# Create a phong renderer by composing a rasterizer and a shader. The textured phong shader will
# interpolate the texture uv coordinates for each vertex, sample from a texture image and
# apply the Phong lighting model
renderer = MeshRenderer(rasterizer=MeshRasterizer(
cameras=cameras, raster_settings=raster_settings),
shader=SoftPhongShader(device=device,
cameras=cameras,
lights=lights))
img = renderer(mesh)
plt.figure(figsize=(10, 10))
plt.imshow(img[0].cpu().numpy())
plt.show()
if __name__ == '__main__':
file = Path(
"/home/lukas/rtm/rtm_files_3d/2020-08-24_11-20-27_111_RESULT.erfh5")
from Pipeline.data_loader_mesh import DataLoaderMesh
sensor_verts_path = Path(
"/home/lukas/rtm/sensor_verts_3d_272_subsampled.dump")
dl = DataLoaderMesh(sensor_verts_path=sensor_verts_path)
data = dl.get_sensor_flowfront_mesh(file)
sample = data[150][1]
mc = MeshCreator(file)
verts, faces, _ = mc.get_mesh_components()
show_vedo_mesh_old(verts, faces, sample)
# save_p3d_mesh(verts, faces, sample)
pass
num_test_samples = 5000
data_root = Path(base_path / "rtm_files")
load_datasets_path = None
cache_path = base_path / "cache"
else:
print("No valid configuration for this machine. Aborting...")
exit()
if "pop-os" in socket.gethostname() and debug:
filepaths = [base_path / "debug"]
batch_size = 4
num_validation_samples = 4
num_test_samples = 4
data_root = Path(base_path / "debug")
dlm = DataLoaderMesh(sensor_indices=((1, 2), (1, 2)))
mc = MeshCreator(batch_size)
mesh = mc.batched_mesh_torch(batch_size)
model = SensorMeshToFlowFrontModel(mesh, batch_size=batch_size)
m = ModelTrainer(
lambda: model,
data_source_paths=filepaths,
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,
data_root = Path(base_path / "rtm_files_3d")
load_datasets_path = None
# cache_path = base_path / "cache"
cache_path = None
else:
print("No valid configuration for this machine. Aborting...")
exit()
if "pop-os" in socket.gethostname() and debug:
filepaths = [base_path / "debug"]
batch_size = 4
num_validation_samples = 4
num_test_samples = 4
data_root = Path(base_path / "debug")
dlm = DataLoaderMesh(sensor_verts_path=sensor_verts_path)
mc = MeshCreator(sample_file)
mesh = mc.batched_mesh_dgl(batch_size)
model = SensorMeshToFlowFrontModel(mesh, batch_size=batch_size)
m = ModelTrainer(
lambda: model,
data_source_paths=filepaths,
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,
train_print_frequency = 50
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,
data_root = Path(base_path / "rtm_files")
load_datasets_path = None
cache_path = base_path / "cache"
else:
print("No valid configuration for this machine. Aborting...")
exit()
if "pop-os" in socket.gethostname() and debug:
filepaths = [base_path / "debug"]
batch_size = 4
num_validation_samples = 4
num_test_samples = 4
data_root = Path(base_path / "debug")
dlm = DataLoaderMesh(sensor_indices=((1, 4), (1, 4)),
third_dim=False,
intermediate_target_size=(38, 30, 2))
mc = MeshCreator(sample_file)
mesh = mc.batched_mesh_dgl(batch_size)
model = SensorMeshToFlowFrontModelDGL(mesh, batch_size=batch_size)
m = ModelTrainer(
lambda: model,
data_source_paths=filepaths,
save_path=save_path,
cache_path=cache_path,
batch_size=batch_size,
train_print_frequency=train_print_frequency,
epochs=epochs,
dummy_epoch=False,
num_workers=num_workers,
x = torch.unsqueeze(x.view(self.batch_size, -1), dim=1)
# x = self.avg_pool(x)
x = self.adaptive_maxpool(x)
x = x.view(size=(self.batch_size, self.bottleneck_dim, 168, 168))
x = x.repeat(1, 3, 1, 1)
x = self.upsample(x)
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_root = Path(base_path / "rtm_files_3d")
load_datasets_path = None
cache_path = base_path / "cache"
else:
print("No valid configuration for this machine. Aborting...")
exit()
if "pop-os" in socket.gethostname() and debug:
filepaths = [base_path / "debug"]
batch_size = 4
num_validation_samples = 4
num_test_samples = 4
data_root = Path(base_path / "debug")
# Sensorgrid: 17*16 = 272
dlm = DataLoaderMesh(sensor_indices=((1, 4), (1, 4)), sensor_verts_path=sensor_verts_path)
mc = MeshCreator(sample_file)
# mesh = mc.batched_mesh_dgl(batch_size)
mesh = mc.subsampled_batched_mesh_dgl(batch_size, faces_percentage=0.8)
subsampled_nodes = None
model = SparseSensorMeshToFlowFrontModelDGL(mesh, batch_size=batch_size)
# model = SensorMeshToFlowFrontModelDGL(mesh, batch_size=batch_size)
m = ModelTrainer(
lambda: model,
data_source_paths=filepaths,
save_path=save_path,
cache_path=cache_path,
batch_size=batch_size,