def test(args):
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# model
if args.model_zoo is not None:
model = get_model(args.model_zoo, pretrained=True)
else:
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
dilated=args.dilated,
lateral=args.lateral,
jpu=args.jpu,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=BatchNorm,
base_size=args.base_size,
crop_size=args.crop_size)
# resuming checkpoint
if args.resume is None or not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
# strict=False, so that it is compatible with old pytorch saved models
model.load_state_dict(checkpoint['state_dict'], strict=False)
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
print(model)
scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \
[0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
if not args.ms:
scales = [1.0]
num_classes = datasets[args.dataset.lower()].NUM_CLASS
evaluator = MultiEvalModule(model,
num_classes,
scales=scales,
flip=args.ms).cuda()
evaluator.eval()
img = input_transform(Image.open(
args.input_path).convert('RGB')).unsqueeze(0)
with torch.no_grad():
output = evaluator.parallel_forward(img)[0]
predict = torch.max(output, 1)[1].cpu().numpy()
mask = utils.get_mask_pallete(predict, args.dataset)
mask.save(args.save_path)
def test(args):
# output folder
outdir = args.save_folder
if not os.path.exists(outdir):
os.makedirs(outdir)
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
testset = get_segmentation_dataset(args.dataset,
split=args.split,
mode=args.mode,
transform=input_transform)
# dataloader
loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
test_data = data.DataLoader(testset,
batch_size=args.test_batch_size,
drop_last=False,
shuffle=False,
collate_fn=test_batchify_fn,
**loader_kwargs)
# model
if args.model_zoo is not None:
model = get_model(args.model_zoo, pretrained=True)
else:
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
dilated=args.dilated,
multi_grid=args.multi_grid,
stride=args.stride,
lateral=args.lateral,
jpu=args.jpu,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=BatchNorm,
base_size=args.base_size,
crop_size=args.crop_size)
# resuming checkpoint
if args.resume is None or not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
# strict=False, so that it is compatible with old pytorch saved models
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
# print(model)
scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \
[0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
if not args.ms:
scales = [1.0]
evaluator = MultiEvalModule(model,
testset.num_class,
scales=scales,
flip=args.ms).cuda()
evaluator.eval()
metric = utils.SegmentationMetric(testset.num_class)
tbar = tqdm(test_data)
for i, (image, dst) in enumerate(tbar):
if 'val' in args.mode:
with torch.no_grad():
predicts = evaluator.parallel_forward(image)
metric.update(dst, predicts)
pixAcc, mIoU = metric.get()
tbar.set_description('pixAcc: %.4f, mIoU: %.4f' %
(pixAcc, mIoU))
else:
# with torch.no_grad():
# outputs = evaluator.parallel_forward(image)
# predicts = [testset.make_pred(torch.max(output, 1)[1].cpu().numpy())
# for output in outputs]
# for predict, impath in zip(predicts, dst):
# mask = utils.get_mask_pallete(predict, args.dataset)
# outname = os.path.splitext(impath)[0] + '.png'
# mask.save(os.path.join(outdir, outname))
with torch.no_grad():
outputs = evaluator.parallel_forward(image)
# predicts = [testset.make_pred(torch.max(output, 1)[1].cpu().numpy())
# for output in outputs]
predicts = [
torch.softmax(output, 1).cpu().numpy()
for output in outputs
]
for predict, impath in zip(predicts, dst):
# mask = utils.get_mask_pallete(predict, args.dataset)
import numpy as np
from PIL import Image
mask = Image.fromarray(
(predict[0, 1, :, :] * 255).astype(np.uint8))
outname = os.path.splitext(impath)[0] + '.bmp'
mask.save(os.path.join(outdir, outname))
