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tuner.py
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tuner.py
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import pytorch_lightning as pl
from transformers import SegformerForSemanticSegmentation
from datasets import load_metric
import torch
from torch import nn
import numpy as np
class SegformerFinetuner(pl.LightningModule):
def __init__(self, id2label, train_dataloader=None, val_dataloader=None, test_dataloader=None, metrics_interval=100):
super(SegformerFinetuner, self).__init__()
self.id2label = id2label
self.metrics_interval = metrics_interval
self.train_dl = train_dataloader
self.val_dl = val_dataloader
self.test_dl = test_dataloader
self.num_classes = len(id2label.keys())
self.label2id = {v:k for k,v in self.id2label.items()}
self.model = SegformerForSemanticSegmentation.from_pretrained(
"nvidia/segformer-b0-finetuned-ade-512-512",
return_dict=False,
num_labels=self.num_classes,
id2label=self.id2label,
label2id=self.label2id,
ignore_mismatched_sizes=True,
)
self.train_mean_iou = load_metric("mean_iou")
self.val_mean_iou = load_metric("mean_iou")
self.test_mean_iou = load_metric("mean_iou")
def forward(self, images, masks):
outputs = self.model(pixel_values=images, labels=masks)
return(outputs)
def training_step(self, batch, batch_nb):
images, masks = batch['pixel_values'], batch['labels']
outputs = self(images, masks)
loss, logits = outputs[0], outputs[1]
upsampled_logits = nn.functional.interpolate(
logits,
size=masks.shape[-2:],
mode="bilinear",
align_corners=False
)
predicted = upsampled_logits.argmax(dim=1)
self.train_mean_iou.add_batch(
predictions=predicted.detach().cpu().numpy(),
references=masks.detach().cpu().numpy()
)
if batch_nb % self.metrics_interval == 0:
metrics = self.train_mean_iou.compute(
num_labels=self.num_classes,
ignore_index=255,
reduce_labels=False,
)
metrics = {'loss': loss, "mean_iou": metrics["mean_iou"], "mean_accuracy": metrics["mean_accuracy"]}
for k,v in metrics.items():
self.log(k,v)
return(metrics)
else:
return({'loss': loss})
def validation_step(self, batch, batch_nb):
images, masks = batch['pixel_values'], batch['labels']
outputs = self(images, masks)
loss, logits = outputs[0], outputs[1]
upsampled_logits = nn.functional.interpolate(
logits,
size=masks.shape[-2:],
mode="bilinear",
align_corners=False
)
predicted = upsampled_logits.argmax(dim=1)
self.val_mean_iou.add_batch(
predictions=predicted.detach().cpu().numpy(),
references=masks.detach().cpu().numpy()
)
return({'val_loss': loss})
# def validation_epoch_end(self, outputs):
# metrics = self.val_mean_iou.compute(
# num_labels=self.num_classes,
# ignore_index=255,
# reduce_labels=False,
# )
# avg_val_loss = torch.stack([x["val_loss"] for x in outputs]).mean()
# val_mean_iou = metrics["mean_iou"]
# val_mean_accuracy = metrics["mean_accuracy"]
# metrics = {"val_loss": avg_val_loss, "val_mean_iou":val_mean_iou, "val_mean_accuracy":val_mean_accuracy}
# for k,v in metrics.items():
# self.log(k,v)
# return metrics
def test_step(self, batch, batch_nb):
images, masks = batch['pixel_values'], batch['labels']
outputs = self(images, masks)
loss, logits = outputs[0], outputs[1]
upsampled_logits = nn.functional.interpolate(
logits,
size=masks.shape[-2:],
mode="bilinear",
align_corners=False
)
predicted = upsampled_logits.argmax(dim=1)
self.test_mean_iou.add_batch(
predictions=predicted.detach().cpu().numpy(),
references=masks.detach().cpu().numpy()
)
return({'test_loss': loss})
def test_epoch_end(self, outputs):
metrics = self.test_mean_iou.compute(
num_labels=self.num_classes,
ignore_index=255,
reduce_labels=False,
)
avg_test_loss = torch.stack([x["test_loss"] for x in outputs]).mean()
test_mean_iou = metrics["mean_iou"]
test_mean_accuracy = metrics["mean_accuracy"]
metrics = {"test_loss": avg_test_loss, "test_mean_iou":test_mean_iou, "test_mean_accuracy":test_mean_accuracy}
for k,v in metrics.items():
self.log(k,v)
return metrics
def configure_optimizers(self):
return torch.optim.Adam([p for p in self.parameters() if p.requires_grad], lr=2e-05, eps=1e-08)
def train_dataloader(self):
return self.train_dl
def val_dataloader(self):
return self.val_dl
def test_dataloader(self):
return self.test_dl