详细的改进教程以及源码，戳这！戳这！！戳这！！！B站：AI学术叫叫兽 源码在相簿的链接中，动态中也有链接，感谢支持！祝科研遥遥领先！
截止到发稿，B站YOLOv8最新改进系列的源码包已更新了22种！
用于移动设备的高效神经网络骨干通常针对 FLOP 或参数计数等指标进行优化。然而，当部署在移动设备上时，这些指标可能与网络的延迟没有很好的相关性。因此，我们通过在移动设备上部署多个移动友好网络来对不同指标进行广泛分析。我们识别和分析最近高效神经网络中的架构和优化瓶颈，并提供缓解这些瓶颈的方法。为此，我们设计了一个高效的骨干 MobileOne，其变体在 iPhone12 上的推理时间低于 1 毫秒，在 ImageNet 上的 top-1 准确率为 75.9%。我们展示了 MobileOne 在高效架构中实现了SOTA性能，同时在移动设备上速度提高了许多倍。我们最好的模型在 ImageNet 上获得了与 MobileFormer 相似的性能，同时速度提高了 38 倍。我们的模型在 ImageNet 上的 top-1 准确率比 EfficientNet 在相似的延迟下高 2.3%。此外，我们展示了我们的模型可以推广到多个任务——图像分类、目标检测和语义分割，与部署在移动设备上的现有高效架构相比，延迟和准确度显著提高。
详细的改进教程以及源码，戳这！戳这！！戳这！！！B站：AI学术叫叫兽 源码在相簿的链接中，动态中也有链接，感谢支持！祝科研遥遥领先！
核心代码示例如下：
def conv_an(in_channels, out_channels, kernel_size, stride, padding, groups=1):
result = nn.Sequential()
result.add_module(
"conv",
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
groups=groups,
bias=False,
),
)
result.add_module("bn", nn.BatchNorm2d(num_features=out_channels))
return result
class DepthWiseConv(nn.Module):
def __init__(self, inc, kernel_size, stride=1):
super().__init__()
padding = 1
if kernel_size == 1:
padding = 0
# self.conv = nn.Sequential(
# nn.Conv2d(inc, inc, kernel_size, stride, padding, groups=inc, bias=False,),
# nn.BatchNorm2d(inc),
# )
self.conv = conv_bn(inc, inc, kernel_size, stride, padding, inc)
def forward(self, x):
return self.conv(x)
class PointWiseConv(nn.Module):
def __init__(self, inc, outc):
super().__init__()
# self.conv = nn.Sequential(
# nn.Conv2d(inc, outc, 1, 1, 0, bias=False),
# nn.BatchNorm2d(outc),
# )
self.conv = conv_bn(inc, outc, 1, 1, 0)
def forward(self, x):
return self.conv(x)
class MobileOneBlock(nn.Module):
def __init__(
self,
in_channels,
out_channels,
k,
stride=1,
dilation=1,
padding_mode="zeros",
deploy=False,
use_se=False,
):
super(MobileOneBlock, self).__init__()
self.deploy = deploy
self.in_channels = in_channels
self.out_channels = out_channels
self.deploy = deploy
kernel_size = 3
padding = 1
assert kernel_size == 3
assert padding == 1
self.k = k
padding_11 = padding - kernel_size // 2
self.nonlinearity = nn.ReLU()
if use_se:
# self.se = SEBlock(out_channels, internal_neurons=out_channels // 16)
...
else:
self.se = nn.Identity()
if deploy:
self.dw_reparam = nn.Conv2d(
in_channels=in_channels,
out_channels=in_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=in_channels,
bias=True,
padding_mode=padding_mode,
)
self.pw_reparam = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=1,
bias=True,
)
else:
self.dw_bn_layer = (
nn.BatchNorm2d(in_channels)
if out_channels == in_channels and stride == 1
else None
)
for k_idx in range(k):
setattr(
self,
f"dw_3x3_{k_idx}",
DepthWiseConv(in_channels, 3, stride=stride),
)
self.dw_1x1 = DepthWiseConv(in_channels, 1, stride=stride)
self.pw_bn_layer = (
nn.BatchNorm2d(in_channels)
if out_channels == in_channels and stride == 1
else None
)
for k_idx in range(k):
setattr(
self, f"pw_1x1_{k_idx}", PointWiseConv(in_channels, out_channels)
)
def forward(self, inputs):
if self.deploy:
x = self.dw_reparam(inputs)
x = self.nonlinearity(x)
x = self.pw_reparam(x)
x = self.nonlinearity(x)
return x
if self.dw_bn_layer is None:
id_out = 0
else:
id_out = self.dw_bn_layer(inputs)
x_conv_3x3 = []
for k_idx in range(self.k):
x = getattr(self, f"dw_3x3_{k_idx}")(inputs)
# print(x.shape)
x_conv_3x3.append(x)
x_conv_1x1 = self.dw_1x1(inputs)
# print(x_conv_1x1.shape, x_conv_3x3[0].shape)
# print(x_conv_1x1.shape)
# print(id_out)
x = id_out + x_conv_1x1 + sum(x_conv_3x3)
x = self.nonlinearity(self.se(x))
# 1x1 conv
if self.pw_bn_layer is None:
id_out = 0
else:
id_out = self.pw_bn_layer(x)
x_conv_1x1 = []
for k_idx in range(self.k):
x_conv_1x1.append(getattr(self, f"pw_1x1_{k_idx}")(x))
x = id_out + sum(x_conv_1x1)
x = self.nonlinearity(x)