def test(args):
# output folder
outdir = 'outdir'
if not os.path.exists(outdir):
os.makedirs(outdir)
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
if args.eval:
testset = get_dataset(args.dataset,
split='val',
mode='testval',
transform=input_transform)
elif args.test_val:
testset = get_dataset(args.dataset,
split='val',
mode='test',
transform=input_transform)
else:
testset = get_dataset(args.dataset,
split='test',
mode='test',
transform=input_transform)
# dataloader
loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
test_data = data.DataLoader(testset,
batch_size=args.test_batch_size,
drop_last=False,
shuffle=False,
collate_fn=test_batchify_fn,
**loader_kwargs)
# model
pretrained = args.resume is None and args.verify is None
if args.model_zoo is not None:
model = get_model(args.model_zoo, pretrained=pretrained)
model.base_size = args.base_size
model.crop_size = args.crop_size
else:
# my
model_kwargs = {}
if args.choice_indices is not None:
assert 'alone_resnest50' in args.backbone
model_kwargs['choice_indices'] = args.choice_indices
#
model = get_segmentation_model(
args.model,
dataset=args.dataset,
backbone=args.backbone,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=torch.nn.BatchNorm2d if args.acc_bn else SyncBatchNorm,
base_size=args.base_size,
crop_size=args.crop_size,
**model_kwargs)
# resuming checkpoint
if args.verify is not None and os.path.isfile(args.verify):
print("=> loading checkpoint '{}'".format(args.verify))
model.load_state_dict(torch.load(args.verify, map_location='cpu'))
elif args.resume is not None and os.path.isfile(args.resume):
checkpoint = torch.load(args.resume, map_location='cpu')
# strict=False, so that it is compatible with old pytorch saved models
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'".format(args.resume))
elif not pretrained:
raise RuntimeError("=> no checkpoint found")
print(model)
if args.acc_bn:
from encoding.utils.precise_bn import update_bn_stats
data_kwargs = {
'transform': input_transform,
'base_size': args.base_size,
'crop_size': args.crop_size
}
trainset = get_dataset(args.dataset,
split=args.train_split,
mode='train',
**data_kwargs)
trainloader = data.DataLoader(ReturnFirstClosure(trainset),
batch_size=args.batch_size,
drop_last=True,
shuffle=True,
**loader_kwargs)
print('Reseting BN statistics')
#model.apply(reset_bn_statistics)
model.cuda()
update_bn_stats(model, trainloader)
if args.export:
torch.save(model.state_dict(), args.export + '.pth')
return
scales = [0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \
[0.5, 0.75, 1.0, 1.25, 1.5, 1.75]#, 2.0
evaluator = MultiEvalModule(model, testset.num_class, scales=scales).cuda()
evaluator.eval()
metric = utils.SegmentationMetric(testset.num_class)
tbar = tqdm(test_data)
for i, (image, dst) in enumerate(tbar):
if args.eval:
with torch.no_grad():
predicts = evaluator.parallel_forward(image)
metric.update(dst, predicts)
pixAcc, mIoU = metric.get()
tbar.set_description('pixAcc: %.4f, mIoU: %.4f' %
(pixAcc, mIoU))
else:
with torch.no_grad():
outputs = evaluator.parallel_forward(image)
predicts = [
testset.make_pred(torch.max(output, 1)[1].cpu().numpy())
for output in outputs
]
for predict, impath in zip(predicts, dst):
mask = utils.get_mask_pallete(predict, args.dataset)
outname = os.path.splitext(impath)[0] + '.png'
mask.save(os.path.join(outdir, outname))
if args.eval:
print('pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU))
def test(args):
# output folder
outdir = '%s/msdanet_vis' % (args.dataset)
if not os.path.exists(outdir):
os.makedirs(outdir)
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
if args.eval:
testset = get_segmentation_dataset(args.dataset,
split='val',
mode='testval',
transform=input_transform)
else: # set split='test' for test set
testset = get_segmentation_dataset(args.dataset,
split='val',
mode='vis',
