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inference.py
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from resnet import get_model
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import torch
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from PIL import Image
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from torchvision.transforms.functional import pil_to_tensor
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model = get_model("r100", dropout=0.0, fp16=True, num_features=512).cuda()
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model.load_state_dict(torch.load("model.pt"))
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model.eval()
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img = pil_to_tensor(Image.open("test.jpg").resize((112,112))).permute(0, 1, 2).to("cuda", torch.float16).unsqueeze(dim = 0)
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embeddings = model(img)
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model.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:460c0b4bb44130276107536c725c0a5a5500c05533bfb2871d9112f69bc7077f
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size 261152502
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resnet.py
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import torch
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from torch import nn
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from torch.utils.checkpoint import checkpoint
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__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']
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using_ckpt = False
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def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
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"""3x3 convolution with padding"""
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return nn.Conv2d(in_planes,
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out_planes,
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kernel_size=3,
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stride=stride,
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padding=dilation,
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groups=groups,
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bias=False,
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dilation=dilation)
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def conv1x1(in_planes, out_planes, stride=1):
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"""1x1 convolution"""
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return nn.Conv2d(in_planes,
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out_planes,
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kernel_size=1,
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stride=stride,
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bias=False)
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class IBasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None,
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groups=1, base_width=64, dilation=1):
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super(IBasicBlock, self).__init__()
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if groups != 1 or base_width != 64:
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raise ValueError('BasicBlock only supports groups=1 and base_width=64')
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if dilation > 1:
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raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
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self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
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self.conv1 = conv3x3(inplanes, planes)
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self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
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self.prelu = nn.PReLU(planes)
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self.conv2 = conv3x3(planes, planes, stride)
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self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
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self.downsample = downsample
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self.stride = stride
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def forward_impl(self, x):
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identity = x
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out = self.bn1(x)
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out = self.conv1(out)
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out = self.bn2(out)
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out = self.prelu(out)
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out = self.conv2(out)
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out = self.bn3(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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return out
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def forward(self, x):
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if self.training and using_ckpt:
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return checkpoint(self.forward_impl, x)
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else:
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return self.forward_impl(x)
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class IResNet(nn.Module):
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fc_scale = 7 * 7
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def __init__(self,
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block, layers, dropout=0, num_features=512, zero_init_residual=False,
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groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
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super(IResNet, self).__init__()
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self.extra_gflops = 0.0
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self.fp16 = fp16
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self.inplanes = 64
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self.dilation = 1
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if replace_stride_with_dilation is None:
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replace_stride_with_dilation = [False, False, False]
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if len(replace_stride_with_dilation) != 3:
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raise ValueError("replace_stride_with_dilation should be None "
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"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
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self.groups = groups
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self.base_width = width_per_group
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self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
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self.prelu = nn.PReLU(self.inplanes)
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self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
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self.layer2 = self._make_layer(block,
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128,
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layers[1],
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stride=2,
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dilate=replace_stride_with_dilation[0])
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self.layer3 = self._make_layer(block,
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256,
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layers[2],
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stride=2,
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dilate=replace_stride_with_dilation[1])
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self.layer4 = self._make_layer(block,
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512,
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layers[3],
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stride=2,
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dilate=replace_stride_with_dilation[2])
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self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
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self.dropout = nn.Dropout(p=dropout, inplace=True)
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self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
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self.features = nn.BatchNorm1d(num_features, eps=1e-05)
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nn.init.constant_(self.features.weight, 1.0)
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self.features.weight.requires_grad = False
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.normal_(m.weight, 0, 0.1)
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, IBasicBlock):
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nn.init.constant_(m.bn2.weight, 0)
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def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
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downsample = None
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previous_dilation = self.dilation
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if dilate:
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self.dilation *= stride
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stride = 1
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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conv1x1(self.inplanes, planes * block.expansion, stride),
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nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
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)
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layers = []
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layers.append(
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block(self.inplanes, planes, stride, downsample, self.groups,
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self.base_width, previous_dilation))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(
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block(self.inplanes,
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planes,
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groups=self.groups,
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base_width=self.base_width,
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dilation=self.dilation))
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return nn.Sequential(*layers)
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def forward(self, x):
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with torch.cuda.amp.autocast(self.fp16):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.prelu(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.bn2(x)
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x = torch.flatten(x, 1)
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x = self.dropout(x)
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x = self.fc(x.float() if self.fp16 else x)
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x = self.features(x)
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return x
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def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
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model = IResNet(block, layers, **kwargs)
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if pretrained:
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raise ValueError()
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return model
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def iresnet18(pretrained=False, progress=True, **kwargs):
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return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained,
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progress, **kwargs)
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def iresnet34(pretrained=False, progress=True, **kwargs):
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return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,
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progress, **kwargs)
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def iresnet50(pretrained=False, progress=True, **kwargs):
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return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
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progress, **kwargs)
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def iresnet100(pretrained=False, progress=True, **kwargs):
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return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,
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progress, **kwargs)
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def iresnet200(pretrained=False, progress=True, **kwargs):
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return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,
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progress, **kwargs)
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def get_model(name, **kwargs):
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# resnet
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if name == "r18":
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return iresnet18(False, **kwargs)
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elif name == "r34":
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return iresnet34(False, **kwargs)
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elif name == "r50":
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return iresnet50(False, **kwargs)
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elif name == "r100":
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return iresnet100(False, **kwargs)
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elif name == "r200":
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return iresnet200(False, **kwargs)
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else:
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raise ValueError
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test.jpg
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