mirror of https://github.com/alibaba/EasyCV.git
101 lines
3.4 KiB
Python
101 lines
3.4 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import torch
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from easycv.utils.checkpoint import load_checkpoint
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from easycv.utils.logger import get_root_logger, print_log
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from easycv.utils.preprocess_function import gaussianBlur, randomGrayScale
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from .. import builder
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from ..base import BaseModel
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from ..registry import MODELS
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from ..utils import GatherLayer
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@MODELS.register_module
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class SimCLR(BaseModel):
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def __init__(self,
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backbone,
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train_preprocess=[],
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neck=None,
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head=None,
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pretrained=None):
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super(SimCLR, self).__init__()
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self.pretrained = pretrained
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self.backbone = builder.build_backbone(backbone)
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self.preprocess_key_map = {
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'randomGrayScale': randomGrayScale,
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'gaussianBlur': gaussianBlur
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}
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self.train_preprocess = [
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self.preprocess_key_map[i] for i in train_preprocess
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]
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self.neck = builder.build_neck(neck)
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self.head = builder.build_head(head)
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self.init_weights()
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@staticmethod
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def _create_buffer(N):
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mask = 1 - torch.eye(N * 2, dtype=torch.uint8).cuda()
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pos_ind = (torch.arange(N * 2).cuda(),
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2 * torch.arange(N, dtype=torch.long).unsqueeze(1).repeat(
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1, 2).view(-1, 1).squeeze().cuda())
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neg_mask = torch.ones((N * 2, N * 2 - 1), dtype=torch.uint8).cuda()
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neg_mask[pos_ind] = 0
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return mask, pos_ind, neg_mask
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def init_weights(self):
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if isinstance(self.pretrained, str):
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logger = get_root_logger()
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load_checkpoint(
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self.backbone, self.pretrained, strict=False, logger=logger)
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else:
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self.backbone.init_weights()
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self.neck.init_weights(init_linear='kaiming')
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def forward_backbone(self, img):
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"""Forward backbone
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Returns:
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x (tuple): 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, **kwargs):
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assert isinstance(img, list)
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img = torch.stack(img, 1)
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img = img.reshape(
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img.size(0) * 2, img.size(2), img.size(3), img.size(4))
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for preprocess in self.train_preprocess:
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img = preprocess(img)
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x = self.forward_backbone(img) # 2n
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z = self.neck(x)[0] # (2n)xd
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z = z / (torch.norm(z, p=2, dim=1, keepdim=True) + 1e-10)
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z = torch.cat(GatherLayer.apply(z), dim=0) # (2N)xd
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assert z.size(0) % 2 == 0
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N = z.size(0) // 2
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s = torch.matmul(z, z.permute(1, 0)) # (2N)x(2N)
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mask, pos_ind, neg_mask = self._create_buffer(N)
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# remove diagonal, (2N)x(2N-1)
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s = torch.masked_select(s, mask == 1).reshape(s.size(0), -1)
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positive = s[pos_ind].unsqueeze(1) # (2N)x1
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# select negative, (2N)x(2N-2)
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negative = torch.masked_select(s, neg_mask == 1).reshape(s.size(0), -1)
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losses = self.head(positive, negative)
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return losses
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def forward_test(self, img, **kwargs):
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pass
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def forward(self, img, mode='train', **kwargs):
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if mode == 'train':
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return self.forward_train(img, **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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