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import torch
from torch import nn
from torch.nn import init
from torchvision.models import resnet34, resnet50
import torchvision.models.vision_transformer as vit
class LightSourceRegressor(nn.Module):
def __init__(self, num_lights=4, alpha=2.0, beta=8.0, **kwargs):
super(LightSourceRegressor, self).__init__()
self.num_lights = num_lights
self.alpha = alpha
self.beta = beta
self.model = resnet34(pretrained=True)
# self.model = resnet50(pretrained=True)
# self.model = vit.vit_b_16(pretrained=True)
self.init_resnet()
# self.init_vit()
self.xyr_mlp = nn.Sequential(
nn.Linear(self.last_dim, 3 * self.num_lights),
)
self.p_mlp = nn.Sequential(
nn.Linear(self.last_dim, self.num_lights),
nn.Sigmoid(), # ensure p is in [0, 1]
)
def init_resnet(self):
self.last_dim = self.model.fc.in_features
self.model.fc = nn.Identity()
def init_vit(self):
self.model.image_size = 512
old_pos_embed = self.model.encoder.pos_embedding
num_patches_old = (224 // 16) ** 2
num_patches_new = (512 // 16) ** 2
if num_patches_new != num_patches_old:
old_pos_embed = old_pos_embed[:, 1:]
old_pos_embed = nn.functional.interpolate(
old_pos_embed.permute(0, 2, 1), size=(num_patches_new,), mode="linear"
)
old_pos_embed = old_pos_embed.permute(0, 2, 1)
# new positional embedding
self.model.encoder.pos_embedding = nn.Parameter(
torch.cat(
[self.model.encoder.pos_embedding[:, :1], old_pos_embed], dim=1
)
)
# num_classes = 4 * self.num_lights # x, y, r, p
# self.model.heads.head = nn.Linear(self.model.hidden_dim, num_classes)
# remove the head
self.last_dim = self.model.hidden_dim
self.model.heads.head = nn.Identity()
def forward(self, x, height=512, width=512, smoothness=0.1, merge=False):
_x = self.model(x) # [B, last_dim]
_xyr = self.xyr_mlp(_x)
_xyr = _xyr.view(-1, self.num_lights, 3)
_p = self.p_mlp(_x)
_p = _p.view(-1, self.num_lights)
output = torch.cat([_xyr, _p.unsqueeze(-1)], dim=-1)
return output
def forward_render(self, x, height=512, width=512, smoothness=0.1, merge=False):
_x = self.forward(x)
_xy = _x[:, :, :2]
_r = _x[:, :, 2]
_p = _x[:, :, 3]
masks = None
masks_merge = None
for b in range(_x.size(0)):
x, y, r = _xy[b, :, 0] * width, _xy[b, :, 1] * width, _r[b] * width / 2
p = _p[b]
mask_list = []
for i in range(self.num_lights):
if r[i] < 0 or r[i] > width or p[i] < 0.5:
continue
y_coords, x_coords = torch.meshgrid(
torch.arange(height, device=x.device),
torch.arange(width, device=x.device),
indexing="ij",
)
distances = torch.sqrt((x_coords - x[i]) ** 2 + (y_coords - y[i]) ** 2)
mask_i = torch.sigmoid(smoothness * (r[i] - distances))
mask_list.append(mask_i)
if len(mask_list) == 0:
_mask_merge = torch.zeros(1, 1, height, width, device=x.device)
else:
_mask_merge = torch.stack(mask_list, dim=0).sum(dim=0).unsqueeze(0)
_mask_merge = _mask_merge.unsqueeze(0)
masks_merge = (
_mask_merge
if masks_merge is None
else torch.cat([masks_merge, _mask_merge], dim=0)
)
masks_merge = torch.clamp(masks_merge, 0, 1)
return masks_merge # [B, 1, H, W]
if __name__ == "__main__":
# pydiffvg.set_use_gpu(torch.cuda.is_available())
model = LightSourceRegressor(num_lights=4).cuda()
x = torch.randn(8, 3, 512, 512, device="cuda")
y = model.forward_render(x)
print(y.shape)
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