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ifa.py
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from model.IFA_matching import IFA_MatchingNet
from util.utils import count_params, set_seed, mIOU
import argparse
from copy import deepcopy
import os
import time
import torch
from torch.nn import CrossEntropyLoss, DataParallel
import torch.nn.functional as F
from torch.optim import SGD
from tqdm import tqdm
from data.dataset import FSSDataset
def parse_args():
parser = argparse.ArgumentParser(description='IFA for CD-FSS')
# basic arguments
parser.add_argument('--data-root',
type=str,
required=True,
help='root path of training dataset')
parser.add_argument('--dataset',
type=str,
default='fss',
choices=['fss', 'deepglobe', 'isic', 'lung'],
help='training dataset')
parser.add_argument('--batch-size',
type=int,
default=4,
help='batch size of training')
parser.add_argument('--lr',
type=float,
default=0.001,
help='learning rate')
parser.add_argument('--crop-size',
type=int,
default=473,
help='cropping size of training samples')
parser.add_argument('--backbone',
type=str,
choices=['resnet50', 'resnet101'],
default='resnet50',
help='backbone of semantic segmentation model')
parser.add_argument('--refine', dest='refine', action='store_true', default=False)
parser.add_argument('--shot',
type=int,
default=1,
help='number of support pairs')
parser.add_argument('--episode',
type=int,
default=24000,
help='total episodes of training')
parser.add_argument('--snapshot',
type=int,
default=1200,
help='save the model after each snapshot episodes')
parser.add_argument('--seed',
type=int,
default=0,
help='random seed to generate tesing samples')
args = parser.parse_args()
return args
def evaluate(model, dataloader, args):
tbar = tqdm(dataloader)
if args.dataset == 'fss':
num_classes = 1000
elif args.dataset == 'deepglobe':
num_classes = 6
elif args.dataset == 'isic':
num_classes = 3
elif args.dataset == 'lung':
num_classes = 1
metric = mIOU(num_classes)
for i, (img_s_list, mask_s_list, img_q, mask_q, cls, _, id_q) in enumerate(tbar):
img_s_list = img_s_list.permute(1,0,2,3,4)
mask_s_list = mask_s_list.permute(1,0,2,3)
img_s_list = img_s_list.numpy().tolist()
mask_s_list = mask_s_list.numpy().tolist()
img_q, mask_q = img_q.cuda(), mask_q.cuda()
for k in range(len(img_s_list)):
img_s_list[k], mask_s_list[k] = torch.Tensor(img_s_list[k]), torch.Tensor(mask_s_list[k])
img_s_list[k], mask_s_list[k] = img_s_list[k].cuda(), mask_s_list[k].cuda()
cls = cls[0].item()
cls = cls + 1
with torch.no_grad():
out_ls = model(img_s_list, mask_s_list, img_q, mask_q)
pred = torch.argmax(out_ls[0], dim=1)
pred[pred == 1] = cls
mask_q[mask_q == 1] = cls
metric.add_batch(pred.cpu().numpy(), mask_q.cpu().numpy())
tbar.set_description("Testing mIOU: %.2f" % (metric.evaluate() * 100.0))
return metric.evaluate() * 100.0
def main():
path_dir = 'ifa'
args = parse_args()
print('\n' + str(args))
