def run_app(cfg: DictConfig) -> None:
dataset = VOCSemanticSegmentationDataset(split=cfg.chainer_eval_set,
data_dir=cfg.voc12_root)
labels = [
dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset))
]
debug = True
if debug:
preds = []
for idx in dataset.ids:
pred = _work(cfg.cam_out_dir, cfg.cv_out_dir, cfg.cam_eval_thres,
cfg.area_threshold, idx)
preds.append(pred)
else:
with mp.Pool(processes=mp.cpu_count() // 2) as pool:
preds = pool.map(
partial(_work, cfg.cam_out_dir, cfg.cv_out_dir,
cfg.cam_eval_thres, cfg.area_threshold),
list(dataset.ids))
print(len(preds))
confusion = calc_semantic_segmentation_confusion(preds, labels)
gtj = confusion.sum(axis=1)
resj = confusion.sum(axis=0)
gtjresj = np.diag(confusion)
denominator = gtj + resj - gtjresj
iou = gtjresj / denominator
print({'iou': iou, 'miou': np.nanmean(iou)})
logging.info({'iou': iou, 'miou': np.nanmean(iou)})
def run(args):
assert args.voc12_root is not None
assert args.chainer_eval_set is not None
assert args.sem_seg_out_dir is not None
dataset = VOCSemanticSegmentationDataset(split=args.chainer_eval_set,
data_dir=args.voc12_root)
labels = [
dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset))
]
preds = []
for id in tqdm(dataset.ids):
cls_labels = imageio.imread(
os.path.join(args.sem_seg_out_dir, id + '.png')).astype(np.uint8)
cls_labels[cls_labels == 255] = 0
preds.append(cls_labels.copy())
confusion = calc_semantic_segmentation_confusion(preds, labels)[:21, :21]
gtj = confusion.sum(axis=1)
resj = confusion.sum(axis=0)
gtjresj = np.diag(confusion)
denominator = gtj + resj - gtjresj
fp = 1. - gtj / denominator
fn = 1. - resj / denominator
iou = gtjresj / denominator
print(fp[0], fn[0])
print(np.mean(fp[1:]), np.mean(fn[1:]))
print({'iou': iou, 'miou': np.nanmean(iou)})
def run(args):
dataset = VOCSemanticSegmentationDataset(split=args.chainer_eval_set,
data_dir=args.voc12_root)
labels = [
dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset))
]
preds = []
for id in dataset.ids:
###################修改代码#########################
cam_dict = np.load(os.path.join(args.cam_out_aug_dir, id + '.npy'),
allow_pickle=True).item()
###################修改代码#########################
cams = cam_dict['high_res']
cams = np.pad(cams, ((1, 0), (0, 0), (0, 0)),
mode='constant',
constant_values=args.cam_eval_thres)
keys = np.pad(cam_dict['keys'] + 1, (1, 0), mode='constant')
cls_labels = np.argmax(cams, axis=0)
cls_labels = keys[cls_labels]
preds.append(cls_labels.copy())
confusion = calc_semantic_segmentation_confusion(preds, labels)
gtj = confusion.sum(axis=1)
resj = confusion.sum(axis=0)
gtjresj = np.diag(confusion)
denominator = gtj + resj - gtjresj
iou = gtjresj / denominator
print({'iou': iou, 'miou': np.nanmean(iou)})
def run_app(cfg: DictConfig) -> None:
dataset = VOCSemanticSegmentationDataset(split=cfg.chainer_eval_set,
data_dir=cfg.voc12_root)
labels = [
dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset))
]
preds = []
for id in dataset.ids:
cls_labels = imageio.imread(
os.path.join(cfg.sem_seg_out_dir, id + '.png')).astype(np.uint8)
cls_labels[cls_labels == 255] = 0
if cfg.cv_out_dir:
cls_labels = add_cv_results(cls_labels.copy(), id, cfg.cv_out_dir,
cfg.area_threshold)
preds.append(cls_labels.copy())
confusion = calc_semantic_segmentation_confusion(preds, labels)[:21, :21]
gtj = confusion.sum(axis=1)
resj = confusion.sum(axis=0)
gtjresj = np.diag(confusion)
denominator = gtj + resj - gtjresj
fp = 1. - gtj / denominator
fn = 1. - resj / denominator
iou = gtjresj / denominator
print(fp[0], fn[0])
print(np.mean(fp[1:]), np.mean(fn[1:]))
print({'iou': iou, 'miou': np.nanmean(iou)})
def run(args):
if args.dataset == 'l8biome':
dataset = l8biome.dataloader.L8BiomeDataset(args.data_root,
'train',
mask_file='mask.tif')
