eval_knn.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.
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# 
#     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 argparse

import torch
from torch import nn
import torch.distributed as dist
import torch.backends.cudnn as cudnn
from torchvision import datasets
from torchvision import transforms as pth_transforms
from torchvision import models as torchvision_models

import utils
from dx_utils import DXDataset4DINO
import vision_transformer as vits


def extract_feature_pipeline(args):
    # ============ preparing data ... ============
    transform = pth_transforms.Compose([
        pth_transforms.Resize(256, interpolation=3),
        pth_transforms.CenterCrop(224),
        pth_transforms.ToTensor(),
        pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
    ])

    dataset_train = ReturnIndexDXDataset(args.csv_path, args.data_path, transform=transform, split="train")
    dataset_val = ReturnIndexDXDataset(args.csv_path, args.data_path, transform=transform, split="val")

    # dataset_train = ReturnIndexDataset(os.path.join(args.data_path, "train"), transform=transform)
    # dataset_val = ReturnIndexDataset(os.path.join(args.data_path, "val"), transform=transform)
    sampler = torch.utils.data.DistributedSampler(dataset_train, shuffle=False)
    data_loader_train = torch.utils.data.DataLoader(
        dataset_train,
        sampler=sampler,
        batch_size=args.batch_size_per_gpu,
        num_workers=args.num_workers,
        pin_memory=True,
        drop_last=False,
    )
    data_loader_val = torch.utils.data.DataLoader(
        dataset_val,
        batch_size=args.batch_size_per_gpu,
        num_workers=args.num_workers,
        pin_memory=True,
        drop_last=False,
    )
    print(f"Data loaded with {len(dataset_train)} train and {len(dataset_val)} val 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)
        model.fc = nn.Identity()
    else:
        print(f"Architecture {args.arch} non supported")
        sys.exit(1)
    model.cuda()
    utils.load_pretrained_weights(model, args.pretrained_weights, args.checkpoint_key, args.arch, args.patch_size)
    model.eval()

    # ============ extract features ... ============
    print("Extracting features for train set...")
    train_features = extract_features(model, data_loader_train, args.use_cuda)
    print("Extracting features for val set...")
    test_features = extract_features(model, data_loader_val, args.use_cuda)

    if utils.get_rank() == 0:
        train_features = nn.functional.normalize(train_features, dim=1, p=2)
        test_features = nn.functional.normalize(test_features, dim=1, p=2)

    train_labels = torch.tensor([s[-1] for s in dataset_train.samples]).long()
    test_labels = torch.tensor([s[-1] for s in dataset_val.samples]).long()
    # save features and labels
    if args.dump_features and dist.get_rank() == 0:
        torch.save(train_features.cpu(), os.path.join(args.dump_features, "trainfeat.pth"))
        torch.save(test_features.cpu(), os.path.join(args.dump_features, "testfeat.pth"))
        torch.save(train_labels.cpu(), os.path.join(args.dump_features, "trainlabels.pth"))
        torch.save(test_labels.cpu(), os.path.join(args.dump_features, "testlabels.pth"))
    return train_features, test_features, train_labels, test_labels


@torch.no_grad()
def extract_features(model, data_loader, use_cuda=True, multiscale=False):
    metric_logger = utils.MetricLogger(delimiter="  ")
    features = None
    for samples, index in metric_logger.log_every(data_loader, 10):
        samples = samples.cuda(non_blocking=True)
        index = index.cuda(non_blocking=True)
        if multiscale:
            feats = utils.multi_scale(samples, model)
        else:
            feats = model(samples).clone()

        # init storage feature matrix
        if dist.get_rank() == 0 and features is None:
            features = torch.zeros(len(data_loader.dataset), feats.shape[-1])
            if use_cuda:
                features = features.cuda(non_blocking=True)
            print(f"Storing features into tensor of shape {features.shape}")

        # get indexes from all processes
        y_all = torch.empty(dist.get_world_size(), index.size(0), dtype=index.dtype, device=index.device)
        y_l = list(y_all.unbind(0))
        y_all_reduce = torch.distributed.all_gather(y_l, index, async_op=True)
        y_all_reduce.wait()
        index_all = torch.cat(y_l)

        # share features between processes
        feats_all = torch.empty(
            dist.get_world_size(),
            feats.size(0),
            feats.size(1),
            dtype=feats.dtype,
            device=feats.device,
        )
        output_l = list(feats_all.unbind(0))
        output_all_reduce = torch.distributed.all_gather(output_l, feats, async_op=True)
        output_all_reduce.wait()

