epochs = 20
num_workers = 8
num_validation_samples = 5000
num_test_samples = 5000
data_root = Path(base_path / "rtm_files")
load_datasets_path = None
cache_path = None
weights_path = Path("/home/lukas/rtm/results/sensor2flow_2020-07-01_16-50-49/checkpoint.pth")
dataset_split_path = Path("/home/lukas/rtm/results/sensor2dryspot/76%_2020-07-09_16-28-35")
checkpoint_path = dataset_split_path / "checkpoint.pth"
else:
print("No valid configuration for this machine. Aborting...")
exit()
dlm = DataLoaderMesh(sensor_verts_path=sensor_verts_path)
mesh = dlm.get_batched_mesh_torch(batch_size, sample_file)
model = SensorMeshToDryspotResnet(mesh, batch_size=batch_size, weights_path=weights_path)
m = ModelTrainer(
lambda: model,
data_source_paths=filepaths,
dataset_split_path=dataset_split_path,
save_path=save_path,
cache_path=cache_path,
batch_size=batch_size,
train_print_frequency=train_print_frequency,
epochs=epochs,
dummy_epoch=True,
num_workers=num_workers,
num_validation_samples=num_validation_samples,
num_test_samples=num_test_samples,
x = torch.sigmoid(self.classifier(x))
return x
if __name__ == '__main__':
from Pipeline.data_loader_mesh import DataLoaderMesh
from pathlib import Path
dl = DataLoaderMesh(
sensor_verts_path=Path("/home/lukas/rtm/sensor_verts.dump"))
file = Path(
"/home/lukas/rtm/rtm_files/2019-07-24_16-32-40_308_RESULT.erfh5")
bs = 4
mesh = dl.get_batched_mesh_torch(bs, file)
# model = SensorMeshToFlowFrontModel(mesh)
# model = SensorMeshToDryspotModel(mesh, bs).cuda()
model = SensorMeshToDryspotResnet(mesh, bs).cuda()
instances = dl.get_sensor_flowfront_mesh(file)
data, labels = [], []
batch = instances[0:bs]
for d, l in batch:
data.append(d)
labels.append(l)
data = torch.Tensor(data).cuda()
lables = torch.Tensor(labels)
for i in tqdm(range(500)):
output = model(data)