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test_tr.py
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test_tr.py
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import argparse
from tqdm import tqdm
from utils.utils import *
from modules.segment_module import transform, untransform
from loader.dataloader import dataloader
from torch.cuda.amp import autocast
from loader.netloader import network_loader, segment_tr_loader, cluster_tr_loader
def test(args, net, segment, cluster, nice, test_loader, cmap):
segment.eval()
prog_bar = tqdm(enumerate(test_loader), total=len(test_loader), leave=True)
with Pool(40) as pool:
for _, batch in prog_bar:
# image and label and self supervised feature
ind = batch["ind"].cuda()
img = batch["img"].cuda()
label = batch["label"].cuda()
with autocast():
# intermediate feature
feat = net(img)[:, 1:, :]
feat_flip = net(img.flip(dims=[3]))[:, 1:, :]
seg_feat = transform(segment.head_ema(feat))
seg_feat_flip = transform(segment.head_ema(feat_flip))
seg_feat = untransform((seg_feat + seg_feat_flip.flip(dims=[3])) / 2)
# interp feat
interp_seg_feat = F.interpolate(transform(seg_feat), label.shape[-2:], mode='bilinear', align_corners=False)
# cluster preds
cluster_preds = cluster.forward_centroid(untransform(interp_seg_feat), crf=True)
# crf
crf_preds = do_crf(pool, img, cluster_preds).argmax(1).cuda()
# nice evaluation
_, desc_nice = nice.eval(crf_preds, label)
# hungarian
hungarian_preds = nice.do_hungarian(crf_preds)
# save images
save_all(args, ind, img, label, cluster_preds.argmax(dim=1), crf_preds, hungarian_preds, cmap, is_tr=True)
# real-time print
desc = f'{desc_nice}'
prog_bar.set_description(desc, refresh=True)
# evaludation metric reset
nice.reset()
def test_without_crf(args, net, segment, cluster, nice, test_loader):
segment.eval()
total_acc = 0
prog_bar = tqdm(enumerate(test_loader), total=len(test_loader), leave=True)
for idx, batch in prog_bar:
# image and label and self supervised feature
ind = batch["ind"].cuda()
img = batch["img"].cuda()
label = batch["label"].cuda()
cmap = create_pascal_label_colormap()
a = invTrans(img)[0].permute(1,2,0)
b = cmap[label[0].cpu()]
# intermediate feature
with autocast():
feat = net(img)[:, 1:, :]
seg_feat_ema = segment.head_ema(feat)
# linear probe loss
linear_logits = segment.linear(seg_feat_ema)
linear_logits = F.interpolate(linear_logits, label.shape[-2:], mode='bilinear', align_corners=False)
flat_label = label.reshape(-1)
flat_label_mask = (flat_label >= 0) & (flat_label < args.n_classes)
# interp feat
interp_seg_feat = F.interpolate(transform(seg_feat_ema), label.shape[-2:], mode='bilinear', align_corners=False)
# cluster
cluster_preds = cluster.forward_centroid(untransform(interp_seg_feat), inference=True)
# nice evaluation
_, desc_nice = nice.eval(cluster_preds, label)
# linear probe acc check
pred_label = linear_logits.argmax(dim=1)
flat_pred_label = pred_label.reshape(-1)
acc = (flat_pred_label[flat_label_mask] == flat_label[flat_label_mask]).sum() / flat_label[
flat_label_mask].numel()
total_acc += acc.item()
# real-time print
desc = f'[TEST] Acc (Linear): {100. * total_acc / (idx + 1):.1f}% | {desc_nice}'
prog_bar.set_description(desc, refresh=True)
# evaludation metric reset
nice.reset()
def test_linear_without_crf(args, net, segment, nice, test_loader):
segment.eval()
prog_bar = tqdm(enumerate(test_loader), total=len(test_loader), leave=True)
with Pool(40) as pool:
for _, batch in prog_bar:
# image and label and self supervised feature
ind = batch["ind"].cuda()
img = batch["img"].cuda()
label = batch["label"].cuda()
with autocast():
# intermediate feature
feat = net(img)[:, 1:, :]
feat_flip = net(img.flip(dims=[3]))[:, 1:, :]
seg_feat = transform(segment.head_ema(feat))
seg_feat_flip = transform(segment.head_ema(feat_flip))
seg_feat = untransform((seg_feat + seg_feat_flip.flip(dims=[3])) / 2)
# interp feat
interp_seg_feat = F.interpolate(transform(seg_feat), label.shape[-2:], mode='bilinear', align_corners=False)
# linear probe interp feat
linear_logits = segment.linear(untransform(interp_seg_feat))
# cluster preds
cluster_preds = linear_logits.argmax(dim=1)
# nice evaluation
_, desc_nice = nice.eval(cluster_preds, label)
# real-time print
desc = f'{desc_nice}'
prog_bar.set_description(desc, refresh=True)
# evaludation metric reset
nice.reset()
def test_linear(args, net, segment, nice, test_loader):
