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Running on Zero
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2680bd5 | 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 | import os
from os.path import join as pjoin
from pathlib import Path
import numpy as np
import hydra
from hydra.utils import instantiate
import torch
from einops import rearrange
from omegaconf import OmegaConf
from .model.cls_free_sampler import ClassifierFreeSampleWrapper
from .utils.mics import get_device, rot_motion_to_dict, fixseed
from .utils.model_utils import create_model_and_diffusion
from ..constant import gesture_list
from .config import DataConfig, ModelConfig, TextConditionModelConfig, ActionConditionModelConfig, GenerateConfig, Config
from ..visualize.visualize import MultiMotionVisualizer
def generate(
gen_cfg: GenerateConfig,
model_cfg: ModelConfig | ActionConditionModelConfig | TextConditionModelConfig,
data_cfg: DataConfig,
seed: int | None = None,
):
if seed:
fixseed(seed)
torch.set_float32_matmul_precision("high")
model, diffusion = create_model_and_diffusion(model_cfg)
state_dict = torch.load(gen_cfg.model_path, map_location='cpu')
model.load_state_dict(state_dict['state_dict'], strict=False)
model = ClassifierFreeSampleWrapper(model, scale=gen_cfg.sample.guidance_param)
device = get_device()
model.to(device)
model.eval()
dataset = instantiate(data_cfg, split='train')
batch_size:int = gen_cfg.num_samples
if data_cfg.repr == 'joint_pos':
njoints = 42
nfeats = 3
elif data_cfg.repr == 'joint_rot':
njoints = 34
nfeats = 6
elif data_cfg.repr == 'joint_pos_w_scalar_rot':
njoints = 42
nfeats = 4
shape = (batch_size, njoints, nfeats, gen_cfg.motion_length)
model_kwargs = dict(
y=dict(
lengths=torch.as_tensor([gen_cfg.motion_length] * batch_size, device=device)
)
)
if model_cfg.cond_mode == 'text':
if model_cfg.arch in ['trans_enc', "trans_dec"]:
model_kwargs['y'].update(
text=[gen_cfg.text_prompt] * batch_size
)
elif model_cfg.arch == "trans_dec_treble_concat" or model_cfg.arch == 'trans_dec_treble_residual':
print(type(gen_cfg.text_prompt))
assert len(gen_cfg.text_prompt) == 3, \
"For treble models, text_prompt should be a tuple/list of 3 strings (left, right, two hands relation)."
model_kwargs['y'].update(
text=dict(
left=[gen_cfg.text_prompt[0]] * batch_size,
right=[gen_cfg.text_prompt[1]] * batch_size,
two_hands_relation=[gen_cfg.text_prompt[2]] * batch_size,
)
)
elif model_cfg.cond_mode == 'action':
assert gen_cfg.action_name in gesture_list, f"action {gen_cfg.action_name} not in {gesture_list}"
action_id = gesture_list.index(gen_cfg.action_name)
actions = torch.ones(batch_size, dtype=torch.long, device=device) * action_id
model_kwargs['y'].update(
actions=actions
)
elif model_cfg.cond_mode != 'no_cond':
raise ValueError(f"cond_mode {model_cfg.cond_mode} not recognized.")
samples = diffusion.p_sample_loop(
model, shape,
clip_denoised=False,
model_kwargs=model_kwargs,
device=device,
skip_timesteps=0,
init_image=None,
progress=True,
noise=None,
const_noise=False
)
samples = rearrange(samples, 'b j f t -> b t (j f)')
samples = dataset.inv_transform(samples.detach().cpu().numpy())
def process_motion(motion, title=None):
'''
motion: (B, T, J*F)
'''
left_motion, right_motion = np.split(
motion.reshape(-1, njoints, nfeats),
indices_or_sections=[njoints // 2],
axis=1
)
cur_motion_to_visualize = dict()
if data_cfg.repr == 'joint_pos':
cur_motion_to_visualize.update(
dict(
type='skeleton',
left_motion=left_motion,
right_motion=right_motion
)
)
elif data_cfg.repr == 'joint_rot':
left_motion = rot_motion_to_dict(left_motion)
right_motion = rot_motion_to_dict(right_motion)
cur_motion_to_visualize.update(
type='mano',
left_motion=left_motion,
right_motion=right_motion
)
elif data_cfg.repr == 'joint_pos_w_scalar_rot':
cur_motion_to_visualize.update(
dict(
type='skeleton',
left_motion=left_motion[:, :, :3],
right_motion=right_motion[:, :, :3],
)
)
if title is not None:
cur_motion_to_visualize.update(title=title)
return cur_motion_to_visualize
motions_to_visualize = []
for i in range(batch_size):
cur_sample = samples[i]
title = f"Sample {i + 1} - Length: {cur_sample.shape[0]}"
motions_to_visualize.append(process_motion(cur_sample, title=title))
text_to_show = ""
if model_cfg.cond_mode == 'text':
if isinstance(gen_cfg.text_prompt, str):
text_to_show = gen_cfg.text_prompt
else:
text_to_show = "[LEFT] " + gen_cfg.text_prompt[0] + " [RIGHT] " + gen_cfg.text_prompt[1] + " [TWO HANDS RELATION] " + gen_cfg.text_prompt[2]
elif model_cfg.cond_mode == 'action':
text_to_show = gen_cfg.action_name
MultiMotionVisualizer.create_3d_animation(
motions=motions_to_visualize,
text=text_to_show,
save_path=pjoin(gen_cfg.output_dir, "generated_motion.gif"),
fps=30
)
@hydra.main(config_path=pjoin(os.getcwd(), "conf"), config_name='generate', version_base=None)
def main(gen_cfg: GenerateConfig):
exp_config_path = Path(gen_cfg.model_path).parent / "config.yaml"
with open(exp_config_path.as_posix(), "r") as f:
exp_config:Config = OmegaConf.load(f)
generate(
gen_cfg,
exp_config.model,
exp_config.data,
exp_config.seed
)
if __name__ == "__main__":
main() |