Files
pytorch-lightning/pytorch_lightning/testing/lm_test_module.py
T
Nic Eggert 64688e1e15 Refactor test modules (#180)
* Expectopatronum implement #89 (#182)

* rename validate -> evaluate; implement test logic; allow multiple test_loaders

* add test_step and test_end to LightningModule

* add in_test_mode to pretraining to implement case 2 (test pretrained model)

* fix code style issues

* LightningTestModel: add optional second test set, implement test_step and test_end

* implemented test for multiple test_dataloaders; fixed typo

* add two test cases for #89

* add documentation for test_step, test_end; fix computation of loss in validation_step example

* Update trainer.py

* Update trainer.py

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* Added proper dp ddp routing calls for test mode

* Update trainer.py

* Update test_models.py

* Update trainer.py

* Update trainer.py

* Update override_data_parallel.py

* Update test_models.py

* Update test_models.py

* Update trainer.py

* Update trainer.py

* Update trainer.py

* Update test_models.py

* Update test_models.py

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* Update trainer.py

* Update override_data_parallel.py

* Update debug.py

* Update lm_test_module.py

* Update test_models.py

* release v0.4.8

* Update README.md

* add training loop docs

* testing loop docs

* testing loop docs

* Convert __dataloader to _dataloader

This will let inherited classes use it

* Factor common test model setup into base class

* Specialized test modules inherit from LightningTestModelBase

* Fix __is_overriden so that it works with more complicated inheritance

* Use mixins to add functionality to test models

* Fix test with no val_dataloader

* Remove unused imports

* Get rid of wild card import

* Update trainer.py

* Update lm_test_module.py
2019-09-02 15:46:16 -04:00

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Python

import os
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torchvision.datasets import MNIST
from torchvision import transforms
from test_tube import HyperOptArgumentParser
from pytorch_lightning.root_module.root_module import LightningModule
from pytorch_lightning import data_loader
from .lm_test_module_base import LightningTestModelBase
from .lm_test_module_mixins import LightningValidationMixin, LightningTestMixin
class LightningTestModel(LightningValidationMixin, LightningTestMixin, LightningTestModelBase):
"""
Most common test case. Validation and test dataloaders
"""
def on_tng_metrics(self, logs):
logs['some_tensor_to_test'] = torch.rand(1)