MrForExample
fa562f1883
- Added 3DGS - Solved the problem of auto_grad fail using with torch.inference_mode(False): since torch in ComfyUI is set to inference mode by default - Testing Example workflows & Updated the documents
120 lines
5.0 KiB
Python
120 lines
5.0 KiB
Python
import random
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import tqdm
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import torch
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import torch.nn.functional as F
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import numpy as np
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from pytorch_msssim import SSIM, MS_SSIM
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from ..diff_rast.diff_mesh_renderer import Renderer
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from ..shared_utils.camera_utils import orbit_camera, OrbitCamera
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class DiffTextureBaker:
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def __init__(self, reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy, mesh,
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training_iterations, batch_size, texture_learning_rate, train_mesh_geometry, geometry_learning_rate, ms_ssim_loss_weight, force_cuda_rasterize):
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self.device = torch.device("cuda")
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self.ref_imgs_num = len(reference_images)
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self.all_ref_cam_poses = reference_orbit_camera_poses
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self.ref_cam_fovy = reference_orbit_camera_fovy
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self.ref_size_H = reference_images[0].shape[0]
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self.ref_size_W = reference_images[0].shape[1]
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self.cam = OrbitCamera(self.ref_size_W, self.ref_size_H, fovy=reference_orbit_camera_fovy)
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# prepare main components for optimization
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self.renderer = Renderer(mesh, force_cuda_rasterize).to(self.device)
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self.optimizer = torch.optim.Adam(self.renderer.get_params(texture_learning_rate, train_mesh_geometry, geometry_learning_rate))
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#self.ssim_loss = SSIM(data_range=1, size_average=True, channel=3)
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self.ms_ssim_loss = MS_SSIM(data_range=1, size_average=True, channel=3)
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self.lambda_ssim = ms_ssim_loss_weight
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self.training_iterations = training_iterations
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self.batch_size = batch_size
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# prepare reference images and masks
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ref_imgs_torch_list = []
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ref_masks_torch_list = []
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for i in range(self.ref_imgs_num):
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ref_imgs_torch_list.append(self.prepare_img(reference_images[i]))
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ref_masks_torch_list.append(self.prepare_img(reference_masks[i].unsqueeze(2)))
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self.ref_imgs_torch = torch.cat(ref_imgs_torch_list, dim=0)
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self.ref_masks_torch = torch.cat(ref_masks_torch_list, dim=0)
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def prepare_img(self, img):
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img_new = img.permute(2, 0, 1).unsqueeze(0).to(self.device)
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img_new = F.interpolate(img_new, (self.ref_size_H, self.ref_size_W), mode="bilinear", align_corners=False).contiguous()
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return img_new
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def training(self):
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starter = torch.cuda.Event(enable_timing=True)
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ender = torch.cuda.Event(enable_timing=True)
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starter.record()
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ref_imgs_masked = []
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for i in range(self.ref_imgs_num):
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ref_imgs_masked.append((self.ref_imgs_torch[i] * self.ref_masks_torch[i]).unsqueeze(0))
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ref_imgs_num_minus_1 = self.ref_imgs_num-1
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for step in tqdm.trange(self.training_iterations):
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### calculate loss between reference and rendered image from known view
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loss = 0
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masked_rendered_img_batch = []
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masked_ref_img_batch = []
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for _ in range(self.batch_size):
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i = random.randint(0, ref_imgs_num_minus_1)
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radius, elevation, azimuth, center_X, center_Y, center_Z = self.all_ref_cam_poses[i]
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# render output
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orbit_target = np.array([center_X, center_Y, center_Z], dtype=np.float32)
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ref_pose = orbit_camera(elevation, azimuth, radius, target=orbit_target)
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ref_cam = (ref_pose, self.cam.perspective)
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out = self.renderer.render(*ref_cam, self.ref_size_H, self.ref_size_W, ssaa=1) #ssaa = min(2.0, max(0.125, 2 * np.random.random()))
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image = out["image"] # [H, W, 3] in [0, 1]
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image = image.permute(2, 0, 1).contiguous() # [3, H, W] in [0, 1]
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image_masked = (image * self.ref_masks_torch[i]).unsqueeze(0)
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masked_rendered_img_batch.append(image_masked)
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masked_ref_img_batch.append(ref_imgs_masked[i])
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masked_rendered_img_batch_torch = torch.cat(masked_rendered_img_batch, dim=0)
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masked_ref_img_batch_torch = torch.cat(masked_ref_img_batch, dim=0)
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# rgb loss
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loss += (1 - self.lambda_ssim) * F.mse_loss(masked_rendered_img_batch_torch, masked_ref_img_batch_torch)
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# D-SSIM loss
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# [1, 3, H, W] in [0, 1]
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#loss += self.lambda_ssim * (1 - self.ssim_loss(X, Y))
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loss += self.lambda_ssim * (1 - self.ms_ssim_loss(masked_ref_img_batch_torch, masked_rendered_img_batch_torch))
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# optimize step
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loss.backward()
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self.optimizer.step()
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self.optimizer.zero_grad()
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torch.cuda.synchronize()
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self.need_update = True
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print(f"Step: {step}")
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self.renderer.update_mesh()
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ender.record()
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#t = starter.elapsed_time(ender)
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def get_mesh_and_texture(self):
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return (self.renderer.mesh, self.renderer.mesh.albedo, ) |