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hubconf.py
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hubconf.py
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dependencies = ['torch', 'torchvision']
import os
import torch
try:
from mmcv.utils import Config, DictAction
except:
from mmengine import Config, DictAction
from mono.model.monodepth_model import get_configured_monodepth_model
metric3d_dir = os.path.dirname(__file__)
MODEL_TYPE = {
'ConvNeXt-Tiny': {
'cfg_file': f'{metric3d_dir}/mono/configs/HourglassDecoder/convtiny.0.3_150.py',
'ckpt_file': 'https://huggingface.co/JUGGHM/Metric3D/resolve/main/convtiny_hourglass_v1.pth',
},
'ConvNeXt-Large': {
'cfg_file': f'{metric3d_dir}/mono/configs/HourglassDecoder/convlarge.0.3_150.py',
'ckpt_file': 'https://huggingface.co/JUGGHM/Metric3D/resolve/main/convlarge_hourglass_0.3_150_step750k_v1.1.pth',
},
'ViT-Small': {
'cfg_file': f'{metric3d_dir}/mono/configs/HourglassDecoder/vit.raft5.small.py',
'ckpt_file': 'https://huggingface.co/JUGGHM/Metric3D/resolve/main/metric_depth_vit_small_800k.pth',
},
'ViT-Large': {
'cfg_file': f'{metric3d_dir}/mono/configs/HourglassDecoder/vit.raft5.large.py',
'ckpt_file': 'https://huggingface.co/JUGGHM/Metric3D/resolve/main/metric_depth_vit_large_800k.pth',
},
'ViT-giant2': {
'cfg_file': f'{metric3d_dir}/mono/configs/HourglassDecoder/vit.raft5.giant2.py',
'ckpt_file': 'https://huggingface.co/JUGGHM/Metric3D/resolve/main/metric_depth_vit_giant2_800k.pth',
},
}
def metric3d_convnext_tiny(pretrain=False, **kwargs):
'''
Return a Metric3D model with ConvNeXt-Large backbone and Hourglass-Decoder head.
For usage examples, refer to: https://github.com/YvanYin/Metric3D/blob/main/hubconf.py
Args:
pretrain (bool): whether to load pretrained weights.
Returns:
model (nn.Module): a Metric3D model.
'''
cfg_file = MODEL_TYPE['ConvNeXt-Tiny']['cfg_file']
ckpt_file = MODEL_TYPE['ConvNeXt-Tiny']['ckpt_file']
cfg = Config.fromfile(cfg_file)
model = get_configured_monodepth_model(cfg)
if pretrain:
model.load_state_dict(
torch.hub.load_state_dict_from_url(ckpt_file)['model_state_dict'],
strict=False,
)
return model
def metric3d_convnext_large(pretrain=False, **kwargs):
'''
Return a Metric3D model with ConvNeXt-Large backbone and Hourglass-Decoder head.
For usage examples, refer to: https://github.com/YvanYin/Metric3D/blob/main/hubconf.py
Args:
pretrain (bool): whether to load pretrained weights.
Returns:
model (nn.Module): a Metric3D model.
'''
cfg_file = MODEL_TYPE['ConvNeXt-Large']['cfg_file']
ckpt_file = MODEL_TYPE['ConvNeXt-Large']['ckpt_file']
cfg = Config.fromfile(cfg_file)
model = get_configured_monodepth_model(cfg)
if pretrain:
model.load_state_dict(
torch.hub.load_state_dict_from_url(ckpt_file)['model_state_dict'],
strict=False,
)
return model
def metric3d_vit_small(pretrain=False, **kwargs):
'''
Return a Metric3D model with ViT-Small backbone and RAFT-4iter head.
For usage examples, refer to: https://github.com/YvanYin/Metric3D/blob/main/hubconf.py
Args:
pretrain (bool): whether to load pretrained weights.
Returns:
model (nn.Module): a Metric3D model.
'''
cfg_file = MODEL_TYPE['ViT-Small']['cfg_file']
ckpt_file = MODEL_TYPE['ViT-Small']['ckpt_file']
cfg = Config.fromfile(cfg_file)
model = get_configured_monodepth_model(cfg)
if pretrain:
model.load_state_dict(
torch.hub.load_state_dict_from_url(ckpt_file)['model_state_dict'],
strict=False,
)
return model
def metric3d_vit_large(pretrain=False, **kwargs):
'''
Return a Metric3D model with ViT-Large backbone and RAFT-8iter head.
For usage examples, refer to: https://github.com/YvanYin/Metric3D/blob/main/hubconf.py
Args:
pretrain (bool): whether to load pretrained weights.
Returns:
model (nn.Module): a Metric3D model.
'''
cfg_file = MODEL_TYPE['ViT-Large']['cfg_file']
ckpt_file = MODEL_TYPE['ViT-Large']['ckpt_file']
cfg = Config.fromfile(cfg_file)
model = get_configured_monodepth_model(cfg)
if pretrain:
model.load_state_dict(
torch.hub.load_state_dict_from_url(ckpt_file)['model_state_dict'],
strict=False,
)
return model
def metric3d_vit_giant2(pretrain=False, **kwargs):
'''
Return a Metric3D model with ViT-Giant2 backbone and RAFT-8iter head.
