UniqAI/gaussian-splatting
1
0
Fork 0
mirror of https://github.com/graphdeco-inria/gaussian-splatting synced 2024-11-22 08:18:17 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

Support for varying image sizes

Browse Source
This commit is contained in:
bkerbl 2023-07-14 01:52:00 +02:00
parent 017da305b1
commit e46b49543f
3 changed files with 9 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
scene/dataset_readers.py
View File

@ -205,8 +205,8 @@ def readCamerasFromTransforms(path, transformsfile, white_background, extension=
image = Image.fromarray(np.array(arr*255.0, dtype=np.byte), "RGB")
fovy = focal2fov(fov2focal(fovx, image.size[0]), image.size[1])
FovY = fovx
FovX = fovy
FovY = fovy
FovX = fovx
cam_infos.append(CameraInfo(uid=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
image_path=image_path, image_name=image_name, width=image.size[0], height=image.size[1]))

2
submodules/diff-gaussian-rasterization

@ -1 +1 @@
Subproject commit fc0cfe904a7870245437d9bfe17f819d9260281d
Subproject commit 936b141c89c3e8cb9f34ca352ce09f1552aa24a2

13
train.py
View File

@ -154,20 +154,19 @@ def training_report(tb_writer, iteration, Ll1, loss, l1_loss, elapsed, testing_i
for config in validation_configs:
if config['cameras'] and len(config['cameras']) > 0:
images = torch.tensor([], device="cuda")
gts = torch.tensor([], device="cuda")
l1_test = 0
psnr_test = 0
for idx, viewpoint in enumerate(config['cameras']):
image = torch.clamp(renderFunc(viewpoint, scene.gaussians, *renderArgs)["render"], 0.0, 1.0)
gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
images = torch.cat((images, image.unsqueeze(0)), dim=0)
gts = torch.cat((gts, gt_image.unsqueeze(0)), dim=0)
if tb_writer and (idx < 5):
tb_writer.add_images(config['name'] + "_view_{}/render".format(viewpoint.image_name), image[None], global_step=iteration)
if iteration == testing_iterations[0]:
tb_writer.add_images(config['name'] + "_view_{}/ground_truth".format(viewpoint.image_name), gt_image[None], global_step=iteration)
l1_test = l1_loss(images, gts)
psnr_test = psnr(images, gts).mean()
l1_test += l1_loss(image, gt_image).mean()
psnr_test += psnr(image, gt_image).mean()
psnr_test /= len(config['cameras'])
l1_test /= len(config['cameras'])
print("\n[ITER {}] Evaluating {}: L1 {} PSNR {}".format(iteration, config['name'], l1_test, psnr_test))
if tb_writer:
tb_writer.add_scalar(config['name'] + '/loss_viewpoint - l1_loss', l1_test, iteration)