| import torch | |
| import torch.nn as nn | |
| from mono.utils.comm import get_func | |
| class DensePredModel(nn.Module): | |
| def __init__(self, cfg) -> None: | |
| super(DensePredModel, self).__init__() | |
| self.encoder = get_func('mono.model.' + cfg.model.backbone.prefix + cfg.model.backbone.type)(**cfg.model.backbone) | |
| self.decoder = get_func('mono.model.' + cfg.model.decode_head.prefix + cfg.model.decode_head.type)(cfg) | |
| def forward(self, input, **kwargs): | |
| # [f_32, f_16, f_8, f_4] | |
| features = self.encoder(input) | |
| out = self.decoder(features, **kwargs) | |
| return out |