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eval_image_retrieval.py
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# 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
# limitations under the License.
###############################################################################
# Copyright (C) 2022 Habana Labs, Ltd. an Intel Company
###############################################################################
# Changes:
# - added support for HPU device
# - moved argsort to CPU
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')
# HPU
parser.add_argument('--device', choices=['hpu', 'cuda', 'cpu'], default='hpu',
help='Device to be used for computation')
parser.add_argument('--batch_size_per_device', default=128, type=int,
help='Per-device batch-size : number of distinct images loaded on one device.')
parser.add_argument('--store_on_device', default=True, type=utils.bool_flag,
help="Should we store the features on device? We recommend setting this to False if you encounter OOM")
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('--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 device.')
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("--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)
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", map_location='cpu'))
else:
print("Warning: We use random weights.")
model.to(args.device)
############################################################################
# Step 1: extract features
global_batch_size = args.batch_size_per_device * utils.get_world_size()
train_features = extract_features(model, data_loader_train, global_batch_size, args.device, args.store_on_device, multiscale=args.multiscale)
query_features = extract_features(model, data_loader_query, global_batch_size, args.device, args.store_on_device, 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)
# Moving sim to CPU due to argsort issues on HPU
ranks = torch.argsort(-sim.cpu(), dim=0).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()