        # update storage feature matrix
        if dist.get_rank() == 0:
            if use_cuda:
                features.index_copy_(0, index_all, torch.cat(output_l))
            else:
                features.index_copy_(0, index_all.cpu(), torch.cat(output_l).cpu())
    return features


@torch.no_grad()
def knn_classifier(train_features, train_labels, test_features, test_labels, k, T, num_classes=1000):
    top1, top5, total = 0.0, 0.0, 0
    train_features = train_features.t()
    num_test_images, num_chunks = test_labels.shape[0], 100
    imgs_per_chunk = num_test_images // num_chunks
    retrieval_one_hot = torch.zeros(k, num_classes).to(train_features.device)
    for idx in range(0, num_test_images, imgs_per_chunk):
        # get the features for test images
        features = test_features[
            idx : min((idx + imgs_per_chunk), num_test_images), :
        ]
        targets = test_labels[idx : min((idx + imgs_per_chunk), num_test_images)]
        batch_size = targets.shape[0]

        # calculate the dot product and compute top-k neighbors
        similarity = torch.mm(features, train_features)
        distances, indices = similarity.topk(k, largest=True, sorted=True)
        candidates = train_labels.view(1, -1).expand(batch_size, -1)
        retrieved_neighbors = torch.gather(candidates, 1, indices)

        retrieval_one_hot.resize_(batch_size * k, num_classes).zero_()
        retrieval_one_hot.scatter_(1, retrieved_neighbors.view(-1, 1), 1)
        distances_transform = distances.clone().div_(T).exp_()
        probs = torch.sum(
            torch.mul(
                retrieval_one_hot.view(batch_size, -1, num_classes),
                distances_transform.view(batch_size, -1, 1),
            ),
            1,
        )
        _, predictions = probs.sort(1, True)

        # find the predictions that match the target
        correct = predictions.eq(targets.data.view(-1, 1))
        top1 = top1 + correct.narrow(1, 0, 1).sum().item()
        top5 = top5 + correct.narrow(1, 0, min(5, k)).sum().item()  # top5 does not make sense if k < 5
        total += targets.size(0)
    top1 = top1 * 100.0 / total
    top5 = top5 * 100.0 / total
    return top1, top5


class ReturnIndexDataset(datasets.ImageFolder):
    def __getitem__(self, idx):
        img, lab = super(ReturnIndexDataset, self).__getitem__(idx)
        return img, idx


class ReturnIndexDXDataset(DXDataset4DINO):
    def __getitem__(self, idx):
        img, lab = super(ReturnIndexDXDataset, self).__getitem__(idx)
        return img, idx


if __name__ == '__main__':
    parser = argparse.ArgumentParser('Evaluation with weighted k-NN on ImageNet')
    parser.add_argument('--batch_size_per_gpu', default=128, type=int, help='Per-GPU batch-size')
    parser.add_argument('--nb_knn', default=[10, 20, 100, 200], nargs='+', type=int,
        help='Number of NN to use. 20 is usually working the best.')
    parser.add_argument('--temperature', default=0.07, type=float,
        help='Temperature used in the voting coefficient')
    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,
        help="Should we store the features on GPU? We recommend setting this to False if you encounter OOM")
    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('--dump_features', default=None,
        help='Path where to save computed features, empty for no saving')
    parser.add_argument('--load_features', default=None, help="""If the features have
        already been computed, where to find them.""")
    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.")
    parser.add_argument('--data_path', default='/path/to/DX/dataset/', type=str,
                        help='Please specify path to the folder with the images.')
    parser.add_argument('--csv_path', default='/path/to/dataset/csv/', type=str,
                        help='Please specify path to the DX dataset CSV file.')
    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

    if args.load_features:
        train_features = torch.load(os.path.join(args.load_features, "trainfeat.pth"))
        test_features = torch.load(os.path.join(args.load_features, "testfeat.pth"))
        train_labels = torch.load(os.path.join(args.load_features, "trainlabels.pth"))
        test_labels = torch.load(os.path.join(args.load_features, "testlabels.pth"))
    else:
        # need to extract features !
        train_features, test_features, train_labels, test_labels = extract_feature_pipeline(args)

    if utils.get_rank() == 0:
        if args.use_cuda:
            train_features = train_features.cuda()
            test_features = test_features.cuda()
            train_labels = train_labels.cuda()
            test_labels = test_labels.cuda()

        print("Features are ready!\nStart the k-NN classification.")
        for k in args.nb_knn:
            top1, top5 = knn_classifier(train_features, train_labels,
                test_features, test_labels, k, args.temperature)
            print(f"{k}-NN classifier result: Top1: {top1}, Top5: {top5}")
    dist.barrier()