def __init__(self, conf, model, dataset, imgdir=None):
self.conf = conf
self.model = model
if imgdir is None:
self.imgdir = os.path.join(model.logdir, "images")
else:
self.imgdir = imgdir
if not os.path.exists(self.imgdir):
os.mkdir(self.imgdir)
loader = self.model.get_loader()
assert torch.cuda.device_count() > 0
self.bs = torch.cuda.device_count()
self.name = "Test"
self.display_iter = 10
class_file = self.model.trainer.loader.dataset.vis_file
self.vis = visualizer.LocalSegVisualizer(
class_file, conf=conf['dataset'])
self.loader = loader.get_data_loader(
conf['dataset'], split="test",
batch_size=torch.cuda.device_count(),
shuffle=False, do_augmentation=False, dataset=dataset)
def __init__(self, conf, model, subsample=None,
name='', split=None, imgdir=None, do_augmentation=False):
self.model = model
self.conf = conf
self.name = name
self.imgdir = imgdir
self.label_coder = self.model.label_coder
if split is None:
split = 'val'
loader = self.model.get_loader()
batch_size = conf['training']['batch_size']
if split == 'val' and batch_size > 8:
batch_size = 8
if conf['evaluation']['reduce_val_bs']:
batch_size = torch.cuda.device_count()
subsampler = partial(
sampler.SubSampler, subsample=subsample)
if subsample is not None:
self.subsample = subsample
else:
self.subsample = 1
self.loader = loader.get_data_loader(
conf['dataset'], split=split, batch_size=batch_size,
sampler=subsampler, do_augmentation=do_augmentation,
pin_memory=False)
self.minor_iter = max(
1, len(self.loader) // conf['evaluation']['num_minor_imgs'])
class_file = self.loader.dataset.vis_file
self.vis = visualizer.LocalSegVisualizer(
class_file, conf=conf['dataset'], label_coder=self.label_coder)
self.binvis = BinarySegVisualizer()
self.bs = batch_size
self.num_step = len(self.loader)
self.count = range(1, len(self.loader) + 5)
self.names = None
self.num_classes = self.loader.dataset.num_classes
self.ignore_idx = -100
self.display_iter = max(
1, len(self.loader) // self.conf['logging']['disp_per_epoch'])
self.smoother = pyvision.utils.MedianSmoother(20)
self.threeDFiles = {}
def __init__(self, conf, model, data_file, max_examples=None,
name='', split=None, imgdir=None):
self.model = model
self.conf = conf
self.name = name
self.imgdir = imgdir
self.imgs_minor = conf['evaluation']['imgs_minor']
self.label_coder = self.model.label_coder
if split is None:
split = 'val'
loader = self.model.get_loader()
batch_size = conf['training']['batch_size']
if split == 'val' and batch_size > 8:
batch_size = 8
if split == 'val' and conf['evaluation']['reduce_val_bs']:
batch_size = conf['training']['num_gpus']
self.loader = loader.get_data_loader(
conf['dataset'], split=split, batch_size=batch_size,
lst_file=data_file, shuffle=False, pin_memory=False)
class_file = conf['dataset']['vis_file']
self.vis = visualizer.LocalSegVisualizer(
class_file, conf=conf['dataset'], label_coder=self.label_coder)
self.bs = batch_size
if max_examples is None:
self.num_step = len(self.loader)
self.count = range(1, len(self.loader) + 5)
else:
max_iter = max_examples // self.bs + 1
self.count = range(1, max_iter + 1)
self.num_step = max_iter
self.names = None
self.num_classes = self.loader.dataset.num_classes
self.ignore_idx = -100
self.display_iter = conf['logging']['display_iter']
self.smoother = pyvision.utils.MedianSmoother(20)
def __init__(self, conf, model, data_file, max_examples=None,
name='', split=None, imgdir=None):
self.model = model
self.conf = conf
self.name = name
self.imgdir = imgdir
self.split = split
self.imgs_minor = conf['evaluation']['imgs_minor']
self.label_coder = self.model.label_coder
if split is None:
split = 'val'
loader = loader_p4
batch_size = conf['training']['batch_size']
if split == 'val' and batch_size > 8:
batch_size = 8
if split == 'val' and conf['evaluation']['reduce_val_bs']:
batch_size = 1
conf['dataset']['transform']['fix_shape'] = False
config = conf['dataset']
# config['val_file'] = 'datasets/camvid3d_p4_one.lst'
config['val_file'] = 'datasets/camvid_p4_full.lst'
config['vis_file'] = 'datasets/camvid360_classes.lst'
config['num_worker'] = 0
config['ignore_label'] = 0
config['idx_offset'] = 1
config['num_classes'] = 308
self.loader = loader.get_data_loader(
config, split='val', batch_size=batch_size,
lst_file=config['val_file'], shuffle=False)
self.loader.dataset.colour_aug = False
self.loader.dataset.shape_aug = False
class_file = conf['dataset']['vis_file']
self.vis = visualizer.LocalSegVisualizer(
class_file, conf=conf['dataset'], label_coder=self.label_coder)
self.bs = batch_size
if max_examples is None:
self.num_step = len(self.loader)
self.count = range(1, len(self.loader) + 5)
else:
max_iter = max_examples // self.bs + 1
self.count = range(1, max_iter + 1)
self.num_step = max_iter
self.names = None
self.num_classes = self.loader.dataset.num_classes
self.ignore_idx = -100
self.display_iter = conf['logging']['display_iter']
self.smoother = pyvision.utils.MedianSmoother(20)