def parse_scene(node):
cam = None
resolution = None
bsdfs = []
bsdf_dict = {}
shapes = []
lights = []
medium_dict = {}
mediums = []
phases = []
for child in node:
if child.tag == 'sensor':
cam = parse_camera(child, medium_dict)
elif child.tag == 'bsdf':
node_id, bsdf = parse_bsdf(child)
if node_id is not None:
bsdf_dict[node_id] = len(bsdfs)
bsdfs.append(bsdf)
elif child.tag == 'medium':
node_id, medium, phase = parse_medium(child)
if node_id is not None:
medium_dict[node_id] = len(mediums)
mediums.append(medium)
medium.phase_id = len(phases)
phases.append(phase)
elif child.tag == 'shape':
shape, light = parse_shape(child, bsdf_dict, medium_dict, len(shapes))
shapes.append(shape)
if light is not None:
lights.append(light)
elif child.tag == 'integrator':
if child.attrib['type'] == 'direct':
integrator = dtrt.DirectIntegrator();
elif child.attrib['type'] == 'path':
integrator = dtrt.PathTracer();
elif child.attrib['type'] == 'volpath_simple':
integrator = dtrt.VolPathTracerSimple();
elif child.attrib['type'] == 'volpath':
integrator = dtrt.VolPathTracer();
elif child.attrib['type'] == 'directAD':
integrator = dtrt.DirectAD();
elif child.attrib['type'] == 'pathAD':
integrator = dtrt.PathTracerAD();
elif child.attrib['type'] == 'volpathAD':
integrator = dtrt.VolPathTracerAD();
elif child.attrib['type'] == 'ptracer':
integrator = dtrt.ParticleTracer();
else:
raise Exception("Integrator type [ %s ] not supported!" % child.attrib['type'])
# print("Rendering using [ %s ] ..." % child.attrib['type'])
return pydtrt.Scene(cam, shapes, bsdfs, mediums, phases, lights), integrator
transform_list.append([T3])
scene_manager = SceneManager(args_init, transform_list)
init = pydtrt.render_scene(integrator, options, *(scene_manager.args))
pydtrt.imwrite(init[0, :, :, :], direc + 'init.exr')
integrator = dtrt.VolPathTracer()
# target_var = torch.tensor([0.7])
target_var = torch.tensor([-1.6, 1.2, -1.5])
print("Target variable values: ", target_var)
scene_manager.set_arguments(target_var)
target = pydtrt.render_scene(integrator, options, *(scene_manager.args))
target = target[0, :, :, :]
pydtrt.imwrite(target, direc + 'target.exr')
integrator = dtrt.VolPathTracerAD()
scene_manager.reset()
lossFunc = pydtrt.ADLossFunc.apply
optimizer = torch.optim.Adam([var_list], lr=1e-1)
grad_out_range = torch.tensor([0] * dtrt.nder, dtype=torch.float)
if output_loss:
file1 = open(direc + 'loss.txt', 'w')
if output_loss:
file2 = open(direc + 'param.txt', 'w')
for t in range(500):
print('iteration:', t)
optimizer.zero_grad()
options.seed = t + 1
img = lossFunc(scene_manager, integrator, options, var_list,
grad_out_range,