def main():
logger = VisdomLogger("train", env=JOB)
logger.add_hook(lambda logger, data: logger.step(),
feature="energy",
freq=16)
logger.add_hook(
lambda logger, data: logger.plot(data["energy"], "free_energy"),
feature="energy",
freq=100)
task_list = [
tasks.rgb,
tasks.normal,
tasks.principal_curvature,
tasks.sobel_edges,
tasks.depth_zbuffer,
tasks.reshading,
tasks.edge_occlusion,
tasks.keypoints3d,
tasks.keypoints2d,
]
reality = RealityTask('ood',
dataset=ImagePairDataset(data_dir=OOD_DIR,
resize=(256, 256)),
tasks=[tasks.rgb, tasks.rgb],
batch_size=28)
# reality = RealityTask('almena',
# dataset=TaskDataset(
# buildings=['almena'],
# tasks=task_list,
# ),
# tasks=task_list,
# batch_size=8
# )
graph = TaskGraph(tasks=[reality, *task_list], batch_size=28)
task = tasks.rgb
images = [reality.task_data[task]]
sources = [task.name]
for _, edge in sorted(
((edge.dest_task.name, edge) for edge in graph.adj[task])):
if isinstance(edge.src_task, RealityTask): continue
reality.task_data[edge.src_task]
x = edge(reality.task_data[edge.src_task])
if edge.dest_task != tasks.normal:
edge2 = graph.edge_map[(edge.dest_task.name, tasks.normal.name)]
x = edge2(x)
images.append(x.clamp(min=0, max=1))
sources.append(edge.dest_task.name)
logger.images_grouped(images, ", ".join(sources), resize=256)
def main():
logger = VisdomLogger("train", env=JOB)
logger.add_hook(lambda logger, data: logger.step(), feature="energy", freq=16)
logger.add_hook(lambda logger, data: logger.plot(data["energy"], "free_energy"), feature="energy", freq=100)
task_list = [
tasks.rgb,
tasks.normal,
tasks.principal_curvature,
tasks.sobel_edges,
tasks.depth_zbuffer,
tasks.reshading,
tasks.edge_occlusion,
tasks.keypoints3d,
tasks.keypoints2d,
]
reality = RealityTask('almena',
dataset=TaskDataset(
buildings=['almena'],
tasks=task_list,
),
tasks=task_list,
batch_size=8
)
graph = TaskGraph(
tasks=[reality, *task_list],
batch_size=8
)
for task in graph.tasks:
if isinstance(task, RealityTask): continue
images = [reality.task_data[task].clamp(min=0, max=1)]
sources = [task.name]
for _, edge in sorted(((edge.src_task.name, edge) for edge in graph.in_adj[task])):
if isinstance(edge.src_task, RealityTask): continue
x = edge(reality.task_data[edge.src_task])
images.append(x.clamp(min=0, max=1))
sources.append(edge.src_task.name)
logger.images_grouped(images, ", ".join(sources), resize=192)
dataset[0][1][:, 0:3],
dataset[1][1][:, 0:3],
dataset[2][1][:, 0:3],
dataset[3][1][:, 0:3],
dataset[4][1][:, 0:3],
dataset[5][1][:, 0:3],
]
images = torch.cat(data, dim=0)
targets = torch.cat(target, dim=0)
if targets.shape[1] == 1:
targets = torch.cat([targets] * 3, dim=1)
print(targets.shape)
print(targets.shape)
images = [images, targets]
images += [
run_viz_suite("imagepercep",
data,
model_file=f"{MODELS_DIR}/rgb2normal_imagepercep.pth",
logger=logger,
percep_mode=False)
]
#images += [run_viz_suite("unet-b", data, graph_file=f"{SHARED_DIR}/results_SAMPLEFF_full_data_baseline_3/graph.pth", logger=logger)]
#images += [run_viz_suite("unet-pc", data, model_file=f"{MODELS_DIR}/unet_percepstep_0.1.pth", logger=logger, old=True)]
#images += [run_viz_suite("geonet", data, graph_file=f"{SHARED_DIR}/results_geonet_lr1e5_1gpu_2/graph.pth", logger=logger)]
# run_eval_suite("unet-b-1m", model_file=f"{SHARED_DIR}/results_SAMPLEFF_baseline1m_3/n.pth", logger=logger)
# run_eval_suite("unet-pc-1m", graph_file=f"{SHARED_DIR}/results_SAMPLEFF_consistency1m_25/graph.pth", logger=logger)
for image in images:
print(image.shape)
logger.images_grouped(images, "imagepercep", resize=320)