return x
# Optional. This improves the accuracy and facilitates quantization.
# 1. Cancel the original weight decay on rbr_dense.conv.weight and rbr_1x1.conv.weight.
# 2. Use like this.
# loss = criterion(....)
# for every RepVGGBlock blk:
# loss += weight_decay_coefficient * 0.5 * blk.get_cust_L2()
# optimizer.zero_grad()
# loss.backward()
def get_custom_L2(self):
# K3 = self.rbr_dense.conv.weight
# K1 = self.rbr_1x1.conv.weight
# t3 = (self.rbr_dense.bn.weight / ((self.rbr_dense.bn.running_var + self.rbr_dense.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach()
# t1 = (self.rbr_1x1.bn.weight / ((self.rbr_1x1.bn.running_var + self.rbr_1x1.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach()
# l2_loss_circle = (K3 ** 2).sum() - (K3[:, :, 1:2, 1:2] ** 2).sum() # The L2 loss of the "circle" of weights in 3x3 kernel. Use regular L2 on them.
# eq_kernel = K3[:, :, 1:2, 1:2] * t3 + K1 * t1 # The equivalent resultant central point of 3x3 kernel.
# l2_loss_eq_kernel = (eq_kernel ** 2 / (t3 ** 2 + t1 ** 2)).sum() # Normalize for an L2 coefficient comparable to regular L2.
# return l2_loss_eq_kernel + l2_loss_circle
...
# This func derives the equivalent kernel and bias in a DIFFERENTIABLE way.
# You can get the equivalent kernel and bias at any time and do whatever you want,
# for example, apply some penalties or constraints during training, just like you do to the other models.
# May be useful for quantization or pruning.
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
dw_kernel_3x3 = []
dw_bias_3x3 = []
for k_idx in range(self.k):
k3, b3 = self._fuse_bn_tensor(getattr(self, f"dw_3x3_{k_idx}").conv)
# print(k3.shape, b3.shape)
dw_kernel_3x3.append(k3)
dw_bias_3x3.append(b3)
dw_kernel_1x1, dw_bias_1x1 = self._fuse_bn_tensor(self.dw_1x1.conv)
dw_kernel_id, dw_bias_id = self._fuse_bn_tensor(
self.dw_bn_layer, self.in_channels
)
dw_kernel = (
sum(dw_kernel_3x3)
+ self._pad_1x1_to_3x3_tensor(dw_kernel_1x1)
+ dw_kernel_id
)
dw_bias = sum(dw_bias_3x3) + dw_bias_1x1 + dw_bias_id
# pw
pw_kernel = []
pw_bias = []
for k_idx in range(self.k):
k1, b1 = self._fuse_bn_tensor(getattr(self, f"pw_1x1_{k_idx}").conv)
# print(k1.shape)
pw_kernel.append(k1)
pw_bias.append(b1)
pw_kernel_id, pw_bias_id = self._fuse_bn_tensor(self.pw_bn_layer, 1)
pw_kernel_1x1 = sum(pw_kernel) + pw_kernel_id
pw_bias_1x1 = sum(pw_bias) + pw_bias_id
return dw_kernel, dw_bias, pw_kernel_1x1, pw_bias_1x1
def _pad_1x1_to_3x3_tensor(self, kernel1x1):
if kernel1x1 is None:
return 0
else:
return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])
def _fuse_bn_tensor(self, branch, groups=None):
if branch is None:
return 0, 0
if isinstance(branch, nn.Sequential):
kernel = branch.conv.weight
bias = branch.conv.bias
running_mean = branch.bn.running_mean
running_var = branch.bn.running_var
gamma = branch.bn.weight
beta = branch.bn.bias
eps = branch.bn.eps
else:
assert isinstance(branch, nn.BatchNorm2d)
# if not hasattr(self, 'id_tensor'):
input_dim = self.in_channels // groups # self.groups
if groups == 1:
ks = 1
else:
ks = 3
kernel_value = np.zeros(
(self.in_channels, input_dim, ks, ks), dtype=np.float32
)
for i in range(self.in_channels):
