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