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 test_epoch(
model: BaseModel,
dataset,
device,
tracker: BaseTracker,
checkpoint: ModelCheckpoint,
voting_runs=1,
tracker_options={},
):
loaders = dataset.test_dataloaders
for loader in loaders:
stage_name = loader.dataset.name
tracker.reset(stage_name)
for i in range(voting_runs):
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, **tracker_options)
tq_test_loader.set_postfix(**tracker.get_metrics(),
color=COLORS.TEST_COLOR)
tracker.finalise(**tracker_options)
tracker.print_summary()
def run_epoch(model: BaseModel, loader, device: str, num_batches: int):
model.eval()
with Ctq(loader) as tq_loader:
for batch_idx, data in enumerate(tq_loader):
if batch_idx < num_batches:
process(model, data, device)
else:
break
def run(model: BaseModel, dataset: BaseDataset, device, cfg):
dataset.create_dataloaders(
model,
1,
False,
cfg.training.num_workers,
False,
)
loader = dataset.test_dataloaders[0]
list_res = []
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()
name_scene, name_pair_source, name_pair_target = dataset.test_dataset[
0].get_name(i)
input, input_target = model.get_input()
xyz, xyz_target = input.pos, input_target.pos
ind, ind_target = input.ind, input_target.ind
matches_gt = torch.stack([ind, ind_target]).transpose(0, 1)
feat, feat_target = model.get_output()
rand = torch.randperm(len(feat))[:cfg.data.num_points]
rand_target = torch.randperm(
len(feat_target))[:cfg.data.num_points]
res = dict(name_scene=name_scene,
name_pair_source=name_pair_source,
name_pair_target=name_pair_target)
T_gt = estimate_transfo(xyz[matches_gt[:, 0]],
xyz_target[matches_gt[:, 1]])
metric = compute_metrics(
xyz[rand],
xyz_target[rand_target],
feat[rand],
feat_target[rand_target],
T_gt,
sym=cfg.data.sym,
tau_1=cfg.data.tau_1,
tau_2=cfg.data.tau_2,
rot_thresh=cfg.data.rot_thresh,
trans_thresh=cfg.data.trans_thresh,
use_ransac=cfg.data.use_ransac,
ransac_thresh=cfg.data.first_subsampling,
use_teaser=cfg.data.use_teaser,
noise_bound_teaser=cfg.data.noise_bound_teaser,
)
res = dict(**res, **metric)
list_res.append(res)
df = pd.DataFrame(list_res)
output_path = os.path.join(cfg.training.checkpoint_dir, cfg.data.name,
"matches")
if not os.path.exists(output_path):
os.makedirs(output_path, exist_ok=True)
df.to_csv(osp.join(output_path, "final_res.csv"))
print(df.groupby("name_scene").mean())
def train_epoch(
epoch: int,
model: BaseModel,
dataset,
device: str,
tracker: BaseTracker,
checkpoint: ModelCheckpoint,
visualizer: Visualizer,
debugging,
):
early_break = getattr(debugging, "early_break", False)
profiling = getattr(debugging, "profiling", False)
model.train()
tracker.reset("train")
visualizer.reset(epoch, "train")
train_loader = dataset.train_dataloader
iter_data_time = time.time()
with Ctq(train_loader) as tq_train_loader:
for i, data in enumerate(tq_train_loader):
model.set_input(data, device)
t_data = time.time() - iter_data_time
iter_start_time = time.time()
model.optimize_parameters(epoch, dataset.batch_size)
if i % 10 == 0:
tracker.track(model)
tq_train_loader.set_postfix(**tracker.get_metrics(),
data_loading=float(t_data),
iteration=float(time.time() -
iter_start_time),
color=COLORS.TRAIN_COLOR)
if visualizer.is_active:
visualizer.save_visuals(model.get_current_visuals())
iter_data_time = time.time()
if early_break:
break
if profiling:
if i > getattr(debugging, "num_batches", 50):
return 0
metrics = tracker.publish(epoch)
checkpoint.save_best_models_under_current_metrics(model, metrics,
tracker.metric_func)
log.info("Learning rate = %f" % model.learning_rate)
def _test_epoch(self, epoch, stage_name: str):
voting_runs = self._cfg.get("voting_runs", 1)
if stage_name == "test":
loaders = self._dataset.test_dataloaders
else:
loaders = [self._dataset.val_dataloader]
self._model.eval()
