lightning/tests/base/model_template.py

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Generic, TypeVar

import torch
import torch.nn as nn
import torch.nn.functional as F

from pytorch_lightning.core.lightning import LightningModule
from tests.base.model_optimizers import ConfigureOptimizersPool
from tests.base.model_test_dataloaders import TestDataloaderVariations
from tests.base.model_test_epoch_ends import TestEpochEndVariations
from tests.base.model_test_steps import TestStepVariations
from tests.base.model_train_dataloaders import TrainDataloaderVariations
from tests.base.model_train_steps import TrainingStepVariations
from tests.base.model_utilities import ModelTemplateData, ModelTemplateUtils
from tests.base.model_valid_dataloaders import ValDataloaderVariations
from tests.base.model_valid_epoch_ends import ValidationEpochEndVariations
from tests.base.model_valid_steps import ValidationStepVariations
from tests.helpers.datasets import PATH_DATASETS, TrialMNIST


class EvalModelTemplate(
    ModelTemplateData,
    ModelTemplateUtils,
    TrainingStepVariations,
    ValidationStepVariations,
    ValidationEpochEndVariations,
    TestStepVariations,
    TestEpochEndVariations,
    TrainDataloaderVariations,
    ValDataloaderVariations,
    TestDataloaderVariations,
    ConfigureOptimizersPool,
    LightningModule,
):
    """
    This template houses all  combinations of model  configurations  we want to test

    >>> model = EvalModelTemplate()
    """

    def __init__(
        self,
        drop_prob: float = 0.2,
        batch_size: int = 32,
        in_features: int = 28 * 28,
        learning_rate: float = 0.001 * 8,
        optimizer_name: str = 'adam',
        data_root: str = PATH_DATASETS,
        out_features: int = 10,
        hidden_dim: int = 1000,
        b1: float = 0.5,
        b2: float = 0.999,
    ):
        # init superclass
        super().__init__()
        self.save_hyperparameters()

        self.drop_prob = drop_prob
        self.batch_size = batch_size
        self.in_features = in_features
        self.learning_rate = learning_rate
        self.optimizer_name = optimizer_name
        self.data_root = data_root
        self.out_features = out_features
        self.hidden_dim = hidden_dim
        self.b1 = b1
        self.b2 = b2
        self.training_step_called = False
        self.training_step_end_called = False
        self.training_epoch_end_called = False
        self.validation_step_called = False
        self.validation_step_end_called = False
        self.validation_epoch_end_called = False
        self.test_step_called = False
        self.test_step_end_called = False
        self.test_epoch_end_called = False

        self.example_input_array = torch.rand(5, 28 * 28)

        # build model
        self.__build_model()

    def __build_model(self):
        """
        Simple model for testing
        :return:
        """
        self.c_d1 = nn.Linear(in_features=self.in_features, out_features=self.hidden_dim)
        self.c_d1_bn = nn.BatchNorm1d(self.hidden_dim)
        self.c_d1_drop = nn.Dropout(self.drop_prob)

        self.c_d2 = nn.Linear(in_features=self.hidden_dim, out_features=self.out_features)

    def forward(self, x):
        x = self.c_d1(x)
        x = torch.tanh(x)
        x = self.c_d1_bn(x)
        x = self.c_d1_drop(x)

        x = self.c_d2(x)
        logits = F.log_softmax(x, dim=1)

        return logits

    def loss(self, labels, logits):
        nll = F.nll_loss(logits, labels)
        return nll

    def prepare_data(self):
        TrialMNIST(root=self.data_root, train=True, download=True)

    @staticmethod
    def get_default_hparams(continue_training: bool = False, hpc_exp_number: int = 0) -> dict:
        args = dict(
            drop_prob=0.2,
            batch_size=32,
            in_features=28 * 28,
            learning_rate=0.001 * 8,
            optimizer_name='adam',
            data_root=PATH_DATASETS,
            out_features=10,
            hidden_dim=1000,
            b1=0.5,
            b2=0.999,
        )

        if continue_training:
            args.update(
                test_tube_do_checkpoint_load=True,
                hpc_exp_number=hpc_exp_number,
            )

        return args


T = TypeVar('T')


class GenericParentEvalModelTemplate(Generic[T], EvalModelTemplate):

    def __init__(
        self,
        drop_prob: float,
        batch_size: int,
        in_features: int,
        learning_rate: float,
        optimizer_name: str,
        data_root: str,
        out_features: int,
        hidden_dim: int,
        b1: float,
        b2: float,
    ):
        super().__init__(
            drop_prob=drop_prob,
            batch_size=batch_size,
            in_features=in_features,
            learning_rate=learning_rate,
            optimizer_name=optimizer_name,
            data_root=data_root,
            out_features=out_features,
            hidden_dim=hidden_dim,
            b1=b1,
            b2=b2,
        )


class GenericEvalModelTemplate(GenericParentEvalModelTemplate[int]):
    pass