# Only compute CAM for cloudy images - we know the segmentation label for clear already.
dataset.images = [img for img in dataset.images if 'cloudy' in img[2]]
labels = [dataset.load_mask(x[0]) for x in dataset.images]
ids = [x[2] for x in dataset.images]
else:
dataset = VOCSemanticSegmentationDataset(split=args.chainer_eval_set,
data_dir=args.data_root)
labels = [
dataset.get_example_by_keys(i, (1, ))[0]
for i in range(len(dataset))
]
ids = dataset.ids
preds = []
for id in tqdm(ids):
cam_dict = np.load(os.path.join(args.cam_out_dir, id + '.npy'),
allow_pickle=True).item()
cams = cam_dict['high_res']
cams = np.pad(cams, ((1, 0), (0, 0), (0, 0)),
mode='constant',
constant_values=args.cam_eval_thres)
if args.dataset == 'l8biome':
# background class (in our case 'clear') corresponds to class 0 already
keys = np.pad(cam_dict['keys'], (1, 0), mode='constant')
else:
keys = np.pad(cam_dict['keys'] + 1, (1, 0), mode='constant')
cls_labels = np.argmax(cams, axis=0)
cls_labels = keys[cls_labels]
preds.append(cls_labels.copy())
if args.dataset == 'l8biome':
# Compute metrics as FCD
pass
else:
confusion = calc_semantic_segmentation_confusion(preds, labels)
gtj = confusion.sum(axis=1)
resj = confusion.sum(axis=0)
gtjresj = np.diag(confusion)
denominator = gtj + resj - gtjresj
iou = gtjresj / denominator
print({'iou': iou, 'miou': np.nanmean(iou)})
def setup(dataset, model, pretrained_model, batchsize, input_size):
dataset_name = dataset
if dataset_name == 'cityscapes':
dataset = CityscapesSemanticSegmentationDataset(
split='val', label_resolution='fine')
label_names = cityscapes_semantic_segmentation_label_names
elif dataset_name == 'ade20k':
dataset = ADE20KSemanticSegmentationDataset(split='val')
label_names = ade20k_semantic_segmentation_label_names
elif dataset_name == 'camvid':
dataset = CamVidDataset(split='test')
label_names = camvid_label_names
elif dataset_name == 'voc':
dataset = VOCSemanticSegmentationDataset(split='val')
label_names = voc_semantic_segmentation_label_names
def eval_(out_values, rest_values):
pred_labels, = out_values
gt_labels, = rest_values
result = eval_semantic_segmentation(pred_labels, gt_labels)
for iu, label_name in zip(result['iou'], label_names):
print('{:>23} : {:.4f}'.format(label_name, iu))
print('=' * 34)
print('{:>23} : {:.4f}'.format('mean IoU', result['miou']))
print('{:>23} : {:.4f}'.format('Class average accuracy',
result['mean_class_accuracy']))
print('{:>23} : {:.4f}'.format('Global average accuracy',
result['pixel_accuracy']))
cls, pretrained_models, default_batchsize = models[model]
if pretrained_model is None:
pretrained_model = pretrained_models.get(dataset_name, dataset_name)
if input_size is None:
input_size = None
else:
input_size = (input_size, input_size)
kwargs = {
'n_class': len(label_names),
'pretrained_model': pretrained_model,
}
if model in ['pspnet_resnet50', 'pspnet_resnet101']:
kwargs.update({'input_size': input_size})
elif model == 'deeplab_v3plus_xception65':
kwargs.update({'min_input_size': input_size})
model = cls(**kwargs)
if batchsize is None:
batchsize = default_batchsize
return dataset, eval_, model, batchsize
def run_app(cfg: DictConfig) -> None:
dataset = VOCSemanticSegmentationDataset(split=cfg.chainer_eval_set,
data_dir=cfg.voc12_root)
os.makedirs(cfg.out_dir, exist_ok=True)
debug = False
if debug:
for i, idx in enumerate(dataset.ids):
_work(dataset, cfg.out_dir, cfg.cv_method, i, idx)
else:
with mp.Pool(processes=mp.cpu_count() // 2) as pool:
pool.starmap(partial(_work, dataset, cfg.out_dir, cfg.cv_method),
enumerate(dataset.ids))
def run(args):
dataset = VOCSemanticSegmentationDataset(split=args.chainer_eval_set,
data_dir=args.voc12_root)
# labels = [dataset.get_example_by_keys(i, (1,))[0] for i in range(len(dataset))]
preds = []
labels = []
n_images = 0
for i, id in enumerate(dataset.ids):
n_images += 1
# print(os.path.join(args.cam_out_dir, id + '.npy'))
cam_dict = np.load(os.path.join(args.cam_out_dir, id + '.npy'),
allow_pickle=True).item()
cams = cam_dict['high_res']
cams = np.pad(cams, ((1, 0), (0, 0), (0, 0)),
mode='constant',
constant_values=args.cam_eval_thres)
keys = np.pad(cam_dict['keys'] + 1, (1, 0), mode='constant')
cls_labels = np.argmax(cams, axis=0)
cls_labels = keys[cls_labels]
preds.append(cls_labels.copy())
labels.append(dataset.get_example_by_keys(i, (1, ))[0])
confusion = calc_semantic_segmentation_confusion(preds, labels)
gtj = confusion.sum(axis=1)
resj = confusion.sum(axis=0)
gtjresj = np.diag(confusion)
denominator = gtj + resj - gtjresj
iou = gtjresj / denominator
print("threshold:", args.cam_eval_thres, 'miou:', np.nanmean(iou),
"i_imgs", n_images)
print('among_predfg_bg',
float((resj[1:].sum() - confusion[1:, 1:].sum()) / (resj[1:].sum())))
return np.nanmean(iou)
def get_dataset_and_model(dataset_name, model_name, pretrained_model,
input_size):
if dataset_name == 'cityscapes':
dataset = CityscapesSemanticSegmentationDataset(
split='val', label_resolution='fine')
label_names = cityscapes_semantic_segmentation_label_names
elif dataset_name == 'ade20k':
dataset = ADE20KSemanticSegmentationDataset(split='val')
label_names = ade20k_semantic_segmentation_label_names
elif dataset_name == 'camvid':
dataset = CamVidDataset(split='test')
label_names = camvid_label_names
elif dataset_name == 'voc':
dataset = VOCSemanticSegmentationDataset(split='val')
label_names = voc_semantic_segmentation_label_names
n_class = len(label_names)
if pretrained_model:
pretrained_model = pretrained_model
else:
pretrained_model = dataset_name
if model_name == 'pspnet_resnet101':