if self.enable_dropout:
self._model.enable_dropout_in_eval()
for loader in loaders:
stage_name = loader.dataset.name
self._tracker.reset(stage_name)
if self.has_visualization:
self._visualizer.reset(epoch, stage_name)
if not self._dataset.has_labels(
stage_name) and not self.tracker_options.get(
"make_submission",
False): # No label, no submission -> do nothing
log.warning("No forward will be run on dataset %s." %
stage_name)
continue
for i in range(voting_runs):
with Ctq(loader) as tq_loader:
for data in tq_loader:
with torch.no_grad():
self._model.set_input(data, self._device)
with torch.cuda.amp.autocast(
enabled=self._model.is_mixed_precision()):
self._model.forward(epoch=epoch)
self._tracker.track(self._model,
data=data,
**self.tracker_options)
tq_loader.set_postfix(**self._tracker.get_metrics(),
color=COLORS.TEST_COLOR)
if self.has_visualization and self._visualizer.is_active:
self._visualizer.save_visuals(
self._model.get_current_visuals())
if self.early_break:
break
if self.profiling:
if i > self.num_batches:
return 0
self._finalize_epoch(epoch)
self._tracker.print_summary()
def _train_epoch(self, epoch: int):
self._model.train()
self._tracker.reset("train")
self._visualizer.reset(epoch, "train")
train_loader = self._dataset.train_dataloader
with self.profiler_profile(epoch) as prof:
iter_data_time = time.time()
with Ctq(train_loader) as tq_train_loader:
for i, data in enumerate(tq_train_loader):
t_data = time.time() - iter_data_time
iter_start_time = time.time()
with self.profiler_record_function('train_step'):
self._model.set_input(data, self._device)
self._model.optimize_parameters(
epoch, self._dataset.batch_size)
with self.profiler_record_function('track/log/visualize'):
if i % 10 == 0:
with torch.no_grad():
self._tracker.track(self._model,
data=data,
**self.tracker_options)
tq_train_loader.set_postfix(
**self._tracker.get_metrics(),
data_loading=float(t_data),
iteration=float(time.time() - iter_start_time),
color=COLORS.TRAIN_COLOR)
if self._visualizer.is_active:
self._visualizer.save_visuals(
self._model.get_current_visuals())
iter_data_time = time.time()
if self.pytorch_profiler_log:
prof.step()
if self.early_break:
break
if self.profiling:
if i > self.num_batches:
return 0
self._finalize_epoch(epoch)
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 val_one_epoch(self, epoch):
self.model.eval()
self.metrics.reset()
with Ctq(self.dataset.val_loader) as tq_loader:
for i, data in enumerate(tq_loader):
tq_loader.set_description('Val epoch[{}]'.format(epoch))
data = data.to(self.device)
y_target = data.y.reshape(-1) - 1
with torch.no_grad():
y_pred = self.model(data)
loss = F.cross_entropy(y_pred,
y_target,
weight=self.cfg.class_weights.to(
self.device),
ignore_index=self.cfg.ignore_index)
tq_loader.set_postfix(loss=loss.item())
self.metrics.update(y_target.cpu().numpy(),
y_pred.max(dim=1)[1].cpu().numpy())
def test_epoch(device):
model.to(device)
model.eval()
tracker.reset("test")
test_loader = dataset.test_dataloaders[0]
iter_data_time = time.time()
with Ctq(test_loader) as tq_test_loader:
for i, data in enumerate(tq_test_loader):
t_data = time.time() - iter_data_time
iter_start_time = time.time()
data.to(device)
model.forward(data)
tracker.track(model)
tq_test_loader.set_postfix(
**tracker.get_metrics(),
data_loading=float(t_data),
iteration=float(time.time() - iter_start_time),
)
iter_data_time = time.time()
def train_one_epoch(self, epoch):
self.model.train()
self.metrics.reset()
with Ctq(self.dataset.train_loader) as tq_loader:
for i, data in enumerate(tq_loader):
tq_loader.set_description('Train epoch[{}]'.format(epoch))
data = data.to(self.device)
self.optimizer.zero_grad()
y_pred = self.model(data)
y_target = data.y.reshape(-1) - 1
loss = F.cross_entropy(y_pred,
y_target,
weight=self.cfg.class_weights.to(
