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提交 abd2999e 编写于 作者: Bubbliiiing's avatar Bubbliiiing
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Showing with 37 addition and 61 deletion
nets/yolo.py
浏览文件 @ abd2999e
from regex import X
import numpy as np
import torch
import torch.nn as nn
import numpy as np
from nets.CSPdarknet import CSPDarknet, Conv, MP, RCSPDark_Block, RCSPDark_Transition, autopad, SiLU
from nets.CSPdarknet import (Conv, CSPDarknet, RCSPDark_Block,
RCSPDark_Transition, SiLU, autopad)
class SPPCSPC(nn.Module):
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 20)):
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
super(SPPCSPC, self).__init__()
c_ = int(2 * c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
......@@ -28,34 +29,28 @@ class SPPCSPC(nn.Module):
class RepConv(nn.Module):
# Represented convolution
# https://arxiv.org/abs/2101.03697
def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=SiLU(), deploy=False):
super(RepConv, self).__init__()
self.deploy = deploy
self.groups = g
self.in_channels = c1
self.out_channels = c2
self.deploy = deploy
self.groups = g
self.in_channels = c1
self.out_channels = c2
assert k == 3
assert autopad(k, p) == 1
padding_11 = autopad(k, p) - k // 2
self.act = nn.LeakyReLU(0.1, inplace=True) if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
padding_11 = autopad(k, p) - k // 2
self.act = nn.LeakyReLU(0.1, inplace=True) if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
if deploy:
self.rbr_reparam = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=True)
self.rbr_reparam = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=True)
else:
self.rbr_identity = (nn.BatchNorm2d(num_features=c1) if c2 == c1 and s == 1 else None)
self.rbr_dense = nn.Sequential(
self.rbr_identity = (nn.BatchNorm2d(num_features=c1) if c2 == c1 and s == 1 else None)
self.rbr_dense = nn.Sequential(
nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False),
nn.BatchNorm2d(num_features=c2),
)
self.rbr_1x1 = nn.Sequential(
self.rbr_1x1 = nn.Sequential(
nn.Conv2d( c1, c2, 1, s, padding_11, groups=g, bias=False),
nn.BatchNorm2d(num_features=c2),
)
......@@ -63,12 +58,10 @@ class RepConv(nn.Module):
def forward(self, inputs):
if hasattr(self, "rbr_reparam"):
return self.act(self.rbr_reparam(inputs))
if self.rbr_identity is None:
id_out = 0
else:
id_out = self.rbr_identity(inputs)
return self.act(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)
def get_equivalent_kernel_bias(self):
......@@ -124,15 +117,14 @@ class RepConv(nn.Module):
)
def fuse_conv_bn(self, conv, bn):
std = (bn.running_var + bn.eps).sqrt()
bias = bn.bias - bn.running_mean * bn.weight / std
std = (bn.running_var + bn.eps).sqrt()
bias = bn.bias - bn.running_mean * bn.weight / std
t = (bn.weight / std).reshape(-1, 1, 1, 1)
t = (bn.weight / std).reshape(-1, 1, 1, 1)
weights = conv.weight * t
bn = nn.Identity()
conv = nn.Conv2d(in_channels = conv.in_channels,
bn = nn.Identity()
conv = nn.Conv2d(in_channels = conv.in_channels,
out_channels = conv.out_channels,
kernel_size = conv.kernel_size,
stride=conv.stride,
......@@ -143,23 +135,21 @@ class RepConv(nn.Module):
padding_mode = conv.padding_mode)
conv.weight = torch.nn.Parameter(weights)
conv.bias = torch.nn.Parameter(bias)
conv.bias = torch.nn.Parameter(bias)
return conv
def fuse_repvgg_block(self):
if self.deploy:
return
print(f"RepConv.fuse_repvgg_block")
self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1])
self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1])
self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1])
rbr_1x1_bias = self.rbr_1x1.bias
self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1])
rbr_1x1_bias = self.rbr_1x1.bias
weight_1x1_expanded = torch.nn.functional.pad(self.rbr_1x1.weight, [1, 1, 1, 1])
# Fuse self.rbr_identity
if (isinstance(self.rbr_identity, nn.BatchNorm2d) or isinstance(self.rbr_identity, nn.modules.batchnorm.SyncBatchNorm)):
# print(f"fuse: rbr_identity == BatchNorm2d or SyncBatchNorm")
identity_conv_1x1 = nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.out_channels,
......@@ -170,25 +160,17 @@ class RepConv(nn.Module):