model = PSPNetResNet101(n_class=n_class,
pretrained_model=pretrained_model,
input_size=input_size)
elif model_name == 'pspnet_resnet50':
model = PSPNetResNet50(n_class=n_class,
pretrained_model=pretrained_model,
input_size=input_size)
elif model_name == 'segnet':
model = SegNetBasic(n_class=n_class, pretrained_model=pretrained_model)
elif model_name == 'deeplab_v3plus_xception65':
model = DeepLabV3plusXception65(n_class=n_class,
pretrained_model=pretrained_model,
min_input_size=input_size)
return dataset, label_names, model
def setUp(self):
self.dataset = VOCSemanticSegmentationDataset(split=self.split)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model',
choices=('multi_task_300', 'multi_task_512'),
default='multi_task_300')
parser.add_argument('--batchsize', type=int, default=32)
parser.add_argument('--iteration', type=int, default=120000)
parser.add_argument('--eval_step',
type=int,
nargs='*',
default=[80000, 100000, 120000])
parser.add_argument('--lr_step',
type=int,
nargs='*',
default=[80000, 100000])
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--snap_step', type=int, default=10000)
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--out',
default='result') # in experiments for real experiment
parser.add_argument('--resume', type=str)
parser.add_argument('--detection', action='store_true', default=False)
parser.add_argument('--segmentation', action='store_true', default=False)
parser.add_argument('--attention', action='store_true', default=False)
parser.add_argument('--dataset', default='voc', type=str)
parser.add_argument('--experiment', type=str, default='final_voc')
parser.add_argument('--multitask_loss', action='store_true', default=False)
parser.add_argument('--dynamic_loss', action='store_true', default=False)
parser.add_argument('--log_interval', type=int, default=10)
parser.add_argument('--debug', action='store_true', default=False)
parser.add_argument('--update_split_interval', type=int, default=100)
parser.add_argument(
'--loss_split', type=float, default=0.5
) # in fact for detection, other task(segmentation) is 1-loss_split
args = parser.parse_args()
snap_step = args.snap_step
args.snap_step = []
for step in range(snap_step, args.iteration + 1, snap_step):
args.snap_step.append(step)
# redefine the output path
import os
import time
args.out = os.path.join(args.out, args.experiment,
time.strftime("%Y%m%d_%H%M%S", time.localtime()))
if args.model == 'multi_task_300':
model = Multi_task_300(n_fg_class=len(voc_bbox_label_names),
pretrained_model='imagenet',
detection=args.detection,
segmentation=args.segmentation,
attention=args.attention)
elif args.model == 'multi_task_512':
model = Multi_task_512(n_fg_class=len(voc_bbox_label_names),
pretrained_model='imagenet',
detection=args.detection,
segmentation=args.segmentation,
attention=args.attention)
model.use_preset('evaluate')
if not (args.segmentation or args.detection):
raise RuntimeError
train_chain = MultiboxTrainChain(model,
gpu=args.gpu >= 0,
use_multi_task_loss=args.multitask_loss,
loss_split=args.loss_split)
train_chain.cleargrads()
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
train = TransformDataset(
Multi_task_VOC(voc_experiments[args.experiment][args.experiment +
'_train']),
Transform(model.coder, model.insize, model.mean))
train_iter = chainer.iterators.MultiprocessIterator(
train, batch_size=args.batchsize)
test = VOCBboxDataset(year='2007',
split='test',
use_difficult=True,
return_difficult=True)
test_iter = chainer.iterators.SerialIterator(test,
args.batchsize,
repeat=False,
shuffle=False)
test_mask = VOCSemanticSegmentationDataset(split='val')
test_mask_iter = chainer.iterators.SerialIterator(test_mask,
args.batchsize,
repeat=False,
shuffle=False)
optimizer = chainer.optimizers.MomentumSGD()
optimizer.setup(train_chain)
# optimizer.add_hook(GradientClipping(0.1))
for param in train_chain.params():
if param.name == 'b':
param.update_rule.add_hook(GradientScaling(2))
else:
param.update_rule.add_hook(WeightDecay(0.0005))
updater = training.updaters.StandardUpdater(train_iter,
optimizer,
device=args.gpu)
trainer = training.Trainer(updater, (args.iteration, 'iteration'),
args.out)
'''if args.resume:
serializers.load_npz(args.resume, trainer)'''
trainer.extend(extensions.ExponentialShift('lr', 0.1, init=args.lr),
trigger=triggers.ManualScheduleTrigger(
args.lr_step, 'iteration'))
if args.dataset == 'voc':
use_07 = True
label_names = voc_bbox_label_names
elif args.dataset == 'coco':
label_names = coco_bbox_label_names
if args.detection and not args.debug:
trainer.extend(MultitaskEvaluator(test_iter,
model,
args.dataset,
use_07,
label_names=label_names),
trigger=triggers.ManualScheduleTrigger(
args.eval_step + [args.iteration], 'iteration'))
if args.segmentation and not args.debug:
trainer.extend(MultitaskEvaluator(test_mask_iter,
model,
dataset=args.dataset,
label_names=label_names,
detection=False),
trigger=triggers.ManualScheduleTrigger(
args.eval_step + [args.iteration], 'iteration'))
log_interval = args.log_interval, 'iteration'
trainer.extend(extensions.LogReport(trigger=log_interval))
if args.segmentation and args.detection and args.dynamic_loss:
trainer.extend(
loss_split.LossSplit(trigger=(args.update_split_interval,
'iteration')))
trainer.extend(extensions.observe_lr(), trigger=log_interval)
trainer.extend(extensions.PrintReport([
'epoch', 'iteration', 'lr', 'main/loss', 'main/loss/mask',
'main/loss/loc', 'main/loss/conf', 'main/loss/split'
]),
trigger=log_interval)
trainer.extend(extensions.ProgressBar(update_interval=10))
