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- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- from timm.models.layers import trunc_normal_, DropPath
- from timm.models.registry import register_model
-
- class Block(nn.Module):
- r""" ConvNeXt Block. There are two equivalent implementations:
- (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
- (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
- We use (2) as we find it slightly faster in PyTorch
-
- Args:
- dim (int): Number of input channels.
- drop_path (float): Stochastic depth rate. Default: 0.0
- layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
- """
- def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
- super().__init__()
- self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
- self.norm = LayerNorm(dim, eps=1e-6)
- self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers
- self.act = nn.GELU()
- self.pwconv2 = nn.Linear(4 * dim, dim)
- self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)),
- requires_grad=True) if layer_scale_init_value > 0 else None
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-
- def forward(self, x):
- input = x
- x = self.dwconv(x)
- x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
- x = self.norm(x)
- x = self.pwconv1(x)
- x = self.act(x)
- x = self.pwconv2(x)
- if self.gamma is not None:
- x = self.gamma * x
- x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
-
- x = input + self.drop_path(x)
- return x
-
- class ConvNeXt(nn.Module):
- r""" ConvNeXt
- A PyTorch impl of : `A ConvNet for the 2020s` -
- https://arxiv.org/pdf/2201.03545.pdf
-
- Args:
- in_chans (int): Number of input image channels. Default: 3
- num_classes (int): Number of classes for classification head. Default: 1000
- depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
- dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
- drop_path_rate (float): Stochastic depth rate. Default: 0.
- layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
- head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
- """
- def __init__(self, in_chans=3, num_classes=1000,
- depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0.,
- layer_scale_init_value=1e-6, head_init_scale=1.,
- ):
- super().__init__()
-
- self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers
- stem = nn.Sequential(
- nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
- LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
- )
- self.downsample_layers.append(stem)
- for i in range(3):
- downsample_layer = nn.Sequential(
- LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
- nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
- )
- self.downsample_layers.append(downsample_layer)
-
- self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks
- dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
- cur = 0
- for i in range(4):
- stage = nn.Sequential(
- *[Block(dim=dims[i], drop_path=dp_rates[cur + j],
- layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]
- )
- self.stages.append(stage)
- cur += depths[i]
-
- self.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer
- self.head = nn.Linear(dims[-1], num_classes)
-
- self.apply(self._init_weights)
- self.head.weight.data.mul_(head_init_scale)
- self.head.bias.data.mul_(head_init_scale)
-
- def _init_weights(self, m):
- if isinstance(m, (nn.Conv2d, nn.Linear)):
- trunc_normal_(m.weight, std=.02)
- nn.init.constant_(m.bias, 0)
-
- def forward_features(self, x):
- for i in range(4):
- x = self.downsample_layers[i](x)
- x = self.stages[i](x)
- return self.norm(x.mean([-2, -1])) # global average pooling, (N, C, H, W) -> (N, C)
-
- def forward(self, x):
- #x = self.forward_features(x)
- #x = self.head(x)
- x0 = self.downsample_layers[0](x)
- x0 = self.stages[0](x0)
-
- x1 = self.downsample_layers[1](x0)
- x1 = self.stages[1](x1)
-
- x2 = self.downsample_layers[2](x1)
- x2 = self.stages[2](x2)
-
- x3 = self.downsample_layers[3](x2)
- x3 = self.stages[3](x3)
- return x3
-
- class LayerNorm(nn.Module):
- r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
- The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
- shape (batch_size, height, width, channels) while channels_first corresponds to inputs
- with shape (batch_size, channels, height, width).
- """
- def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
- super().__init__()
- self.weight = nn.Parameter(torch.ones(normalized_shape))
- self.bias = nn.Parameter(torch.zeros(normalized_shape))
- self.eps = eps
- self.data_format = data_format
- if self.data_format not in ["channels_last", "channels_first"]:
- raise NotImplementedError
- self.normalized_shape = (normalized_shape, )
-
- def forward(self, x):
- if self.data_format == "channels_last":
- return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
- elif self.data_format == "channels_first":
- u = x.mean(1, keepdim=True)
- s = (x - u).pow(2).mean(1, keepdim=True)
- x = (x - u) / torch.sqrt(s + self.eps)
- x = self.weight[:, None, None] * x + self.bias[:, None, None]
- return x
-
-
- model_urls = {
- "convnext_tiny_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
- "convnext_small_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth",
- "convnext_base_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth",
- "convnext_large_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth",
- "convnext_tiny_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth",
- "convnext_small_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth",
- "convnext_base_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth",
- "convnext_large_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth",
- "convnext_xlarge_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth",
- }
-
- @register_model
- def convnext_tiny(pretrained=False,in_22k=False, **kwargs):
- model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
- if pretrained:
- '''
- url = model_urls['convnext_tiny_22k'] if in_22k else model_urls['convnext_tiny_1k']
- checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
- model.load_state_dict(checkpoint["model"])
- '''
- pretrained_dict = torch.load("/dataset/convnext_base_1k_224_ema.pth")
- model_dict = model.state_dict()
- # 1. filter out unnecessary keys
- pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
- # 2. overwrite entries in the existing state dict
- model_dict.update(pretrained_dict)
- model.load_state_dict(model_dict)
- return model
-
- @register_model
- def convnext_small(pretrained=False,in_22k=False, **kwargs):
- model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
- if pretrained:
- url = model_urls['convnext_small_22k'] if in_22k else model_urls['convnext_small_1k']
- checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
- model.load_state_dict(checkpoint["model"])
- return model
-
- @register_model
- def convnext_base(pretrained=False, in_22k=False, **kwargs):
- model = ConvNeXt(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
- if pretrained:
- url = model_urls['convnext_base_22k'] if in_22k else model_urls['convnext_base_1k']
- checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
- model.load_state_dict(checkpoint["model"])
- return model
-
- @register_model
- def convnext_large(pretrained=False, in_22k=False, **kwargs):
- model = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
- if pretrained:
- url = model_urls['convnext_large_22k'] if in_22k else model_urls['convnext_large_1k']
- checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
- model.load_state_dict(checkpoint["model"])
- return model
-
- @register_model
- def convnext_xlarge(pretrained=False, in_22k=False, **kwargs):
- model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
- if pretrained:
- assert in_22k, "only ImageNet-22K pre-trained ConvNeXt-XL is available; please set in_22k=True"
- url = model_urls['convnext_xlarge_22k']
- checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
- model.load_state_dict(checkpoint["model"])
- return model
-
-
- class ei_Net_Resnet50(nn.Module):
- def __init__(self):
- super(ei_Net_Resnet50, self).__init__()
-
- self.convnext_backbone = convnext_tiny(pretrained=True)
-
- self.down1 = nn.Conv2d(768, 256, 3, 1, 1)
- self.down2 = nn.Conv2d(256, 32, 3, 1, 1)
- self.down3 = nn.Conv2d(32, 1, 3, 1, 1)
-
-
- def forward(self, x):
-
- output = self.convnext_backbone(x)
- output = self.down3(self.down2(self.down1(output)))
- output = F.interpolate(output, x.size()[2:], mode="bilinear", align_corners=False)
-
- return output
-
- def ei_net():
- model = ei_Net_Resnet50()
- return model
- '''
- data = torch.randn(1,3,320,320)
- net = ei_Net_Resnet50()
- output = net(data)
- print(output.shape)
- print(sum([x.nelement() for x in net.parameters()]))
- print('Total params: %.2fM' % (sum(p.numel() for p in net.parameters())/1000000.0))
- '''
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