transform=input_transform)
# dataloader
loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
test_data = data.DataLoader(testset,
batch_size=args.test_batch_size,
drop_last=False,
shuffle=False,
collate_fn=test_batchify_fn,
**loader_kwargs)
if args.model_zoo is not None:
model = get_model(args.model_zoo, pretrained=True)
else:
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=BatchNorm2d,
base_size=args.base_size,
crop_size=args.crop_size,
multi_grid=args.multi_grid,
multi_dilation=args.multi_dilation)
# resuming checkpoint
if args.resume is None or not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
# strict=False, so that it is compatible with old pytorch saved models
model.load_state_dict(checkpoint['state_dict'], strict=False)
print(model)
num_class = testset.num_class
evaluator = MultiEvalModule(model,
testset.num_class,
multi_scales=args.multi_scales).cuda()
evaluator.eval()
tbar = tqdm(test_data)
def eval_batch(image, dst, evaluator, eval_mode):
if eval_mode:
# evaluation mode on validation set
targets = dst
outputs = evaluator.parallel_forward(image)
batch_inter, batch_union, batch_correct, batch_label = 0, 0, 0, 0
for output, target in zip(outputs, targets):
correct, labeled = utils.batch_pix_accuracy(
output.data.cpu(), target)
inter, union = utils.batch_intersection_union(
output.data.cpu(), target, testset.num_class)
batch_correct += correct
batch_label += labeled
batch_inter += inter
batch_union += union
return batch_correct, batch_label, batch_inter, batch_union
else:
# Visualize and dump the results
im_paths = dst
outputs = evaluator.parallel_forward(image)
predicts = [
torch.max(output, 1)[1].cpu().numpy() + testset.pred_offset
for output in outputs
]
for predict, impath in zip(predicts, im_paths):
mask = utils.get_mask_pallete(predict, args.dataset)
outname = os.path.splitext(impath)[0] + '.png'
mask.save(os.path.join(outdir, outname))
# dummy outputs for compatible with eval mode
return 0, 0, 0, 0
total_inter, total_union, total_correct, total_label = \
np.int64(0), np.int64(0), np.int64(0), np.int64(0)
for i, (image, dst) in enumerate(tbar):
if torch_ver == "0.3":
image = Variable(image, volatile=True)
correct, labeled, inter, union = eval_batch(
image, dst, evaluator, args.eval)
else:
with torch.no_grad():
correct, labeled, inter, union = eval_batch(
image, dst, evaluator, args.eval)
pixAcc, mIoU, IoU = 0, 0, 0
if args.eval:
total_correct += correct.astype('int64')
total_label += labeled.astype('int64')
total_inter += inter.astype('int64')
total_union += union.astype('int64')
pixAcc = np.float64(1.0) * total_correct / (
np.spacing(1, dtype=np.float64) + total_label)
IoU = np.float64(1.0) * total_inter / (
np.spacing(1, dtype=np.float64) + total_union)
mIoU = IoU.mean()
tbar.set_description('pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU))
return pixAcc, mIoU, IoU, num_class
def test(args):
# output folder
outdir = 'outdir'
if not os.path.exists(outdir):
os.makedirs(outdir)
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
if args.eval:
testset = get_segmentation_dataset(args.dataset,
split='val',
mode='val',
transform=input_transform,
return_file=True)
else:
testset = get_segmentation_dataset(args.dataset,
split='test',
mode='test',
transform=input_transform)
# dataloader
loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
test_data = data.DataLoader(testset,
batch_size=args.test_batch_size,
drop_last=False,
shuffle=False,
collate_fn=test_batchify_fn,
**loader_kwargs)
# model
if args.model_zoo is not None:
model = get_model(args.model_zoo, pretrained=True)
else:
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=BatchNorm2d,
base_size=args.base_size,
crop_size=args.crop_size)
# resuming checkpoint
if args.resume is None or not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
