(
self,
head,
features=256,
backbone="vitb_rn50_384",
readout="project",
channels_last=False,
use_bn=False,
)
| 25 | |
| 26 | class DPT(BaseModel): |
| 27 | def __init__( |
| 28 | self, |
| 29 | head, |
| 30 | features=256, |
| 31 | backbone="vitb_rn50_384", |
| 32 | readout="project", |
| 33 | channels_last=False, |
| 34 | use_bn=False, |
| 35 | ): |
| 36 | |
| 37 | super(DPT, self).__init__() |
| 38 | |
| 39 | self.channels_last = channels_last |
| 40 | |
| 41 | hooks = { |
| 42 | "vitb_rn50_384": [0, 1, 8, 11], |
| 43 | "vitb16_384": [2, 5, 8, 11], |
| 44 | "vitl16_384": [5, 11, 17, 23], |
| 45 | } |
| 46 | |
| 47 | # Instantiate backbone and reassemble blocks |
| 48 | self.pretrained, self.scratch = _make_encoder( |
| 49 | backbone, |
| 50 | features, |
| 51 | False, # Set to true of you want to train from scratch, uses ImageNet weights |
| 52 | groups=1, |
| 53 | expand=False, |
| 54 | exportable=False, |
| 55 | hooks=hooks[backbone], |
| 56 | use_readout=readout, |
| 57 | ) |
| 58 | |
| 59 | self.scratch.refinenet1 = _make_fusion_block(features, use_bn) |
| 60 | self.scratch.refinenet2 = _make_fusion_block(features, use_bn) |
| 61 | self.scratch.refinenet3 = _make_fusion_block(features, use_bn) |
| 62 | self.scratch.refinenet4 = _make_fusion_block(features, use_bn) |
| 63 | |
| 64 | self.scratch.output_conv = head |
| 65 | |
| 66 | |
| 67 | def forward(self, x): |
no test coverage detected