trainer.extend(extensions.snapshot(),
trigger=triggers.ManualScheduleTrigger(
args.snap_step + [args.iteration], 'iteration'))
trainer.extend(extensions.snapshot_object(
model, 'model_iter_{.updater.iteration}'),
trigger=triggers.ManualScheduleTrigger(
args.snap_step + [args.iteration], 'iteration'))
if args.resume:
if 'model' in args.resume:
serializers.load_npz(args.resume, model)
else:
serializers.load_npz(args.resume, trainer)
print(args)
trainer.run()
def run(args):
if args.dataset == 'voc12':
dataset = VOCSemanticSegmentationDataset(split=args.chainer_eval_set, data_dir=args.dev_root)
outsize = None
elif args.dataset in ['adp_morph', 'adp_func']:
dataset = ADPSemanticSegmentationDataset(split=args.chainer_eval_set, data_dir=args.dev_root,
htt_type=args.dataset.split('_')[-1])
outsize = (1088, 1088)
elif args.dataset in ['deepglobe', 'deepglobe_balanced']:
dataset = DeepGlobeSemanticSegmentationDataset(split=args.chainer_eval_set, data_dir=args.dev_root,
is_balanced=args.dataset == 'deepglobe_balanced')
outsize = (2448, 2448)
else:
raise KeyError('Dataset %s not yet implemented' % args.dataset)
labels = [dataset.get_example_by_keys(i, (1,))[0] for i in range(len(dataset))]
preds = []
with tqdm(total=len(dataset)) as pbar:
for id in dataset.ids:
if args.dataset == 'voc12':
img_path = voc12.dataloader.get_img_path(id, args.dev_root)
elif args.dataset in ['adp_morph', 'adp_func']:
img_path = adp.dataloader.get_img_path(id, args.dev_root, args.split == 'evaluation')
elif args.dataset in ['deepglobe', 'deepglobe_balanced']:
img_path = deepglobe.dataloader.get_img_path(id, args.dev_root)
else:
raise KeyError('Dataset %s not yet implemented' % args.dataset)
cam_dict = np.load(os.path.join(args.cam_out_dir, id + '.npy'), allow_pickle=True).item()
if args.dataset == 'voc12':
cams = cam_dict['high_res']
cams = np.pad(cams, ((1, 0), (0, 0), (0, 0)), mode='constant', constant_values=args.cam_eval_thres)
keys = np.pad(cam_dict['keys'] + 1, (1, 0), mode='constant')
elif args.dataset in ['adp_morph', 'adp_func']:
keys = cam_dict['keys']
cams = cam_dict['high_res']
elif args.dataset in ['deepglobe', 'deepglobe_balanced']:
keys = cam_dict['keys']
cams = cam_dict['cam']
else:
raise KeyError('Dataset %s not yet implemented' % args.dataset)
cls_labels = np.argmax(cams, axis=0)
cls_labels = keys[cls_labels]
if outsize is not None:
cls_labels = cv2.resize(cls_labels, outsize, interpolation=cv2.INTER_NEAREST)
imageio.imsave(os.path.join(args.cam_clr_out_dir, id + '.png'), cls_labels.astype(np.uint8))
# Save with colour
rw_pred_clr = np.zeros(list(cls_labels.shape) + [3], dtype=np.uint8)
off = 0
for t in ['bg', 'fg']:
for i, c in enumerate(args.class_colours[t]):
for ch in range(3):
rw_pred_clr[:, :, ch] += c[ch] * np.uint8(cls_labels == (i + off))
off += len(args.class_colours[t])
imageio.imsave(os.path.join(args.cam_clr_out_dir, id + '.png'), rw_pred_clr)
# Save with colour, overlaid on original image
if args.dataset not in ['deepglobe', 'deepglobe_balanced']:
orig_img = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)
else:
orig_img = cv2.resize(cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB), rw_pred_clr.shape[:2])
if args.dataset in ['adp_morph', 'adp_func']:
rw_pred_clr = cv2.resize(rw_pred_clr, orig_img.shape[:2])
rw_pred_clr_over = np.uint8((1 - args.overlay_r) * np.float32(orig_img) +
args.overlay_r * np.float32(rw_pred_clr))
imageio.imsave(os.path.join(args.cam_clr_out_dir, id + '_overlay.png'), rw_pred_clr_over)
preds.append(cls_labels.copy())
pbar.update(1)
confusion = calc_semantic_segmentation_confusion(preds, labels)
gtj = confusion.sum(axis=1)
resj = confusion.sum(axis=0)
gtjresj = np.diag(confusion)
denominator = gtj + resj - gtjresj
iou = gtjresj / denominator
precision = gtjresj / resj
recall = gtjresj / gtj
miou = np.array([np.nanmean(iou)])
mprecision = np.array([np.nanmean(precision)])
mrecall = np.array([np.nanmean(recall)])
iou_data = np.concatenate((iou, miou), axis=0)
pr_data = np.concatenate((precision, mprecision), axis=0)
re_data = np.concatenate((recall, mrecall), axis=0)
data = np.column_stack((iou_data, pr_data, re_data))
if args.dataset in ['deepglobe', 'deepglobe_balanced']:
row_names = args.class_names['bg'] + args.class_names['fg'][:-1] + ['mean']
else:
row_names = args.class_names['bg'] + args.class_names['fg'] + ['mean']
df = pd.DataFrame(data, index=row_names, columns=['iou', 'precision', 'recall'])
df.to_csv(os.path.join(args.eval_dir, args.run_name + '_' + args.split + '_cam_iou.csv'), index=True)
with open(args.logfile, 'a') as f:
f.write('[eval_cam, ' + args.split + '] iou: ' + str(list(iou)) + '\n')
f.write('[eval_cam, ' + args.split + '] miou: ' + str(miou[0]) + '\n')
# args.logger.write('[eval_cam] iou: ' + iou + '\n')
# args.logger.write('[eval_cam] miou: ' + miou+ '\n')
def run(args):
if args.dataset == 'voc12':
dataset = VOCSemanticSegmentationDataset(split=args.chainer_eval_set,
data_dir=args.dev_root)
outsize = None
elif args.dataset in ['adp_morph', 'adp_func']:
dataset = ADPSemanticSegmentationDataset(
split=args.chainer_eval_set,
data_dir=args.dev_root,
htt_type=args.dataset.split('_')[-1])
outsize = (1088, 1088)
elif args.dataset in ['deepglobe', 'deepglobe_balanced']:
dataset = DeepGlobeSemanticSegmentationDataset(
split=args.chainer_eval_set,
data_dir=args.dev_root,
is_balanced=args.dataset == 'deepglobe_balanced')
outsize = (2448, 2448)
else:
raise KeyError('Dataset %s not yet implemented' % args.dataset)
labels = [
dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset))
]
preds = []
