Spaces:
Runtime error
Runtime error
File size: 8,961 Bytes
1f418ff |
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 |
# MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# File author: Zhenyu Li
import torch
import torch.nn as nn
import torch.nn.functional as F
# from zoedepth.models.layers.swin_layers import G2LFusion
from estimator.models.blocks.swin_layers import G2LFusion
from torchvision.ops import roi_align as torch_roi_align
from estimator.registry import MODELS
class DoubleConvWOBN(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=True),
# nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=True),
# nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True))
def forward(self, x):
return self.double_conv(x)
class DoubleConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
"""Downscaling with maxpool then double conv"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels)
)
def forward(self, x):
return self.maxpool_conv(x)
class Upv1(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
# self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
if mid_channels is not None:
self.conv = DoubleConvWOBN(in_channels, out_channels, mid_channels)
else:
self.conv = DoubleConvWOBN(in_channels, out_channels, in_channels)
def forward(self, x1, x2):
# x1 = self.up(x1)
x1 = F.interpolate(x1, size=x2.shape[-2:], mode='bilinear', align_corners=True)
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
@MODELS.register_module()
class GuidedFusionPatchFusion(nn.Module):
def __init__(
self,
n_channels,
g2l,
in_channels=[32, 256, 256, 256, 256, 256],
depth=[2, 2, 3, 3, 4, 4],
num_heads=[8, 8, 16, 16, 32, 32],
# num_patches=[12*16, 24*32, 48*64, 96*128, 192*256, 384*512],
num_patches=[384*512, 192*256, 96*128, 48*64, 24*32, 12*16],
patch_process_shape=[384, 512]):
super(GuidedFusionPatchFusion, self).__init__()
self.n_channels = n_channels
self.inc = DoubleConv(n_channels, in_channels[0])
self.down_conv_list = nn.ModuleList()
for idx in range(len(in_channels) - 1):
lay = Down(in_channels[idx], in_channels[idx+1])
self.down_conv_list.append(lay)
in_channels_inv = in_channels[::-1]
self.up_conv_list = nn.ModuleList()
for idx in range(1, len(in_channels)):
lay = Upv1(in_channels_inv[idx] + in_channels_inv[idx-1] + in_channels_inv[idx-1], in_channels_inv[idx])
self.up_conv_list.append(lay)
self.g2l = g2l
if self.g2l:
self.g2l_att = nn.ModuleList()
win = 12
self.patch_process_shape = patch_process_shape
num_heads_inv = num_heads[::-1]
depth_inv = depth[::-1]
num_patches_inv = num_patches[::-1]
self.g2l_list = nn.ModuleList()
self.convs = nn.ModuleList()
for idx in range(len(in_channels_inv)):
g2l_layer = G2LFusion(input_dim=in_channels_inv[idx], embed_dim=in_channels_inv[idx], window_size=win, num_heads=num_heads_inv[idx], depth=depth_inv[idx], num_patches=num_patches_inv[idx])
self.g2l_list.append(g2l_layer)
layer = DoubleConvWOBN(in_channels_inv[idx] * 2, in_channels_inv[idx], in_channels_inv[idx])
self.convs.append(layer)
# self.g2l5 = G2LFusion(input_dim=in_channels[5], embed_dim=crf_dims[5], window_size=win, num_heads=32, depth=4, num_patches=num_patches[0])
# self.g2l4 = G2LFusion(input_dim=in_channels[4], embed_dim=crf_dims[4], window_size=win, num_heads=32, depth=4, num_patches=num_patches[1])
# self.g2l3 = G2LFusion(input_dim=in_channels[3], embed_dim=crf_dims[3], window_size=win, num_heads=16, depth=3, num_patches=num_patches[2])
# self.g2l2 = G2LFusion(input_dim=in_channels[2], embed_dim=crf_dims[2], window_size=win, num_heads=16, depth=3, num_patches=num_patches[3])
# self.g2l1 = G2LFusion(input_dim=in_channels[1], embed_dim=crf_dims[1], window_size=win, num_heads=8, depth=2, num_patches=num_patches[4])
# self.g2l0 = G2LFusion(input_dim=in_channels[0], embed_dim=crf_dims[0], window_size=win, num_heads=8, depth=2, num_patches=num_patches[5])
# self.conv5 = DoubleConvWOBN(in_channels[5] * 2, in_channels[5], in_channels[5])
# self.conv4 = DoubleConvWOBN(in_channels[4] * 2, in_channels[4], in_channels[4])
# self.conv3 = DoubleConvWOBN(in_channels[3] * 2, in_channels[3], in_channels[3])
# self.conv2 = DoubleConvWOBN(in_channels[2] * 2, in_channels[2], in_channels[2])
# self.conv1 = DoubleConvWOBN(in_channels[1] * 2, in_channels[1], in_channels[1])
# self.conv0 = DoubleConvWOBN(in_channels[0] * 2, in_channels[0], in_channels[0])
def forward(self,
input_tensor,
guide_plus,
guide_cat,
bbox=None,
fine_feat_crop=None,
coarse_feat_whole=None,
coarse_feat_whole_hack=None,
coarse_feat_crop=None):
# apply unscaled feat to swin
if coarse_feat_whole_hack is not None:
coarse_feat_whole = coarse_feat_whole_hack
feat_list = []
x = self.inc(input_tensor)
feat_list.append(x)
for layer in self.down_conv_list:
x = layer(x)
feat_list.append(x)
output = []
feat_inv_list = feat_list[::-1]
for idx, (feat_enc, feat_c) in enumerate(zip(feat_inv_list, coarse_feat_whole)):
# in case for depth-anything
_, _, h, w = feat_enc.shape
if h != feat_c.shape[-2] or w != feat_c.shape[-1]:
feat_enc = F.interpolate(feat_enc, size=feat_c.shape[-2:], mode='bilinear', align_corners=True)
if idx == 0:
pass
else:
feat_enc = self.up_conv_list[idx-1](torch.cat([temp_feat, guide_cat[idx-1]], dim=1), feat_enc)
_, _, h, w = feat_c.shape
feat_c = self.g2l_list[idx](feat_c, None)
feat_c = torch_roi_align(feat_c, bbox, (h, w), h/self.patch_process_shape[0], aligned=True)
x = self.convs[idx](torch.cat([feat_enc, feat_c], dim=1))
temp_feat = x
output.append(x)
return output[::-1] |