File size: 9,126 Bytes
3ef1661 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 |
import torch
import logging
import os
from mono.utils.avg_meter import MetricAverageMeter
from mono.utils.visualization import save_val_imgs, visual_train_data, create_html, save_raw_imgs, save_normal_val_imgs
import cv2
from tqdm import tqdm
import numpy as np
from mono.utils.logger import setup_logger
from mono.utils.comm import main_process
#from scipy.optimize import minimize
#from torchmin import minimize
import torch.optim as optim
from torch.autograd import Variable
def to_cuda(data: dict):
for k, v in data.items():
if isinstance(v, torch.Tensor):
data[k] = v.cuda(non_blocking=True)
if isinstance(v, list) and len(v)>=1 and isinstance(v[0], torch.Tensor):
for i, l_i in enumerate(v):
data[k][i] = l_i.cuda(non_blocking=True)
return data
def align_scale(pred: torch.tensor, target: torch.tensor):
mask = target > 0
if torch.sum(mask) > 10:
scale = torch.median(target[mask]) / (torch.median(pred[mask]) + 1e-8)
else:
scale = 1
pred_scale = pred * scale
return pred_scale, scale
def align_shift(pred: torch.tensor, target: torch.tensor):
mask = target > 0
if torch.sum(mask) > 10:
shift = torch.median(target[mask]) - (torch.median(pred[mask]) + 1e-8)
else:
shift = 0
pred_shift = pred + shift
return pred_shift, shift
def align_scale_shift(pred: torch.tensor, target: torch.tensor):
mask = target > 0
target_mask = target[mask].cpu().numpy()
pred_mask = pred[mask].cpu().numpy()
if torch.sum(mask) > 10:
scale, shift = np.polyfit(pred_mask, target_mask, deg=1)
if scale < 0:
scale = torch.median(target[mask]) / (torch.median(pred[mask]) + 1e-8)
shift = 0
else:
scale = 1
shift = 0
pred = pred * scale + shift
return pred, scale
def get_prediction(
model: torch.nn.Module,
input: torch.tensor,
cam_model: torch.tensor,
pad_info: torch.tensor,
scale_info: torch.tensor,
gt_depth: torch.tensor,
normalize_scale: float,
intrinsic = None,
clip_range = None,
flip_aug = False):
#clip_range = [0, 10],
#flip_aug = True):
data = dict(
input=input,
#ref_input=ref_input,
cam_model=cam_model
)
#output = model.module.inference(data)
output = model.module.inference(data)
pred_depth, confidence = output['prediction'], output['confidence']
pred_depth = torch.abs(pred_depth)
pred_depth = pred_depth.squeeze()
if flip_aug == True:
output_flip = model.module.inference(dict(
input=torch.flip(input, [3]),
#ref_input=ref_input,
cam_model=cam_model
))
if clip_range != None:
output['prediction'] = torch.clamp(output['prediction'], clip_range[0], clip_range[1])
output_flip['prediction'] = torch.clamp(output_flip['prediction'], clip_range[0] / normalize_scale * scale_info , clip_range[1] / normalize_scale * scale_info)
output['prediction'] = 0.5 * (output['prediction'] + torch.flip(output_flip['prediction'], [3]))
output['confidence'] = 0.5 * (output['confidence'] + torch.flip(output_flip['confidence'], [3]))
output['pad'] = torch.Tensor(pad_info).cuda().unsqueeze(0).int()
output['mask'] = torch.ones_like(pred_depth).bool().unsqueeze(0).unsqueeze(1)
output['scale_info'] = scale_info
if intrinsic is not None:
output['intrinsic'] = intrinsic
pred_depth = pred_depth[pad_info[0]: pred_depth.shape[0]-pad_info[1], pad_info[2]: pred_depth.shape[1]-pad_info[3]]
pred_depth = torch.nn.functional.interpolate(pred_depth[None, None, :, :], gt_depth.shape, mode='bilinear').squeeze() # to orginal size
pred_depth = pred_depth * normalize_scale / scale_info
if clip_range != None:
pred_depth = torch.clamp(pred_depth, clip_range[0], clip_range[1])
pred_depth_scale, scale = align_scale(pred_depth, gt_depth) #align_scale_shift(pred_depth, gt_depth)
if clip_range != None:
pred_depth_scale = torch.clamp(pred_depth_scale, clip_range[0], clip_range[1])
