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Saining Xie
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## MoCo v3 for Self-supervised ResNet and ViT
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This folder includes the transfer learning experiments on CIFAR-10, CIFAR-100, Flowers and Pets datasets. We provide finetuning recipes for the ViT-Base model.
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### Transfer Results
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The following results are based on ImageNet-1k self-supervised pre-training, followed by end-to-end fine-tuning on downstream datasets. All results are based on a batch size of 128 and 100 training epochs.
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#### ViT, transfer learning
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<table><tbody>
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<!-- START TABLE -->
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<!-- TABLE HEADER -->
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<th valign="center">dataset</th>
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<th valign="center">pretrain<br/>epochs</th>
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<th valign="center">pretrain<br/>crops</th>
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<th valign="center">e2e<br/>acc</th>
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<!-- TABLE BODY -->
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<tr>
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<td align="left">CIFAR-10</td>
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<td align="right">300</td>
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<td align="center">2x224</td>
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<td align="center">98.9%</td>
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</tr>
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<tr>
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<td align="left">CIFAR-100</td>
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<td align="right">300</td>
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<td align="center">2x224</td>
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<td align="center">90.5%</td>
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</tr>
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<tr>
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<td align="left">Flowers</td>
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<td align="right">300</td>
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<td align="center">2x224</td>
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<td align="center">97.7%</td>
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</tr>
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<tr>
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<td align="left">Pets</td>
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<td align="right">300</td>
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<td align="center">2x224</td>
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<td align="center">93.2%</td>
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</tr>
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</tbody></table>
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Similar to the end-to-end fine-tuning on ImageNet, the transfer learning results are also obtained using the [DeiT](https://github.com/facebookresearch/deit) repo.
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### Usage: Transfer learning with ViT
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To perform transfer learning for ViT, use our script to convert the pre-trained ViT checkpoint to [DEiT](https://github.com/facebookresearch/deit) format:
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```
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python convert_to_deit.py \
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--input [your checkpoint path]/[your checkpoint file].pth.tar \
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--output [target checkpoint file].pth
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```
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Then run the training (in the DeiT repo) with the converted checkpoint:
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1. copy (or replace) the following files to the DeiT folder:
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```
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datasets.py
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oxford_flowers_dataset.py
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oxford_pets_dataset.py
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```
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1. download and prepare the datasets
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Pets [Homepage](https://www.robots.ox.ac.uk/~vgg/data/pets/)
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```
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.
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└── ./pets/
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├── ./pets/annotations/ # split and label files
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└── ./pets/images/ # data images
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```
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Flowers [Homepage](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/)
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```
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.
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└── ./flowers/
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├── ./flowers/jpg/ # jpg images
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├── ./flowers/setid.mat # dataset split
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└── ./flowers/imagelabels.mat # labels
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```
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CIFAR-10/CIFAR-100 datasets will be downloaded automatically.
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### Transfer learning scripts (with a 8-GPU machine):
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#### CIFAR-10
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```
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python -u -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
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--batch-size 128 --output_dir [your output dir path] --epochs 100 --lr 3e-4 --weight-decay 0.1 --eval-freq 10 \
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--no-pin-mem --warmup-epochs 3 --data-set cifar10 --data-path [cifar-10 data path] --no-repeated-aug \
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--reprob 0.0 --drop-path 0.1 --mixup 0.8 --cutmix 1
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```
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#### CIFAR-100
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```
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python -u -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
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--batch-size 128 --output_dir [your output dir path] --epochs 100 --lr 3e-4 --weight-decay 0.1 --eval-freq 10 \
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--no-pin-mem --warmup-epochs 3 --data-set cifar10 --data-path [cifar-100 data path] --no-repeated-aug \
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--reprob 0.0 --drop-path 0.1 --mixup 0.5 --cutmix 1
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```
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#### Flowers
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```
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python -u -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
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--batch-size 128 --output_dir [your output dir path] --epochs 100 --lr 3e-4 --weight-decay 0.3 --eval-freq 10 \
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--no-pin-mem --warmup-epochs 3 --data-set cifar10 --data-path [oxford-flowers data path] --no-repeated-aug \
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--reprob 0.25 --drop-path 0.1 --mixup 0 --cutmix 0
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```
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#### Pets
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```
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python -u -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
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--batch-size 128 --output_dir [your output dir path] --epochs 100 --lr 3e-4 --weight-decay 0.1 --eval-freq 10 \
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--no-pin-mem --warmup-epochs 3 --data-set cifar10 --data-path [oxford-pets data path] --no-repeated-aug \
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--reprob 0 --drop-path 0 --mixup 0.8 --cutmix 0
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```
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**Note**:
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Similar to ImageNet end-to-end finetuning experiment, We use `--resume` rather than `--finetune` in the DeiT repo, as its `--finetune` option trains under eval mode. When loading the pre-trained model, revise `model_without_ddp.load_state_dict(checkpoint['model'])` with `strict=False`.