# strict=False, so that it is compatible with old pytorch saved models
pretrained_dict = checkpoint['state_dict']
model_dict = model.state_dict()
for name, param in pretrained_dict.items():
if name not in model_dict:
continue
if isinstance(param, Parameter):
# backwards compatibility for serialized parameters
param = param.data
model_dict[name].copy_(param)
#model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
print(model)
# count parameter number
pytorch_total_params = sum(p.numel() for p in model.parameters())
print("Total number of parameters: %d" % pytorch_total_params)
evaluator = MultiEvalModule(model, testset.num_class).cuda()
evaluator.eval()
tbar = tqdm(test_data)
def eval_batch(image, dst, im_paths, evaluator, eval_mode):
if eval_mode:
# evaluation mode on validation set
targets = dst
outputs = evaluator.parallel_forward(image)
batch_inter, batch_union, batch_correct, batch_label = 0, 0, 0, 0
for output, target in zip(outputs, targets):
correct, labeled = utils.batch_pix_accuracy(
output.data.cpu(), target)
inter, union = utils.batch_intersection_union(
output.data.cpu(), target, testset.num_class)
batch_correct += correct
batch_label += labeled
batch_inter += inter
batch_union += union
# save outputs
predicts = [
torch.max(output, 1)[1].cpu().numpy() # + testset.pred_offset
for output in outputs
]
for predict, impath, target in zip(predicts, im_paths, targets):
mask = utils.get_mask_pallete(predict, args.dataset)
outname = os.path.splitext(impath)[0] + '.png'
mask.save(os.path.join(outdir, outname))
# save ground truth into png format
target = target.data.cpu().numpy()
target = utils.get_mask_pallete(target, args.dataset)
outname = os.path.splitext(impath)[0] + '_gtruth.png'
target.save(os.path.join(outdir, outname))
return batch_correct, batch_label, batch_inter, batch_union
else:
# test mode, dump the results
im_paths = dst
outputs = evaluator.parallel_forward(image)
predicts = [
torch.max(output, 1)[1].cpu().numpy() # + testset.pred_offset
for output in outputs
]
for predict, impath in zip(predicts, im_paths):
mask = utils.get_mask_pallete(predict, args.dataset)
outname = os.path.splitext(impath)[0] + '.png'
mask.save(os.path.join(outdir, outname))
# dummy outputs for compatible with eval mode
return 0, 0, 0, 0
total_inter, total_union, total_correct, total_label = \
np.int64(0), np.int64(0), np.int64(0), np.int64(0)
for i, (image, dst, img_paths) in enumerate(tbar):
if torch_ver == "0.3":
image = Variable(image, volatile=True)
correct, labeled, inter, union = eval_batch(
image, dst, img_paths, evaluator, args.eval)
else:
with torch.no_grad():
correct, labeled, inter, union = eval_batch(
image, dst, img_paths, evaluator, args.eval)
if args.eval:
total_correct += correct.astype('int64')
total_label += labeled.astype('int64')
total_inter += inter.astype('int64')
total_union += union.astype('int64')
pixAcc = np.float64(1.0) * total_correct / (
np.spacing(1, dtype=np.float64) + total_label)
IoU = np.float64(1.0) * total_inter / (
np.spacing(1, dtype=np.float64) + total_union)
mIoU = IoU.mean()
tbar.set_description('pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU))
def test(args):
# output folder
outdir = 'outdir'
if not os.path.exists(outdir):
os.makedirs(outdir)
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
data_kwargs = {'root': args.data_root}
if args.eval:
testset = get_segmentation_dataset(args.dataset,
split='val',
mode='testval',
transform=input_transform,
**data_kwargs)
elif args.test_val:
testset = get_segmentation_dataset(args.dataset,
split='val',
mode='test',
transform=input_transform,
**data_kwargs)
else:
testset = get_segmentation_dataset(args.dataset,
split='test',
mode='test',
transform=input_transform,
**data_kwargs)
# dataloader
loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
test_data = data.DataLoader(testset,
batch_size=args.test_batch_size,
drop_last=False,
