Add bake texture related nodes and classes

diff_rast/diff_texturing.py

@@ -1,4 +1,135 @@
-import sys
+import random
 
-from ..shared_utils.common_utils import get_persistent_directory
+import torch
+import torch.nn.functional as F
+import numpy as np
+from pytorch_msssim import SSIM, MS_SSIM
+
+from ..diff_rast.diff_mesh_renderer import Renderer
 from ..shared_utils.camera_utils import orbit_camera, OrbitCamera
+
+class DiffTextureBaker:        
+    
+    def __init__(self, reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy, mesh, 
+                     training_iterations, batch_size, texture_learning_rate, train_mesh_geometry, geometry_learning_rate, ms_ssim_loss_weight, force_cuda_rasterize):
+        self.device = torch.device("cuda")
+        
+        self.ref_imgs_num = len(reference_images)
+
+        self.all_ref_cam_poses = reference_orbit_camera_poses
+        self.ref_cam_fovy = reference_orbit_camera_fovy
+    
+        self.ref_size_H = reference_images[0].shape[0]
+        self.ref_size_W = reference_images[0].shape[1]
+        
+        self.cam = OrbitCamera(self.ref_size_W, self.ref_size_H, fovy=reference_orbit_camera_fovy)
+        
+        # prepare main components for optimization
+        self.renderer = Renderer(mesh, force_cuda_rasterize).to(self.device)
+
+        self.optimizer = torch.optim.Adam(self.renderer.get_params(texture_learning_rate, train_mesh_geometry, geometry_learning_rate))
+        #self.ssim_loss = SSIM(data_range=1, size_average=True, channel=3)
+        self.ms_ssim_loss = MS_SSIM(data_range=1, size_average=True, channel=3)
+        self.lambda_ssim = ms_ssim_loss_weight
+        
+        self.training_iterations = training_iterations
+        
+        self.batch_size = batch_size
+        
+        # prepare reference images and masks
+        ref_imgs_torch_list = []
+        ref_masks_torch_list = []
+        for i in range(self.ref_imgs_num):
+            ref_imgs_torch_list.append(self.prepare_img(reference_images[i]))
+            ref_masks_torch_list.append(self.prepare_img(reference_masks[i].unsqueeze(2)))
+            
+        self.ref_imgs_torch = torch.cat(ref_imgs_torch_list, dim=0)
+        self.ref_masks_torch = torch.cat(ref_masks_torch_list, dim=0)
+    
+    def prepare_img(self, img):
+        img_new = img.permute(2, 0, 1).unsqueeze(0).to(self.device)
+        img_new = F.interpolate(img_new, (self.ref_size_H, self.ref_size_W), mode="bilinear", align_corners=False).contiguous()
+        return img_new
+    
+    def training(self):
+        starter = torch.cuda.Event(enable_timing=True)
+        ender = torch.cuda.Event(enable_timing=True)
+        starter.record()
+        
+        ref_imgs_masked = []
+        for i in range(self.ref_imgs_num):
+            ref_imgs_masked.append((self.ref_imgs_torch[i] * self.ref_masks_torch[i]).unsqueeze(0))
+            
+        ref_imgs_num_minus_1 = self.ref_imgs_num-1
+
+        for step in range(self.training_iterations):
+
+            ### calculate loss between reference and rendered image from known view
+            loss = 0
+            masked_rendered_img_batch = []
+            masked_ref_img_batch = []
+            for _ in range(self.batch_size):
+                
+                i = random.randint(0, ref_imgs_num_minus_1)
+
+                radius, elevation, azimuth, center_X, center_Y, center_Z = self.all_ref_cam_poses[i]
+                
+                # render output
+                orbit_target = np.array([center_X, center_Y, center_Z], dtype=np.float32)
+                ref_pose = orbit_camera(elevation, azimuth, radius, target=orbit_target)
+                ref_cam = (ref_pose, self.cam.perspective)
+                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()))
+
+                image = out["image"]    # [H, W, 3] in [0, 1]
+                image = image.permute(2, 0, 1).contiguous()  # [3, H, W] in [0, 1]
+                
+                #print(f"image.requires_grad: {image.requires_grad}")
+                
+                image_masked = (image * self.ref_masks_torch[i]).unsqueeze(0)
+                
+                #print(f"image_masked.requires_grad: {image_masked.requires_grad}")
+                #print(f"ref_imgs_masked[i].requires_grad: {ref_imgs_masked[i].requires_grad}")
+                
+                masked_rendered_img_batch.append(image_masked)
+                masked_ref_img_batch.append(ref_imgs_masked[i])
+            
+            masked_rendered_img_batch_torch = torch.cat(masked_rendered_img_batch, dim=0)
+            masked_ref_img_batch_torch = torch.cat(masked_ref_img_batch, dim=0)
+                
+            # rgb loss
+            loss += (1 - self.lambda_ssim) * F.mse_loss(masked_rendered_img_batch_torch, masked_ref_img_batch_torch)
+            
+            # D-SSIM loss
+            # [1, 3, H, W] in [0, 1]
+            #loss += self.lambda_ssim * (1 - self.ssim_loss(X, Y))
+            loss += self.lambda_ssim * (1 - self.ms_ssim_loss(masked_ref_img_batch_torch, masked_rendered_img_batch_torch))
+            
+            print(f"masked_rendered_img_batch_torch.requires_grad: {masked_rendered_img_batch_torch.requires_grad}")
+            print(f"masked_ref_img_batch_torch.requires_grad: {masked_ref_img_batch_torch.requires_grad}")
+
+            print(f"loss.requires_grad: {loss.requires_grad}")
+            
+            print(f"self.renderer.raw_albedo.requires_grad: {self.renderer.raw_albedo.requires_grad}")
+
+            # import kiui
+            # kiui.lo(hor, ver)
+            # kiui.vis.plot_image(image)
+
+            # optimize step
+            loss.backward()
+            self.optimizer.step()
+            self.optimizer.zero_grad()
+            
+            torch.cuda.synchronize()
+            
+            self.need_update = True
+            
+            print(f"Step: {step}")
+
+        self.renderer.update_mesh()
+        
+        ender.record()
+        t = starter.elapsed_time(ender)
+        
+    def get_mesh_and_texture(self):
+        return (self.renderer.mesh, self.renderer.mesh.albedo, )