self.device),
ignore_index=self.cfg.ignore_index)
loss.backward()
self.optimizer.step()
tq_loader.set_postfix(loss=loss.item())
self.metrics.update(y_target.cpu().numpy(),
y_pred.max(dim=1)[1].cpu().numpy())
def test_epoch(
epoch: int,
model: BaseModel,
dataset,
device,
tracker: BaseTracker,
checkpoint: ModelCheckpoint,
visualizer: Visualizer,
debugging,
):
early_break = getattr(debugging, "early_break", False)
model.eval()
loaders = dataset.test_dataloaders
for loader in loaders:
stage_name = loader.dataset.name
tracker.reset(stage_name)
visualizer.reset(epoch, 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)
if visualizer.is_active:
visualizer.save_visuals(model.get_current_visuals())
if early_break:
break
tracker.finalise()
metrics = tracker.publish(epoch)
tracker.print_summary()
checkpoint.save_best_models_under_current_metrics(
model, metrics, tracker.metric_func)
def eval_epoch(
epoch: int,
model: BaseModel,
dataset,
device,
tracker: BaseTracker,
checkpoint: ModelCheckpoint,
visualizer: Visualizer,
debugging,
):
early_break = getattr(debugging, "early_break", False)
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:
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)
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,
tracker.metric_func)
def train_epoch(device):
model.to(device)
model.train()
tracker.reset("train")
train_loader = dataset.train_dataloader
iter_data_time = time.time()
with Ctq(train_loader) as tq_train_loader:
for i, data in enumerate(tq_train_loader):
t_data = time.time() - iter_data_time
iter_start_time = time.time()
optimizer.zero_grad()
data.to(device)
model.forward(data)
model.backward()
optimizer.step()
if i % 10 == 0:
tracker.track(model)
tq_train_loader.set_postfix(
**tracker.get_metrics(),
data_loading=float(t_data),
iteration=float(time.time() - iter_start_time),
)
iter_data_time = time.time()
def eval_epoch(
model: BaseModel,
dataset,
device,
tracker: BaseTracker,
checkpoint: ModelCheckpoint,
voting_runs=1,
tracker_options={},
):
tracker.reset("val")
loader = dataset.val_dataloader
for i in range(voting_runs):
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, **tracker_options)
tq_val_loader.set_postfix(**tracker.get_metrics(),
color=COLORS.VAL_COLOR)
tracker.finalise(**tracker_options)
tracker.print_summary()
def test(self, num_votes=100):
logging.info('Test {} on {} ...'.format(self.cfg.model_name,
self.cfg.dataset))
test_smooth = 0.98
saving_path = 'results/Semantic3D/predictions'
os.makedirs(saving_path) if not os.path.exists(saving_path) else None
# load model checkpoints
self.model.load(
'checkpoints/PointConvBig_on_Semantic3D_bs_8_epochs_100_big_crf.ckpt'
)
self.model.to(self.device)
self.model.eval()
epoch = 0
last_min = -0.5
while last_min < num_votes:
# test one epoch
with Ctq(self.dataset.val_loader) as tq_loader:
for i, data in enumerate(tq_loader):
tq_loader.set_description('Evaluation')
# model inference
data = data.to(self.device)
with torch.no_grad():
probs = F.softmax(self.model(data),
dim=-1) # get pred probs
# running means for each epoch on Test set
point_idx = data.point_idx.cpu().numpy() # the point idx
cloud_idx = data.cloud_idx.cpu().numpy() # the cloud idx
probs = probs.reshape(
self.cfg.batch_size, -1,
self.cfg.num_classes).cpu().numpy() # [B, N, C]
for b in range(
self.cfg.batch_size): # for each sample in batch
prob = probs[b, :, :] # [N, C]
p_idx = point_idx[b, :] # [N]
c_idx = cloud_idx[b][0] # int
self.test_probs[c_idx][p_idx] = test_smooth * self.test_probs[c_idx][p_idx] \
+ (1 - test_smooth) * prob # running means
# after each epoch
new_min = np.min(self.dataset.val_set.min_possibility)
print('Epoch {:3d} end, current min possibility = {:.2f}'.format(
epoch, new_min))
if last_min + 4 < new_min:
print('Test procedure done, saving predicted clouds ...')
