File size: 10,226 Bytes
9965bf6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 |
"""
Generate a large batch of image samples from a model and save them as a large
numpy array. This can be used to produce samples for FID evaluation.
"""
import argparse
import os
import numpy as np
import torch as th
import pandas as pd
import torch.distributed as dist
import torch.nn.functional as F
import multiprocessing
from guided_diffusion import dist_util, midi_util, logger
from guided_diffusion.dit import DiT_models
from guided_diffusion.script_util import (
model_and_diffusion_defaults,
create_model_and_diffusion,
add_dict_to_argparser,
args_to_dict,
create_classifier,
)
from guided_diffusion.gaussian_diffusion import _encode, _extract_rule
from guided_diffusion.pr_datasets_all import load_data
from load_utils import load_model
import diff_collage as dc
from guided_diffusion.condition_functions import (
model_fn, dc_model_fn, composite_nn_zt, composite_rule)
from functools import partial
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (20, 3)
plt.rcParams['figure.dpi'] = 300
plt.rcParams['savefig.dpi'] = 300
def main():
args = create_argparser().parse_args()
root_dir = 'cond_table/'
args.dir = root_dir + os.path.splitext(args.config_path.split(root_dir)[-1])[0] + f'_cls_{args.class_label}'
comm = dist_util.setup_dist(port=args.port)
logger.configure(args=args, comm=comm)
config = midi_util.load_config(args.config_path)
if config.sampling.use_ddim:
args.timestep_respacing = config.sampling.timestep_respacing
logger.log("creating model and diffusion...")
model, diffusion = create_model_and_diffusion(
**args_to_dict(args, model_and_diffusion_defaults().keys())
)
model.load_state_dict(
dist_util.load_state_dict(args.model_path, map_location="cpu"), strict=False
)
model.to(dist_util.dev())
if args.use_fp16:
model.convert_to_fp16()
model.eval()
# create embed model
embed_model = None
cond_fn_config = config.guidance.cond_fn
if config.guidance.nn:
logger.log("loading classifier...")
classifier_config = cond_fn_config.classifiers
num_classifiers = len(classifier_config.paths)
classifiers = []
for i in range(num_classifiers):
classifier = create_classifier(
image_size=args.image_size,
in_channels=args.in_channels,
classifier_use_fp16=False,
classifier_width=128,
classifier_depth=2,
classifier_attention_resolutions='32,16,8',
classifier_use_scale_shift_norm=True,
classifier_resblock_updown=True,
classifier_pool='attention',
num_classes=classifier_config.num_classes[i],
chord=True if 'chord' in cond_fn_config.rule_names[i] else False,
)
classifier.load_state_dict(
dist_util.load_state_dict(classifier_config.paths[i], map_location="cpu")
)
classifier.to(dist_util.dev())
classifier.eval()
classifiers.append(classifier)
if cond_fn_config is not None:
if config.guidance.nn:
cond_fn_used = partial(composite_nn_zt, fns=cond_fn_config.fns,
classifier_scales=cond_fn_config.classifier_scales,
classifiers=classifiers, rule_names=cond_fn_config.rule_names)
else:
cond_fn_used = partial(composite_rule, fns=cond_fn_config.fns,
classifier_scales=cond_fn_config.classifier_scales,
rule_names=cond_fn_config.rule_names)
else:
cond_fn_used = None
if config.sampling.diff_collage:
def eps_fn(x, t, y=None, half=False):
# since our backbone takes 128x16 as input
return model(x.permute(0, 1, 3, 2), t, y=y, half=half).permute(0, 1, 3, 2)
# circle need one more num_img than linear
img_shape = (args.in_channels, args.image_size[1], args.image_size[0]) # 4 x 16 x 128
if config.dc.type == 'circle':
worker = dc.CondIndCircle(img_shape, eps_fn, config.dc.num_img + 1, overlap_size=config.dc.overlap_size)
else:
worker = dc.CondIndSimple(img_shape, eps_fn, config.dc.num_img, overlap_size=config.dc.overlap_size)
model_long_fn = worker.eps_scalar_t_fn
gen_shape = (args.batch_size, worker.shape[0], worker.shape[2], worker.shape[1])
model_fn_used = partial(dc_model_fn, model=model_long_fn, num_classes=args.num_classes,
class_cond=args.class_cond, cfg=args.cfg, w=args.w)
else:
gen_shape = (args.batch_size, args.in_channels, args.image_size[0], args.image_size[1])
model_fn_used = partial(model_fn, model=model, num_classes=args.num_classes,
class_cond=args.class_cond, cfg=args.cfg, w=args.w)
if args.class_cond:
# only generate one class
classes = th.ones(size=(args.batch_size,), device=dist_util.dev(), dtype=th.int) * args.class_label
save_dir = logger.get_dir()
save_dir_gt = os.path.join(save_dir, 'gt')
os.makedirs(os.path.expanduser(save_dir), exist_ok=True)
os.makedirs(os.path.expanduser(save_dir_gt), exist_ok=True)
ddim_stochastic = partial(diffusion.ddim_sample_loop, eta=1.)