shuffle=False,
collate_fn=test_batchify_fn,
**loader_kwargs)
# model
if args.model_zoo is not None:
model = get_model(args.model_zoo, pretrained=True)
#model.base_size = args.base_size
#model.crop_size = args.crop_size
else:
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=SyncBatchNorm,
base_size=args.base_size,
crop_size=args.crop_size)
# resuming checkpoint
if args.resume is None or not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
# strict=False, so that it is compatible with old pytorch saved models
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
print(model)
# scales = [0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \
# [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0]
scales = [1.0]
evaluator = MultiEvalModule(model, testset.num_class, scales=scales).cuda()
evaluator.eval()
metric = utils.SegmentationMetric(testset.num_class)
tbar = tqdm(test_data)
for i, (image, dst) in enumerate(tbar):
if args.eval:
with torch.no_grad():
predicts = evaluator.parallel_forward(image)
metric.update(dst, predicts)
pixAcc, mIoU = metric.get()
tbar.set_description('pixAcc: %.4f, mIoU: %.4f' %
(pixAcc, mIoU))
else:
with torch.no_grad():
outputs = evaluator.parallel_forward(image)
predicts = [
testset.make_pred(torch.max(output, 1)[1].cpu().numpy())
for output in outputs
]
for predict, impath in zip(predicts, dst):
mask = utils.get_mask_pallete(predict, args.dataset)
outname = os.path.splitext(impath)[0] + '.png'
mask.save(os.path.join(outdir, outname))
def test(args):
directory = "runs/val_summary/%s/%s/%s/" % (args.dataset, args.model,
args.resume)
if not os.path.exists(directory):
os.makedirs(directory)
writer = SummaryWriter(directory)
# output folder
outdir = 'outdir'
if not os.path.exists(outdir):
os.makedirs(outdir)
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
if args.eval:
testset = get_segmentation_dataset(args.dataset,
split='val',
mode='testval',
transform=input_transform)
elif args.test_val:
testset = get_segmentation_dataset(args.dataset,
split='val',
mode='test',
transform=input_transform)
else:
testset = get_segmentation_dataset(args.dataset,
split='test',
mode='test',
transform=input_transform)
# dataloader
loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
test_data = data.DataLoader(testset,
batch_size=args.test_batch_size,
drop_last=False,
shuffle=False,
collate_fn=test_batchify_fn,
**loader_kwargs)
Norm_method = torch.nn.BatchNorm2d
# model
if args.model_zoo is not None:
model = get_model(args.model_zoo, pretrained=True)
#model.base_size = args.base_size
#model.crop_size = args.crop_size
else:
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
aux=args.aux,
multi_grid=args.multi_grid,
num_center=args.num_center,
norm_layer=Norm_method,
root=args.backbone_path,
base_size=args.base_size,
crop_size=args.crop_size)
# resuming checkpoint
if args.resume is None or not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
# strict=False, so that it is compatible with old pytorch saved models
#model.module.load_state_dict(checkpoint['state_dict'])
old_state_dict = checkpoint['state_dict']
new_state_dict = dict()
for k, v in old_state_dict.items():
if k.startswith('module.'):
#new_state_dict[k[len('module.'):]] = old_state_dict[k]
new_state_dict[k[len('model.module.'):]] = old_state_dict[k]
#new_state_dict[k] = old_state_dict[k]
else:
new_state_dict[k] = old_state_dict[k]
#new_k = 'module.' + k
#new_state_dict[new_k] = old_state_dict[k]
model.load_state_dict(new_state_dict)
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
print(model)
scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \
[0.75, 1.0, 1.25, 1.5, 1.75, 2.0]
if args.dataset == 'ade20k':
scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0]
if not args.ms:
scales = [1.0]
if args.dataset == 'ade20k':
evaluator = MultiEvalModule2(model,
testset.num_class,
scales=scales,
