def eval_epoch(
epoch: int,
model: BaseModel,
dataset,
device,
tracker: BaseTracker,
checkpoint: ModelCheckpoint,
visualizer: Visualizer,
early_break: bool,
):
model.eval()
tracker.reset("val")
visualizer.reset(epoch, "val")
loader = dataset.val_dataloader()
with Ctq(loader) as tq_val_loader:
for data in tq_val_loader:
data = data.to(device)
with torch.no_grad():
model.set_input(data)
model.forward()
tracker.track(model)
tq_val_loader.set_postfix(**tracker.get_metrics(),
color=COLORS.VAL_COLOR)
if visualizer.is_active:
visualizer.save_visuals(model.get_current_visuals())
if early_break:
break
metrics = tracker.publish(epoch)
tracker.print_summary()
checkpoint.save_best_models_under_current_metrics(model, metrics)
def eval_epoch(model: BaseModel, dataset, device, tracker: BaseTracker, checkpoint: ModelCheckpoint):
tracker.reset("val")
loader = dataset.val_dataloader
with Ctq(loader) as tq_val_loader:
for data in tq_val_loader:
with torch.no_grad():
model.set_input(data, device)
model.forward()
tracker.track(model)
tq_val_loader.set_postfix(**tracker.get_metrics(), color=COLORS.VAL_COLOR)
tracker.print_summary()
def test_epoch(model: BaseModel, dataset, device, tracker: BaseTracker, checkpoint: ModelCheckpoint):
tracker.reset("test")
loader = dataset.test_dataloader()
with Ctq(loader) as tq_test_loader:
for data in tq_test_loader:
data = data.to(device)
with torch.no_grad():
model.set_input(data)
model.forward()
tracker.track(model)
tq_test_loader.set_postfix(**tracker.get_metrics(), color=COLORS.TEST_COLOR)
tracker.print_summary()
def run(model: BaseModel, dataset: BaseDataset, device, output_path, cfg):
# Set dataloaders
num_fragment = dataset.num_fragment
if cfg.data.is_patch:
for i in range(num_fragment):
dataset.set_patches(i)
dataset.create_dataloaders(
model,
cfg.batch_size,
False,
cfg.num_workers,
False,
)
loader = dataset.test_dataloaders()[0]
features = []
scene_name, pc_name = dataset.get_name(i)
with Ctq(loader) as tq_test_loader:
for data in tq_test_loader:
# pcd = open3d.geometry.PointCloud()
# pcd.points = open3d.utility.Vector3dVector(data.pos[0].numpy())
# open3d.visualization.draw_geometries([pcd])
with torch.no_grad():
model.set_input(data, device)
model.forward()
features.append(model.get_output().cpu())
features = torch.cat(features, 0).numpy()
log.info("save {} from {} in {}".format(pc_name, scene_name,
output_path))
save(output_path, scene_name, pc_name,
dataset.base_dataset[i].to("cpu"), features)
else:
dataset.create_dataloaders(
model,
1,
False,
cfg.num_workers,
False,
)
loader = dataset.test_dataloaders()[0]
with Ctq(loader) as tq_test_loader:
for i, data in enumerate(tq_test_loader):
with torch.no_grad():
model.set_input(data, device)
model.forward()
features = model.get_output()[0] # batch of 1
save(output_path, scene_name, pc_name, data.to("cpu"),
features)
def run(model: BaseModel, dataset: BaseDataset, device, output_path):
loaders = dataset.test_dataloaders
predicted = {}
for loader in loaders:
loader.dataset.name
with Ctq(loader) as tq_test_loader:
for data in tq_test_loader:
with torch.no_grad():
model.set_input(data, device)
model.forward()
predicted = {
**predicted,
**dataset.predict_original_samples(data, model.conv_type,
model.get_output())
}
save(output_path, predicted)
def test_epoch(model: BaseModel, dataset, device, tracker: BaseTracker, checkpoint: ModelCheckpoint):
loaders = dataset.test_dataloaders
for loader in loaders:
stage_name = loader.dataset.name
tracker.reset(stage_name)
with Ctq(loader) as tq_test_loader:
for data in tq_test_loader:
with torch.no_grad():
model.set_input(data, device)
model.forward()
tracker.track(model)
tq_test_loader.set_postfix(**tracker.get_metrics(), color=COLORS.TEST_COLOR)
tracker.print_summary()
def run(model: BaseModel, dataset: BaseDataset, device, output_path):
loaders = dataset.test_dataloaders()
predicted = {}
for idx, loader in enumerate(loaders):
dataset.get_test_dataset_name(idx)
with Ctq(loader) as tq_test_loader:
for data in tq_test_loader:
data = data.to(device)
with torch.no_grad():
model.set_input(data)
model.forward()
predicted = {
**predicted,
**dataset.predict_original_samples(data, model.conv_type,
model.get_output())
}
save(output_path, predicted)
def eval_epoch(model: BaseModel, dataset, device, tracker: BaseTracker,
checkpoint: ModelCheckpoint, log):
model.eval()
tracker.reset("val")
loader = dataset.val_dataloader()
with Ctq(loader) as tq_val_loader:
for data in tq_val_loader:
data = data.to(device)
with torch.no_grad():
model.set_input(data)
model.forward()
tracker.track(model)
tq_val_loader.set_postfix(**tracker.get_metrics(),
color=COLORS.VAL_COLOR)
metrics = tracker.publish()
tracker.print_summary()
checkpoint.save_best_models_under_current_metrics(model, metrics)
def test_epoch(
epoch: int,
model: BaseModel,
dataset,
device,
tracker: BaseTracker,
checkpoint: ModelCheckpoint,
visualizer: Visualizer,
early_break: bool,
):
model.eval()
loaders = dataset.test_dataloaders()
for idx, loader in enumerate(loaders):
stage_name = dataset.get_test_dataset_name(idx)
tracker.reset(stage_name)
visualizer.reset(epoch, stage_name)
with Ctq(loader) as tq_test_loader:
for data in tq_test_loader:
data = data.to(device)
with torch.no_grad():
model.set_input(data)
model.forward()
tracker.track(model)
tq_test_loader.set_postfix(**tracker.get_metrics(),
color=COLORS.TEST_COLOR)
if visualizer.is_active:
visualizer.save_visuals(model.get_current_visuals())
if early_break:
break
metrics = tracker.publish(epoch)
tracker.print_summary()
checkpoint.save_best_models_under_current_metrics(model, metrics)
