PuzzleTuning/Counterpart PreTrain Methods/dino-main/eval_image_retrieval.py

# Copyright (c) Facebook, Inc. and its affiliates.
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     http://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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import os
import sys
import pickle
import argparse

import torch
from torch import nn
import torch.distributed as dist
import torch.backends.cudnn as cudnn
from torchvision import models as torchvision_models
from torchvision import transforms as pth_transforms
from PIL import Image, ImageFile
import numpy as np

import utils
import vision_transformer as vits
from eval_knn import extract_features


class OxfordParisDataset(torch.utils.data.Dataset):
    def __init__(self, dir_main, dataset, split, transform=None, imsize=None):
        if dataset not in ['roxford5k', 'rparis6k']:
            raise ValueError('Unknown dataset: {}!'.format(dataset))

        # loading imlist, qimlist, and gnd, in cfg as a dict
        gnd_fname = os.path.join(dir_main, dataset, 'gnd_{}.pkl'.format(dataset))
        with open(gnd_fname, 'rb') as f:
            cfg = pickle.load(f)
        cfg['gnd_fname'] = gnd_fname
        cfg['ext'] = '.jpg'
        cfg['qext'] = '.jpg'
        cfg['dir_data'] = os.path.join(dir_main, dataset)
        cfg['dir_images'] = os.path.join(cfg['dir_data'], 'jpg')
        cfg['n'] = len(cfg['imlist'])
        cfg['nq'] = len(cfg['qimlist'])
        cfg['im_fname'] = config_imname
        cfg['qim_fname'] = config_qimname
        cfg['dataset'] = dataset
        self.cfg = cfg

        self.samples = cfg["qimlist"] if split == "query" else cfg["imlist"]
        self.transform = transform
        self.imsize = imsize

    def __len__(self):
        return len(self.samples)

    def __getitem__(self, index):
        path = os.path.join(self.cfg["dir_images"], self.samples[index] + ".jpg")
        ImageFile.LOAD_TRUNCATED_IMAGES = True
        with open(path, 'rb') as f:
            img = Image.open(f)
            img = img.convert('RGB')
        if self.imsize is not None:
            img.thumbnail((self.imsize, self.imsize), Image.ANTIALIAS)
        if self.transform is not None:
            img = self.transform(img)
        return img, index


def config_imname(cfg, i):
    return os.path.join(cfg['dir_images'], cfg['imlist'][i] + cfg['ext'])


def config_qimname(cfg, i):
    return os.path.join(cfg['dir_images'], cfg['qimlist'][i] + cfg['qext'])


if __name__ == '__main__':
    parser = argparse.ArgumentParser('Image Retrieval on revisited Paris and Oxford')
    parser.add_argument('--data_path', default='/path/to/revisited_paris_oxford/', type=str)
    parser.add_argument('--dataset', default='roxford5k', type=str, choices=['roxford5k', 'rparis6k'])
    parser.add_argument('--multiscale', default=False, type=utils.bool_flag)
    parser.add_argument('--imsize', default=224, type=int, help='Image size')
    parser.add_argument('--pretrained_weights', default='', type=str, help="Path to pretrained weights to evaluate.")
    parser.add_argument('--use_cuda', default=True, type=utils.bool_flag)
    parser.add_argument('--arch', default='vit_small', type=str, help='Architecture')
    parser.add_argument('--patch_size', default=16, type=int, help='Patch resolution of the model.')
    parser.add_argument("--checkpoint_key", default="teacher", type=str,
        help='Key to use in the checkpoint (example: "teacher")')
    parser.add_argument('--num_workers', default=10, type=int, help='Number of data loading workers per GPU.')
    parser.add_argument("--dist_url", default="env://", type=str, help="""url used to set up
        distributed training; see https://pytorch.org/docs/stable/distributed.html""")
    parser.add_argument("--local_rank", default=0, type=int, help="Please ignore and do not set this argument.")
    args = parser.parse_args()

    utils.init_distributed_mode(args)
    print("git:\n  {}\n".format(utils.get_sha()))
    print("\n".join("%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(args)).items())))
    cudnn.benchmark = True