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import os
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import json
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from torchvision import datasets, transforms
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from torchvision.datasets.folder import ImageFolder, default_loader
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from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.data import create_transform
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import oxford_flowers_dataset, oxford_pets_dataset
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def build_transform(is_train, args):
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transform_train = transforms.Compose([
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transforms.RandomResizedCrop((args.input_size, args.input_size), scale=(0.05, 1.0)),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
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])
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transform_test = transforms.Compose([
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transforms.Resize(int((256 / 224) * args.input_size)),
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transforms.CenterCrop(args.input_size),
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transforms.ToTensor(),
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transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
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])
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return transform_train if is_train else transform_test
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def build_dataset(is_train, args):
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transform = build_transform(is_train, args)
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if args.data_set == 'imagenet':
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raise NotImplementedError("Only [cifar10, cifar100, flowers, pets] are supported; \
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for imagenet end-to-end finetuning, please refer to the instructions in the main README.")
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if args.data_set == 'imagenet':
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root = os.path.join(args.data_path, 'train' if is_train else 'val')
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dataset = datasets.ImageFolder(root, transform=transform)
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nb_classes = 1000
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elif args.data_set == 'cifar10':
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dataset = datasets.CIFAR10(root=args.data_path,
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train=is_train,
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download=True,
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transform=transform)
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nb_classes = 10
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elif args.data_set == "cifar100":
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dataset = datasets.CIFAR100(root=args.data_path,
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train=is_train,
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download=True,
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transform=transform)
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nb_classes = 100
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elif args.data_set == "flowers":
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dataset = oxford_flowers_dataset.Flowers(root=args.data_path,
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train=is_train,
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download=False,
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transform=transform)
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nb_classes = 102
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elif args.data_set == "pets":
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dataset = oxford_pets_dataset.Pets(root=args.data_path,
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train=is_train,
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download=False,
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transform=transform)
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nb_classes = 37
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else:
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raise NotImplementedError("Only [cifar10, cifar100, flowers, pets] are supported; \
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for imagenet end-to-end finetuning, please refer to the instructions in the main README.")
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return dataset, nb_classes
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from __future__ import print_function
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from PIL import Image
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import os
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import os.path
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import numpy as np
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import pickle
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import scipy.io
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from typing import Any, Callable, Optional, Tuple
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from torchvision.datasets.vision import VisionDataset
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class Flowers(VisionDataset):
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def __init__(
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self,
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root,
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train=True,
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transform=None,
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target_transform=None,
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download=False,
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):
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super(Flowers, self).__init__(root, transform=transform,
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target_transform=target_transform)
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base_folder = root
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self.image_folder = os.path.join(base_folder, "jpg")
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label_file = os.path.join(base_folder, "imagelabels.mat")
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setid_file = os.path.join(base_folder, "setid.mat")
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self.train = train
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self.labels = scipy.io.loadmat(label_file)["labels"][0]
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train_list = scipy.io.loadmat(setid_file)["trnid"][0]
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val_list = scipy.io.loadmat(setid_file)["valid"][0]
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test_list = scipy.io.loadmat(setid_file)["tstid"][0]
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trainval_list = np.concatenate([train_list, val_list])
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if self.train:
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self.img_files = trainval_list
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else:
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self.img_files = test_list
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def __getitem__(self, index):
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img_name = "image_%05d.jpg" % self.img_files[index]
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target = self.labels[self.img_files[index] - 1] - 1
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img = Image.open(os.path.join(self.image_folder, img_name))
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if self.transform is not None:
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img = self.transform(img)
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if self.target_transform is not None:
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target = self.target_transform(target)
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return img, target
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def __len__(self):
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return len(self.img_files)
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from PIL import Image
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import os
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import os.path
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import numpy as np
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import pickle
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import scipy.io
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from typing import Any, Callable, Optional, Tuple
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from torchvision.datasets.vision import VisionDataset
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class Pets(VisionDataset):
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def __init__(
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self,
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root: str,
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train: bool = True,
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transform: Optional[Callable] = None,
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target_transform: Optional[Callable] = None,
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download: bool = False,
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) -> None:
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super(Pets, self).__init__(root, transform=transform,
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target_transform=target_transform)
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base_folder = root
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self.train = train
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annotations_path_dir = os.path.join(base_folder, "annotations")
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self.image_path_dir = os.path.join(base_folder, "images")
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if self.train:
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split_file = os.path.join(annotations_path_dir, "trainval.txt")
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with open(split_file) as f:
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self.images_list = f.readlines()
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else:
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split_file = os.path.join(annotations_path_dir, "test.txt")
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with open(split_file) as f:
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self.images_list = f.readlines()
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def __getitem__(self, index: int) -> Tuple[Any, Any]:
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img_name, label, species, _ = self.images_list[index].strip().split(" ")
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img_name += ".jpg"
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target = int(label) - 1
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img = Image.open(os.path.join(self.image_path_dir, img_name))
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img = img.convert('RGB')
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if self.transform is not None:
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img = self.transform(img)
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if self.target_transform is not None:
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target = self.target_transform(target)
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return img, target
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def __len__(self) -> int:
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return len(self.images_list)
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