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import argparse
import inspect
from . import gaussian_diffusion as gd
from .respace import SpacedDiffusion, space_timesteps
from .transformer import TransformerModel
def diffusion_defaults():
"""
Defaults for image and classifier training.
"""
return dict(
analog_bit=False,
learn_sigma=False,
# diffusion_steps=25,
diffusion_steps=1000,
noise_schedule="cosine",
timestep_respacing="ddim100",
use_kl=False,
predict_xstart=False,
rescale_timesteps=False,
rescale_learned_sigmas=False,
# target_set=-1,
# target_set=4,
# target_set=5,
# target_set=6,
# target_set=7,
target_set=8,
set_name='',
)
def update_arg_parser(args):
args.num_channels = 512
num_coords = 16 if args.analog_bit else 2
if args.dataset=='rplan':
args.input_channels = num_coords + (2*8 if not args.analog_bit else 0) # . , . , . , . , '
args.condition_channels = 89
args.out_channels = num_coords * 1
args.use_unet = False
elif args.dataset=='st3d':
args.input_channels = num_coords + (2*8 if not args.analog_bit else 0) # . , . , . , . , '
args.condition_channels = 89
args.out_channels = num_coords * 1
args.use_unet = False
elif args.dataset=='zind':
args.input_channels = num_coords + 2 * 8
args.condition_channels = 89
args.out_channels = num_coords * 1
args.use_unet = False
elif args.dataset=='layout':
args.use_unet = True
pass #TODO NEED TO COMPLETE
elif args.dataset=='outdoor':
args.use_unet = True
pass #TODO NEED TO COMPLETE
else:
assert False, "DATASET NOT FOUND"
def model_and_diffusion_defaults():
"""
Defaults for image training.
"""
res = dict(
dataset='rplan',
# dataset='',
use_checkpoint=False,
input_channels=0,
condition_channels=0,
out_channels=0,
use_unet=False,
num_channels=128
)
res.update(diffusion_defaults())
return res
def create_model_and_diffusion(
input_channels,
condition_channels,
num_channels,
out_channels,
dataset,
use_checkpoint,
use_unet,
learn_sigma,
diffusion_steps,
noise_schedule,
timestep_respacing,
use_kl,
predict_xstart,
rescale_timesteps,
rescale_learned_sigmas,
analog_bit,
target_set,
set_name,
):
model = TransformerModel(input_channels, condition_channels, num_channels, out_channels, dataset, use_checkpoint, use_unet, analog_bit)
diffusion = create_gaussian_diffusion(
steps=diffusion_steps,
learn_sigma=learn_sigma,
noise_schedule=noise_schedule,
use_kl=use_kl,
predict_xstart=predict_xstart,
rescale_timesteps=rescale_timesteps,
rescale_learned_sigmas=rescale_learned_sigmas,
timestep_respacing=timestep_respacing,
)
return model, diffusion
def create_gaussian_diffusion(
*,
steps=1000,
learn_sigma=False,
sigma_small=False,
noise_schedule="linear",
use_kl=False,
predict_xstart=False,
rescale_timesteps=False,
rescale_learned_sigmas=False,
timestep_respacing="",
):
betas = gd.get_named_beta_schedule(noise_schedule, steps)
if use_kl:
loss_type = gd.LossType.RESCALED_KL
elif rescale_learned_sigmas:
loss_type = gd.LossType.RESCALED_MSE
else:
loss_type = gd.LossType.MSE
if not timestep_respacing:
timestep_respacing = [steps]
return SpacedDiffusion(
use_timesteps=space_timesteps(steps, timestep_respacing),
betas=betas,
model_mean_type=(
gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X
),
model_var_type=(
(
gd.ModelVarType.FIXED_LARGE
if not sigma_small
else gd.ModelVarType.FIXED_SMALL
)
if not learn_sigma
else gd.ModelVarType.LEARNED_RANGE
),
loss_type=loss_type,
rescale_timesteps=rescale_timesteps,
)
def add_dict_to_argparser(parser, default_dict):
for k, v in default_dict.items():
v_type = type(v)
if v is None:
v_type = str
elif isinstance(v, bool):
v_type = str2bool
parser.add_argument(f"--{k}", default=v, type=v_type)
def args_to_dict(args, keys):
return {k: getattr(args, k) for k in keys}
def str2bool(v):
"""
https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
"""
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("boolean value expected")
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