    # ============ preparing data ... ============
    transform = pth_transforms.Compose([
        pth_transforms.ToTensor(),
        pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
    ])
    dataset_train = OxfordParisDataset(args.data_path, args.dataset, split="train", transform=transform, imsize=args.imsize)
    dataset_query = OxfordParisDataset(args.data_path, args.dataset, split="query", transform=transform, imsize=args.imsize)
    sampler = torch.utils.data.DistributedSampler(dataset_train, shuffle=False)
    data_loader_train = torch.utils.data.DataLoader(
        dataset_train,
        sampler=sampler,
        batch_size=1,
        num_workers=args.num_workers,
        pin_memory=True,
        drop_last=False,
    )
    data_loader_query = torch.utils.data.DataLoader(
        dataset_query,
        batch_size=1,
        num_workers=args.num_workers,
        pin_memory=True,
        drop_last=False,
    )
    print(f"train: {len(dataset_train)} imgs / query: {len(dataset_query)} imgs")

    # ============ building network ... ============
    if "vit" in args.arch:
        model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=0)
        print(f"Model {args.arch} {args.patch_size}x{args.patch_size} built.")
    elif "xcit" in args.arch:
        model = torch.hub.load('facebookresearch/xcit:main', args.arch, num_classes=0)
    elif args.arch in torchvision_models.__dict__.keys():
        model = torchvision_models.__dict__[args.arch](num_classes=0)
    else:
        print(f"Architecture {args.arch} non supported")
        sys.exit(1)
    if args.use_cuda:
        model.cuda()
    model.eval()

    # load pretrained weights
    if os.path.isfile(args.pretrained_weights):
        state_dict = torch.load(args.pretrained_weights, map_location="cpu")
        if args.checkpoint_key is not None and args.checkpoint_key in state_dict:
            print(f"Take key {args.checkpoint_key} in provided checkpoint dict")
            state_dict = state_dict[args.checkpoint_key]
        # remove `module.` prefix
        state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
        # remove `backbone.` prefix induced by multicrop wrapper
        state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()}
        msg = model.load_state_dict(state_dict, strict=False)
        print('Pretrained weights found at {} and loaded with msg: {}'.format(args.pretrained_weights, msg))
    elif args.arch == "vit_small" and args.patch_size == 16:
        print("Since no pretrained weights have been provided, we load pretrained DINO weights on Google Landmark v2.")
        model.load_state_dict(torch.hub.load_state_dict_from_url(url="https://dl.fbaipublicfiles.com/dino/dino_vitsmall16_googlelandmark_pretrain/dino_vitsmall16_googlelandmark_pretrain.pth"))
    else:
        print("Warning: We use random weights.")

    ############################################################################
    # Step 1: extract features
    train_features = extract_features(model, data_loader_train, args.use_cuda, multiscale=args.multiscale)
    query_features = extract_features(model, data_loader_query, args.use_cuda, multiscale=args.multiscale)

    if utils.get_rank() == 0:  # only rank 0 will work from now on
        # normalize features
        train_features = nn.functional.normalize(train_features, dim=1, p=2)
        query_features = nn.functional.normalize(query_features, dim=1, p=2)

        ############################################################################
        # Step 2: similarity
        sim = torch.mm(train_features, query_features.T)
        ranks = torch.argsort(-sim, dim=0).cpu().numpy()

        ############################################################################
        # Step 3: evaluate
        gnd = dataset_train.cfg['gnd']
        # evaluate ranks
        ks = [1, 5, 10]
        # search for easy & hard
        gnd_t = []
        for i in range(len(gnd)):
            g = {}
            g['ok'] = np.concatenate([gnd[i]['easy'], gnd[i]['hard']])
            g['junk'] = np.concatenate([gnd[i]['junk']])
            gnd_t.append(g)
        mapM, apsM, mprM, prsM = utils.compute_map(ranks, gnd_t, ks)
        # search for hard
        gnd_t = []
        for i in range(len(gnd)):
            g = {}
            g['ok'] = np.concatenate([gnd[i]['hard']])
            g['junk'] = np.concatenate([gnd[i]['junk'], gnd[i]['easy']])
            gnd_t.append(g)
        mapH, apsH, mprH, prsH = utils.compute_map(ranks, gnd_t, ks)
        print('>> {}: mAP M: {}, H: {}'.format(args.dataset, np.around(mapM*100, decimals=2), np.around(mapH*100, decimals=2)))
        print('>> {}: mP@k{} M: {}, H: {}'.format(args.dataset, np.array(ks), np.around(mprM*100, decimals=2), np.around(mprH*100, decimals=2)))
    dist.barrier()