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9e14838 | 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 | import torch
import torch.nn as nn
import lightning.pytorch as pl
from torch import optim
from functools import partial
from torchmetrics import Metric
from torchmetrics.aggregation import MeanMetric
from torchmetrics.classification import AUROC, Accuracy, BinaryConfusionMatrix, AveragePrecision
class GenericStatistics(Metric):
def __init__(self):
super().__init__()
self.add_state("values", default=torch.tensor([]), dist_reduce_fx="sum")
def update(self, values: torch.Tensor):
assert len(values.shape) == 1
self.values = torch.cat([self.values, values])
def compute(self):
return torch.stack(
[
torch.mean(self.values),
torch.std(self.values)
]
)
class ODClassifier(pl.LightningModule):
def __init__(self):
super().__init__()
params = dict(add_dataloader_idx=False, rank_zero_only=True)
self.log = partial(self.log, **params)
self.log_dict = partial(self.log_dict, **params)
self.model = None
def forward(self, *args, **kargs):
return self.model(*args, **kargs)
@property
def transform(self):
raise NotImplementedError()
@property
def n_px(self):
raise NotImplementedError()
def training_step(self, batch, batch_idx):
results = [self.shared_step(batch[dts_name], 'train') for dts_name in batch]
return sum([_results['loss'] for _results in results])
def test_step(self, batch, batch_idx, dataloader_idx=0):
result = self.shared_step(batch, 'test')
return result['loss']
def validation_step(self, batch, batch_idx, dataloader_idx=0):
result = self.shared_step(batch, 'valid')
return result['loss']
def shared_step(self, batch, stage):
x, y, z = batch["xyz"]
indices = batch["indices"]
dts_name = batch["dts_name"]
logits = self(x, **z)
loss = nn.functional.cross_entropy(logits, y)
self.log(
f"{stage}/{dts_name}/loss",
loss,
batch_size=x.shape[0]
)
return {
"logits": logits,
"labels": y,
"loss": loss,
"dts_name": dts_name,
"indices": indices
}
def evaluate(self, x, **kargs):
return self.model(x, **kargs)
def on_validation_model_train(self):
self.model.train()
class ODBinaryMetricClassifier(ODClassifier):
def __init__(self):
super().__init__()
self.dts_metrics = {}
self.metric_map = {
"auc": partial(AUROC, task="BINARY", num_classes=2),
"acc": partial(Accuracy, task="BINARY", num_classes=2),
"cm": partial(BinaryConfusionMatrix, normalize="true"),
"ap": partial(AveragePrecision, task="BINARY", num_classes=2),
"loss": MeanMetric,
# "stats/real": GenericStatistics,
# "stats/fake": GenericStatistics
}
def get_metric(self, dts_name, metric_name, device):
if (not dts_name in self.dts_metrics):
self.dts_metrics[dts_name] = {}
if (not metric_name in self.dts_metrics[dts_name]):
self.dts_metrics[dts_name][metric_name] = self.metric_map[metric_name]().to(device)
return self.dts_metrics[dts_name][metric_name]
def reset_metrics(self):
for dts_name, metrics in self.dts_metrics.items():
for metric_name, metric_obj in metrics.items():
metric_obj.reset()
self.dts_metrics.clear()
# shared procedures
def shared_metric_update_procedure(self, result):
# save metrics
logits = result['logits'].detach().softmax(dim=-1)
labels = result['labels'].detach()
loss = result["loss"].detach()
self.get_metric(result['dts_name'], 'auc', logits.device).update(logits[:, 1], labels)
self.get_metric(result['dts_name'], 'acc', logits.device).update(logits[:, 1], labels)
self.get_metric(result['dts_name'], 'ap', logits.device).update(logits[:, 1], labels)
self.get_metric(result['dts_name'], 'cm', logits.device).update(logits[:, 1], labels)
self.get_metric(result['dts_name'], 'loss', logits.device).update(loss)
labels = labels.to(dtype=bool)
# self.get_metric(result['dts_name'], 'stats/real', logits.device).update(logits[~labels, 0])
# self.get_metric(result['dts_name'], 'stats/fake', logits.device).update(logits[labels, 1])
def shared_beg_epoch_procedure(self, phase):
self.reset_metrics()
def shared_end_epoch_procedure(self, phase):
log_datas = {}
for dts_name, metrics in self.dts_metrics.items():
for metric_name, metric_obj in metrics.items():
values = metric_obj.compute().flatten()
name = f'{phase}/{dts_name}/{metric_name}'
if (len(values) == 1):
log_datas[name] = values
else:
for i, v in enumerate(values):
log_datas[f"{name}/#{i}"] = v
self.log_dict(log_datas)
self.reset_metrics()
# validation
def on_validation_epoch_start(self) -> None:
self.shared_beg_epoch_procedure('valid')
def validation_step(self, batch, batch_idx, dataloader_idx=0):
result = self.shared_step(batch, 'valid')
self.shared_metric_update_procedure(result)
return result['loss']
def on_validation_epoch_end(self) -> None:
self.shared_end_epoch_procedure('valid')
# test
def on_test_epoch_start(self) -> None:
self.shared_beg_epoch_procedure('test')
def test_step(self, batch, batch_idx, dataloader_idx=0):
result = self.shared_step(batch, 'test')
self.shared_metric_update_procedure(result)
return result['loss']
def on_test_epoch_end(self) -> None:
self.shared_end_epoch_procedure('test')
# predict
def predict_step(self, batch, batch_idx, dataloader_idx=0):
x, y, z = batch["xyz"]
dts_name = batch['dts_name']
names = batch["names"]
z = {
_k: z[_k]
for _k in z
}
y = y.tolist()
results = self.evaluate(
x,
**z
)
probs = results["logits"].softmax(dim=-1)[:, 1].flatten().cpu()
return dict(
y=y,
probs=probs,
names=names,
dts_name=dts_name
)
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