tensor(
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kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
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def _pad_1x1_to_3x3_tensor(self, kernel1x1):
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if kernel1x1 is None:
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return 0
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else:
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return nn.functional.pad(kernel1x1, [1, 1, 1, 1])
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def _fuse_bn_tensor(self, branch):
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if branch is None:
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return 0, 0
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if isinstance(branch, ConvBN):
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kernel = branch.conv.weight
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running_mean = branch.bn._mean
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running_var = branch.bn._variance
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gamma = branch.bn.weight
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beta = branch.bn.bias
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eps = branch.bn._epsilon
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else:
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assert isinstance(branch, nn.BatchNorm2D)
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if not hasattr(self, 'id_tensor'):
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input_dim = self.in_channels // self.groups
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kernel_value = np.zeros(
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(self.in_channels, input_dim, 3, 3), dtype=np.float32)
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for i in range(self.in_channels):
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kernel_value[i, i % input_dim, 1, 1] = 1
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self.id_tensor = paddle.to_tensor(kernel_value)
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kernel = self.id_tensor
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running_mean = branch._mean
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running_var = branch._variance
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gamma = branch.weight
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beta = branch.bias
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eps = branch._epsilon
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std = (running_var + eps).sqrt()
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t = (gamma / std).reshape((-1, 1, 1, 1))
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return kernel * t, beta - running_mean * gamma / std
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class RepVGG(nn.Layer):
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def __init__(self,
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num_blocks,
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width_multiplier=None,
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override_groups_map=None,
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class_dim=1000):
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super(RepVGG, self).__init__()
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assert len(width_multiplier) == 4
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self.override_groups_map = override_groups_map or dict()
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assert 0 not in self.override_groups_map
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self.in_planes = min(64, int(64 * width_multiplier[0]))
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self.stage0 = RepVGGBlock(
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in_channels=3,
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out_channels=self.in_planes,
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kernel_size=3,
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stride=2,
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padding=1)
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self.cur_layer_idx = 1
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self.stage1 = self._make_stage(
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int(64 * width_multiplier[0]), num_blocks[0], stride=2)
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self.stage2 = self._make_stage(
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int(128 * width_multiplier[1]), num_blocks[1], stride=2)
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self.stage3 = self._make_stage(
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int(256 * width_multiplier[2]), num_blocks[2], stride=2)
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self.stage4 = self._make_stage(
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int(512 * width_multiplier[3]), num_blocks[3], stride=2)
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self.gap = nn.AdaptiveAvgPool2D(output_size=1)
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self.linear = nn.Linear(int(512 * width_multiplier[3]), class_dim)
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def _make_stage(self, planes, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1)
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blocks = []
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for stride in strides:
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cur_groups = self.override_groups_map.get(self.cur_layer_idx, 1)
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blocks.append(
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RepVGGBlock(
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in_channels=self.in_planes,
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out_channels=planes,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=cur_groups))
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self.in_planes = planes
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self.cur_layer_idx += 1
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return nn.Sequential(*blocks)
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def eval(self):
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self.training = False
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for layer in self.sublayers():
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layer.training = False
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layer.eval()
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def forward(self, x):
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out = self.stage0(x)
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out = self.stage1(out)
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out = self.stage2(out)
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out = self.stage3(out)
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out = self.stage4(out)
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out = self.gap(out)
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out = paddle.flatten(out, start_axis=1)
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out = self.linear(out)
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return out
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optional_groupwise_layers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26]
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g2_map = {l: 2 for l in optional_groupwise_layers}
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g4_map = {l: 4 for l in optional_groupwise_layers}
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def RepVGG_A0(**kwargs):
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return RepVGG(
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num_blocks=[2, 4, 14, 1],
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width_multiplier=[0.75, 0.75, 0.75, 2.5],
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override_groups_map=None,
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**kwargs)
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def RepVGG_A1(**kwargs):
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return RepVGG(
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num_blocks=[2, 4, 14, 1],
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width_multiplier=[1, 1, 1, 2.5],
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override_groups_map=None,
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**kwargs)
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def RepVGG_A2(**kwargs):
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return RepVGG(
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num_blocks=[2, 4, 14, 1],
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width_multiplier=[1.5, 1.5, 1.5, 2.75],
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override_groups_map=None,
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**kwargs)
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def RepVGG_B0(**kwargs):
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return RepVGG(
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num_blocks=[4, 6, 16, 1],
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width_multiplier=[1, 1, 1, 2.5],
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override_groups_map=None,
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**kwargs)
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def RepVGG_B1(**kwargs):
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return RepVGG(
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num_blocks=[4, 6, 16, 1],
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width_multiplier=[2, 2, 2, 4],
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override_groups_map=None,
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**kwargs)
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def RepVGG_B1g2(**kwargs):
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return RepVGG(
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num_blocks=[4, 6, 16, 1],
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width_multiplier=[2, 2, 2, 4],
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override_groups_map=g2_map,
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**kwargs)
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def RepVGG_B1g4(**kwargs):
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return RepVGG(
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num_blocks=[4, 6, 16, 1],
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width_multiplier=[2, 2, 2, 4],
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override_groups_map=g4_map,
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**kwargs)
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def RepVGG_B2(**kwargs):
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return RepVGG(
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num_blocks=[4, 6, 16, 1],
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width_multiplier=[2.5, 2.5, 2.5, 5],
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override_groups_map=None,
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**kwargs)
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def RepVGG_B2g2(**kwargs):
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return RepVGG(
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num_blocks=[4, 6, 16, 1],
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width_multiplier=[2.5, 2.5, 2.5, 5],
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override_groups_map=g2_map,
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**kwargs)
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def RepVGG_B2g4(**kwargs):
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return RepVGG(
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num_blocks=[4, 6, 16, 1],
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width_multiplier=[2.5, 2.5, 2.5, 5],
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override_groups_map=g4_map,
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**kwargs)
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def RepVGG_B3(**kwargs):
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return RepVGG(
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num_blocks=[4, 6, 16, 1],
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width_multiplier=[3, 3, 3, 5],
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override_groups_map=None,
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**kwargs)
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def RepVGG_B3g2(**kwargs):
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return RepVGG(
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num_blocks=[4, 6, 16, 1],
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width_multiplier=[3, 3, 3, 5],
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override_groups_map=g2_map,
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**kwargs)
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def RepVGG_B3g4(**kwargs):
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return RepVGG(
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num_blocks=[4, 6, 16, 1],
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width_multiplier=[3, 3, 3, 5],
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override_groups_map=g4_map,
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**kwargs)
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