lightning/tests/base/model_test_steps.py

94 lines
2.5 KiB
Python

from abc import ABC
from collections import OrderedDict
import torch
class TestStepVariations(ABC):
"""
Houses all variations of test steps
"""
def test_step(self, batch, batch_idx, *args, **kwargs):
"""
Default, baseline test_step
:param batch:
:return:
"""
x, y = batch
x = x.view(x.size(0), -1)
y_hat = self(x)
loss_test = self.loss(y, y_hat)
# acc
labels_hat = torch.argmax(y_hat, dim=1)
test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
test_acc = torch.tensor(test_acc)
test_acc = test_acc.type_as(x)
# alternate possible outputs to test
if batch_idx % 1 == 0:
output = OrderedDict({
'test_loss': loss_test,
'test_acc': test_acc,
})
return output
if batch_idx % 2 == 0:
return test_acc
if batch_idx % 3 == 0:
output = OrderedDict({
'test_loss': loss_test,
'test_acc': test_acc,
'test_dic': {'test_loss_a': loss_test}
})
return output
def test_step__multiple_dataloaders(self, batch, batch_idx, dataloader_idx, **kwargs):
"""
Default, baseline test_step
:param batch:
:return:
"""
x, y = batch
x = x.view(x.size(0), -1)
y_hat = self(x)
loss_test = self.loss(y, y_hat)
# acc
labels_hat = torch.argmax(y_hat, dim=1)
test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
test_acc = torch.tensor(test_acc)
test_acc = test_acc.type_as(x)
# alternate possible outputs to test
if batch_idx % 1 == 0:
output = OrderedDict({
'test_loss': loss_test,
'test_acc': test_acc,
})
return output
if batch_idx % 2 == 0:
return test_acc
if batch_idx % 3 == 0:
output = OrderedDict({
'test_loss': loss_test,
'test_acc': test_acc,
'test_dic': {'test_loss_a': loss_test}
})
return output
if batch_idx % 5 == 0:
output = OrderedDict({
f'test_loss_{dataloader_idx}': loss_test,
f'test_acc_{dataloader_idx}': test_acc,
})
return output
def test_step__empty(self, batch, batch_idx, *args, **kwargs):
return {}