Replies: 1 comment 3 replies
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Hi @miangoleh This should happen by default, if you don't mess with the parameters/optimizers. Here's an example in one of our tutorials: https://mitsuba.readthedocs.io/en/stable/src/inverse_rendering/radiance_field_reconstruction.html#Optimization |
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I have a very simple scene with a mesh and envmap. I am optimizing the "scale" parameter of an environment map. When I tried printing the grads after each loss.backward call with {dr.ravel(dr.grad(opt[k]))} I was expecting the grad values to change each time (accumulate) but the first computed gradient stays the same untill opt.step is called. I noticed however the optimization seems to be fine! (The results are very similar to the case where opt.step is called after each loss.backward). for it in tqdm(range(args.niters), desc="Optimization Iterations"):
inner_tqdm = tqdm(range(nviews), desc="Per-view Loss", leave=False)
for view_idx in inner_tqdm:
# render
rendered = mi.render(scene, params=scene_params, sensor=view_idx, spp=args.opt_spp, seed=it)
# reference
ref_image, ref_mask = dataset.get_reference_image(view_idx)
ref_mask = np.stack([ref_mask]*3, axis=-1)
# convert to mitsuba
ref_image_mi = mi.TensorXf(ref_image)
ref_mask_mi = mi.TensorXf(ref_mask)
loss = l1loss(rendered, ref_image_mi, ref_mask_mi)
inner_tqdm.set_description(f"(it:{it} view:{view_idx}) loss: {np.array(loss):.4f}")
dr.backward(loss)
opt.step()
scene_params.update(opt) |
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That sounds like a bug, could you provide us a with a small reproducer ? |
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It seems to happen when I am optimizing a latent_variable or geometry optimization as in "u" parameter of the LargeStep algorithm. For example I am trying to optimize the radiance of an area light. When I directly use the scene param key as the parameter gradients are updated after each call to backward. However when I define a latent variable and update the scene params using that the gradients only update after opt.step() is called. *** I am using "light2.emitter.radiance_value" as latent variable to update scene_param[light2.emitter.radiance.value] |
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I am trying to only perform the opt.step() after accumulating grads from a few different samples.
However by probing the opt it seems that gradients are not accumulated and only the first computation is kept. Is there a way to achieve the similar effect as of using batches/mini batches in training neural networks?
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