flip=args.ms).cuda()
else:
evaluator = MultiEvalModule(model,
testset.num_class,
scales=scales,
flip=args.ms).cuda()
evaluator.eval()
metric = utils.SegmentationMetric(testset.num_class)
tbar = tqdm(test_data)
for i, (image, dst) in enumerate(tbar):
if args.eval:
with torch.no_grad():
predicts = evaluator.parallel_forward(image)
metric.update(dst, predicts)
pixAcc, mIoU = metric.get()
tbar.set_description('pixAcc: %.4f, mIoU: %.4f' %
(pixAcc, mIoU))
writer.add_scalar('pixAcc', pixAcc, i)
writer.add_scalar('mIoU', mIoU, i)
else:
with torch.no_grad():
outputs = evaluator.parallel_forward(image)
predicts = [
testset.make_pred(torch.max(output, 1)[1].cpu().numpy())
for output in outputs
]
for predict, impath in zip(predicts, dst):
mask = utils.get_mask_pallete(predict, args.dataset)
outname = os.path.splitext(impath)[0] + '.png'
mask.save(os.path.join(outdir, outname))
writer.close()
def test(args):
# output folder
outdir = args.save_folder
if not os.path.exists(outdir):
os.makedirs(outdir)
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
testset = get_segmentation_dataset(args.dataset,
split=args.split,
mode=args.mode,
transform=input_transform)
# dataloader
loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
test_data = data.DataLoader(testset,
batch_size=args.test_batch_size,
drop_last=False,
shuffle=False,
collate_fn=test_batchify_fn,
**loader_kwargs)
# model
if args.model_zoo is not None:
model = get_model(args.model_zoo, pretrained=True)
else:
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
dilated=args.dilated,
multi_grid=args.multi_grid,
stride=args.stride,
lateral=args.lateral,
jpu=args.jpu,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=BatchNorm,
base_size=args.base_size,
crop_size=args.crop_size)
# resuming checkpoint
if args.resume is None or not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
# strict=False, so that it is compatible with old pytorch saved models
model.load_state_dict(checkpoint['state_dict'], strict=False)
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
# print(model)
scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \
[0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
if not args.ms:
scales = [1.0]
evaluator = MultiEvalModule(model,
testset.num_class,
scales=scales,
flip=args.ms).cuda()
evaluator.eval()
tbar = tqdm(test_data)
total_inter, total_union, total_correct, total_label = 0, 0, 0, 0
result = []
for i, (image, dst) in enumerate(tbar):
# print(dst)
with torch.no_grad():
if i > 20:
st = time.time()
outputs = evaluator.forward(image[0].unsqueeze(0).cuda())
if i > 20:
result.append(1 / (time.time() - st))
print(np.mean(result), np.std(result))
if 'val' in args.mode:
# compute image IoU metric
inter, union, area_pred, area_lab = batch_intersection_union(
outputs, dst[0], testset.num_class)
total_label += area_lab
total_inter += inter
total_union += union
class_pixAcc = 1.0 * inter / (np.spacing(1) + area_lab)
class_IoU = 1.0 * inter / (np.spacing(1) + union)
print("img Classes pixAcc:", class_pixAcc)
print("img Classes IoU:", class_IoU)
else:
# save prediction results
predict = testset.make_pred(
torch.max(output, 1)[1].cpu().numpy())
mask = utils.get_mask_pallete(predict, args.dataset)
outname = os.path.splitext(dst[0])[0] + '.png'
mask.save(os.path.join(outdir, outname))
if 'val' in args.mode:
# compute set IoU metric
pixAcc = 1.0 * total_inter / (np.spacing(1) + total_label)
IoU = 1.0 * total_inter / (np.spacing(1) + total_union)
mIoU = IoU.mean()
print("set Classes pixAcc:", pixAcc)
print("set Classes IoU:", IoU)
print("set mean IoU:", mIoU)
def semseg(input_path, output_path=None, with_L0=False):
"""
param:
input_path: str, path of input image
output_path: str, path to save output image
return: tuple, [animal_name, "background"] if pixels of "background" dominate,
["background", animal_name] else.