with tqdm(total=len(dataset)) as pbar:
for id in dataset.ids:
cls_labels = imageio.imread(
os.path.join(args.sem_seg_out_dir,
id + '.png')).astype(np.uint8)
cls_labels[cls_labels == 255] = 0
if outsize is not None:
cls_labels = cv2.resize(cls_labels,
outsize,
interpolation=cv2.INTER_NEAREST)
preds.append(cls_labels.copy())
pbar.update(1)
confusion = calc_semantic_segmentation_confusion(preds,
labels) #[:21, :21]
gtj = confusion.sum(axis=1)
resj = confusion.sum(axis=0)
gtjresj = np.diag(confusion)
denominator = gtj + resj - gtjresj
fp = 1. - gtj / denominator
fn = 1. - resj / denominator
iou = gtjresj / denominator
miou = np.array([np.nanmean(iou)])
print(fp[0], fn[0])
print(np.mean(fp[1:]), np.mean(fn[1:]))
data = np.concatenate((iou, miou), axis=0)
if args.dataset in ['deepglobe', 'deepglobe_balanced']:
row_names = args.class_names['bg'] + args.class_names['fg'][:-1] + [
'miou'
]
else:
row_names = args.class_names['bg'] + args.class_names['fg'] + ['miou']
df = pd.DataFrame(data, index=row_names, columns=['iou'])
df.to_csv(os.path.join(args.eval_dir,
args.run_name + '_' + args.split + '_iou.csv'),
index=True)
with open(args.logfile, 'a') as f:
f.write('[eval_sem_seg, ' + args.split + '] iou: ' + str(list(iou)) +
'\n')
f.write('[eval_sem_seg, ' + args.split + '] miou: ' + str(miou[0]) +
'\n')
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--device', type=int, default=-1, help='gpu id')
parser.add_argument('--lr_init',
type=float,
default=5 * 1e-5,
help='init learning rate')
# parser.add_argument('--lr_trigger', type=float, default=5, help='trigger to decreace learning rate')
# parser.add_argument('--lr_target', type=float, default=5*1e-5, help='target learning rate')
# parser.add_argument('--lr_factor', type=float, default=.75, help='decay factor')
parser.add_argument('--name',
type=str,
default='classifier',
help='name of the experiment')
parser.add_argument('--resume',
type=bool,
default=False,
help='resume training or not')
parser.add_argument('--snapshot',
type=str,
help='snapshot file of the trainer to resume from')
args = parser.parse_args()
resume = args.resume
device = args.device
if resume:
load_snapshot_path = args.snapshot
experiment = args.name
lr_init = args.lr_init
# lr_target = args.lr_target
# lr_factor = args.lr_factor
# lr_trigger_interval = (args.lr_trigger, 'epoch')
os.makedirs('result/' + experiment, exist_ok=True)
f = open('result/' + experiment + '/details.txt', "w+")
f.write("lr - " + str(lr_init) + "\n")
f.write("optimizer - " + str(Adam))
# f.write("lr_trigger_interval - "+str(lr_trigger_interval)+"\n")
f.close()
if not resume:
# Add the FC layers to original FCN for GAIN
model_own = FCN8s()
model_original = fcn.models.FCN8s()
model_file = fcn.models.FCN8s.download()
chainer.serializers.load_npz(model_file, model_original)
for layers in model_original._children:
setattr(model_own, layers, getattr(model_original, layers))
del (model_original, model_file)
else:
model_own = FCN8s()
if device >= 0:
model_own.to_gpu(device)
dataset = VOCSemanticSegmentationDataset()
iterator = SerialIterator(dataset, 1)
optimizer = Adam(alpha=lr_init)
optimizer.setup(model_own)
updater = VOC_ClassificationUpdater(iterator, optimizer, device=device)
trainer = Trainer(updater, (100, 'epoch'))
log_keys = ['epoch', 'iteration', 'main/Loss']
trainer.extend(
extensions.LogReport(log_keys, (100, 'iteration'),
log_name='log_' + experiment))
trainer.extend(extensions.PrintReport(log_keys),
trigger=(100, 'iteration'))
trainer.extend(extensions.snapshot(filename=experiment +
"_snapshot_{.updater.iteration}"),
trigger=(5, 'epoch'))
trainer.extend(extensions.snapshot_object(
trainer.updater._optimizers['main'].target,
experiment + "_model_{.updater.iteration}"),
trigger=(5, 'epoch'))
trainer.extend(
extensions.PlotReport(['main/Loss'],
'iteration', (100, 'iteration'),
file_name='trainer_' + experiment + '/loss.png',
grid=True,
marker=" "))
# trainer.extend(extensions.ExponentialShift('lr', lr_factor, target=lr_target), trigger=lr_trigger_interval)
if resume:
chainer.serializers.load_npz(load_snapshot_path, trainer)
print("Running - - ", experiment)
print('initial lr ', lr_init)
# print('lr_trigger_interval ', lr_trigger_interval)
trainer.run()
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--gpu', type=int, default=0, help='gpu id')
parser.add_argument('--modelfile', help='pretrained model file of FCN8')
parser.add_argument('--lr',
type=float,
default=1e-7,
help='init learning rate')
parser.add_argument('--name',
type=str,
default='exp',
help='name of the experiment')
parser.add_argument('--resume',
type=int,
default=0,
help='resume training or not')
parser.add_argument('--snapshot',
type=str,
help='snapshot file to resume from')
parser.add_argument('--lambda1',
default=5,
type=float,
help='lambda1 param')
parser.add_argument('--lambda2',
default=1,
type=float,
help='lambda2 param')
parser.add_argument('--lambda3',
default=1.5,
type=float,
help='lambda3 param')
args = parser.parse_args()
resume = args.resume
device = args.gpu
if resume:
load_snapshot_path = args.snapshot
load_model_path = args.modelfile
else:
pretrained_model_path = args.modelfile
experiment = args.name
lr = args.lr
optim = Adam
training_interval = (20000, 'iteration')
snapshot_interval = (1000, 'iteration')
lambd1 = args.lambda1
lambd2 = args.lambda2
lambd3 = args.lambda3
updtr = VOC_GAIN_Updater2
os.makedirs('result/' + experiment, exist_ok=True)
f = open('result/' + experiment + '/details.txt', "w+")
f.write("lr - " + str(lr) + "\n")
f.write("optimizer - " + str(optim) + "\n")
