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aff3c6f f10f497 aff3c6f f10f497 aff3c6f f10f497 aff3c6f f10f497 aff3c6f f10f497 aff3c6f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import clip
import sys
import numpy as np
from models.seg_post_model.models import SegModel
from torchvision.ops import roi_align
class Counting_with_SD_features_loca(nn.Module):
def __init__(self, scale_factor):
super(Counting_with_SD_features_loca, self).__init__()
self.adapter = adapter_roi_loca()
self.regressor = regressor_with_SD_features()
class Counting_with_SD_features_dino_vit_c3(nn.Module):
def __init__(self, scale_factor, vit=None):
super(Counting_with_SD_features_dino_vit_c3, self).__init__()
self.adapter = adapter_roi_loca()
self.regressor = regressor_with_SD_features_seg_vit_c3()
class Counting_with_SD_features_track(nn.Module):
def __init__(self, scale_factor, vit=None):
super(Counting_with_SD_features_track, self).__init__()
self.adapter = adapter_roi_loca()
self.regressor = regressor_with_SD_features_tra()
class adapter_roi_loca(nn.Module):
def __init__(self, pool_size=[3, 3]):
super(adapter_roi_loca, self).__init__()
self.pool_size = pool_size
self.conv1 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(2)
self.fc = nn.Linear(256 * 3 * 3, 768)
self.initialize_weights()
def forward(self, x, boxes):
num_of_boxes = boxes.shape[1]
rois = []
bs, _, h, w = x.shape
if h != 512 or w != 512:
x = F.interpolate(x, size=(512, 512), mode='bilinear', align_corners=False)
if bs == 1:
boxes = torch.cat([
torch.arange(
bs, requires_grad=False
).to(boxes.device).repeat_interleave(num_of_boxes).reshape(-1, 1),
boxes.flatten(0, 1),
], dim=1)
rois = roi_align(
x,
boxes=boxes, output_size=3,
spatial_scale=1.0 / 8, aligned=True
)
rois = torch.mean(rois, dim=0, keepdim=True)
else:
boxes = torch.cat([
boxes.flatten(0, 1),
], dim=1).split(num_of_boxes, dim=0)
rois = roi_align(
x,
boxes=boxes, output_size=3,
spatial_scale=1.0 / 8, aligned=True
)
rois = rois.split(num_of_boxes, dim=0)
rois = torch.stack(rois, dim=0)
rois = torch.mean(rois, dim=1, keepdim=False)
x = self.conv1(rois)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def forward_boxes(self, x, boxes):
num_of_boxes = boxes.shape[1]
rois = []
bs, _, h, w = x.shape
if h != 512 or w != 512:
x = F.interpolate(x, size=(512, 512), mode='bilinear', align_corners=False)
if bs == 1:
boxes = torch.cat([
torch.arange(
bs, requires_grad=False
).to(boxes.device).repeat_interleave(num_of_boxes).reshape(-1, 1),
boxes.flatten(0, 1),
], dim=1)
rois = roi_align(
x,
boxes=boxes, output_size=3,
spatial_scale=1.0 / 8, aligned=True
)
# rois = torch.mean(rois, dim=0, keepdim=True)
else:
raise NotImplementedError
x = self.conv1(rois)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
class regressor_with_SD_features(nn.Module):
def __init__(self):
super(regressor_with_SD_features, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(324, 256, kernel_size=1, stride=1),
nn.LeakyReLU(),
nn.LayerNorm((64, 64))
)
self.layer2 = nn.Sequential(
nn.Conv2d(256, 128, kernel_size=3, padding=1),
nn.LeakyReLU(),
nn.ConvTranspose2d(in_channels=128, out_channels=128, kernel_size=4, stride=2, padding=1),
)
self.layer3 = nn.Sequential(
nn.Conv2d(128, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(in_channels=64, out_channels=64, kernel_size=4, stride=2, padding=1),
)
self.layer4 = nn.Sequential(
nn.Conv2d(64, 32, kernel_size=3, padding=1),
nn.LeakyReLU(),
nn.ConvTranspose2d(in_channels=32, out_channels=32, kernel_size=4, stride=2, padding=1),
)
self.conv = nn.Sequential(
nn.Conv2d(32, 1, kernel_size=1),
