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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)
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