f.write("lambd1 - " + str(lambd1) + "\n")
f.write("lambd2 - " + str(lambd2) + "\n")
f.write("lambd3 - " + str(lambd3) + "\n")
f.write("training_interval - " + str(training_interval) + "\n")
f.write("Updater - " + str(updtr) + "\n")
f.close()
if resume:
model = FCN8s()
chainer.serializers.load_npz(load_model_path, model)
else:
model = FCN8s()
chainer.serializers.load_npz(pretrained_model_path, model)
if device >= 0:
model.to_gpu(device)
dataset = VOCSemanticSegmentationDataset()
iterator = SerialIterator(dataset, 1, shuffle=False)
optimizer = Adam(alpha=lr)
optimizer.setup(model)
updater = updtr(iterator,
optimizer,
device=device,
lambd1=lambd1,
lambd2=lambd2)
trainer = Trainer(updater, training_interval)
log_keys = [
'epoch', 'iteration', 'main/AM_Loss', 'main/CL_Loss', 'main/TotalLoss'
]
trainer.extend(
extensions.LogReport(log_keys, (10, 'iteration'),
log_name='log' + experiment))
trainer.extend(extensions.PrintReport(log_keys),
trigger=(100, 'iteration'))
trainer.extend(
extensions.ProgressBar(training_length=training_interval,
update_interval=100))
trainer.extend(extensions.snapshot(filename='snapshot' + experiment),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
trainer.updater._optimizers['main'].target, "model" + experiment),
trigger=snapshot_interval)
trainer.extend(
extensions.PlotReport(['main/AM_Loss'],
'iteration', (20, 'iteration'),
file_name=experiment + '/am_loss.png',
grid=True,
marker=" "))
trainer.extend(
extensions.PlotReport(['main/CL_Loss'],
'iteration', (20, 'iteration'),
file_name=experiment + '/cl_loss.png',
grid=True,
marker=" "))
trainer.extend(
extensions.PlotReport(['main/SG_Loss'],
'iteration', (20, 'iteration'),
file_name=experiment + '/sg_loss.png',
grid=True,
marker=" "))
trainer.extend(
extensions.PlotReport(['main/TotalLoss'],
'iteration', (20, 'iteration'),
file_name=experiment + '/total_loss.png',
grid=True,
marker=" "))
trainer.extend(
extensions.PlotReport(log_keys[2:],
'iteration', (20, 'iteration'),
file_name=experiment + '/all_loss.png',
grid=True,
marker=" "))
if resume:
chainer.serializers.load_npz(load_snapshot_path, trainer)
print("Running - - ", experiment)
print('initial lr ', lr)
print('optimizer ', optim)
print('lambd1 ', lambd1)
print('lambd2 ', lambd2)
print('lambd3', lambd3)
trainer.run()
from chainercv.visualizations import vis_image
from chainercv.visualizations import vis_label
import matplotlib.pyplot as plot
fig = plot.figure(figsize=(26, 10))
ax1 = fig.add_subplot(1, 2, 1)
plot.axis('off')
ax2 = fig.add_subplot(1, 2, 2)
plot.axis('off')
dataset = VOCDetectionDataset()
img, bbox, label = dataset[310]
vis_bbox(img, bbox, label,
label_names=voc_detection_label_names,
ax=ax1)
dataset = VOCSemanticSegmentationDataset()
img, label = dataset[30]
vis_image(img, ax=ax2)
_, legend_handles = vis_label(
label,
label_names=voc_semantic_segmentation_label_names,
label_colors=voc_semantic_segmentation_label_colors,
alpha=0.9, ax=ax2)
# ax2.legend(handles=legend_handles, bbox_to_anchor=(1, 1), loc=2)
plot.tight_layout()
plot.savefig('../images/vis_visualization.png')
plot.show()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model',
choices=('multi_task_300', 'multi_task_512'),
default='multi_task_300')
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--model_path', type=str)
parser.add_argument('--detection', action='store_true', default=False)
parser.add_argument('--segmentation', action='store_true', default=False)
parser.add_argument('--attention', action='store_true', default=False)
parser.add_argument('--dataset', default='voc', type=str)
parser.add_argument('--eval_seg', default=False, action='store_true')
parser.add_argument('--eval_det', default=False, action='store_true')
parser.add_argument('--batchsize', type=int, default=32)
args = parser.parse_args()
print(args)
if not (args.segmentation or args.detection):
raise RuntimeError
if not args.model_path:
raise RuntimeError
if args.model == 'multi_task_300':
model = Multi_task_300(n_fg_class=len(voc_bbox_label_names),
pretrained_model='imagenet',
detection=args.detection,
segmentation=args.segmentation,
attention=args.attention)
elif args.model == 'multi_task_512':
model = Multi_task_512(n_fg_class=len(voc_bbox_label_names),
pretrained_model='imagenet',
detection=args.detection,
segmentation=args.segmentation,
attention=args.attention)
model.use_preset('evaluate')
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
if args.dataset == 'voc':
use_07 = True
label_names = voc_bbox_label_names
elif args.dataset == 'coco':
label_names = coco_bbox_label_names
if args.model_path:
serializers.load_npz(args.model_path, model)
if args.detection and args.eval_det:
test = VOCBboxDataset(year='2007',
split='test',
use_difficult=True,
return_difficult=True)
test_iter = chainer.iterators.SerialIterator(test,
args.batchsize,
repeat=False,
shuffle=False)
det_evaluator = MultitaskEvaluator(test_iter,
model,
use_07_metric=use_07,
label_names=label_names,
detection=True)
result = det_evaluator()
print('detection result')
print(result)
if args.segmentation and args.eval_seg:
test_mask = VOCSemanticSegmentationDataset(split='val')
test_mask_iter = chainer.iterators.SerialIterator(test_mask,
args.batchsize,
repeat=False,
shuffle=False)
seg_evaluator = MultitaskEvaluator(test_mask_iter,
model,
use_07_metric=use_07,
label_names=label_names,
detection=False)
result_mask = seg_evaluator()
print('segmentation result')
print(result_mask)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--gpu', type=int, default=0, help='gpu id')
