def test_dataset(dataset_name):
config = edict()
config.dataset_name = dataset_name
config.with_path = True
config.input_shape = (512, 1024)
config.max_crop_size = (1024, 2048)
config.aug_library = 'imgaug'
config = get_dataset_generalize_config(config, dataset_name)
config = get_default_augmentor_config(config)
augmentations = Augmentations(config)
dataset = dataset_generalize(config,
split='train',
augmentations=augmentations)
N = len(dataset)
idx = np.random.randint(0, N)
data = dataset.__getitem__(idx)
img, ann = data['image']
img = img.transpose((1, 2, 0))
ann = add_color_map(ann)
img_path, ann_path = data['filename']
ori_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
ori_img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)
ori_ann = read_ann_file(ann_path)
ori_ann = add_color_map(ori_ann)
show_images([img, ann, ori_img, ori_ann],
['img', 'ann', 'origin img', 'origin ann'])
def class_weight_count(dataset_name, split='train'):
config = get_dataset_generalize_config(None, dataset_name)
dataset = dataset_generalize(config, split=split, bchw=False)
N = len(dataset)
class_number = len(config.foreground_class_ids)
COUNT = dataset_class_count(class_number)
for idx in trange(N):
img, ann = dataset.__getitem__(idx)
COUNT.update(ann)
count = COUNT.summary()
return count
def static_dataset(dataset_name, split='train'):
config = get_dataset_generalize_config(None, dataset_name)
dataset = dataset_generalize(config, split=split, bchw=False)
N = len(dataset)
MEAN = dataset_mean()
for idx in trange(N):
img, ann = dataset.__getitem__(idx)
MEAN.update(img)
mean = MEAN.summary()
STD = dataset_std(mean)
for idx in trange(N):
img, ann = dataset.__getitem__(idx)
STD.update(img)
std = STD.summary()
return mean, std
def test_dataset_loader(dataset_name):
config = edict()
config.dataset_name = dataset_name
config.print_path = True
config.input_shape = (224, 224)
config.ignore_index = 255
config.with_edge = False
config.batch_size = 2
config = get_dataset_generalize_config(config, dataset_name)
config = get_default_augmentor_config(config)
augmentations = Augmentations(config)
dataset = dataset_generalize(config,
split='train',
augmentations=augmentations)
loader = TD.DataLoader(dataset=dataset,
batch_size=config.batch_size,
shuffle=True,
drop_last=False)
plt.ion()
for i, data in enumerate(loader):
imgs, labels = data
print(i, imgs.shape, labels.shape)
show_tensor_list([imgs], ['img'])
show_tensor_list([labels], ['labels'])
np_labels = labels.data.cpu().numpy()
print('label id: ', np.unique(np_labels))
image_list = np.split(np_labels, config.batch_size)
image_list = [np.squeeze(img) for img in image_list]
image_list = [add_color_map(img) for img in image_list]
show_images(image_list, ['label'] * config.batch_size)
if i > 1:
break
plt.ioff()
plt.show()
def test_cityscapes_category(self):
"""
the cityscapes category id != huawei class index, need remap them
and the cityscape dataset may ignore some object like parking
(class id=9, category=flat ...)
Returns
-------
None.
"""
config=get_dataset_generalize_config(None,'Cityscapes_Category')
config.with_path=True
cat_val_dataset = dataset_generalize(config,
split='val',
augmentations=None,
normalizations=None)
config=get_dataset_generalize_config(None,'Cityscapes_Fine')
config.with_path=True
class_val_dataset = dataset_generalize(config,
split='val',
augmentations=None,
normalizations=None)
assert len(cat_val_dataset)==len(class_val_dataset)
N=len(cat_val_dataset)
for key,value in id2catId.items():
print('key={}, value={}'.format(key,value))
for idx in range(min(N,3)):
cat_data=cat_val_dataset.__getitem__(idx)
cat_img,cat_ann=cat_data['image']
class_data=class_val_dataset.__getitem__(idx)
class_img,class_ann=class_data['image']
assert cat_data['filename'][1]==class_data['filename'][1]
origin_ann=Image.open(class_data['filename'][1])
origin_ann=np.array(origin_ann,np.uint8)
class2cat=origin_ann.copy()
for key,value in id2catId.items():
if key>=0:
class2cat[origin_ann==key]=value
for class_id in config.foreground_class_ids:
print('class_id={},catId={}'.format(class_id,id2catId[class_id]))
class2cat[origin_ann==0]=255
class2cat[origin_ann!=255]-=1
diff=(class2cat!=cat_ann)
negative=(origin_ann==255)
print(np.unique(origin_ann[diff]))
cat_img=cat_img.transpose((1,2,0))
class_img=class_img.transpose((1,2,0))
cat_ann=add_color_map(cat_ann)
class_ann=add_color_map(class_ann)
class2cat=add_color_map(class2cat)
show_images([cat_img,cat_ann,class_img,class_ann,class2cat,diff,negative],['cat','cat','class','class','class2cat','diff','negative'])
fractal_fusion_type = 'mean'
config.before_upsample = before_upsample
config.fractal_depth = fractal_depth
config.fractal_fusion_type = fractal_fusion_type
location_str = 'before' if before_upsample else 'after'
config.note = '_'.join([
config.note, location_str, 'depth',
str(fractal_depth), 'fusion', fractal_fusion_type
])
net = psp_fractal(config)
keras_fit(net, train_loader, val_loader)
elif test == 'coarse':
net = get_net(config)
for dataset_name in ['Cityscapes_Coarse', 'Cityscapes_Fine']:
config = get_dataset_generalize_config(config, dataset_name)
#config.dataset_name = dataset_name.lower()
coarse_train_loader, coarse_val_loader = get_loaders(config)
keras_fit(net, coarse_train_loader, coarse_val_loader)
elif test == 'summary':
net = get_net(config)
config_str = json.dumps(config, indent=2, sort_keys=True)
print(config_str)
print('args is ' + '*' * 30)
print(args)
height, width = config.input_shape
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torchsummary.summary(net.to(device), (3, height, width))
elif test == 'huawei':
# train on cityscapes, validation on huawei
# -*- coding: utf-8 -*-
"""
experiment on downsample and miou
"""
from torchseg.dataset.dataset_generalize import dataset_generalize, \
get_dataset_generalize_config
from torchseg.utils.metrics import runningScore
import cv2
from easydict import EasyDict as edict
if __name__ == '__main__':
config = edict()
config.with_edge = False
config = get_dataset_generalize_config(config, 'HuaWei')
val_dataset = dataset_generalize(config,
split='val',
augmentations=None,
normalizations=None)
N = len(val_dataset)
if config.ignore_index == 0:
config.class_number = len(config.foreground_class_ids) + 1
else:
config.class_number = len(config.foreground_class_ids)
metric = runningScore(config.class_number)
print('| scale | miou |')
print('| -\t | -\t |')