last_min = new_min
# projection prediction to original point cloud
t1 = time.time()
for i, file in enumerate(self.dataset.val_set.val_files):
proj_idx = self.dataset.val_set.test_proj[
i] # already get the shape
probs = self.test_probs[i][
proj_idx, :] # same shape with proj_idx
# [0 ~ 7] + 1 -> [1 ~ 8], because 0 for unlabeled
preds = np.argmax(probs, axis=1).astype(np.uint8) + 1
# saving prediction results
cloud_name = file.split('/')[-1]
# ascii_name = os.path.join(saving_path, self.dataset.test_set.ascii_files[cloud_name])
# np.savetxt(ascii_name, preds, fmt='%d')
# print('Save {:s} succeed !'.format(ascii_name))
filename = os.path.join(saving_path, cloud_name)
write_ply(filename, [preds], ['pred'])
print('Save {:s} succeed !'.format(filename))
t2 = time.time()
print('Done in {:.2f} s.'.format(t2 - t1))
return
epoch += 1
return
def test_s3dis(self, num_votes=100):
logging.info('Evaluating {} on {} ...'.format(self.cfg.model_name,
self.cfg.dataset))
test_smooth = 0.95
# statistic label proportions in test set
class_proportions = np.zeros(self.cfg.num_classes, dtype=np.float32)
for i, label in enumerate(self.dataset.test_set.label_values):
class_proportions[i] = np.sum([
np.sum(labels == label)
for labels in self.dataset.test_set.val_labels
])
# load model checkpoints
self.model.load(
'checkpoints/RandLANet_on_S3DIS_bs_8_epochs_100_big.ckpt')
self.model.to(self.device)
self.model.eval()
epoch = 0
last_min = -0.5
while last_min < num_votes:
# test one epoch
with Ctq(self.dataset.test_loader) as tq_loader:
for i, data in enumerate(tq_loader):
tq_loader.set_description('Evaluation')
# model inference
data = data.to(self.device)
with torch.no_grad():
logits = self.model(data) # get pred
y_pred = F.softmax(logits, dim=-1)
y_pred = y_pred.cpu().numpy()
y_target = data.y.cpu().numpy().reshape(-1) # get target
point_idx = data.point_idx.cpu().numpy() # the point idx
cloud_idx = data.cloud_idx.cpu().numpy() # the cloud idx
# compute batch accuracy
correct = np.sum(np.argmax(y_pred, axis=1) == y_target)
acc = correct / float(np.prod(
np.shape(y_target))) # accurate for each test batch
tq_loader.set_postfix(ACC=acc)
y_pred = y_pred.reshape(self.cfg.batch_size, -1,
self.cfg.num_classes) # [B, N, C]
for b in range(
self.cfg.batch_size): # for each sample in batch
probs = y_pred[b, :, :] # [N, C]
p_idx = point_idx[b, :] # [N]
c_idx = cloud_idx[b][0] # int
self.test_probs[c_idx][p_idx] = test_smooth * self.test_probs[c_idx][p_idx] \
+ (1 - test_smooth) * probs # running means
new_min = np.min(self.dataset.test_set.min_possibility)
print('Epoch {:3d} end, current min possibility = {:.2f}'.format(
epoch, new_min))
if last_min + 1 < new_min:
# update last_min
last_min += 1
# show vote results
print('Confusion on sub clouds.')
confusion_list = []
num_clouds = len(
self.dataset.test_set.input_labels) # test cloud number
for i in range(num_clouds):
probs = self.test_probs[i]
preds = self.dataset.test_set.label_values[np.argmax(
probs, axis=1)].astype(np.int32)
labels = self.dataset.test_set.input_labels[i]
confusion_list += [
confusion_matrix(labels, preds,
self.dataset.test_set.label_values)
]
# re-group confusions
C = np.sum(np.stack(confusion_list), axis=0).astype(np.float32)
# re-scale with the right number of point per class
C *= np.expand_dims(
class_proportions / (np.sum(C, axis=1) + 1e-6), 1)
# compute IoU
IoUs = self._iou_from_confusions(C)
m_IoU = np.mean(IoUs)
s = '{:5.2f} | '.format(100 * m_IoU)
for IoU in IoUs:
s += '{:5.2f} '.format(100 * IoU)
print(s)
if int(np.ceil(new_min) % 1) == 0: # ???
