| | import torch |
| |
|
| | from src.visualization.color_map import apply_color_map_to_image |
| | import torch.distributed as dist |
| |
|
| | def inverse_normalize(tensor, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)): |
| | mean = torch.as_tensor(mean, dtype=tensor.dtype, device=tensor.device).view(-1, 1, 1) |
| | std = torch.as_tensor(std, dtype=tensor.dtype, device=tensor.device).view(-1, 1, 1) |
| | return tensor.mul(std).add(mean) |
| |
|
| |
|
| | |
| | def vis_depth_map(result, near=None, far=None): |
| | if near is None and far is None: |
| | far = result.view(-1)[:16_000_000].quantile(0.99).log() |
| | try: |
| | near = result[result > 0][:16_000_000].quantile(0.01).log() |
| | except: |
| | print("No valid depth values found.") |
| | near = torch.zeros_like(far) |
| | else: |
| | near = near.log() |
| | far = far.log() |
| | |
| | result = result.log() |
| | result = 1 - (result - near) / (far - near) |
| | return apply_color_map_to_image(result, "turbo") |
| |
|
| |
|
| | def confidence_map(result): |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | result = result / result.view(-1).max() |
| | return apply_color_map_to_image(result, "magma") |
| |
|
| |
|
| | def get_overlap_tag(overlap): |
| | if 0.05 <= overlap <= 0.3: |
| | overlap_tag = "small" |
| | elif overlap <= 0.55: |
| | overlap_tag = "medium" |
| | elif overlap <= 0.8: |
| | overlap_tag = "large" |
| | else: |
| | overlap_tag = "ignore" |
| |
|
| | return overlap_tag |
| |
|
| |
|
| | def is_dist_avail_and_initialized(): |
| | if not dist.is_available(): |
| | return False |
| | if not dist.is_initialized(): |
| | return False |
| | return True |
| |
|
| |
|
| | def get_world_size(): |
| | if not is_dist_avail_and_initialized(): |
| | return 1 |
| | return dist.get_world_size() |
| |
|
| |
|
| | def get_rank(): |
| | if not is_dist_avail_and_initialized(): |
| | return 0 |
| | return dist.get_rank() |