return pred_depth, pred_depth_scale, scale, output
# def depth_normal_consistency_optimization(output_dict, consistency_fn):
# s = torch.zeros_like(output_dict['scale_info'])
# def closure(x):
# output_dict['scale'] = torch.exp(x) * output_dict['scale_info']
# error = consistency_fn(**output_dict)
# return error + x * x
# result = minimize(closure, s, method='newton-exact', disp=1, options={'max_iter':10, 'lr':0.1})
# return float(torch.exp(-result.x))
def do_test_with_dataloader(
model: torch.nn.Module,
cfg: dict,
dataloader: torch.utils.data,
logger: logging.RootLogger,
is_distributed: bool = True,
local_rank: int = 0):
show_dir = cfg.show_dir
save_interval = 100
save_html_path = show_dir + '/index.html'
save_imgs_dir = show_dir + '/vis'
os.makedirs(save_imgs_dir, exist_ok=True)
save_raw_dir = show_dir + '/raw'
os.makedirs(save_raw_dir, exist_ok=True)
normalize_scale = cfg.data_basic.depth_range[1]
dam = MetricAverageMeter(cfg.test_metrics)
dam_scale = MetricAverageMeter(cfg.test_metrics)
try:
depth_range = cfg.data_basic.clip_depth_range if cfg.clip_depth else None
except:
depth_range = None
for i, data in enumerate(tqdm(dataloader)):
# logger.info(f'{local_rank}: {i}/{len(dataloader)}')
data = to_cuda(data)
gt_depth = data['target'].squeeze()
mask = gt_depth > 1e-6
pad_info = data['pad']
pred_depth, pred_depth_scale, scale, output = get_prediction(
model,
data['input'],
data['cam_model'],
pad_info,
data['scale'],
gt_depth,
normalize_scale,
data['intrinsic'],
)
logger.info(f'{data["filename"]}: {scale}')
# optimization
#if "normal_out_list" in output.keys():
#scale_opt = depth_normal_consistency_optimization(output, consistency_loss)
#print('scale', scale_opt, float(scale))
scale_opt = 1.0
# update depth metrics
dam_scale.update_metrics_gpu(pred_depth_scale, gt_depth, mask, is_distributed)
dam.update_metrics_gpu(pred_depth, gt_depth, mask, is_distributed)
# save evaluation results
if i % save_interval == 0:
# save
rgb = data['input'][:, :, pad_info[0]: data['input'].shape[2]-pad_info[1], pad_info[2]: data['input'].shape[3]-pad_info[3]]
rgb = torch.nn.functional.interpolate(rgb, gt_depth.shape, mode='bilinear').squeeze()
max_scale = save_val_imgs(i,
pred_depth,
gt_depth,
rgb,
data['filename'][0],
save_imgs_dir,
)
logger.info(f'{data["filename"]}, {"max_scale"}: {max_scale}')
# # save original depth/rgb
# save_raw_imgs(
# pred_depth.cpu().squeeze().numpy(),
# data['raw_rgb'].cpu().squeeze().numpy(),
# data['filename'][0],
# save_raw_dir,
# )
# surface normal metrics
if "normal_out_list" in output.keys():
normal_out_list = output['normal_out_list']
gt_normal = data['normal']
pred_normal = normal_out_list[-1][:, :3, :, :] # (B, 3, H, W)
H, W = pred_normal.shape[2:]
pred_normal = pred_normal[:, :, pad_info[0]:H-pad_info[1], pad_info[2]:W-pad_info[3]]
pred_normal = torch.nn.functional.interpolate(pred_normal, size=gt_normal.shape[2:], mode='bilinear', align_corners=True)
gt_normal_mask = ~torch.all(gt_normal == 0, dim=1, keepdim=True)
dam.update_normal_metrics_gpu(pred_normal, gt_normal, gt_normal_mask, cfg.distributed)# save valiad normal
if i % save_interval == 0:
save_normal_val_imgs(iter,
pred_normal,
gt_normal,
rgb, # data['input'],
'normal_' + data['filename'][0],
save_imgs_dir,
)
# get validation error
if main_process():
eval_error = dam.get_metrics()
print('>>>>>W/o scale: ', eval_error)
eval_error_scale = dam_scale.get_metrics()
print('>>>>>W scale: ', eval_error_scale)
# disp_eval_error = dam_disp.get_metrics()
# print('>>>>>Disp to depth: ', disp_eval_error)
# for i, dam in enumerate(dams):
# print(f'>>>>>W/o scale gru{i}: ', dam.get_metrics())
logger.info(eval_error)
logger.info(eval_error_scale)
# logger.info(disp_eval_error)
# [logger.info(dam.get_metrics()) for dam in dams]
|