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run.py
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run.py
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import os
import argparse
import random
import torch
import torch.optim as optim
import torchvision.transforms as transforms
import dataset
import model.backbone as backbone
import metric.loss as loss
import metric.pairsampler as pair
from tqdm import tqdm
from torch.utils.data import DataLoader
from metric.utils import recall
from metric.batchsampler import NPairs
from model.embedding import LinearEmbedding
parser = argparse.ArgumentParser()
LookupChoices = type('', (argparse.Action, ), dict(__call__=lambda a, p, n, v, o: setattr(n, a.dest, a.choices[v])))
parser.add_argument('--mode',
choices=["train", "eval"],
default="train")
parser.add_argument('--load',
default=None)
parser.add_argument('--dataset',
choices=dict(cub200=dataset.CUB2011Metric,
cars196=dataset.Cars196Metric,
stanford=dataset.StanfordOnlineProductsMetric),
default=dataset.CUB2011Metric,
action=LookupChoices)
parser.add_argument('--base',
choices=dict(googlenet=backbone.GoogleNet,
inception_v1bn=backbone.InceptionV1BN,
resnet18=backbone.ResNet18,
resnet50=backbone.ResNet50),
default=backbone.ResNet50,
action=LookupChoices)
parser.add_argument('--sample',
choices=dict(random=pair.RandomNegative,
hard=pair.HardNegative,
all=pair.AllPairs,
semihard=pair.SemiHardNegative,
distance=pair.DistanceWeighted),
default=pair.AllPairs,
action=LookupChoices)
parser.add_argument('--loss',
choices=dict(l1_triplet=loss.L1Triplet,
l2_triplet=loss.L2Triplet,
contrastive=loss.ContrastiveLoss),
default=loss.L2Triplet,
action=LookupChoices)
parser.add_argument('--margin', type=float, default=0.2)
parser.add_argument('--embedding_size', type=int, default=128)
parser.add_argument('--l2normalize', choices=['true', 'false'], default='true')
parser.add_argument('--lr', default=1e-5, type=float)
parser.add_argument('--lr_decay_epochs', type=int, default=[25, 30, 35], nargs='+')
parser.add_argument('--lr_decay_gamma', default=0.5, type=float)
parser.add_argument('--batch', default=64, type=int)
parser.add_argument('--num_image_per_class', default=5, type=int)
parser.add_argument('--epochs', default=40, type=int)
parser.add_argument('--iter_per_epoch', type=int, default=100)
parser.add_argument('--recall', default=[1], type=int, nargs='+')
parser.add_argument('--seed', default=random.randint(1, 1000), type=int)
parser.add_argument('--data', default='data')
parser.add_argument('--save_dir', default=None)
opts = parser.parse_args()
for set_random_seed in [random.seed, torch.manual_seed, torch.cuda.manual_seed_all]:
set_random_seed(opts.seed)
base_model = opts.base(pretrained=True)
if isinstance(base_model, backbone.InceptionV1BN) or isinstance(base_model, backbone.GoogleNet):
normalize = transforms.Compose([
transforms.Lambda(lambda x: x[[2, 1, 0], ...] * 255.0),
transforms.Normalize(mean=[104, 117, 128], std=[1, 1, 1]),
])
else:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
])
test_transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize
])
dataset_train = opts.dataset(opts.data, train=True, transform=train_transform, download=True)
dataset_train_eval = opts.dataset(opts.data, train=True, transform=test_transform, download=True)
dataset_eval = opts.dataset(opts.data, train=False, transform=test_transform, download=True)
print("Number of images in Training Set: %d" % len(dataset_train))
print("Number of images in Test set: %d" % len(dataset_eval))
loader_train_sample = DataLoader(dataset_train, batch_sampler=NPairs(dataset_train,
opts.batch,
m=opts.num_image_per_class,
iter_per_epoch=opts.iter_per_epoch),
pin_memory=True, num_workers=8)
loader_train_eval = DataLoader(dataset_train_eval, shuffle=False, batch_size=opts.batch, drop_last=False,
pin_memory=False, num_workers=8)
loader_eval = DataLoader(dataset_eval, shuffle=False, batch_size=opts.batch, drop_last=False,
pin_memory=True, num_workers=8)
model = LinearEmbedding(base_model,
output_size=base_model.output_size,
embedding_size=opts.embedding_size,
normalize=opts.l2normalize == 'true').cuda()
if opts.load is not None:
model.load_state_dict(torch.load(opts.load))
print("Loaded Model from %s" % opts.load)
criterion = opts.loss(sampler=opts.sample(), margin=opts.margin)
optimizer = optim.Adam(model.parameters(), lr=opts.lr, weight_decay=1e-5)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=opts.lr_decay_epochs, gamma=opts.lr_decay_gamma)
def train(net, loader, ep):
lr_scheduler.step()
net.train()
loss_all, norm_all = [], []
train_iter = tqdm(loader, ncols=80)
for images, labels in train_iter:
images, labels = images.cuda(), labels.cuda()
embedding = net(images)
loss = criterion(embedding, labels)
loss_all.append(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_iter.set_description("[Train][Epoch %d] Loss: %.5f" % (ep, loss.item()))
print('[Epoch %d] Loss: %.5f\n' % (ep, torch.Tensor(loss_all).mean()))
def eval(net, loader, ep):
K = opts.recall
net.eval()
test_iter = tqdm(loader, ncols=80)
embeddings_all, labels_all = [], []
test_iter.set_description("[Eval][Epoch %d]" % ep)
with torch.no_grad():
for images, labels in test_iter:
images, labels = images.cuda(), labels.cuda()
embedding = net(images)
embeddings_all.append(embedding.data)
labels_all.append(labels.data)
embeddings_all = torch.cat(embeddings_all).cpu()
labels_all = torch.cat(labels_all).cpu()
rec = recall(embeddings_all, labels_all, K=K)
for k, r in zip(K, rec):
print('[Epoch %d] Recall@%d: [%.4f]\n' % (ep, k, 100 * r))
return rec[0]
if opts.mode == "eval":
eval(model, loader_train_eval, 0)
eval(model, loader_eval, 0)
else:
train_recall = eval(model, loader_train_eval, 0)
val_recall = eval(model, loader_eval, 0)
best_rec = val_recall
for epoch in range(1, opts.epochs+1):
train(model, loader_train_sample, epoch)
train_recall = eval(model, loader_train_eval, epoch)
val_recall = eval(model, loader_eval, epoch)
if best_rec < val_recall:
best_rec = val_recall
if opts.save_dir is not None:
if not os.path.isdir(opts.save_dir):
os.mkdir(opts.save_dir)
torch.save(model.state_dict(), "%s/%s"%(opts.save_dir, "best.pth"))
if opts.save_dir is not None:
if not os.path.isdir(opts.save_dir):
os.mkdir(opts.save_dir)
torch.save(model.state_dict(), "%s/%s"%(opts.save_dir, "last.pth"))
with open("%s/result.txt"%opts.save_dir, 'w') as f:
f.write("Best Recall@1: %.4f\n" % (best_rec * 100))
f.write("Final Recall@1: %.4f\n" % (val_recall * 100))
print("Best Recall@1: %.4f" % best_rec)