Pikaliov
4ff36ce188
Add dataloaders (v1/v2), analysis scripts, and documentation for working with UAV-GeoLoc (World-UAV). Co-authored-by: Cursor <cursoragent@cursor.com>
95 lines
3.3 KiB
Python
95 lines
3.3 KiB
Python
import torch
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import torch.nn as nn
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DINOV2_ARCHS = {
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'dinov2_vits14': 384,
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'dinov2_vitb14': 768,
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'dinov2_vitl14': 1024,
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'dinov2_vitg14': 1536,
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}
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class DINOv2(nn.Module):
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"""
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DINOv2 model
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Args:
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model_name (str): The name of the model architecture
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should be one of ('dinov2_vits14', 'dinov2_vitb14', 'dinov2_vitl14', 'dinov2_vitg14')
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num_trainable_blocks (int): The number of last blocks in the model that are trainable.
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norm_layer (bool): If True, a normalization layer is applied in the forward pass.
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return_token (bool): If True, the forward pass returns both the feature map and the token.
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"""
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def __init__(
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self,
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model_name='dinov2_vitb14',
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num_trainable_blocks=2,
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norm_layer=False,
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return_token=False,
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pretrain_flag=False
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):
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super().__init__()
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assert model_name in DINOV2_ARCHS.keys(), f'Unknown model name {model_name}'
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self.model = torch.hub.load('facebookresearch/dinov2', "dinov2_vits14")
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# torch.hub.load('/home/Shen/.cache/torch/hub/facebookresearch_dinov2_main/',
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# model_name,
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# source='local')
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# self.model = torch.hub.load('facebookresearch/dinov2', model_name)
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self.num_channels = DINOV2_ARCHS[model_name]
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self.num_trainable_blocks = num_trainable_blocks
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self.norm_layer = norm_layer
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self.return_token = return_token
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self.flag = pretrain_flag
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def forward(self, x):
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"""
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The forward method for the DINOv2 class
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Parameters:
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x (torch.Tensor): The input tensor [B, 3, H, W]. H and W should be divisible by 14.
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Returns:
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f (torch.Tensor): The feature map [B, C, H // 14, W // 14].
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t (torch.Tensor): The token [B, C]. This is only returned if return_token is True.
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"""
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B, C, H, W = x.shape
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x = self.model.prepare_tokens_with_masks(x)
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if self.flag:
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# When flag is True, freeze all parameters
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for param in self.model.parameters():
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param.requires_grad = False
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with torch.no_grad():
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for blk in self.model.blocks:
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x = blk(x)
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else:
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# When flag is False, freeze part of the parameters (e.g., first blocks)
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for param in self.model.parameters():
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param.requires_grad = False # Freeze all layers initially
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# Unfreeze the last few blocks (trainable)
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for param in self.model.blocks[-self.num_trainable_blocks:].parameters():
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param.requires_grad = True
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with torch.no_grad():
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for blk in self.model.blocks[:-self.num_trainable_blocks]: # Freeze these blocks
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x = blk(x)
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# Last blocks are trained
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for blk in self.model.blocks[-self.num_trainable_blocks:]: # Train these blocks
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x = blk(x)
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if self.norm_layer:
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x = self.model.norm(x)
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t = x[:, 0]
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f = x[:, 1:]
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# Reshape to (B, C, H, W)
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f = f.reshape((B, H // 14, W // 14, self.num_channels)).permute(0, 3, 1, 2)
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if self.return_token:
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return f, t
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return f
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