if ks == 1:
kernel_value[i, i % input_dim, 0, 0] = 1
else:
kernel_value[i, i % input_dim, 1, 1] = 1
self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
kernel = self.id_tensor
running_mean = branch.running_mean
running_var = branch.running_var
gamma = branch.weight
beta = branch.bias
eps = branch.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1, 1)
return kernel * t, beta - running_mean * gamma / std
def switch_to_deploy(self):
dw_kernel, dw_bias, pw_kernel, pw_bias = self.get_equivalent_kernel_bias()
self.dw_reparam = nn.Conv2d(
in_channels=self.pw_1x1_0.conv.conv.in_channels,
out_channels=self.pw_1x1_0.conv.conv.in_channels,
kernel_size=self.dw_3x3_0.conv.conv.kernel_size,
stride=self.dw_3x3_0.conv.conv.stride,
padding=self.dw_3x3_0.conv.conv.padding,
groups=self.dw_3x3_0.conv.conv.in_channels,
bias=True,
)
self.pw_reparam = nn.Conv2d(
in_channels=self.pw_1x1_0.conv.conv.in_channels,
out_channels=self.pw_1x1_0.conv.conv.out_channels,
kernel_size=1,
stride=1,
bias=True,
)
self.dw_reparam.weight.data = dw_kernel
self.dw_reparam.bias.data = dw_bias
self.pw_reparam.weight.data = pw_kernel
self.pw_reparam.bias.data = pw_bias
for para in self.parameters():
para.detach_()
self.__delattr__("dw_1x1")
for k_idx in range(self.k):
self.__delattr__(f"dw_3x3_{k_idx}")
self.__delattr__(f"pw_1x1_{k_idx}")
if hasattr(self, "dw_bn_layer"):
self.__delattr__("dw_bn_layer")
if hasattr(self, "pw_bn_layer"):
self.__delattr__("pw_bn_layer")
if hasattr(self, "id_tensor"):
self.__delattr__("id_tensor")
self.deploy = True
class MobileOneNet(nn.Module):
def __init__(
self, blocks, ks, channels, strides, width_muls, num_classes=None, deploy=False
):
super().__init__()
self.stage_num = len(blocks)
# self.stage0 = MobileOneBlock(3, int(channels[0] * width_muls[0]), ks[0], stride=strides[0], deploy=deploy)
self.stage0 = nn.Sequential(
nn.Conv2d(3, int(channels[0] * width_muls[0]), 3, 2, 1, bias=False),
nn.BatchNorm2d(int(channels[0] * width_muls[0])),
nn.ReLU(),
)
in_channels = int(channels[0] * width_muls[0])
for idx, block_num in enumerate(blocks[1:]):
idx += 1
module = []
out_channels = int(channels[idx] * width_muls[idx])
for b_idx in range(block_num):
stride = strides[idx] if b_idx == 0 else 1
block = MobileOneBlock(
in_channels, out_channels, ks[idx], stride, deploy=deploy
)
in_channels = out_channels
module.append(block)
setattr(self, f"stage{idx}", nn.Sequential(*module))
if num_classes is not None:
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Sequential(
nn.Linear(
out_channels,
num_classes,
),
)
def forward(self, x):
# for s_idx in range(self.stage_num):
# x = getattr(self, f'stage{s_idx}')(x)
x0 = self.stage0(x)
# print(x0[0,:,0,0])
# return x0
x1 = self.stage1(x0)
x2 = self.stage2(x1)
x3 = self.stage3(x2)
x4 = self.stage4(x3)
x5 = self.stage5(x4)
assert x5.shape[-1] == 7
x = self.avg_pool(x5)
x = torch.flatten(x, start_dim=1) # b, c
x = self.fc1(x)
return x
详细的改进教程以及源码，戳这！戳这！！戳这！！！B站：AI学术叫叫兽 源码在相簿的链接中，动态中也有链接，感谢支持！祝科研遥遥领先！
执行命令
python train.py
改完收工！
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遥遥领先同行！！！！
详细的改进教程以及源码，戳这！戳这！！戳这！！！B站：AI学术叫叫兽 源码在相簿的链接中，动态中也有链接，感谢支持！祝科研遥遥领先！