sample_fn = (
diffusion.p_sample_loop if not config.sampling.use_ddim else ddim_stochastic
)
logger.log(f"loading midi from test set cls {args.class_label} to edit...")
val_data = load_data(
data_dir=args.data_dir + "_test_cls_" + str(args.class_label) + ".csv",
batch_size=args.batch_size,
class_cond=True,
image_size=gen_shape[2],
rule=None,
)
logger.log("sampling...")
count_samples = 0
# target rules are dummy for get editing table
target_rules = vars(config.target_rules)
if 'vertical_nd' in target_rules.keys():
# create a new dummy rule name and delete the old names
target_rules['note_density_pixel'] = None
target_rules.pop('vertical_nd')
target_rules.pop('horizontal_nd')
model_kwargs = {"rule": {k: v for k, v in target_rules.items()}}
if args.class_cond:
model_kwargs["y"] = classes
all_results = pd.DataFrame()
while count_samples < args.num_samples:
with th.no_grad():
gt, extra = next(val_data)
gt = gt.to(dist_util.dev())
for rule_name in target_rules.keys():
target_rule = _extract_rule(rule_name, gt)
model_kwargs["rule"][rule_name] = target_rule
sample = sample_fn(
model_fn_used,
gen_shape,
clip_denoised=args.clip_denoised,
model_kwargs=model_kwargs,
device=dist_util.dev(),
cond_fn=cond_fn_used,
# None for NN(z_0), embed_model for rule(decoder(z_0))
embed_model=embed_model if config.guidance.vae else None,
scale_factor=args.scale_factor,
guidance_kwargs=config.guidance,
scg_kwargs=vars(config.scg) if config.guidance.scg else None,
t_end=config.sampling.t_end,
record=args.record,
progress=True
)
sample = midi_util.decode_sample_for_midi(sample, embed_model=embed_model,
scale_factor=args.scale_factor, threshold=-0.95)
arr = sample.cpu().numpy()
arr = arr.transpose(0, 3, 1, 2)
gt = ((gt + 1) * 63.5).clamp(0, 127).to(th.uint8)
arr_gt = gt.cpu().numpy()
if args.save_files:
if args.class_cond:
label_arr = classes.cpu().numpy()
midi_util.save_piano_roll_midi(arr, save_dir, args.fs, y=label_arr, save_ind=count_samples)
midi_util.save_piano_roll_midi(arr_gt, save_dir_gt, args.fs, y=label_arr, save_ind=count_samples)
else:
midi_util.save_piano_roll_midi(arr, save_dir, args.fs, save_ind=count_samples)
midi_util.save_piano_roll_midi(arr_gt, save_dir_gt, args.fs, save_ind=count_samples)
# test distance between generated samples and target
generated_samples = th.from_numpy(arr) / 63.5 - 1
results = midi_util.eval_rule_loss(generated_samples, model_kwargs["rule"])
all_results = pd.concat([all_results, results], ignore_index=True)
# save every step
if args.save_files:
all_results.to_csv(os.path.join(save_dir, 'results.csv'), index=False)
count_samples += args.batch_size
if args.save_files:
all_results.to_csv(os.path.join(save_dir, 'results.csv'), index=False)
# Create the DataFrame for loss_stats
loss_columns = [col for col in all_results.columns if '.loss' in col]
rows = []
for col in loss_columns:
rows.append({'Attr': col, 'Mean': all_results[col].mean(), 'Std': all_results[col].std()})
loss_stats = pd.DataFrame(rows, columns=['Attr', 'Mean', 'Std'])
loss_stats.to_csv(os.path.join(save_dir, 'summary.csv'))
print(loss_stats)
logger.log("sampling complete")
def create_argparser():
defaults = dict(
project="music-sampling",
dir="",
data_dir="", # use to load in val data to extract rule
config_path="",
model="", # DiT model names
model_path="",
clip_denoised=False,
num_samples=128,
batch_size=16,
scale_factor=1.,
fs=12.5,
num_classes=0,
class_label=1, # class to generate
cfg=False,
w=4., # for cfg
classifier_scale=1.0,
record=False,
save_files=True,
training=False, # not training, so don't need to create more folders than needed
port=None,
)
defaults.update(model_and_diffusion_defaults())
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
return parser
if __name__ == "__main__":
multiprocessing.set_start_method('spawn', force=True)
main()
|