File size: 7,903 Bytes
4409449 |
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 |
import os
import numpy as np
import torch
import logging
from pathlib import Path
from pytorch_lightning import LightningModule
from os.path import join as pjoin
from collections import OrderedDict
from mGPT.metrics import BaseMetrics
from mGPT.config import get_obj_from_str
class BaseModel(LightningModule):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# self.configure_metrics()
# Ablation
self.test_step_outputs = []
self.times = []
self.rep_i = 0
def training_step(self, batch, batch_idx):
return self.allsplit_step("train", batch, batch_idx)
def validation_step(self, batch, batch_idx):
return self.allsplit_step("val", batch, batch_idx)
def test_step(self, batch, batch_idx):
outputs = self.allsplit_step("test", batch, batch_idx)
self.test_step_outputs.append(outputs)
return outputs
def predict_step(self, batch, batch_idx):
return self.forward(batch)
def on_train_epoch_end(self):
# Log steps and losses
dico = self.step_log_dict()
# Log losses
dico.update(self.loss_log_dict('train'))
# Write to log only if not sanity check
if not self.trainer.sanity_checking:
self.log_dict(dico, sync_dist=True, rank_zero_only=True)
def on_validation_epoch_end(self):
# Log steps and losses
dico = self.step_log_dict()
# Log losses
dico.update(self.loss_log_dict('train'))
dico.update(self.loss_log_dict('val'))
# Log metrics
dico.update(self.metrics_log_dict())
# Write to log only if not sanity check
if not self.trainer.sanity_checking:
self.log_dict(dico, sync_dist=True, rank_zero_only=True)
def on_test_epoch_end(self):
# Log metrics
dico = self.metrics_log_dict()
# Write to log only if not sanity check
if not self.trainer.sanity_checking:
self.log_dict(dico, sync_dist=True, rank_zero_only=True)
self.save_npy(self.test_step_outputs)
self.rep_i = self.rep_i + 1
# Free up the memory
self.test_step_outputs.clear()
def preprocess_state_dict(self, state_dict):
new_state_dict = OrderedDict()
# metric_state_dict = self.metrics.state_dict()
loss_state_dict = self._losses.state_dict()
# for k, v in metric_state_dict.items():
# new_state_dict['metrics.' + k] = v
for k, v in loss_state_dict.items():
new_state_dict['_losses.' + k] = v
for k, v in state_dict.items():
if '_losses' not in k and 'Metrics' not in k:
new_state_dict[k] = v
return new_state_dict
def load_state_dict(self, state_dict, strict=True):
new_state_dict = self.preprocess_state_dict(state_dict)
super().load_state_dict(new_state_dict, strict)
def step_log_dict(self):
return {
"epoch": float(self.trainer.current_epoch),
"step": float(self.trainer.current_epoch)
}
def loss_log_dict(self, split: str):
losses = self._losses['losses_' + split]
loss_dict = losses.compute(split)
return loss_dict
def metrics_log_dict(self):
# For TM2TMetrics MM
if self.trainer.datamodule.is_mm and "TM2TMetrics" in self.hparams.metrics_dict:
metrics_dicts = ['MMMetrics']
else:
metrics_dicts = self.hparams.metrics_dict
# Compute all metrics
metrics_log_dict = {}
for metric in metrics_dicts:
metrics_dict = getattr(
self.metrics,
metric).compute(sanity_flag=self.trainer.sanity_checking)
metrics_log_dict.update({
f"Metrics/{metric}": value.item()
for metric, value in metrics_dict.items()
})
return metrics_log_dict
def configure_optimizers(self):
# Optimizer
optim_target = self.hparams.cfg.TRAIN.OPTIM.target
if len(optim_target.split('.')) == 1:
optim_target = 'torch.optim.' + optim_target
optimizer = get_obj_from_str(optim_target)(
params=self.parameters(), **self.hparams.cfg.TRAIN.OPTIM.params)
# Scheduler
scheduler_target = self.hparams.cfg.TRAIN.LR_SCHEDULER.target
if len(scheduler_target.split('.')) == 1:
scheduler_target = 'torch.optim.lr_scheduler.' + scheduler_target
lr_scheduler = get_obj_from_str(scheduler_target)(
optimizer=optimizer, **self.hparams.cfg.TRAIN.LR_SCHEDULER.params)
return {'optimizer': optimizer, 'lr_scheduler': lr_scheduler}
def configure_metrics(self):
self.metrics = BaseMetrics(datamodule=self.datamodule, **self.hparams)
def save_npy(self, outputs):
cfg = self.hparams.cfg
output_dir = Path(
os.path.join(
cfg.FOLDER,
str(cfg.model.target.split('.')[-2].lower()),
str(cfg.NAME),
"samples_" + cfg.TIME,
))
if cfg.TEST.SAVE_PREDICTIONS:
lengths = [i[1] for i in outputs]
outputs = [i[0] for i in outputs]
if cfg.TEST.DATASETS[0].lower() in ["humanml3d", "kit"]:
keyids = self.trainer.datamodule.test_dataset.name_list
for i in range(len(outputs)):
for bid in range(
min(cfg.TEST.BATCH_SIZE, outputs[i].shape[0])):
keyid = keyids[i * cfg.TEST.BATCH_SIZE + bid]
data = self.trainer.datamodule.test_dataset.data_dict[
keyid]
motion = torch.tensor(data['motion'],
device=outputs[i].device)
motion = self.datamodule.normalize(motion)
length = data['length']
text_list = data['text']
gen_joints = outputs[i][bid][:lengths[i][bid]].cpu(
).numpy()
if cfg.TEST.REPLICATION_TIMES > 1:
name = f"{keyid}.npy"
else:
name = f"{keyid}.npy"
# save predictions results
npypath = output_dir / name
np.save(npypath, gen_joints)
npypath = output_dir / f"{keyid}_gt.npy"
joints = self.feats2joints(motion).cpu().numpy()
np.save(npypath, joints)
with open(output_dir / f"{keyid}.txt", "a") as f:
for text in text_list:
f.write(f"{text['caption']}\n")
elif cfg.TEST.DATASETS[0].lower() in ["humanact12", "uestc"]:
keyids = range(len(self.trainer.datamodule.test_dataset))
for i in range(len(outputs)):
for bid in range(
min(cfg.TEST.BATCH_SIZE, outputs[i].shape[0])):
keyid = keyids[i * cfg.TEST.BATCH_SIZE + bid]
gen_joints = outputs[i][bid].cpu()
gen_joints = gen_joints.permute(2, 0,
1)[:lengths[i][bid],
...].numpy()
if cfg.TEST.REPLICATION_TIMES > 1:
name = f"{keyid}_{self.rep_i}"
else:
name = f"{keyid}.npy"
# save predictions results
npypath = output_dir / name
np.save(npypath, gen_joints)
|