For usage examples, refer to: https://github.com/YvanYin/Metric3D/blob/main/hubconf.py
Args:
pretrain (bool): whether to load pretrained weights.
Returns:
model (nn.Module): a Metric3D model.
'''
cfg_file = MODEL_TYPE['ViT-giant2']['cfg_file']
ckpt_file = MODEL_TYPE['ViT-giant2']['ckpt_file']
cfg = Config.fromfile(cfg_file)
model = get_configured_monodepth_model(cfg)
if pretrain:
model.load_state_dict(
torch.hub.load_state_dict_from_url(ckpt_file)['model_state_dict'],
strict=False,
)
return model
if __name__ == '__main__':
import cv2
import numpy as np
#### prepare data
rgb_file = 'data/kitti_demo/rgb/0000000050.png'
depth_file = 'data/kitti_demo/depth/0000000050.png'
intrinsic = [707.0493, 707.0493, 604.0814, 180.5066]
gt_depth_scale = 256.0
rgb_origin = cv2.imread(rgb_file)[:, :, ::-1]
#### ajust input size to fit pretrained model
# keep ratio resize
input_size = (616, 1064) # for vit model
# input_size = (544, 1216) # for convnext model
h, w = rgb_origin.shape[:2]
scale = min(input_size[0] / h, input_size[1] / w)
rgb = cv2.resize(rgb_origin, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_LINEAR)
# remember to scale intrinsic, hold depth
intrinsic = [intrinsic[0] * scale, intrinsic[1] * scale, intrinsic[2] * scale, intrinsic[3] * scale]
# padding to input_size
padding = [123.675, 116.28, 103.53]
h, w = rgb.shape[:2]
pad_h = input_size[0] - h
pad_w = input_size[1] - w
pad_h_half = pad_h // 2
pad_w_half = pad_w // 2
rgb = cv2.copyMakeBorder(rgb, pad_h_half, pad_h - pad_h_half, pad_w_half, pad_w - pad_w_half, cv2.BORDER_CONSTANT, value=padding)
pad_info = [pad_h_half, pad_h - pad_h_half, pad_w_half, pad_w - pad_w_half]
#### normalize
mean = torch.tensor([123.675, 116.28, 103.53]).float()[:, None, None]
std = torch.tensor([58.395, 57.12, 57.375]).float()[:, None, None]
rgb = torch.from_numpy(rgb.transpose((2, 0, 1))).float()
rgb = torch.div((rgb - mean), std)
rgb = rgb[None, :, :, :].cuda()
###################### canonical camera space ######################
# inference
model = torch.hub.load('yvanyin/metric3d', 'metric3d_vit_small', pretrain=True)
model.cuda().eval()
with torch.no_grad():
pred_depth, confidence, output_dict = model.inference({'input': rgb})
# un pad
pred_depth = pred_depth.squeeze()
pred_depth = pred_depth[pad_info[0] : pred_depth.shape[0] - pad_info[1], pad_info[2] : pred_depth.shape[1] - pad_info[3]]
# upsample to original size
pred_depth = torch.nn.functional.interpolate(pred_depth[None, None, :, :], rgb_origin.shape[:2], mode='bilinear').squeeze()
###################### canonical camera space ######################
#### de-canonical transform
canonical_to_real_scale = intrinsic[0] / 1000.0 # 1000.0 is the focal length of canonical camera
pred_depth = pred_depth * canonical_to_real_scale # now the depth is metric
pred_depth = torch.clamp(pred_depth, 0, 300)
#### you can now do anything with the metric depth
# such as evaluate predicted depth
if depth_file is not None:
gt_depth = cv2.imread(depth_file, -1)
gt_depth = gt_depth / gt_depth_scale
gt_depth = torch.from_numpy(gt_depth).float().cuda()
assert gt_depth.shape == pred_depth.shape
mask = (gt_depth > 1e-8)
abs_rel_err = (torch.abs(pred_depth[mask] - gt_depth[mask]) / gt_depth[mask]).mean()
print('abs_rel_err:', abs_rel_err.item())
#### normal are also available
if 'prediction_normal' in output_dict: # only available for Metric3Dv2, i.e. vit model
pred_normal = output_dict['prediction_normal'][:, :3, :, :]
normal_confidence = output_dict['prediction_normal'][:, 3, :, :] # see https://arxiv.org/abs/2109.09881 for details
# un pad and resize to some size if needed
pred_normal = pred_normal.squeeze()
pred_normal = pred_normal[:, pad_info[0] : pred_normal.shape[1] - pad_info[1], pad_info[2] : pred_normal.shape[2] - pad_info[3]]
# you can now do anything with the normal
# such as visualize pred_normal
pred_normal_vis = pred_normal.cpu().numpy().transpose((1, 2, 0))
pred_normal_vis = (pred_normal_vis + 1) / 2
cv2.imwrite('normal_vis.png', (pred_normal_vis * 255).astype(np.uint8))