### Please modify the following paths with your trained model paths.
if args.dataset == 'deepglobe':
if args.backbone == 'resnet50':
if args.shot == 1:
checkpoint_path = './outdir/models/deepglobe/resnet50_1shot_avg_44.40.pth'
if args.shot == 5:
checkpoint_path = './outdir/models/deepglobe/resnet50_5shot_avg_52.78.pth'
if args.dataset == 'isic':
if args.backbone == 'resnet50':
if args.shot == 1:
checkpoint_path = './outdir/models/isic/resnet50_1shot_avg_55.50.pth'
if args.shot == 5:
checkpoint_path = './outdir/models/isic/resnet50_5shot_avg_62.60.pth'
if args.dataset == 'lung':
if args.backbone == 'resnet50':
if args.shot == 1:
checkpoint_path = './outdir/models/lung/resnet50_1shot_avg_72.64.pth'
if args.shot == 5:
checkpoint_path = './outdir/models/lung/resnet50_5shot_avg_73.07.pth'
if args.dataset == 'fss':
if args.backbone == 'resnet50':
if args.shot == 1:
checkpoint_path = './outdir/models/fss/resnet50_1shot_avg_77.16.pth'
if args.shot == 5:
checkpoint_path = './outdir/models/fss/resnet50_5shot_avg_79.37.pth'
miou = 0
save_path = 'outdir/models/%s/%s' % (args.dataset, path_dir)
os.makedirs(save_path, exist_ok=True)
FSSDataset.initialize(img_size=400, datapath=args.data_root)
train_dataset = args.dataset+'ifa'
trainloader = FSSDataset.build_dataloader(train_dataset, args.batch_size, 4, '0', 'val', args.shot)
FSSDataset.initialize(img_size=400, datapath=args.data_root)
testloader = FSSDataset.build_dataloader(args.dataset, args.batch_size, 4, '0', 'val', args.shot)
print('Do we use SSP refinement?', args.refine)
model = IFA_MatchingNet(args.backbone, args.refine, args.shot)
print('\nParams: %.1fM' % count_params(model))
print('Loaded model:', checkpoint_path)
checkpoint = torch.load(checkpoint_path)
model.load_state_dict(checkpoint)
for param in model.layer0.parameters():
param.requires_grad = False
for param in model.layer1.parameters():
param.requires_grad = False
for module in model.modules():
if isinstance(module, torch.nn.BatchNorm2d):
for param in module.parameters():
param.requires_grad = False
criterion = CrossEntropyLoss(ignore_index=255)
optimizer = SGD([param for param in model.parameters() if param.requires_grad],
lr=args.lr, momentum=0.9, weight_decay=5e-4)
model = DataParallel(model).cuda()
best_model = None
iters = 0
total_iters = args.episode // args.batch_size
lr_decay_iters = [total_iters // 3, total_iters * 2 // 3]
previous_best = float(miou)
# each snapshot is considered as an epoch
for epoch in range(args.episode // args.snapshot):
print("\n==> Epoch %i, learning rate = %.5f\t\t\t\t Previous best = %.2f"
% (epoch, optimizer.param_groups[0]["lr"], previous_best))
model.train()
for module in model.modules():
if isinstance(module, torch.nn.BatchNorm2d):
module.eval()
total_loss = 0.0
tbar = tqdm(trainloader)
set_seed(int(time.time()))
for i, (img_s_list, mask_s_list, img_q, mask_q, _, _, _) in enumerate(tbar):
img_s_list = img_s_list.permute(1,0,2,3,4)
mask_s_list = mask_s_list.permute(1,0,2,3)
img_s_list = img_s_list.numpy().tolist()
mask_s_list = mask_s_list.numpy().tolist()
img_q, mask_q = img_q.cuda(), mask_q.cuda()
for k in range(len(img_s_list)):
img_s_list[k], mask_s_list[k] = torch.Tensor(img_s_list[k]), torch.Tensor(mask_s_list[k])
img_s_list[k], mask_s_list[k] = img_s_list[k].cuda(), mask_s_list[k].cuda()
out_ls = model(img_s_list, mask_s_list, img_q, mask_q)
mask_s = torch.cat(mask_s_list, dim=0)
mask_s = mask_s.long()
if args.refine:
### iter = 3
loss = criterion(out_ls[0], mask_q) + criterion(out_ls[1], mask_q) + criterion(out_ls[2], mask_q) + criterion(out_ls[3], mask_s) * 0.2 + \
criterion(out_ls[4], mask_s) * 0.4 + criterion(out_ls[5], mask_q) * 0.1 + criterion(out_ls[6], mask_s) * 0.1 + \
criterion(out_ls[7], mask_q) * 0.1 + criterion(out_ls[8], mask_s) * 0.1
else:
loss = criterion(out_ls[0], mask_q) + criterion(out_ls[1], mask_q) + criterion(out_ls[2], mask_s) * 0.4
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
iters += 1
if iters in lr_decay_iters:
optimizer.param_groups[0]['lr'] /= 10.0
tbar.set_description('Loss: %.3f' % (total_loss / (i + 1)))
model.eval()
set_seed(args.seed)
miou = evaluate(model, testloader, args)
if miou >= previous_best:
best_model = deepcopy(model)
previous_best = miou
torch.save(best_model.module.state_dict(),
os.path.join(save_path, '%s_%ishot_%.2f.pth' % (args.backbone, args.shot, miou)))
print('\nEvaluating on 5 seeds.....')
total_miou = 0.0
for seed in range(5):
print('\nRun %i:' % (seed + 1))
set_seed(args.seed + seed)
miou = evaluate(best_model, testloader, args)
total_miou += miou
print('\n' + '*' * 32)
print('Averaged mIOU on 5 seeds: %.2f' % (total_miou / 5))
print('*' * 32 + '\n')
torch.save(best_model.module.state_dict(),
os.path.join(save_path, '%s_%ishot_avg_%.2f.pth' % (args.backbone, args.shot, total_miou / 5)))
if __name__ == '__main__':
main()