parser.add_argument('--modelfile', help='pretrained model file of FCN8')
parser.add_argument('--lr',
type=float,
default=5 * 1e-4,
help='init learning rate')
parser.add_argument('--name',
type=str,
default='exp',
help='init learning rate')
parser.add_argument('--resume',
type=int,
default=0,
help='resume training or not')
parser.add_argument('--snapshot',
type=str,
help='snapshot file to resume from')
args = parser.parse_args()
resume = args.resume
device = args.gpu
if resume:
load_snapshot_path = args.snapshot
load_model_path = args.modelfile
else:
load_model_path = args.modelfile
experiment = args.name
lr = args.lr
lr_trigger_interval = (5, 'epoch')
optim = Adam
os.makedirs('result/' + experiment, exist_ok=True)
f = open('result/' + experiment + '/details.txt', "w+")
f.write("lr - " + str(lr) + "\n")
f.write("optimizer - " + str(optim))
f.write("lr_trigger_interval - " + str(lr_trigger_interval) + "\n")
f.close()
if not resume:
# Add the FC layers to original FConvN for GAIN
model_own = FCN8s()
model_original = fcn.models.FCN8s()
model_file = fcn.models.FCN8s.download()
chainer.serializers.load_npz(model_file, model_original)
for layers in model_original._children:
setattr(model_own, layers, getattr(model_original, layers))
del (model_original, model_file)
else:
model_own = FCN8s()
chainer.serializers.load_npz(load_model_path, model_own)
if device >= 0:
model_own.to_gpu(device)
dataset = VOCSemanticSegmentationDataset()
iterator = SerialIterator(dataset, 1)
optimizer = Adam(alpha=lr)
optimizer.setup(model_own)
updater = VOC_ClassificationUpdater(iterator, optimizer, device=device)
trainer = Trainer(updater, (50, 'epoch'))
log_keys = ['epoch', 'iteration', 'main/Loss']
trainer.extend(
extensions.LogReport(log_keys, (100, 'iteration'),
log_name='log' + experiment))
trainer.extend(extensions.PrintReport(log_keys),
trigger=(100, 'iteration'))
trainer.extend(
extensions.ProgressBar(training_length=(50, 'epoch'),
update_interval=500))
trainer.extend(extensions.snapshot(filename='snapshot' + experiment),
trigger=(5, 'epoch'))
trainer.extend(extensions.snapshot_object(
trainer.updater._optimizers['main'].target, "model" + experiment),
trigger=(5, 'epoch'))
trainer.extend(
extensions.PlotReport(['main/Loss'],
'iteration', (100, 'iteration'),
file_name=experiment + '/loss.png',
grid=True,
marker=" "))
if resume:
chainer.serializers.load_npz(load_snapshot_path, trainer)
print("Running - - ", experiment)
print('initial lr ', lr)
print('lr_trigger_interval ', lr_trigger_interval)
trainer.run()
def main():
parser = argparse.ArgumentParser(
description='ChainerCV Semantic Segmentation example with FCN')
parser.add_argument('--batch_size',
'-b',
type=int,
default=1,
help='Number of images in each mini-batch')
parser.add_argument('--iteration',
'-i',
type=int,
default=50000,
help='Number of iteration to carry out')
parser.add_argument('--gpu',
'-g',
type=int,
default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--lr',
'-l',
type=float,
default=1e-10,
help='Learning rate of the optimizer')
parser.add_argument('--out',
'-o',
default='result',
help='Directory to output the result')
parser.add_argument('--resume',
'-r',
default='',
help='Resume the training from snapshot')
args = parser.parse_args()
print('GPU: {}'.format(args.gpu))
print('# Minibatch-size: {}'.format(args.batch_size))
print('# iteration: {}'.format(args.iteration))
print('')
batch_size = args.batch_size
iteration = args.iteration
gpu = args.gpu
lr = args.lr
out = args.out
resume = args.resume
# prepare datasets
def transform(in_data):
img, label = in_data
vgg_subtract_bgr = np.array([103.939, 116.779, 123.68],
np.float32)[:, None, None]
img -= vgg_subtract_bgr
img = transforms.pad(img, max_size=(512, 512), bg_value=0)
label = transforms.pad(label, max_size=(512, 512), bg_value=-1)
return img, label
train_data = VOCSemanticSegmentationDataset(mode='train')
test_data = VOCSemanticSegmentationDataset(mode='val')
train_data = TransformDataset(train_data, transform)
test_data = TransformDataset(test_data, transform)
# set up FCN32s
n_class = 21
model = FCN32s(n_class=n_class)
if gpu != -1:
model.to_gpu(gpu)
chainer.cuda.get_device(gpu).use()
# prepare an optimizer
optimizer = chainer.optimizers.MomentumSGD(lr=lr, momentum=0.99)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(rate=0.0005))
# prepare iterators
train_iter = chainer.iterators.SerialIterator(train_data,
batch_size=batch_size)
test_iter = chainer.iterators.SerialIterator(test_data,
batch_size=1,
repeat=False,
shuffle=False)
updater = training.StandardUpdater(train_iter, optimizer, device=gpu)
trainer = training.Trainer(updater, (iteration, 'iteration'), out=out)
val_interval = 3000, 'iteration'
log_interval = 100, 'iteration'
trainer.extend(TestModeEvaluator(test_iter, model, device=gpu),
trigger=val_interval)
# reporter related
trainer.extend(extensions.LogReport(trigger=log_interval))
trainer.extend(extensions.PrintReport([
'iteration', 'main/time', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'main/accuracy_cls',
'validation/main/accuracy_cls', 'main/iu', 'validation/main/iu',
'main/fwavacc', 'validation/main/fwavacc'
]),
trigger=log_interval)
trainer.extend(extensions.ProgressBar(update_interval=10))
# visualize training
trainer.extend(
extensions.PlotReport(['main/loss', 'validation/main/loss'],
trigger=log_interval,
file_name='loss.png'))
trainer.extend(
extensions.PlotReport(['main/accuracy', 'validation/main/accuracy'],
trigger=log_interval,
file_name='accuracy.png'))
trainer.extend(
extensions.PlotReport(
