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experiments.py
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import mitsuba as mi
mi.set_variant('cuda_ad_rgb')
import drjit as dr
import numpy as np
import os
import sys
import argparse
import plugins
from optimize import run_opt
from largesteps import *
from mitsuba.scalar_rgb import Transform4f as T
SCENES_DIR = os.path.join(os.path.dirname(__file__), "scenes")
OUTPUT_DIR = os.path.join(os.path.dirname(__file__), "output")
def scene_janga(method, output_dir):
sensor_count = 8
# Visualisation resolution
final_res = (720*sensor_count, 720)
# Training resolution
resx = 256
resy = 256
batch_sensor = {
'type': 'batch',
'film': {
'type': 'hdrfilm',
'width': resx*sensor_count, 'height': resy,
},
'sampler': {
'type': 'independent',
'sample_count': 1
}
}
scale = [1.0, 1.2, 1.5]
target = [0.5 * s for s in scale]
d = 2
h = 0
for i in range(sensor_count):
theta = 2*np.pi / sensor_count * i + 0.1
batch_sensor[f"sensor_{i:02d}"] = {
'type': 'perspective',
'fov': 45,
'to_world': T.look_at(target=target, origin=[target[0]+d*np.cos(theta), h, target[2]+d*np.sin(theta)], up=[0, 1, 0]),
}
medium = {
'type': 'heterogeneous',
'sigma_t': {
'type': 'gridvolume',
'filename': os.path.join(SCENES_DIR, 'janga-smoke/volumes/janga-smoke-264-136-136.vol'),
'to_world': T.scale(scale),
'use_grid_bbox': False,
'accel': False
},
'albedo': {
'type': 'gridvolume',
'filename': os.path.join(SCENES_DIR, 'janga-smoke/volumes/albedo-noise-256-128-128.vol'),
'to_world': T.scale(scale),
},
'scale': 20.0,
'sample_emitters': True,
'has_spectral_extinction': False,
'majorant_resolution_factor': 0
}
scene_dict = {
'type': 'scene',
'object': {
'type': 'obj',
'filename': os.path.join(SCENES_DIR, 'common/meshes/cube_unit.obj'),
'bsdf': {'type': 'null'},
'to_world': T.scale(scale),
},
'envmap': {
'type': 'envmap',
'filename': os.path.join(SCENES_DIR, 'common/textures/alps_field_4k.exr'),
'scale': 0.5,
},
'sensor': batch_sensor
}
if method == 'cv_ps':
scene_dict['object']['interior'] = {
'type': 'twostatemedium',
'old': medium,
'new': medium,
'incoming': medium
}
else:
scene_dict['object']['interior'] = medium
v_res = 16
scene = mi.load_dict(scene_dict)
params = {
'scene': scene,
'variables': {
'object.interior_medium.sigma_t.data': {
'init': dr.full(mi.TensorXf, 0.04, (v_res, v_res, v_res, 1)),
'clamp': (0.0, 250.0),
},
'object.interior_medium.albedo.data': {
'init': dr.full(mi.TensorXf, 0.6, (v_res, v_res, v_res, 3)),
'clamp': (0.0, 1.0),
'lr_factor': 2.0
},
},
'use_majorant_supergrid': True,
'recomp_freq': 50,
'upsample': [0.04, 0.16, 0.36, 0.64],
'schedule': [0.75, 0.85, 0.95],
'save': True,
'benchmark': True,
'n_steps': 2000,
'method': method,
'lr': 5e-3,
'spp': 4,
'spp_grad': 4,
'spp_ref': 128,
'ref_passes': 16,
'spp_inf': 64,
'final_res': final_res,
'output_dir': output_dir,
'integrator': mi.load_dict({'type': 'twostateprbvolpath' if method == 'cv_ps' else 'prbvolpath', 'max_depth': 64, 'rr_depth': 64}),
'adjoint_integrator': mi.load_dict({'type': 'volpathsimple', 'max_depth': 64, 'rr_depth': 64})
}
return params
def scene_dust_devil(method, output_dir):
v_res = 16
sensor_count = 8
final_res = (720*sensor_count, 1280)
resx = 256
resy = 456
batch_sensor = {
'type': 'batch',
'film': {
'type': 'hdrfilm',
'width': resx*sensor_count, 'height': resy,
},
'sampler': {
'type': 'independent',
'sample_count': 1
}
}
target = [0.5, 0.5, 0.5]
d = 1
h = 0.5
for i in range(sensor_count):
theta = 2*np.pi / sensor_count * i
batch_sensor[f"sensor_{i:02d}"] = {
'type': 'perspective',
'fov': 45,
'to_world': T.look_at(target=target, origin=[target[0]+d*np.cos(theta), h, target[2]+d*np.sin(theta)], up=[0, 1, 0]),
}
medium = {
'type': 'heterogeneous',
'sigma_t': {
'type': 'gridvolume',
'filename': os.path.join(SCENES_DIR, 'dust-devil/volumes/embergen_dust_devil_tornado_a_50-256-256-256.vol'),
'use_grid_bbox': False,
'accel': False
},
'albedo': {
'type': 'gridvolume',
'filename': os.path.join(SCENES_DIR, 'dust-devil/volumes/albedo-constant-sand-256-256-256.vol'),
'use_grid_bbox': False,
'accel': False
},
'phase': {
'type': 'hg',
'g': 0.877
},
'scale': 100.0,
'sample_emitters': True,
'has_spectral_extinction': False,
'majorant_resolution_factor': 0
}
scene_dict = {
'type': 'scene',
'object': {
'type': 'obj',
'filename': os.path.join(SCENES_DIR, 'common/meshes/cube_unit.obj'),
'bsdf': {'type': 'null'},
},
'envmap': {
'type': 'envmap',
'filename': os.path.join(SCENES_DIR, 'common/textures/kloofendal_38d_partly_cloudy_4k.exr'),
'scale': 1.0,
},
'sensor': batch_sensor
}
if method == 'cv_ps':
scene_dict['object']['interior'] = {
'type': 'twostatemedium',
'old': medium,
'new': medium,
'incoming': medium
}
else:
scene_dict['object']['interior'] = medium
scene = mi.load_dict(scene_dict)
params = {
'scene': scene,
'variables': {
'object.interior_medium.sigma_t.data': {
'init': dr.full(mi.TensorXf, 0.04, (v_res, v_res, v_res, 1)),
'clamp': (0.0, 250.0),
},
'object.interior_medium.albedo.data': {
'init': dr.full(mi.TensorXf, 0.6, (v_res, v_res, v_res, 3)),
'clamp': (0.0, 1.0),
'lr_factor': 2.0
},
},
'use_majorant_supergrid': True,
'recomp_freq': 50,
'upsample': [0.04, 0.16, 0.36, 0.64],
'schedule': [0.75, 0.85, 0.95],
'save': True,
'benchmark': True,
'n_steps': 2000,
'method': method,
'lr': 5e-4,
'spp': 16,
'spp_grad': 16,
'spp_ref': 128,
'ref_passes': 16,
'spp_inf': 64,
'final_res': final_res,
'output_dir': output_dir,
'integrator': mi.load_dict({'type': 'twostateprbvolpath' if method == 'cv_ps' else 'prbvolpath', 'max_depth': 64, 'rr_depth': 64}),
'adjoint_integrator': mi.load_dict({'type': 'volpathsimple', 'max_depth': 64, 'rr_depth': 64})
}
return params
def scene_rover(method, output_dir):
sensor_count = 10
final_res = (720*sensor_count), 720
resx = 256
resy = 256
v_res = 16
batch_sensor = {
'type': 'batch',
'film': {
'type': 'hdrfilm',
'width': resx*sensor_count, 'height': resy,
},
'sampler': {
'type': 'independent',
'sample_count': 1
}
}
scale = [6.5, 7.5, 10]
tr = [-3, -1, -5]
target = [-0.25, 2.75, 0]
d = 12
h = 10.0
for i in range(sensor_count):
theta = 2*np.pi / sensor_count * i + 0.1
batch_sensor[f"sensor_{i:02d}"] = {
'type': 'perspective',
'fov': 45,
'to_world': T.look_at(target=target, origin=[target[0]+d*np.cos(theta), h, target[2]+d*np.sin(theta)], up=[0, 1, 0]),
}
# Render scene ref here
to_world = T.translate(tr).scale(scale)
medium = {
'type': 'heterogeneous',
'sigma_t': {
'type': 'gridvolume',
'filename': os.path.join(SCENES_DIR, 'common/volumes/sigma_t-constant-sand-256-256-256.vol'),
'to_world': to_world,
'use_grid_bbox': False,
'accel': False
},
'albedo': {
'type': 'gridvolume',
'filename': os.path.join(SCENES_DIR, 'common/volumes/albedo-constant-sand-256-256-256.vol'),
'to_world': to_world,
'use_grid_bbox': False,
'accel': False
},
'scale': 1.0,
'sample_emitters': True,
'has_spectral_extinction': False,
'majorant_resolution_factor': 8
}
scene_dict = {
'type': 'scene',
'object': {
'type': 'obj',
'filename': os.path.join(SCENES_DIR, 'common/meshes/cube_unit.obj'),
'bsdf': {'type': 'null'},
'to_world': to_world,
},
'envmap': {
'type': 'envmap',
'filename': os.path.join(SCENES_DIR, 'common/textures/clarens_night_02_4k.exr'),
},
'sensor': batch_sensor
}
if method == 'cv_ps':
scene_dict['object']['interior'] = {
'type': 'twostatemedium',
'old': medium,
'new': medium,
'incoming': medium
}
else:
scene_dict['object']['interior'] = medium
params = {
'scene': mi.load_dict(scene_dict),
'variables': {
'object.interior_medium.sigma_t.data': {
'init': dr.full(mi.TensorXf, 0.04, (v_res, v_res, v_res, 1)),
'clamp': (0.0, 250.0),
},
'object.interior_medium.albedo.data': {
'init': dr.full(mi.TensorXf, 0.6, (v_res, v_res, v_res, 3)),
'clamp': (0.0, 1.0),
'lr_factor': 2.0
},
},
'upsample': [0.04, 0.16, 0.36, 0.64],
'schedule': [0.75, 0.85, 0.95],
'save': True,
'benchmark': True,
'n_steps': 2000,
'recomp_freq': 50,
'method': method,
'lr': 1e-2,
'spp': 8,
'spp_grad': 8,
'spp_ref': 128,
'ref_passes': 16,
'spp_inf': 64,
'final_res': final_res,
'output_dir': output_dir,
'integrator': mi.load_dict({'type': 'twostateprbvolpath' if method == 'cv_ps' else 'prbvolpath', 'max_depth': 64, 'rr_depth': 64}),
'adjoint_integrator': mi.load_dict({'type': 'volpathsimple', 'max_depth': 64, 'rr_depth': 64})
}
ref_name = os.path.join(os.path.dirname(output_dir), "img_ref.exr")
if not os.path.exists(ref_name):
sensor = mi.load_dict(batch_sensor)
scene = mi.load_file(os.path.join(SCENES_DIR, "rover", "rover-ref.xml"), envmap_filename=os.path.join(SCENES_DIR, scene_dict['envmap']['filename']))
img_ref = mi.TensorXf(0.0)
ref_passes = params['ref_passes']
spp_ref = params['spp_ref']
from tqdm import trange
for j in trange(ref_passes):
img_ref += mi.render(scene, seed = 17843 + j, sensor=sensor, integrator=params['integrator'], spp=spp_ref) / ref_passes
mi.Bitmap(img_ref).write_async(ref_name)
sensor_params = mi.traverse(sensor)
sensor_params['film.size'] = final_res
sensor_params.update()
img = mi.TensorXf(0.0)
for j in trange(ref_passes):
img += mi.render(scene, seed = 17843 + j, sensor=sensor, integrator=params['integrator'], spp=spp_ref) / ref_passes
mi.Bitmap(img).write_async(os.path.join(os.path.dirname(output_dir), "img_ref_display.exr"))
del scene
del sensor
return params
def scene_bunnies(method, output_dir):
scene = mi.load_file(os.path.join(SCENES_DIR, "bunnies", "scene_twostates.xml" if method == 'cv_ps' else "scene.xml"), resx=1280, resy=720)
params = {
'scene': scene,
'variables': {
'PLYMesh_1.interior_medium.sigma_t.value.value': {
'init': 1.0,
'clamp': (1e-4, 10.0),
},
'PLYMesh_2.bsdf.alpha.value': {
'init': 0.5,
'clamp': (1e-4, 1.0),
},
'PLYMesh_3.bsdf.reflectance.value': {
'init': [0.5, 0.5, 0.5],
'clamp': (1e-4, 1.0),
}
},
'save': True,
'n_steps': 300,
'recomp_freq': 1,
'method': method,
'lr': 5e-2,
'spp': 1,
'spp_grad': 16,
'spp_ref': 4096,
'spp_inf': 1024,
'output_dir': output_dir,
'integrator': mi.load_dict({'type': 'twostateprbvolpath' if method == 'cv_ps' else 'prbvolpath', 'max_depth': 64, 'rr_depth': 64}),
}
return params
def scene_ajar(method, output_dir):
scene = mi.load_file(os.path.join(SCENES_DIR, "veach-ajar", "scene_twostates.xml" if method == 'cv_ps' else "scene.xml"), resx=1280, resy=720)
lambda_ = 29.0
return {
'scene': scene,
'variables': {
'LandscapeBSDF.reflectance.data': {
'init': dr.full(mi.TensorXf, 0.1, (256, 512, 3)),
'clamp': (1e-4, 1.0),
'largesteps': CholeskySolver(dr.full(mi.TensorXf, 0.1, (256, 512, 3)), lambda_)
},
'tea.albedo.value.value': {
'init': [0.1, 0.1, 0.1],
'clamp': (1e-4, 1.0)
},
'tea.sigma_t.value.value': {
'init': 0.1,
'clamp': (1e-4, 10.0),
'lr_factor': 2.0
}
},
'save': True,
'schedule': [0.75, 0.85, 0.95],
'n_steps': 500,
'recomp_freq': 5,
'method': method,
'denoise': False,
'lr': 2e-2,
'spp': 16,
'spp_grad': 16,
'spp_ref': 4096,
'ref_passes': 16,
'spp_inf': 4096,
'output_dir': output_dir,
'integrator': mi.load_dict({'type': 'twostateprbvolpath' if method == 'cv_ps' else 'prbvolpath', 'max_depth': 64})
}
def scene_ajar_bias(method, output_dir):
params = scene_ajar(method, output_dir)
params['bias_steps'] = [255]
return params
def scene_dragon(method, output_dir):
scene = mi.load_file(os.path.join(SCENES_DIR, "dragon", "scene_twostates.xml" if method == 'cv_ps' else "scene.xml"), resx=500, resy=300)
params = {
'scene': scene,
'variables': {
'PLYMesh_2.interior_medium.sigma_t.value.value': {
'init': 0.1,
'clamp': (1e-4, 10.0),
}
},
'save': True,
'n_steps': 750,
'method': method,
'lr': 5e-2,
'spp': 1,
'spp_grad': 16,
'spp_ref': 2048,
'spp_inf': 256,
'output_dir': output_dir,
'integrator': mi.load_dict({'type': 'twostateprbvolpath' if method == 'cv_ps' else 'prbvolpath', 'max_depth': 64, 'rr_depth': 64}),
}
return params
def scene_teapot(method, output_dir):
scene = mi.load_file(os.path.join(SCENES_DIR, "teapot-full", "scene_twostates" if method == 'cv_ps' else "scene.xml"), resx=500, resy=300)
params = {
'scene': scene,
'variables': {
'tea.sigma_t.value.value': {
'init': 0.1,
'clamp': (1e-4, 1.0),
},
'tea.albedo.value.value': {
'init': [0.2, 0.2, 0.2],
'clamp': (1e-4, 1.0),
}
},
'save': True,
'n_steps': 250,
'method': method,
'lr': 1e-2,
'spp': 1,
'spp_grad': 16,
'spp_ref': 8192,
'spp_inf': 64,
'output_dir': output_dir,
'integrator': mi.load_dict({'type': 'twostateprbvolpath' if method == 'cv_ps' else 'prbvolpath', 'max_depth': 64, 'rr_depth': 64})
}
return params
def scene_cornell_vgg(method, output_dir):
scene_dict = mi.cornell_box()
scene_dict['sensor']['film']['width'] = 512
scene_dict['sensor']['film']['height'] = 512
back_bsdf = {
'type': 'diffuse',
'reflectance': {
'type': 'bitmap',
'filename': os.path.join(SCENES_DIR, 'concrete.exr'),
'to_uv': mi.ScalarTransform4f.rotate([1,0,0], 180)
}
}
if method == 'cv_ps':
scene_dict['back']['bsdf'] = {
'type': 'twostate',
'old': back_bsdf,
'new': back_bsdf,
'incoming': back_bsdf
}
else:
scene_dict['back']['bsdf'] = back_bsdf
params = {
'scene': mi.load_dict(scene_dict),
'variables': {
'back.bsdf.reflectance.data': {
'init': dr.full(mi.TensorXf, 0.5, (512, 512, 3)),
'clamp': (1e-3, 1.0),
}
},
'save': True,
'n_steps': 500,
'final_res': (1280, 1280),
'loss': 'VGG',
'method': method,
'lr': 2e-2,
'spp': 1,
'spp_grad': 16,
'ref_passes': 8,
'spp_ref': 1024,
'spp_inf': 128,
'output_dir': output_dir,
'integrator': mi.load_dict({'type': 'twostateprb' if method == 'cv_ps' else 'prb', 'max_depth': 64, 'rr_depth': 64}),
}
return params
def scene_kitchen(method, output_dir):
scene = mi.load_file(os.path.join(SCENES_DIR, "kitchen", "scene.xml"))
return {
'scene': scene,
'variables': {},
'save': True,
'n_steps': 1,
'method': method,
'denoise': False,
'lr': 2e-2,
'spp': 1,
'spp_grad': 16,
'spp_ref': 4096,
'ref_passes': 4,
'output_dir': output_dir,
'integrator': mi.load_dict({'type': 'twostateprb' if method == 'cv_ps' else 'prb', 'max_depth': 64, 'rr_depth': 64}),
}
AVAILABLE_SCENES = [name[6:] for name in globals() if name.startswith('scene_')]
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Run a Mitsuba experiment')
parser.add_argument('scene', type=str, choices=AVAILABLE_SCENES, help='Name of the scene to use')
parser.add_argument('method', type=str, help='Name of the optimization method to use')
# Optional overrides of scene parameters
parser.add_argument('--n_steps', type=int, help='Number of optimization steps')
parser.add_argument('--lr', type=float, help='Learning rate')
parser.add_argument('--spp', type=int, help='Samples per pixel for the primal rendering')
parser.add_argument('--spp_grad', type=int, help='Samples per pixel for the adjoint rendering')
parser.add_argument('--beta1', type=float, help='β₁ parameter for statistics')
parser.add_argument('--beta2', type=float, help='β₂ parameter for statistics')
args = parser.parse_args()
output_dir = os.path.join(os.path.dirname(__file__), "output", args.scene, args.method)
params = globals()[f"scene_{args.scene}"](args.method, output_dir)
# Override the parameters if necessary
for arg in ['n_steps', 'lr', 'spp', 'spp_grad', 'beta1', 'beta2']:
if getattr(args, arg) is not None:
params[arg] = getattr(args, arg)
result_dict = run_opt(params)
np.savez(os.path.join(output_dir, "result.npz"), **result_dict)