print('re-project vote #{:d}'.format(int(
np.floor(new_min))))
proj_prob_list = []
for i in range(num_clouds):
proj_idx = self.dataset.test_set.val_proj[i]
probs = self.test_probs[i][proj_idx, :]
proj_prob_list += [probs]
# show vote results
print('confusion on full cloud')
confusion_list = []
for i in range(num_clouds):
preds = self.dataset.test_set.label_values[np.argmax(
proj_prob_list[i], axis=1)].astype(np.uint8)
labels = self.dataset.test_set.val_labels[i]
acc = np.sum(preds == labels) / len(labels)
print(self.dataset.test_set.input_names[i] + 'ACC:' +
str(acc))
confusion_list += [
confusion_matrix(
labels, preds,
self.dataset.test_set.label_values)
]
# name = self.dataset.test_set.label_values + '.ply'
# write_ply(join(path, 'val_preds', name), [preds, labels], ['pred', 'label'])
# re-group confusions
C = np.sum(np.stack(confusion_list), axis=0)
IoUs = self._iou_from_confusions(C)
m_IoU = np.mean(IoUs)
s = '{:5.2f} | '.format(100 * m_IoU)
for IoU in IoUs:
s += '{:5.2f} '.format(100 * IoU)
print(s)
print('finished.')
return
epoch += 1
continue
return
def run(model: BaseModel, dataset: BaseDataset, device, cfg):
reg_thresh = cfg.data.registration_recall_thresh
if reg_thresh is None:
reg_thresh = 0.2
print(time.strftime("%Y%m%d-%H%M%S"))
dataset.create_dataloaders(
model, 1, False, cfg.training.num_workers, False,
)
loader = dataset.test_dataloaders[0]
list_res = []
with Ctq(loader) as tq_test_loader:
for i, data in enumerate(tq_test_loader):
with torch.no_grad():
t0 = time.time()
model.set_input(data, device)
model.forward()
t1 = time.time()
name_scene, name_pair_source, name_pair_target = dataset.test_dataset[0].get_name(i)
input, input_target = model.get_input()
xyz, xyz_target = input.pos, input_target.pos
ind, ind_target = input.ind, input_target.ind
matches_gt = torch.stack([ind, ind_target]).transpose(0, 1)
feat, feat_target = model.get_output()
# rand = voxel_selection(xyz, grid_size=0.06, min_points=cfg.data.min_points)
# rand_target = voxel_selection(xyz_target, grid_size=0.06, min_points=cfg.data.min_points)
rand = torch.randperm(len(feat))[: cfg.data.num_points]
rand_target = torch.randperm(len(feat_target))[: cfg.data.num_points]
res = dict(name_scene=name_scene, name_pair_source=name_pair_source, name_pair_target=name_pair_target)
T_gt = estimate_transfo(xyz[matches_gt[:, 0]], xyz_target[matches_gt[:, 1]])
t2 = time.time()
metric = compute_metrics(
xyz[rand],
xyz_target[rand_target],
feat[rand],
feat_target[rand_target],
T_gt,
sym=cfg.data.sym,
tau_1=cfg.data.tau_1,
tau_2=cfg.data.tau_2,
rot_thresh=cfg.data.rot_thresh,
trans_thresh=cfg.data.trans_thresh,
use_ransac=cfg.data.use_ransac,
ransac_thresh=cfg.data.first_subsampling,
use_teaser=cfg.data.use_teaser,
noise_bound_teaser=cfg.data.noise_bound_teaser,
xyz_gt=xyz[matches_gt[:, 0]],
xyz_target_gt=xyz_target[matches_gt[:, 1]],
registration_recall_thresh=reg_thresh,
)
res = dict(**res, **metric)
res["time_feature"] = t1 - t0
res["time_feature_per_point"] = (t1 - t0) / (len(input.pos) + len(input_target.pos))
res["time_prep"] = t2 - t1
list_res.append(res)
df = pd.DataFrame(list_res)
output_path = os.path.join(cfg.training.checkpoint_dir, cfg.data.name, "matches")
if not os.path.exists(output_path):
os.makedirs(output_path, exist_ok=True)
df.to_csv(osp.join(output_path, "final_res_{}.csv".format(time.strftime("%Y%m%d-%H%M%S"))))
print(df.groupby("name_scene").mean())