nn.ReLU()
)
self.norm = nn.LayerNorm(normalized_shape=(64, 64))
self.initialize_weights()
def forward(self, attn_stack, feature_list):
attn_stack = self.norm(attn_stack)
unet_feature = feature_list[-1]
attn_stack_mean = torch.mean(attn_stack, dim=1, keepdim=True)
unet_feature = unet_feature * attn_stack_mean
unet_feature = torch.cat([unet_feature, attn_stack], dim=1) # [1, 324, 64, 64]
x = self.layer1(unet_feature)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
out = self.conv(x)
return out / 100
def initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
from models.enc_model.unet_parts import *
class regressor_with_SD_features_seg_vit_c3(nn.Module):
def __init__(self, n_channels=3, n_classes=2, bilinear=False):
super(regressor_with_SD_features_seg_vit_c3, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.norm = nn.LayerNorm(normalized_shape=(64, 64))
self.inc_0 = nn.Conv2d(n_channels, 3, kernel_size=3, padding=1)
self.vit_model = SegModel(gpu=True, nchan=3, pretrained_model="", use_bfloat16=False)
self.vit = self.vit_model.net
def forward(self, img, attn_stack, feature_list):
attn_stack = attn_stack[:, [1,3], ...]
attn_stack = self.norm(attn_stack)
unet_feature = feature_list[-1]
unet_feature_mean = torch.mean(unet_feature, dim=1, keepdim=True)
x = torch.cat([unet_feature_mean, attn_stack], dim=1) # [1, 324, 64, 64]
if x.shape[-1] != 512:
x = F.interpolate(x, size=(512, 512), mode="bilinear")
x = self.inc_0(x)
out = self.vit_model.eval(img.squeeze().cpu().numpy(), feat=x.squeeze().cpu().numpy())
if out.dtype == np.uint16:
out = out.astype(np.int16)
out = torch.from_numpy(out).unsqueeze(0).to(x.device)
return out
def initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
class regressor_with_SD_features_tra(nn.Module):
def __init__(self, n_channels=2, n_classes=2, bilinear=False):
super(regressor_with_SD_features_tra, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.norm = nn.LayerNorm(normalized_shape=(64, 64))
# segmentation
self.inc_0 = nn.Conv2d(3, 3, kernel_size=3, padding=1)
self.vit_model = SegModel(gpu=True, nchan=3, pretrained_model="", use_bfloat16=False)
self.vit = self.vit_model.net
self.inc_1 = nn.Conv2d(n_channels, 1, kernel_size=3, padding=1)
self.mlp = nn.Linear(64 * 64, 320)
def forward_seg(self, img, attn_stack, feature_list, mask, training=False):
attn_stack = attn_stack[:, [1,3], ...]
attn_stack = self.norm(attn_stack)
unet_feature = feature_list[-1]
unet_feature_mean = torch.mean(unet_feature, dim=1, keepdim=True)
x = torch.cat([unet_feature_mean, attn_stack], dim=1) # [1, 324, 64, 64]
if x.shape[-1] != 512:
x = F.interpolate(x, size=(512, 512), mode="bilinear")
x = self.inc_0(x)
feat = x
out = self.vit_model.eval(img.squeeze().cpu().numpy(), feat=x.squeeze().cpu().numpy())
if out.dtype == np.uint16:
out = out.astype(np.int16)
out = torch.from_numpy(out).unsqueeze(0).to(x.device)
return out, 0., feat
def forward(self, attn_prev, feature_list_prev, attn_after, feature_list_after):
assert attn_prev.shape == attn_after.shape, "attn_prev and attn_after must have the same shape"
n_instances = attn_prev.shape[0]
attn_prev = self.norm(attn_prev) # [n_instances, 1, 64, 64]
attn_after = self.norm(attn_after)
x = torch.cat([attn_prev, attn_after], dim=1) # n_instances, 2, 64, 64
x = self.inc_1(x)
x = x.view(1, n_instances, -1) # Flatten the tensor to [n_instances, 64*64*4]
x = self.mlp(x) # Apply the MLP to get the output
return x # Output shape will be [n_instances, 4]
def initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
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