"""
sys.argv = sys.argv[:1]
option = Options()
args = option.parse()
args.aux = True
args.se_loss = True
args.resume = "./checkpoints/encnet_jpu_res101_pcontext.pth.tar" # model checkpoint
torch.manual_seed(args.seed)
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# using L0_smooth to transform the orignal picture
if with_L0:
mid_result = os.path.join(os.path.dirname(input_path), "L0_result.png")
L0_smooth(input_path, mid_result)
input_path = mid_result
# model
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
dilated=args.dilated,
lateral=args.lateral,
jpu=args.jpu,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=BatchNorm,
base_size=args.base_size,
crop_size=args.crop_size)
# resuming checkpoint
if args.resume is None or not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume, map_location=torch.device('cpu'))
# strict=False, so that it is compatible with old pytorch saved models
model.load_state_dict(checkpoint['state_dict'], strict=False)
print("semseg model loaded successfully!")
scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \
[0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
if not args.ms:
scales = [1.0]
num_classes = datasets[args.dataset.lower()].NUM_CLASS
evaluator = MultiEvalModule(model,
num_classes,
scales=scales,
flip=args.ms).cuda()
evaluator.eval()
classes = np.array([
'empty', 'aeroplane', 'bag', 'bed', 'bedclothes', 'bench', 'bicycle',
'bird', 'boat', 'book', 'bottle', 'building', 'bus', 'cabinet', 'car',
'cat', 'ceiling', 'chair', 'cloth', 'computer', 'cow', 'cup',
'curtain', 'dog', 'door', 'fence', 'floor', 'flower', 'food', 'grass',
'ground', 'horse', 'keyboard', 'light', 'motorbike', 'mountain',
'mouse', 'person', 'plate', 'platform', 'pottedplant', 'road', 'rock',
'sheep', 'shelves', 'sidewalk', 'sign', 'sky', 'snow', 'sofa', 'table',
'track', 'train', 'tree', 'truck', 'tvmonitor', 'wall', 'water',
'window', 'wood'
])
animals = ['bird', 'cat', 'cow', 'dog', 'horse', 'mouse', 'sheep']
img = input_transform(Image.open(input_path).convert('RGB')).unsqueeze(0)
with torch.no_grad():
output = evaluator.parallel_forward(img)[0]
predict = torch.max(output, 1)[1].cpu().numpy() + 1
pred_idx = np.unique(predict)
pred_label = classes[pred_idx]
print("[SemSeg] ", input_path, ": ", pred_label, sep='')
main_pixels = 0
main_idx = -1
for idx, label in zip(pred_idx, pred_label):
if label in animals:
pixels = np.sum(predict == idx)
if pixels > main_pixels:
main_pixels = pixels
main_idx = idx
background_pixels = np.sum(predict != main_idx)
main_animal = classes[main_idx]
predict[predict != main_idx] = 29
mask_matrix = predict.copy()
if output_path is not None:
mask_matrix[np.where(mask_matrix != 29)] = 1
mask_matrix[np.where(mask_matrix == 29)] = 0
mask = utils.get_mask_pallete(mask_matrix, args.dataset)
mask.save(output_path)
if main_idx < 29:
return predict, (main_animal, "background")
else:
return predict, ("background", main_animal)
def test(args):
# output folder
outdir = args.save_folder
if not os.path.exists(outdir):
os.makedirs(outdir)
# data transforms
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
# dataset
testset = get_segmentation_dataset(args.dataset,
split=args.split,
mode=args.mode,
transform=input_transform)
# dataloader
loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
test_data = data.DataLoader(testset,
batch_size=args.test_batch_size,
drop_last=False,
shuffle=False,
collate_fn=test_batchify_fn,
**loader_kwargs)
# model
if args.model_zoo is not None:
model = get_model(args.model_zoo, pretrained=True)
else:
model = get_segmentation_model(args.model,
dataset=args.dataset,
backbone=args.backbone,
dilated=args.dilated,
multi_grid=args.multi_grid,
stride=args.stride,
lateral=args.lateral,
jpu=args.jpu,
aux=args.aux,
se_loss=args.se_loss,
norm_layer=BatchNorm,
base_size=args.base_size,
crop_size=args.crop_size)
# resuming checkpoint
if args.resume is None or not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
# strict=False, so that it is compatible with old pytorch saved models
model.load_state_dict(checkpoint['state_dict'], strict=False)
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
# print(model)
scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \
[0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
if not args.ms:
scales = [1.0]
evaluator = MultiEvalModule(model,
testset.num_class,
scales=scales,
flip=args.ms).cuda()
evaluator.eval()
metric = utils.SegmentationMetric(testset.num_class)
tbar = tqdm(test_data)
total_inter, total_union, total_correct, total_label, all_label = 0, 0, 0, 0, 0
# for i, (image, dst) in enumerate(tbar):
# # print(dst)
# with torch.no_grad():
# outputs = evaluator.parallel_forward(image)[0]
# correct, labeled = batch_pix_accuracy(outputs, dst[0])
# total_correct += correct
# all_label += labeled
# img_pixAcc = 1.0 * correct / (np.spacing(1) + labeled)
# inter, union, area_pred, area_lab = batch_intersection_union(outputs, dst[0], testset.num_class)
# total_label += area_lab
# total_inter += inter
# total_union += union
# class_pixAcc = 1.0 * inter / (np.spacing(1) + area_lab)
# class_IoU = 1.0 * inter / (np.spacing(1) + union)
# class_mIoU = class_IoU.mean()
# print("img pixAcc:", img_pixAcc)
# print("img Classes pixAcc:", class_pixAcc)
# print("img Classes IoU:", class_IoU)
# total_pixAcc = 1.0 * total_correct / (np.spacing(1) + all_label)
# pixAcc = 1.0 * total_inter / (np.spacing(1) + total_label)
# IoU = 1.0 * total_inter / (np.spacing(1) + total_union)
# mIoU = IoU.mean()
# print("set pixAcc:", pixAcc)
# print("set Classes pixAcc:", pixAcc)
# print("set Classes IoU:", IoU)
# print("set mean IoU:", mIoU)
for i, (image, dst) in enumerate(tbar):
if 'val' in args.mode:
with torch.no_grad():
predicts = evaluator.parallel_forward(image)
# metric.update(dst[0], predicts[0])
# pixAcc, mIoU = metric.get()
# tbar.set_description( 'pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU))
else:
with torch.no_grad():
outputs = evaluator.parallel_forward(image)
'crop_size': args.crop_size}
trainset = get_dataset(args.dataset, split=args.train_split, mode='train', **data_kwargs)
trainloader = data.DataLoader(ReturnFirstClosure(trainset), batch_size=args.batch_size,
drop_last=True, shuffle=True, **loader_kwargs)
print('Reseting BN statistics')
#model.apply(reset_bn_statistics)
model.cuda()
update_bn_stats(model, trainloader)
if args.export:
torch.save(model.state_dict(), args.export + '.pth')
return
scales = [0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \
[0.5, 0.75, 1.0, 1.25, 1.5, 1.75]#, 2.0
evaluator = MultiEvalModule(model, testset.num_class, scales=scales).cuda()
evaluator.eval()
metric = utils.SegmentationMetric(testset.num_class)
tbar = tqdm(test_data)
for i, (image, dst) in enumerate(tbar):
if args.eval:
with torch.no_grad():
predicts = evaluator.parallel_forward(image)
metric.update(dst, predicts)
pixAcc, mIoU = metric.get()
tbar.set_description( 'pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU))
else:
with torch.no_grad():
outputs = evaluator.parallel_forward(image)
predicts = [testset.make_pred(torch.max(output, 1)[1].cpu().numpy())