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import torch
from torch import nn
__all__ = ["iresnet18", "iresnet34", "iresnet50", "iresnet100", "iresnet200"]
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation,
)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class IBasicBlock(nn.Module):
expansion = 1
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
groups=1,
base_width=64,
dilation=1,
):
super(IBasicBlock, self).__init__()
if groups != 1 or base_width != 64:
raise ValueError("BasicBlock only supports groups=1 and base_width=64")
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
self.conv1 = conv3x3(inplanes, planes)
self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
self.prelu = nn.PReLU(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.bn1(x)
out = self.conv1(out)
out = self.bn2(out)
out = self.prelu(out)
out = self.conv2(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
class IResNet(nn.Module):
def __init__(
self,
block,
layers,
dropout=0,
num_features=512,
zero_init_residual=False,
groups=1,
width_per_group=64,
replace_stride_with_dilation=None,
fp16=False,
fc_scale = 7 * 7,
):
super(IResNet, self).__init__()
self.fp16 = fp16
self.inplanes = 64
self.dilation = 1
self.fc_scale = fc_scale
if replace_stride_with_dilation is None:
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError(
"replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation)
)
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(
3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False
)
self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
self.prelu = nn.PReLU(self.inplanes)
self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
self.layer2 = self._make_layer(
block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]
)
self.layer3 = self._make_layer(
block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]
)
self.layer4 = self._make_layer(
block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]
)
self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
self.dropout = nn.Dropout(p=dropout, inplace=True)
self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
self.features = nn.BatchNorm1d(num_features, eps=1e-05)
nn.init.constant_(self.features.weight, 1.0)
self.features.weight.requires_grad = False
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, 0, 0.1)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
if zero_init_residual:
for m in self.modules():
if isinstance(m, IBasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
nn.BatchNorm2d(planes * block.expansion, eps=1e-05,),
)
layers = []
layers.append(
block(
self.inplanes,
planes,
stride,
downsample,
self.groups,
self.base_width,
previous_dilation,
)
)
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(
self.inplanes,
planes,
groups=self.groups,
base_width=self.base_width,
dilation=self.dilation,
)
)
return nn.Sequential(*layers)
def forward(self, x):
with torch.cuda.amp.autocast(self.fp16):
x = self.conv1(x)
x = self.bn1(x)
x = self.prelu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.bn2(x)
# print(x.shape)
x = torch.flatten(x, 1)
x = self.dropout(x)
x = self.fc(x.float() if self.fp16 else x)
x = self.features(x)
return x
def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
model = IResNet(block, layers, **kwargs)
if pretrained:
model_dir = {
'iresnet18': './weights/r18-backbone.pth',
'iresnet34': './weights/r34-backbone.pth',
'iresnet50': './weights/r50-backbone.pth',
'iresnet100': './weights/r100-backbone.pth',
}
pre_trained_weights = torch.load(model_dir[arch], map_location=torch.device('cpu'))
tmp_dict = {}
for key in pre_trained_weights:
# if 'features' in key or 'fc' in key:
# print('skip %s' % key)
# continue
tmp_dict[key] = pre_trained_weights[key]
# get 'iresnet' model layers which don't exist in 'arcxx' and insert to tmp
model_dict = model.state_dict()
for key in model_dict:
if key not in tmp_dict:
tmp_dict[key] = model_dict[key]
model.load_state_dict(tmp_dict, strict=False)
print("load pre-trained iresnet from %s" % model_dir[arch])
return model
def iresnet18(pretrained=False, progress=True, **kwargs):
return _iresnet(
"iresnet18", IBasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs
)
def iresnet34(pretrained=False, progress=True, **kwargs):
return _iresnet(
"iresnet34", IBasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs
)
def iresnet50(pretrained=False, progress=True, **kwargs):
return _iresnet(
"iresnet50", IBasicBlock, [3, 4, 14, 3], pretrained, progress, **kwargs
)
def iresnet100(pretrained=False, progress=True, **kwargs):
return _iresnet(
"iresnet100", IBasicBlock, [3, 13, 30, 3], pretrained, progress, **kwargs
)
def iresnet200(pretrained=False, progress=True, **kwargs):
return _iresnet(
"iresnet200", IBasicBlock, [6, 26, 60, 6], pretrained, progress, **kwargs
)
@torch.no_grad()
def identification(folder: str = './images', target_idx: int = 0):
import os
from PIL import Image
import torch
import torchvision.transforms as transforms
import torch.nn.functional as F
import kornia
import numpy as np
os.makedirs('crop', exist_ok=True)
img_list = os.listdir(folder)
img_list.sort()
n = len(img_list)
trans = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
# transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
trans_matrix = torch.tensor(
[[[1.07695457, -0.03625215, -1.56352194],
[0.03625215, 1.07695457, -5.32134629]]],
requires_grad=False).float().cuda()
fid_model = iresnet50(pretrained=True).cuda().eval()
def save_tensor_to_img(tensor: torch.Tensor, path: str, scale=255):
tensor = tensor.permute(0, 2, 3, 1)[0] # in [0,1]
tensor = tensor.clamp(0, 1)
tensor = tensor * scale
tensor_np = tensor.cpu().numpy().astype(np.uint8)
if tensor_np.shape[-1] == 1: # channel dim
tensor_np = tensor_np.repeat(3, axis=-1)
tensor_img = Image.fromarray(tensor_np)
tensor_img.save(path)
feats = torch.zeros((n, 512), dtype=torch.float32).cuda()
for idx, img_path in enumerate(img_list):
img_pil = Image.open(os.path.join(folder, img_path)).convert('RGB')
img_tensor = trans(img_pil).unsqueeze(0).cuda()
# img_tensor = kornia.geometry.transform.warp_affine(img_tensor, trans_matrix, (256, 256))
save_tensor_to_img(img_tensor / 2 + 0.5, path=os.path.join('./crop', img_path))
img_tensor = F.interpolate(img_tensor, size=112, mode="bilinear", align_corners=True) # to 112
feat = fid_model(img_tensor)
feats[idx] = feat
target_feat = feats[target_idx].unsqueeze(0)
cosine_sim = F.cosine_similarity(target_feat, feats, 1)
print(cosine_sim.shape)
print('====== similarity with %s ======' % img_list[target_idx])
for idx in range(n):
print('[%d] %s = %.2f' % (idx, img_list[idx], float(cosine_sim[idx].cpu())))
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description="arcface")
parser.add_argument("-i", "--target_idx", type=int, default=0)
args = parser.parse_args()
identification(target_idx=args.target_idx)
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