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import sys
import yaml
import json
import torch
import pickle
import shutil
import logging
import warnings
import argparse
from os import path
from datetime import datetime
from torchmetrics.classification import AUROC, Accuracy
from src.utility.builtin import ODTrainer, ODLightningCLI
def parse_args(args=None):
parser = argparse.ArgumentParser()
parser.add_argument("model_cfg_path", type=str)
parser.add_argument("data_cfg_path", type=str)
parser.add_argument("model_ckpt_path", type=str)
parser.add_argument("--precision", type=str, default="16")
parser.add_argument("--devices", type=int, default=-1)
parser.add_argument("--notes", type=str, default='')
return parser.parse_args(args=args)
class StatsRecorder:
def __init__(self, label):
self.label = label
self.prob = 0
self.count = 0
def update(self, prob, label):
assert label == self.label
self.prob += prob
self.count += 1
def compute(self):
return {
"label": self.label,
"prob": self.prob / self.count
}
def configure_logging():
logging_fmt = "[%(levelname)s][%(filename)s:%(lineno)d]: %(message)s"
logging.basicConfig(level="INFO", format=logging_fmt)
warnings.filterwarnings(action="ignore")
@torch.inference_mode()
def inference_driver(cli, cfg_dir, ckpt_path, notes=None):
timestamp = datetime.now().strftime("%m%dT%H%M%S")
trainer = cli.trainer
# setup model
model = cli.model
try:
model = model.__class__.load_from_checkpoint(ckpt_path)
except Exception as e:
print(f"Unable to load model from checkpoint in strict mode: {e}")
print(f"Loading model from checkpoint in non-strict mode.")
model = model.__class__.load_from_checkpoint(ckpt_path, strict=False)
model.eval()
# setup dataset
datamodule = cli.datamodule
datamodule.prepare_data()
datamodule.affine_model(cli.model)
datamodule.setup('test')
stats = {}
report = {}
test_dataloaders = datamodule.test_dataloader()
for dts_name, dataloader in test_dataloaders.items():
# iterate all videos
auc_calc = AUROC(task="BINARY", num_classes=2)
acc_calc = Accuracy(task="BINARY", num_classes=2)
dataset = dataloader.dataset
dts_stats = {}
# perform ddp prediction
batch_results = trainer.predict(
model=model,
dataloaders=[dataloader]
)
gathered_results = [None] * torch.distributed.get_world_size()
torch.distributed.all_gather_object(gathered_results, batch_results)
torch.distributed.barrier()
if (trainer.is_global_zero):
# fetch predict results and aggregate.
for batch_results in gathered_results:
for batch_result in batch_results:
probs = batch_result["probs"]
names = batch_result["names"]
y = batch_result["y"]
for prob, label, name in zip(probs, y, names):
if (not name in dts_stats):
dts_stats[name] = StatsRecorder(label)
dts_stats[name].update(prob, label)
# compute the average probability.
for k in dts_stats:
dts_stats[k] = dts_stats[k].compute()
# add straying videos into metric calculation
for k, v in dataset.stray_videos.items():
dts_stats[k] = dict(
label=v,
prob=0.5,
stray=1
)
# compute the metric scores
dataset_labels = []
dataset_probs = []
for v in dts_stats.values():
dataset_labels.append(v["label"])
dataset_probs.append(v["prob"])
dataset_labels = torch.tensor(dataset_labels)
dataset_probs = torch.tensor(dataset_probs)
accuracy = acc_calc(dataset_probs, dataset_labels).item()
roc_auc = auc_calc(dataset_probs, dataset_labels).item()
accuracy = round(accuracy, 3)
roc_auc = round(roc_auc, 3)
logging.info(f'[{dts_name}] accuracy: {accuracy}, roc_auc: {roc_auc}')
stats[dts_name] = dts_stats
report[dts_name] = {
"accuracy": accuracy,
"roc_auc": roc_auc
}
if (trainer.is_global_zero):
# save report and stats.
with open(path.join(cfg_dir, f'report_{timestamp}.json'), "w") as f:
json.dump(report, f, sort_keys=True, indent=4, separators=(',', ': '))
with open(path.join(cfg_dir, f'stats_{timestamp}.pickle'), "wb") as f:
pickle.dump(stats, f)
return report
if __name__ == "__main__":
configure_logging()
params = parse_args()
cli = ODLightningCLI(
run=False,
trainer_class=ODTrainer,
save_config_callback=None,
parser_kwargs={
"parser_mode": "omegaconf"
},
auto_configure_optimizers=False,
seed_everything_default=1019,
args=[
'-c', params.model_cfg_path,
'-c', params.data_cfg_path,
'--trainer.logger=null',
f'--trainer.devices={params.devices}',
f'--trainer.precision={params.precision}',
],
)
cfg_dir = os.path.split(params.model_cfg_path)[0]
ckpt_path = params.model_ckpt_path
notes = params.notes
inference_driver(
cli=cli,
cfg_dir=cfg_dir,
ckpt_path=ckpt_path,
notes=notes
)
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