File size: 4,563 Bytes
a9289c0 |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_model import BaseModel
from .blocks import (
FeatureFusionBlock,
FeatureFusionBlock_custom,
Interpolate,
_make_encoder,
forward_vit,
)
def _make_fusion_block(features, use_bn):
return FeatureFusionBlock_custom(
features,
nn.ReLU(False),
deconv=False,
bn=use_bn,
expand=False,
align_corners=True,
)
class DPT(BaseModel):
def __init__(
self,
head,
features=256,
backbone="vitb_rn50_384",
readout="project",
channels_last=False,
use_bn=False,
enable_attention_hooks=False,
):
super(DPT, self).__init__()
self.channels_last = channels_last
hooks = {
"vitb_rn50_384": [0, 1, 8, 11],
"vitb16_384": [2, 5, 8, 11],
"vitl16_384": [5, 11, 17, 23],
}
# Instantiate backbone and reassemble blocks
self.pretrained, self.scratch = _make_encoder(
backbone,
features,
False, # Set to true of you want to train from scratch, uses ImageNet weights
groups=1,
expand=False,
exportable=False,
hooks=hooks[backbone],
use_readout=readout,
enable_attention_hooks=enable_attention_hooks,
)
self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
self.scratch.output_conv = head
def forward(self, x):
if self.channels_last == True:
x.contiguous(memory_format=torch.channels_last)
layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
layer_1_rn = self.scratch.layer1_rn(layer_1)
layer_2_rn = self.scratch.layer2_rn(layer_2)
layer_3_rn = self.scratch.layer3_rn(layer_3)
layer_4_rn = self.scratch.layer4_rn(layer_4)
path_4 = self.scratch.refinenet4(layer_4_rn)
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
out = self.scratch.output_conv(path_1)
return out
class DPTDepthModel(DPT):
def __init__(
self, path=None, non_negative=True, scale=1.0, shift=0.0, invert=False, **kwargs
):
features = kwargs["features"] if "features" in kwargs else 256
self.scale = scale
self.shift = shift
self.invert = invert
head = nn.Sequential(
nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(True),
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
nn.ReLU(True) if non_negative else nn.Identity(),
nn.Identity(),
)
super().__init__(head, **kwargs)
if path is not None:
self.load(path)
def forward(self, x):
inv_depth = super().forward(x).squeeze(dim=1)
if self.invert:
depth = self.scale * inv_depth + self.shift
depth[depth < 1e-8] = 1e-8
depth = 1.0 / depth
return depth
else:
return inv_depth
class DPTSegmentationModel(DPT):
def __init__(self, num_classes, path=None, **kwargs):
features = kwargs["features"] if "features" in kwargs else 256
kwargs["use_bn"] = True
head = nn.Sequential(
nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(features),
nn.ReLU(True),
nn.Dropout(0.1, False),
nn.Conv2d(features, num_classes, kernel_size=1),
Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
)
super().__init__(head, **kwargs)
self.auxlayer = nn.Sequential(
nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(features),
nn.ReLU(True),
nn.Dropout(0.1, False),
nn.Conv2d(features, num_classes, kernel_size=1),
)
if path is not None:
self.load(path)
|