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import torch
import pytorch_lightning as L
from timm import create_model
class LitClassification(L.LightningModule):
def __init__(self, drop_path=0.05):
super().__init__()
self.model = create_model(
"resnet50", pretrained=False, drop_path_rate=drop_path
)
self.loss_fn = torch.nn.CrossEntropyLoss()
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx):
images, targets = batch["image"], batch["targets"]
outputs = self.model(images)
loss = self.loss_fn(outputs, targets)
acc1, acc5 = self.__accuracy(outputs, targets, topk=(1, 5))
self.log("train_loss", loss)
self.log(
"train_acc1", acc1, on_step=True, prog_bar=True, on_epoch=True, logger=True
)
self.log("train_acc5", acc5, on_step=True, on_epoch=True, logger=True)
return loss
def validation_step(self, batch, batch_idx):
images, targets = batch["image"], batch["targets"]
outputs = self(images)
loss = self.loss_fn(outputs, targets)
acc1, acc5 = self.__accuracy(outputs, targets, topk=(1, 5))
self.log("valid_loss", loss)
self.log("val_acc1", acc1, on_step=True, prog_bar=True, on_epoch=True)
self.log("val_acc5", acc5, on_step=True, on_epoch=True)
@staticmethod
def __accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k."""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res |