File size: 11,513 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 |
import torch
import torch.nn as nn
from mono.utils.comm import get_func
import numpy as np
import torch.nn.functional as F
class BaseDepthModel(nn.Module):
def __init__(self, cfg, criterions, **kwards):
super(BaseDepthModel, self).__init__()
model_type = cfg.model.type
self.depth_model = get_func('mono.model.model_pipelines.' + model_type)(cfg)
self.criterions_main = criterions['decoder_losses'] if criterions and 'decoder_losses' in criterions else None
self.criterions_auxi = criterions['auxi_losses'] if criterions and 'auxi_losses' in criterions else None
self.criterions_pose = criterions['pose_losses'] if criterions and 'pose_losses' in criterions else None
self.criterions_gru = criterions['gru_losses'] if criterions and 'gru_losses' in criterions else None
try:
self.downsample = cfg.prediction_downsample
except:
self.downsample = None
self.training = True
def forward(self, data):
if self.downsample != None:
self.label_downsample(self.downsample, data)
output = self.depth_model(**data)
losses_dict = {}
if self.training:
output.update(data)
losses_dict = self.get_loss(output)
if self.downsample != None:
self.pred_upsample(self.downsample, output)
return output['prediction'], losses_dict, output['confidence']
def inference(self, data):
with torch.no_grad():
output = self.depth_model(**data)
output.update(data)
if self.downsample != None:
self.pred_upsample(self.downsample, output)
output['dataset'] = 'wild'
return output
def get_loss(self, paras):
losses_dict = {}
# Losses for training
if self.training:
# decode branch
losses_dict.update(self.compute_decoder_loss(paras))
# auxilary branch
losses_dict.update(self.compute_auxi_loss(paras))
# pose branch
losses_dict.update(self.compute_pose_loss(paras))
# GRU sequence branch
losses_dict.update(self.compute_gru_loss(paras))
total_loss = sum(losses_dict.values())
losses_dict['total_loss'] = total_loss
return losses_dict
def compute_gru_loss(self, paras_):
losses_dict = {}
if self.criterions_gru is None or len(self.criterions_gru) == 0:
return losses_dict
paras = {k:v for k,v in paras_.items() if k!='prediction' and k!='prediction_normal'}
n_predictions = len(paras['predictions_list'])
for i, pre in enumerate(paras['predictions_list']):
if i == n_predictions-1:
break
#if i % 3 != 0:
#continue
if 'normal_out_list' in paras.keys():
pre_normal = paras['normal_out_list'][i]
else:
pre_normal = None
iter_dict = self.branch_loss(
prediction=pre,
prediction_normal=pre_normal,
criterions=self.criterions_gru,
branch=f'gru_{i}',
**paras
)
# We adjust the loss_gamma so it is consistent for any number of RAFT-Stereo iterations
adjusted_loss_gamma = 0.9**(15/(n_predictions - 1))
i_weight = adjusted_loss_gamma**(n_predictions - i - 1)
iter_dict = {k:v*i_weight for k,v in iter_dict.items()}
losses_dict.update(iter_dict)
return losses_dict
def compute_decoder_loss(self, paras):
losses_dict = {}
decode_losses_dict = self.branch_loss(
criterions=self.criterions_main,
branch='decode',
**paras
)
return decode_losses_dict
def compute_auxi_loss(self, paras):
losses_dict = {}
if len(self.criterions_auxi) == 0:
return losses_dict
args = dict(
target=paras['target'],
data_type=paras['data_type'],
sem_mask=paras['sem_mask'],
)
for i, auxi_logit in enumerate(paras['auxi_logit_list']):
auxi_losses_dict = self.branch_loss(
prediction=paras['auxi_pred'][i],
criterions=self.criterions_auxi,
pred_logit=auxi_logit,
branch=f'auxi_{i}',
**args
)
losses_dict.update(auxi_losses_dict)
return losses_dict
def compute_pose_loss(self, paras):
losses_dict = {}
if self.criterions_pose is None or len(self.criterions_pose) == 0:
return losses_dict
# valid_flg = paras['tmpl_flg']
# if torch.sum(valid_flg) == 0:
# return losses_dict
# else:
# # sample valid batch
# samples = {}
# for k, v in paras.items():
# if isinstance(v, torch.Tensor):
# samples.update({k: v[valid_flg]})
# elif isinstance(v, list) and isinstance(v[0], torch.Tensor):
# samples.update({k: [i[valid_flg] for i in v]})
for loss_method in self.criterions_pose:
loss_tmp = loss_method(**paras)
losses_dict['pose_' + loss_method._get_name()] = loss_tmp
return losses_dict
def branch_loss(self, prediction, pred_logit, criterions, branch='decode', **kwargs):
B, _, _, _ = prediction.shape
losses_dict = {}
args = dict(pred_logit=pred_logit)
target = kwargs.pop('target')
args.update(kwargs)
# data type for each batch
batches_data_type = np.array(kwargs['data_type'])
