地址链接:https://www.flyai.com/m/rexnet_3.0_82.8.pth
查看来源:https://github.com/clovaai/rexnet#pretrained
确定自己使用的框架并导入对应的库。导入库实现样例代码可参考 文档中心-预训练模型使用教程
在代码中实现加载预训练模型地址
"""
ReXNet
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import torch
import torch.nn as nn
from math import ceil
# Memory-efficient Siwsh using torch.jit.script borrowed from the code in (https://twitter.com/jeremyphoward/status/1188251041835315200)
# Currently use memory-efficient SiLU as default:
USE_MEMORY_EFFICIENT_SiLU = True
if USE_MEMORY_EFFICIENT_SiLU:
@torch.jit.script
def silu_fwd(x):
return x.mul(torch.sigmoid(x))
@torch.jit.script
def silu_bwd(x, grad_output):
x_sigmoid = torch.sigmoid(x)
return grad_output * (x_sigmoid * (1. + x * (1. - x_sigmoid)))
class SiLUJitImplementation(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return silu_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return silu_bwd(x, grad_output)
def silu(x, inplace=False):
return SiLUJitImplementation.apply(x)
else:
def silu(x, inplace=False):
return x.mul_(x.sigmoid()) if inplace else x.mul(x.sigmoid())
class SiLU(nn.Module):
def __init__(self, inplace=True):
super(SiLU, self).__init__()
self.inplace = inplace
def forward(self, x):
return silu(x, self.inplace)
def ConvBNAct(out, in_channels, channels, kernel=1, stride=1, pad=0,
num_group=1, active=True, relu6=False):
out.append(nn.Conv2d(in_channels, channels, kernel,
stride, pad, groups=num_group, bias=False))
out.append(nn.BatchNorm2d(channels))
if active:
out.append(nn.ReLU6(inplace=True) if relu6 else nn.ReLU(inplace=True))
def ConvBNSiLU(out, in_channels, channels, kernel=1, stride=1, pad=0, num_group=1):
out.append(nn.Conv2d(in_channels, channels, kernel,
stride, pad, groups=num_group, bias=False))
out.append(nn.BatchNorm2d(channels))
out.append(SiLU(inplace=True))
class SE(nn.Module):
def __init__(self, in_channels, channels, se_ratio=12):
super(SE, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Conv2d(in_channels, channels // se_ratio, kernel_size=1, padding=0),
nn.BatchNorm2d(channels // se_ratio),
nn.ReLU(inplace=True),
nn.Conv2d(channels // se_ratio, channels, kernel_size=1, padding=0),
nn.Sigmoid()
)
def forward(self, x):
y = self.avg_pool(x)
y = self.fc(y)
return x * y
class LinearBottleneck(nn.Module):
def __init__(self, in_channels, channels, t, stride, use_se=True, se_ratio=12,
**kwargs):
super(LinearBottleneck, self).__init__(**kwargs)
self.use_shortcut = stride == 1 and in_channels <= channels
self.in_channels = in_channels
self.out_channels = channels
out = []
if t != 1:
dw_channels = in_channels * t
ConvBNSiLU(out, in_channels=in_channels, channels=dw_channels)
else:
dw_channels = in_channels
ConvBNAct(out, in_channels=dw_channels, channels=dw_channels, kernel=3, stride=stride, pad=1,
num_group=dw_channels, active=False)
if use_se:
out.append(SE(dw_channels, dw_channels, se_ratio))
out.append(nn.ReLU6())
ConvBNAct(out, in_channels=dw_channels, channels=channels, active=False, relu6=True)
self.out = nn.Sequential(*out)
def forward(self, x):
out = self.out(x)
if self.use_shortcut:
out[:, 0:self.in_channels] += x
return out
class ReXNetV1(nn.Module):
def __init__(self, input_ch=16, final_ch=180, width_mult=1.0, depth_mult=1.0, classes=1000,
use_se=True,
se_ratio=12,
dropout_ratio=0.2,
bn_momentum=0.9):
super(ReXNetV1, self).__init__()
layers = [1, 2, 2, 3, 3, 5]
strides = [1, 2, 2, 2, 1, 2]
use_ses = [False, False, True, True, True, True]
layers = [ceil(element * depth_mult) for element in layers]
strides = sum([[element] + [1] * (layers[idx] - 1)
for idx, element in enumerate(strides)], [])
if use_se:
use_ses = sum([[element] * layers[idx] for idx, element in enumerate(use_ses)], [])
else:
use_ses = [False] * sum(layers[:])
ts = [1] * layers[0] + [6] * sum(layers[1:])
self.depth = sum(layers[:]) * 3
stem_channel = 32 / width_mult if width_mult < 1.0 else 32
inplanes = input_ch / width_mult if width_mult < 1.0 else input_ch
features = []
in_channels_group = []
channels_group = []
# The following channel configuration is a simple instance to make each layer become an expand layer.
for i in range(self.depth // 3):
if i == 0:
in_channels_group.append(int(round(stem_channel * width_mult)))
channels_group.append(int(round(inplanes * width_mult)))
else:
in_channels_group.append(int(round(inplanes * width_mult)))
inplanes += final_ch / (self.depth // 3 * 1.0)
channels_group.append(int(round(inplanes * width_mult)))
ConvBNSiLU(features, 3, int(round(stem_channel * width_mult)), kernel=3, stride=2, pad=1)
for block_idx, (in_c, c, t, s, se) in enumerate(zip(in_channels_group, channels_group, ts, strides, use_ses)):
features.append(LinearBottleneck(in_channels=in_c,
channels=c,
t=t,
stride=s,
use_se=se, se_ratio=se_ratio))
pen_channels = int(1280 * width_mult)
ConvBNSiLU(features, c, pen_channels)
features.append(nn.AdaptiveAvgPool2d(1))
self.features = nn.Sequential(*features)
self.output = nn.Sequential(
nn.Dropout(dropout_ratio),
nn.Conv2d(pen_channels, classes, 1, bias=True))
def extract_features(self, x):
return self.features[:-1](x)
def forward(self, x):
x = self.features(x)
x = self.output(x).flatten(1)
return x
if __name__ == '__main__':
model = ReXNetV1(width_mult=1.0)
out = model(torch.randn(2, 3, 224, 224))
loss = out.sum()
loss.backward()
print('Checked a single forward/backward iteration')
tf_efficientnetv2_s_21k-6337ad01.pth
查看来源:https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21k-6337ad01.pthtf_efficientnetv2_l_21k-91a19ec9.pth
查看来源:https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21k-91a19ec9.pthmobilenetv3-large-1cd25616.pth
查看来源:https://github.com/bubbliiiing/mobilenet-yolov4-lite-pytorch#%E6%96%87%E4%BB%B6%E4%B8%8B%E8%BD%BDinception_v3_google-0cc3c7bd.pth
查看来源:https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pthdeit_base_patch16_224-b5f2ef4d.pth
查看来源:https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pthswin_large_patch4_window12_384_22k.pth
查看来源:https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth