def calc_tab3():
ddir = sys.argv[1]
inds = os.listdir(ddir)
outs = []
if len(sys.argv) > 2:
inds = inds[:int(sys.argv[2])]
for ind in tqdm(inds):
if '.txt' in ind:
hp = utils.loadHPFromFile(f'{ddir}/{ind}')
verts, faces = hier_execute(hp)
else:
verts, faces = utils.loadObj(f'{ddir}/{ind}')
verts = torch.tensor(verts)
faces = torch.tensor(faces)
outs.append((verts, faces))
misses = 0.
results = {
'num_parts': [],
'rootedness': [],
'stability': [],
}
samples = []
for (verts, faces) in tqdm(outs):
results['num_parts'].append(verts.shape[0] / 8.0)
samples.append((verts, faces))
if check_rooted(verts, faces):
results['rootedness'].append(1.)
else:
results['rootedness'].append(0.)
if check_stability(verts, faces):
results['stability'].append(1.)
else:
results['stability'].append(0.)
for key in results:
if len(results[key]) > 0:
res = torch.tensor(results[key]).mean().item()
else:
res = 0.
results[key] = res
results['variance'] = eval_get_var(samples)
for key in results:
print(f"Result {key} : {results[key]}")
def main(ind):
sa = ShapeAssembly()
root_lines, has_mid = make_skel()
prog_lines = ['Assembly Program_0 {']
for b in root_lines:
prog_lines.append('\t'+b)
prog_lines.append('}')
RP = Program()
for l in root_lines:
RP.execute(l)
base_par = 'mid' if has_mid else 'top'
base_lines = make_base_prog(
RP.cuboids['base'],
RP.cuboids[base_par]
)
for i in range(len(prog_lines)):
prog_lines[i] = prog_lines[i].replace('base', 'Program_1')
prog_lines += base_lines
#for b in base_lines:
# prog_lines.append('\t'+b)
#prog_lines.append('}')
cube_count = -1
switches = []
for line in prog_lines:
if 'Cuboid' in line:
if not ('Program_' in line or "bbox" in line):
switches.append((
f'cube{cube_count}', line.split()[0]
))
if "bbox" in line:
cube_count = -1
cube_count += 1
for a, b in switches:
prog_lines = [line.replace(b,a) for line in prog_lines]
hier_prog = make_hier_prog(prog_lines)
verts, faces = hier_execute(hier_prog)
if check_rooted(verts, faces) and check_stability(verts, faces):
# writeObj(verts, faces, f'out_{ind}.obj')
writeHierProg(hier_prog, f"random_hier_data/{ind}.txt")
return True
return False
def main(dataset_path, outdir):
indices, progs = load_progs(dataset_path)
os.system(f'mkdir {outdir}')
os.system(f'mkdir {outdir}/valid')
os.system(f'mkdir {outdir}/non_valid')
count = 0
for ind, prog in tqdm(list(zip(indices, progs))):
count += 1
if count < 0:
continue
lc = simplifyHP(prog)
verts, faces = hier_execute(prog)
bbdims = torch.tensor([
float(a)
for a in re.split(r'[()]', prog['prog'][0])[1].split(',')[:3]
])
bb_viol = (verts.abs().max(dim=0).values / (bbdims / 2)).max()
try:
rooted = check_rooted(verts, faces)
except Exception as e:
print(f"Failed rooted check with {e}")
rooted = False
if DO_STABLE:
stable = check_stability(verts, faces)
else:
stable = True
if lc <= MAX_LEAVES and lc >= MIN_LEAVES and bb_viol < BB_THRESH and rooted and stable:
utils.writeHierProg(prog, f'{outdir}/valid/{ind}.txt')
utils.writeObj(verts, faces, f'{outdir}/valid/{ind}.obj')
else:
utils.writeHierProg(prog, f'{outdir}/non_valid/{ind}.txt')
utils.writeObj(verts, faces, f'{outdir}/non_valid/{ind}.obj')
def gen_metrics(gen_progs,
outpath,
exp_name,
epoch,
VERBOSE,
write_progs=True):
misses = 0.
results = {
'num_parts': [],
'rootedness': [],
'stability': [],
}
samples = []
for i, prog in enumerate(gen_progs):
try:
verts, faces = hier_execute(prog)
assert not torch.isnan(verts).any(), 'saw nan vert'
if write_progs:
utils.writeObj(
verts, faces,
f"{outpath}/{exp_name}/objs/gen/{epoch}_{i}.obj")
utils.writeHierProg(
prog, f"{outpath}/{exp_name}/programs/gen/{epoch}_{i}.txt")
results['num_parts'].append(verts.shape[0] / 8.0)
samples.append((verts, faces))
except Exception as e:
misses += 1.
if VERBOSE:
print(f"failed gen metrics for {i} with {e}")
continue
try:
if check_rooted(verts, faces):
results['rootedness'].append(1.)
else:
results['rootedness'].append(0.)
if check_stability(verts, faces):
results['stability'].append(1.)
else:
results['stability'].append(0.)
except Exception as e:
if VERBOSE:
print(f"failed rooted/stable with {e}")
for key in results:
if len(results[key]) > 0:
res = torch.tensor(results[key]).mean().item()
else:
res = 0.
results[key] = res
try:
results['variance'] = eval_get_var(samples)
except Exception as e:
results['variance'] = 0.
if VERBOSE:
print(f"failed getting variance with {e}")
return results, misses