['main/accuracy_cls', 'validation/main/accuracy_cls'],
trigger=log_interval,
file_name='accuracy_cls.png'))
trainer.extend(
extensions.PlotReport(['main/iu', 'validation/main/iu'],
trigger=log_interval,
file_name='iu.png'))
trainer.extend(
extensions.PlotReport(['main/fwavacc', 'validation/main/fwavacc'],
trigger=log_interval,
file_name='fwavacc.png'))
def vis_transform(in_data):
vgg_subtract_bgr = np.array([103.939, 116.779, 123.68],
np.float32)[:, None, None]
img, label = in_data
img += vgg_subtract_bgr
img, label = transforms.chw_to_pil_image_tuple((img, label),
indices=[0, 1])
return img, label
trainer.extend(
SemanticSegmentationVisReport(
range(10), # visualize outputs for the first 10 data of test_data
test_data,
model,
n_class=n_class,
predict_func=model.predict, # a function to predict output
vis_transform=vis_transform),
trigger=val_interval,
invoke_before_training=True)
trainer.extend(extensions.dump_graph('main/loss'))
if resume:
chainer.serializers.load_npz(osp.expanduser(resume), trainer)
trainer.run()
continue
if x.shape[i] == dim:
start = 0
elif x.shape[i] > dim:
start = random.choice(range(x.shape[i] - dim))
else:
raise ValueError('shape of image is larger than output_shape')
slices.append(slice(start, start + dim))
slices = tuple(slices)
outs = []
for x in xs:
outs.append(x[slices])
if force_array:
outs = outs[0]
else:
outs = tuple(outs)
if return_slices:
return outs, slices
else:
return outs
if __name__ == '__main__':
from chainercv.datasets import VOCSemanticSegmentationDataset
dataset = VOCSemanticSegmentationDataset()
img, label = dataset.get_example(0)
img, label = random_crop((img, label), (None, 256, 256))
def main(gpu=-1, batch_size=1, iterations=100000,
lr=1e-10, out='result', resume=''):
# prepare datasets
def transform(in_data):
img, label = in_data
vgg_subtract_bgr = np.array(
[103.939, 116.779, 123.68], np.float32)[:, None, None]
img -= vgg_subtract_bgr
img = pad(img, max_size=(512, 512), bg_value=0)
label = pad(label, max_size=(512, 512), bg_value=-1)
return img, label
train_data = VOCSemanticSegmentationDataset(mode='train')
test_data = VOCSemanticSegmentationDataset(mode='val')
extend(train_data, transform)
extend(test_data, transform)
# set up FCN32s
n_class = 21
model = FCN32s(n_class=n_class)
if gpu != -1:
model.to_gpu(gpu)
chainer.cuda.get_device(gpu).use()
# prepare an optimizer
optimizer = chainer.optimizers.MomentumSGD(lr=lr, momentum=0.99)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(rate=0.0005))
# prepare iterators
train_iter = chainer.iterators.SerialIterator(
train_data, batch_size=batch_size)
test_iter = chainer.iterators.SerialIterator(
test_data, batch_size=1, repeat=False, shuffle=False)
updater = training.StandardUpdater(train_iter, optimizer, device=gpu)
trainer = training.Trainer(updater, (iterations, 'iteration'), out=out)
val_interval = 3000, 'iteration'
log_interval = 100, 'iteration'
trainer.extend(
TestModeEvaluator(test_iter, model, device=gpu), trigger=val_interval)
# reporter related
trainer.extend(extensions.LogReport(trigger=log_interval))
trainer.extend(extensions.PrintReport(
['iteration', 'main/time',
'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy',
'main/accuracy_cls', 'validation/main/accuracy_cls',
'main/iu', 'validation/main/iu',
'main/fwavacc', 'validation/main/fwavacc']),
trigger=log_interval)
trainer.extend(extensions.ProgressBar(update_interval=10))
# visualize training
trainer.extend(
extensions.PlotReport(
['main/loss', 'validation/main/loss'],
trigger=log_interval, file_name='loss.png')
)
trainer.extend(
extensions.PlotReport(
['main/accuracy', 'validation/main/accuracy'],
trigger=log_interval, file_name='accuracy.png')
)
trainer.extend(
extensions.PlotReport(
['main/accuracy_cls', 'validation/main/accuracy_cls'],
trigger=log_interval, file_name='accuracy_cls.png')
)
trainer.extend(
extensions.PlotReport(
['main/iu', 'validation/main/iu'],
trigger=log_interval, file_name='iu.png')
)
trainer.extend(
extensions.PlotReport(
['main/fwavacc', 'validation/main/fwavacc'],
trigger=log_interval, file_name='fwavacc.png')
)
trainer.extend(
SemanticSegmentationVisReport(
range(10), # visualize outputs for the first 10 data of test_data
test_data,
model,
n_class=n_class,
predict_func=model.extract # use FCN32s.extract to get a score map
),
trigger=val_interval, invoke_before_training=True)
trainer.extend(extensions.dump_graph('main/loss'))
if resume:
chainer.serializers.load_npz(osp.expanduser(resume), trainer)
trainer.run()
from config import *
import numpy as np
import os
from chainercv.datasets import VOCSemanticSegmentationDataset
from chainercv.evaluations import calc_semantic_segmentation_confusion
from PIL import Image
import cv2
rgb_dict = [[0, 0, 0]] + [[255, 0, 0], [0, 255, 0], [0, 0, 255], [255, 255, 0],
[255, 0, 255], [0, 255, 255]] * 20
# rgb_dict = [[0, 0, 0]] + [[0, 0, 0], [0, 255, 0], [0, 0, 0], [255, 255, 0], [ 255, 0, 255], [0, 255,255]]*20
if __name__ == "__main__":
dataset = VOCSemanticSegmentationDataset(split=chainer_eval_set,
data_dir=voc12_root)
labels = [
dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset))
]
preds = []
for idx, id in enumerate(dataset.ids):
cam_dict = np.load(os.path.join(cam_out_dir, id + '.npy'),
allow_pickle=True).item()
cams = cam_dict['high_res']
cams[:2, :] = 0
cams[3:, :] = 0
cams = np.pad(cams, ((1, 0), (0, 0), (0, 0)),
mode='constant',
constant_values=cam_eval_thres) # 添加背景的阈值
keys = np.pad(cam_dict['keys'] + 1, (1, 0), mode='constant') # 添加背景
cls_labels = np.argmax(cams, axis=0)
cls_labels = keys[cls_labels]