95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
import torch
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import torch.nn as nn
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from openselfsup.utils import print_log
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from . import builder
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from .registry import MODELS
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@MODELS.register_module
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class RotationPred(nn.Module):
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"""Rotation prediction.
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Implementation of "Unsupervised Representation Learning
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by Predicting Image Rotations (https://arxiv.org/abs/1803.07728)".
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Args:
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backbone (dict): Config dict for module of backbone ConvNet.
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head (dict): Config dict for module of loss functions. Default: None.
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pretrained (str, optional): Path to pre-trained weights. Default: None.
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"""
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def __init__(self, backbone, head=None, pretrained=None):
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super(RotationPred, self).__init__()
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self.backbone = builder.build_backbone(backbone)
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if head is not None:
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self.head = builder.build_head(head)
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self.init_weights(pretrained=pretrained)
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def init_weights(self, pretrained=None):
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"""Initialize the weights of model.
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Args:
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pretrained (str, optional): Path to pre-trained weights.
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Default: None.
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"""
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if pretrained is not None:
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print_log('load model from: {}'.format(pretrained), logger='root')
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self.backbone.init_weights(pretrained=pretrained)
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self.head.init_weights(init_linear='kaiming')
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def forward_backbone(self, img):
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"""Forward backbone.
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Args:
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img (Tensor): Input images of shape (N, C, H, W).
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Typically these should be mean centered and std scaled.
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Returns:
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tuple[Tensor]: backbone outputs.
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"""
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x = self.backbone(img)
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return x
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def forward_train(self, img, rot_label, **kwargs):
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"""Forward computation during training.
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Args:
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img (Tensor): Input images of shape (N, C, H, W).
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Typically these should be mean centered and std scaled.
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rot_label (Tensor): Labels for the rotations.
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kwargs: Any keyword arguments to be used to forward.
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Returns:
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dict[str, Tensor]: A dictionary of loss components.
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"""
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x = self.forward_backbone(img)
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outs = self.head(x)
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loss_inputs = (outs, rot_label)
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losses = self.head.loss(*loss_inputs)
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return losses
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def forward_test(self, img, **kwargs):
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x = self.forward_backbone(img) # tuple
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outs = self.head(x)
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keys = ['head{}'.format(i) for i in range(len(outs))]
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out_tensors = [out.cpu() for out in outs] # NxC
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return dict(zip(keys, out_tensors))
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def forward(self, img, rot_label=None, mode='train', **kwargs):
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if mode != "extract" and img.dim() == 5: # Nx4xCxHxW
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assert rot_label.dim() == 2 # Nx4
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img = img.view(
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img.size(0) * img.size(1), img.size(2), img.size(3),
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img.size(4)) # (4N)xCxHxW
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rot_label = torch.flatten(rot_label) # (4N)
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if mode == 'train':
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return self.forward_train(img, rot_label, **kwargs)
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elif mode == 'test':
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return self.forward_test(img, **kwargs)
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elif mode == 'extract':
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return self.forward_backbone(img)
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else:
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raise Exception("No such mode: {}".format(mode))
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