# batches_data_names = np.array(kwargs['dataset'])
# resize the target
# if target.shape[2] != prediction.shape[2] and target.shape[3] != prediction.shape[3]:
# _, _, H, W = prediction.shape
# target = nn.functional.interpolate(target, (H,W), mode='nearest')
mask = target > 1e-8
for loss_method in criterions:
# sample batches, which satisfy the loss requirement for data types
new_mask = self.create_mask_as_loss(loss_method, mask, batches_data_type)
loss_tmp = loss_method(
prediction=prediction,
target=target,
mask=new_mask,
**args)
losses_dict[branch + '_' + loss_method._get_name()] = loss_tmp
return losses_dict
def create_mask_as_loss(self, loss_method, mask, batches_data_type):
data_type_req = np.array(loss_method.data_type)[:, None]
batch_mask = torch.tensor(np.any(data_type_req == batches_data_type, axis=0), device="cuda") #torch.from_numpy(np.any(data_type_req == batches_data_type, axis=0)).cuda()
new_mask = mask * batch_mask[:, None, None, None]
return new_mask
def label_downsample(self, downsample_factor, data_dict):
scale_factor = float(1.0 / downsample_factor)
downsample_target = F.interpolate(data_dict['target'], scale_factor=scale_factor)
downsample_stereo_depth = F.interpolate(data_dict['stereo_depth'], scale_factor=scale_factor)
data_dict['target'] = downsample_target
data_dict['stereo_depth'] = downsample_stereo_depth
return data_dict
def pred_upsample(self, downsample_factor, data_dict):
scale_factor = float(downsample_factor)
upsample_prediction = F.interpolate(data_dict['prediction'], scale_factor=scale_factor).detach()
upsample_confidence = F.interpolate(data_dict['confidence'], scale_factor=scale_factor).detach()
data_dict['prediction'] = upsample_prediction
data_dict['confidence'] = upsample_confidence
return data_dict
# def mask_batches(self, prediction, target, mask, batches_data_names, data_type_req):
# """
# Mask the data samples that satify the loss requirement.
# Args:
# data_type_req (str): the data type required by a loss.
# batches_data_names (list): the list of data types in a batch.
# """
# batch_mask = np.any(data_type_req == batches_data_names, axis=0)
# prediction = prediction[batch_mask]
# target = target[batch_mask]
# mask = mask[batch_mask]
# return prediction, target, mask, batch_mask
# def update_mask_g8(self, target, mask, prediction, batches_data_names, absRel=0.5):
# data_type_req=np.array(['Golf8_others'])[:, None]
# pred, target, mask_sample, batch_mask = self.mask_batches(prediction, target, mask, batches_data_names, data_type_req)
# if pred.numel() == 0:
# return mask
# scale_batch = []
# for i in range(mask_sample.shape[0]):
# scale = torch.median(target[mask_sample]) / (torch.median(pred[mask_sample]) + 1e-8)
# abs_rel = torch.abs(pred[i:i+1, ...] * scale - target[i:i+1, ...]) / (pred[i:i+1, ...] * scale + 1e-6)
# if target[i, ...][target[i, ...]>0].min() < 0.041:
# mask_valid_i = ((abs_rel < absRel) | ((target[i:i+1, ...]<0.02) & (target[i:i+1, ...]>1e-6))) & mask_sample[i:i+1, ...]
# else:
# mask_valid_i = mask_sample[i:i+1, ...]
# mask_sample[i:i+1, ...] = mask_valid_i
# # print(target.max(), target[target>0].min())
# # self.visual_g8(target, mask_valid_i)
# mask[batch_mask] = mask_sample
# return mask
# def update_mask_g8_v2(self, target, mask, prediction, batches_data_names,):
# data_type_req=np.array(['Golf8_others'])[:, None]
# pred, target, mask_sample, batch_mask = self.mask_batches(prediction, target, mask, batches_data_names, data_type_req)
# if pred.numel() == 0:
# return mask
# raw_invalid_mask = target < 1e-8
# target[raw_invalid_mask] = 1e8
# kernal = 31
# pool = min_pool2d(target, kernal)
# diff = target- pool
# valid_mask = (diff < 0.02) & mask_sample & (target<0.3)
# target_min = target.view(target.shape[0], -1).min(dim=1)[0]
# w_close = target_min < 0.04
# valid_mask[~w_close] = mask_sample[~w_close]
# mask[batch_mask]= valid_mask
# target[raw_invalid_mask] = -1
# #self.visual_g8(target, mask[batch_mask])
# return mask
# def visual_g8(self, gt, mask):
# import matplotlib.pyplot as plt
# from mono.utils.transform import gray_to_colormap
# gt = gt.cpu().numpy().squeeze()
# mask = mask.cpu().numpy().squeeze()
# if gt.ndim >2:
# gt = gt[0, ...]
# mask = mask[0, ...]
# name = np.random.randint(1000000)
# print(gt.max(), gt[gt>0].min(), name)
# gt_filter = gt.copy()
# gt_filter[~mask] = 0
# out = np.concatenate([gt, gt_filter], axis=0)
# out[out<0] = 0
# o = gray_to_colormap(out)
# o[out<1e-8]=0
# plt.imsave(f'./tmp/{name}.png', o)
def min_pool2d(tensor, kernel, stride=1):
tensor = tensor * -1.0
tensor = F.max_pool2d(tensor, kernel, padding=kernel//2, stride=stride)
tensor = -1.0 * tensor
return tensor |