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import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualRenderBlock(nn.Module):
def __init__(self, dim):
super().__init__()
self.block = nn.Sequential(
nn.Conv2d(dim, dim, kernel_size=3, padding=1),
nn.GroupNorm(8, dim),
nn.SiLU(),
nn.Conv2d(dim, dim, kernel_size=3, padding=1),
nn.GroupNorm(8, dim)
)
def forward(self, x):
return x + self.block(x)
class RenderEncoder(nn.Module):
def __init__(self, encoder_type="1d", in_channels=768, out_channels=3):
super().__init__()
self.encoder_type = encoder_type
if encoder_type == "1d":
self.model = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1),
nn.Sigmoid()
)
elif encoder_type == "residual":
self.model = ResidualBlockRender(in_channels, out_channels)
elif encoder_type == "expressive":
mid_channels = 256
self.model = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
nn.GroupNorm(8, mid_channels),
nn.SiLU(),
ResidualRenderBlock(mid_channels),
ResidualRenderBlock(mid_channels),
ResidualRenderBlock(mid_channels),
nn.Conv2d(mid_channels, out_channels, kernel_size=1),
nn.Sigmoid()
)
else:
raise ValueError(f"Unknown encoder_type '{encoder_type}'. Use '1d', 'residual', or 'expressive'.")
def forward(self, x):
return self.model(x)
class ResidualBlockRender(nn.Module):
def __init__(self, in_channels=768, out_channels=3):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, 256, kernel_size=3, padding=1)
self.relu1 = nn.ReLU()
self.conv2 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.relu2 = nn.ReLU()
self.conv3 = nn.Conv2d(256, out_channels, kernel_size=1)
self.out = nn.Sigmoid()
if in_channels != out_channels:
self.residual_proj = nn.Conv2d(in_channels, out_channels, kernel_size=1)
else:
self.residual_proj = nn.Identity()
def forward(self, x):
residual = self.residual_proj(x)
h = self.relu1(self.conv1(x))
h = self.relu2(self.conv2(h))
h = self.conv3(h)
h = h + residual
return self.out(h)
def load_render_encoder(checkpoint_path, device='cpu'):
"""Load standalone RenderEncoder from checkpoint"""
checkpoint = torch.load(checkpoint_path, map_location=device)
config = checkpoint['model_config']
model = RenderEncoder(
encoder_type=config['encoder_type'],
in_channels=config['in_channels'],
out_channels=config['out_channels']
)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
print(f"Loaded RenderEncoder: {config}")
return model |