segment.eval()
prog_bar = tqdm(enumerate(test_loader), total=len(test_loader), leave=True)
with Pool(40) as pool:
for _, batch in prog_bar:
# image and label and self supervised feature
ind = batch["ind"].cuda()
img = batch["img"].cuda()
label = batch["label"].cuda()
with autocast():
# intermediate feature
feat = net(img)[:, 1:, :]
feat_flip = net(img.flip(dims=[3]))[:, 1:, :]
seg_feat = transform(segment.head_ema(feat))
seg_feat_flip = transform(segment.head_ema(feat_flip))
seg_feat = untransform((seg_feat + seg_feat_flip.flip(dims=[3])) / 2)
# interp feat
interp_seg_feat = F.interpolate(transform(seg_feat), label.shape[-2:], mode='bilinear', align_corners=False)
# linear probe interp feat
linear_logits = segment.linear(untransform(interp_seg_feat))
# cluster preds
cluster_preds = torch.log_softmax(linear_logits, dim=1)
# crf
crf_preds = do_crf(pool, img, cluster_preds).argmax(1).cuda()
# nice evaluation
_, desc_nice = nice.eval(crf_preds, label)
# real-time print
desc = f'{desc_nice}'
prog_bar.set_description(desc, refresh=True)
# evaludation metric reset
nice.reset()
def main(rank, args):
# setting gpu id of this process
torch.cuda.set_device(rank)
# print argparse
print_argparse(args, rank=0)
# dataset loader
train_loader, test_loader, _ = dataloader(args, False)
# network loader
net = network_loader(args, rank)
segment = segment_tr_loader(args, rank)
cluster = cluster_tr_loader(args, rank)
# evaluation
nice = NiceTool(args.n_classes)
# color map
cmap = create_cityscapes_colormap() if args.dataset == 'cityscapes' else create_pascal_label_colormap()
###################################################################################
# First, run train_mediator.py
path, is_exist = pickle_path_and_exist(args)
# early save for time
if is_exist:
# load
codebook = np.load(path)
cb = torch.from_numpy(codebook).cuda()
cluster.codebook.data = cb
cluster.codebook.requires_grad = False
segment.head.codebook = cb
segment.head_ema.codebook = cb
# print successful loading modularity
rprint(f'Modularity {path} loaded', rank)
else:
rprint('Train Modularity-based Codebook First', rank)
return
###################################################################################
# param size
print(f'# of Parameters: {num_param(segment)/10**6:.2f}(M)')
# post-processing with crf and hungarian matching
test_without_crf(
args,
net,
segment,
cluster,
nice,
test_loader)
# post-processing with crf and hungarian matching
test(
args,
net,
segment,
cluster,
nice,
test_loader,
cmap)
# post-processing with crf and hungarian matching
# test_linear_without_crf(
# args,
# net,
# segment,
# nice,
# test_loader)
# test_linear(
# args,
# net,
# segment,
# nice,
# test_loader)
if __name__ == "__main__":
# fetch args
parser = argparse.ArgumentParser()
# model parameter
parser.add_argument('--NAME-TAG', default='CAUSE-TR', type=str)
parser.add_argument('--data_dir', default='/mnt/hard2/lbk-iccv/datasets', type=str)
parser.add_argument('--dataset', default='pascalvoc', type=str)
parser.add_argument('--port', default='12355', type=str)
parser.add_argument('--ckpt', default='checkpoint/dino_vit_small_8.pth', type=str)
parser.add_argument('--distributed', default=False, type=str2bool)
parser.add_argument('--load_segment', default=True, type=str2bool)
parser.add_argument('--load_cluster', default=True, type=str2bool)
parser.add_argument('--train_resolution', default=320, type=int)
parser.add_argument('--test_resolution', default=320, type=int)
parser.add_argument('--batch_size', default=16, type=int)
parser.add_argument('--num_workers', default=int(os.cpu_count() / 8), type=int)
parser.add_argument('--gpu', default='4', type=str)
parser.add_argument('--num_codebook', default=2048, type=int)
# model parameter
parser.add_argument('--reduced_dim', default=90, type=int)
parser.add_argument('--projection_dim', default=2048, type=int)
args = parser.parse_args()
if 'dinov2' in args.ckpt:
args.train_resolution=322
args.test_resolution=322
if 'small' in args.ckpt:
args.dim=384
elif 'base' in args.ckpt:
args.dim=768
args.num_queries=args.train_resolution**2 // int(args.ckpt.split('_')[-1].split('.')[0])**2
# the number of gpus for multi-process
gpu_list = list(map(int, args.gpu.split(',')))
ngpus_per_node = len(gpu_list)
# first gpu index is activated once there are several gpu in args.gpu
main(rank=gpu_list[0], args=args)