bias=False)
identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.to(self.rbr_1x1.weight.data.device)
identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.squeeze().squeeze()
# print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}")
identity_conv_1x1.weight.data.fill_(0.0)
identity_conv_1x1.weight.data.fill_diagonal_(1.0)
identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.unsqueeze(2).unsqueeze(3)
# print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}")
identity_conv_1x1 = self.fuse_conv_bn(identity_conv_1x1, self.rbr_identity)
bias_identity_expanded = identity_conv_1x1.bias
weight_identity_expanded = torch.nn.functional.pad(identity_conv_1x1.weight, [1, 1, 1, 1])
else:
# print(f"fuse: rbr_identity != BatchNorm2d, rbr_identity = {self.rbr_identity}")
bias_identity_expanded = torch.nn.Parameter( torch.zeros_like(rbr_1x1_bias) )
weight_identity_expanded = torch.nn.Parameter( torch.zeros_like(weight_1x1_expanded) )
#print(f"self.rbr_1x1.weight = {self.rbr_1x1.weight.shape}, ")
#print(f"weight_1x1_expanded = {weight_1x1_expanded.shape}, ")
#print(f"self.rbr_dense.weight = {self.rbr_dense.weight.shape}, ")
self.rbr_dense.weight = torch.nn.Parameter(self.rbr_dense.weight + weight_1x1_expanded + weight_identity_expanded)
self.rbr_dense.bias = torch.nn.Parameter(self.rbr_dense.bias + rbr_1x1_bias + bias_identity_expanded)
......@@ -208,7 +190,6 @@ class RepConv(nn.Module):
self.rbr_dense = None
def fuse_conv_and_bn(conv, bn):
# Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
fusedconv = nn.Conv2d(conv.in_channels,
conv.out_channels,
kernel_size=conv.kernel_size,
......@@ -217,16 +198,13 @@ def fuse_conv_and_bn(conv, bn):
groups=conv.groups,
bias=True).requires_grad_(False).to(conv.weight.device)
# prepare filters
w_conv = conv.weight.clone().view(conv.out_channels, -1)
w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
w_conv = conv.weight.clone().view(conv.out_channels, -1)
w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
# prepare spatial bias
b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
return fusedconv
#---------------------------------------------------#
......@@ -275,16 +253,15 @@ class YoloBody(nn.Module):
self.yolo_head_P4 = nn.Conv2d(base_channels * 16, len(anchors_mask[1]) * (5 + num_classes), 1)
self.yolo_head_P5 = nn.Conv2d(base_channels * 32, len(anchors_mask[0]) * (5 + num_classes), 1)
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
def fuse(self):
print('Fusing layers... ')
for m in self.modules():
if isinstance(m, RepConv):
#print(f" fuse_repvgg_block")
m.fuse_repvgg_block()
elif type(m) is Conv and hasattr(m, 'bn'):
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
delattr(m, 'bn') # remove batchnorm
m.forward = m.fuseforward # update forward
m.conv = fuse_conv_and_bn(m.conv, m.bn)
delattr(m, 'bn')
m.forward = m.fuseforward
return self
def forward(self, x):
......@@ -315,17 +292,17 @@ class YoloBody(nn.Module):
P5 = self.rep_conv_3(P5)
#---------------------------------------------------#
# 第三个特征层
# y3=(batch_size,75,80,80)
# y3=(batch_size, 75, 80, 80)
#---------------------------------------------------#
out2 = self.yolo_head_P3(P3)
#---------------------------------------------------#
# 第二个特征层
# y2=(batch_size,75,40,40)
# y2=(batch_size, 75, 40, 40)
#---------------------------------------------------#
out1 = self.yolo_head_P4(P4)
#---------------------------------------------------#
# 第一个特征层
# y1=(batch_size,75,20,20)
# y1=(batch_size, 75, 20, 20)
#---------------------------------------------------#
out0 = self.yolo_head_P5(P5)
return out0, out1, out2
......
summary.py
浏览文件 @ abd2999e
......@@ -11,10 +11,9 @@ if __name__ == "__main__":
input_shape = [640, 640]
anchors_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
num_classes = 80
phi = 'l'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
m = YoloBody(anchors_mask, num_classes, phi).to(device)
m = YoloBody(anchors_mask, num_classes, False).to(device)
summary(m, (3, input_shape[0], input_shape[1]))
dummy_input = torch.randn(1, 3, input_shape[0], input_shape[1]).to(device)
......
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