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78d2329 | 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 | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class ResizeConvFeatureUpsampler(nn.Module):
"""
https://distill.pub/2016/deconv-checkerboard/
"""
def __init__(self, num_scales=1,
lowest_feature_resolution=8,
out_channels=128,
vit_type='vits',
no_mono_feature=False,
gaussian_downsample=None,
monodepth_backbone=False,
):
super(ResizeConvFeatureUpsampler, self).__init__()
self.num_scales = num_scales
self.monodepth_backbone = monodepth_backbone
self.upsampler = nn.ModuleList()
vit_feature_channel_dict = {
'vits': 384,
'vitb': 768,
'vitl': 1024
}
vit_feature_channel = vit_feature_channel_dict[vit_type]
if monodepth_backbone:
vit_feature_channel = 384
out_channels = out_channels // num_scales
for i in range(num_scales):
cnn_feature_channels = 128 - (32 * i)
mv_transformer_feature_channels = 128 // (2 ** i)
if no_mono_feature:
mono_feature_channels = 0
else:
mono_feature_channels = vit_feature_channel // (2 ** i)
in_channels = cnn_feature_channels + \
mv_transformer_feature_channels + mono_feature_channels
if monodepth_backbone:
in_channels = 384
curr_upsample_factor = lowest_feature_resolution // (2 ** i)
num_upsample = int(math.log(curr_upsample_factor, 2))
modules = []
if num_upsample == 1:
curr_in_channels = out_channels * 2
else:
curr_in_channels = out_channels * 2 * (num_upsample - 1)
modules.append(nn.Conv2d(in_channels, curr_in_channels, 1))
for i in range(num_upsample):
modules.append(nn.Upsample(scale_factor=2, mode='nearest'))
if i == num_upsample - 1:
modules.append(nn.Conv2d(curr_in_channels,
out_channels, 3, 1, 1, padding_mode='replicate'))
else:
modules.append(nn.Conv2d(curr_in_channels,
curr_in_channels // 2, 3, 1, 1, padding_mode='replicate'))
curr_in_channels = curr_in_channels // 2
modules.append(nn.GELU())
if gaussian_downsample is not None:
if gaussian_downsample == 2:
del modules[-3:]
elif gaussian_downsample == 4:
del modules[-6:]
else:
raise NotImplementedError
self.upsampler.append(nn.Sequential(*modules))
def forward(self, features_list_cnn, features_list_mv, features_list_mono=None):
out = []
for i in range(self.num_scales):
if self.monodepth_backbone:
concat = features_list_cnn[i]
elif features_list_mono is None:
concat = torch.cat(
(features_list_cnn[i], features_list_mv[i]), dim=1)
else:
concat = torch.cat(
(features_list_cnn[i], features_list_mv[i], features_list_mono[i]), dim=1)
concat = self.upsampler[i](concat)
out.append(concat)
out = torch.cat(out, dim=1)
return out
def _test():
device = torch.device('cuda:0')
model = ResizeConvFeatureUpsampler(num_scales=2,
lowest_feature_resolution=4,
).to(device)
print(model)
b, h, w = 2, 32, 64
features_list_cnn = [torch.randn(b, 128, h, w).to(device)]
features_list_mv = [torch.randn(b, 128, h, w).to(device)]
features_list_mono = [torch.randn(b, 384, h, w).to(device)]
# scale 2
features_list_cnn.append(torch.randn(b, 96, h * 2, w * 2).to(device))
features_list_mv.append(torch.randn(b, 64, h * 2, w * 2).to(device))
features_list_mono.append(torch.randn(b, 192, h * 2, w * 2).to(device))
out = model(features_list_cnn,
features_list_mv, features_list_mono)
print(out.shape)
if __name__ == '__main__':
_test()
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