run_exp_dl.py

#
# mDKL
#
# Copyright (c) Siemens AG, 2021
# Authors:
# Zhiliang Wu <zhiliang.wu@siemens.com>
# License-Identifier: MIT

import gc
from pathlib import Path
import shutil
import uuid

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import mlflow

import torch
from torch import nn
from torch.optim.lr_scheduler import StepLR

import ignite
from ignite.contrib.handlers.mlflow_logger import MLflowLogger, \
    global_step_from_engine
from ignite.contrib.handlers import FastaiLRFinder, ProgressBar
from ignite.contrib.handlers.param_scheduler import LRScheduler
from ignite.contrib.metrics.regression import R2Score
from ignite.engine import Events, create_supervised_trainer, \
    create_supervised_evaluator
from ignite.handlers import Checkpoint, DiskSaver
from ignite.metrics import MeanSquaredError

from data_utils import AugmBBoxDatasetAll, get_tranform, \
    get_augm_data_loaders_in_file, prepare_batch
from logging_conf import logger
from model_utils import get_model, EpochOutputStore, CheckPointAfter
from plot_utils import joint_plot_xy, residual_plot


def lr_finder(batch_size, alpha, end_lr=10, diverge_th=5, model_name='resnet',
              pretrain=True, fold_idx=0, device=torch.device('cpu'),
              exp_name='dataset_xxx',
              run_name='model_lrf', seed=42):
    """Find a suitable learning rate.
    More theory see https://www.jeremyjordan.me/nn-learning-rate/.

    Args:
        batch_size (int): Batch size.
        alpha (float): The value of weight decay (a.k.a. regularization).
        end_lr (int or float): The upper bound of the tested learning rate.
        diverge_th (int or float): The threshold for the divergence.
        model_name (str): The name of the backbone.
        pretrain (bool): Whether load the weights in pretrained models.
        fold_idx (int): The index of the training/validation set.
        device (torch.device or str): The device to load the models.
        exp_name (str): The name of the experiments with a format of
            dataset+xxx, which defines the experiment name inside MLflow.
        run_name (str): The name of the run with a format of [model_name]_lrf,
            which defines the run name inside MLflow.
        seed (int): The number of the random seed to ensure the reproducibility.

    Returns:
        None: The plot of the lrfinder will be saved.

    """

    np.random.seed(seed)
    torch.manual_seed(seed)

    logger.info(f'Running learning rate finder with {exp_name} on model '
                f'{model_name}.')

    df_path = Path('../DL_lung_0.5.csv')
    im_path = Path(f'{str(Path.home())}/Key_DL/')

    df_split = pd.read_csv('../DL_lung_0.5_idx_split.csv',
                           index_col=0)

    custom_tranform = get_tranform(mean=(0.389, 0.389, 0.389),
                                   std=(0.240, 0.240, 0.240),
                                   bbox=True)

    _, train_evaluator_loader, _, _ = \
        get_augm_data_loaders_in_file(df_path, im_path, df_split,
                                      train_batch_size=batch_size,
                                      valid_batch_size=128,
                                      custom_tranform=custom_tranform,
                                      datasetclass=AugmBBoxDatasetAll,
                                      fname_col='File_name',
                                      n_fold=fold_idx, augm=True,
                                      albu_format=True)

    model = get_model(model_name, pretrain=pretrain, output_dim=4)
    model = model.to(device)

    # start from small learning rates
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-6,
                                 weight_decay=alpha)

    trainer = create_supervised_trainer(model, optimizer,
                                        nn.MSELoss(),
                                        device=device,
                                        prepare_batch=prepare_batch)

    pbar = ProgressBar(persist=True)
    pbar.attach(trainer, output_transform=lambda x: {'batch loss': x})

    mlflow.set_experiment(exp_name)
    with mlflow.start_run(run_name=run_name):

        mlflow_logger = MLflowLogger()
        mlflow_logger.log_params({
            'seed': seed,
            'batch_size': batch_size,
            'model': model_name,
            'weight_decay': alpha,
            'fold_index': fold_idx,
            'pytorch version': torch.__version__,
            'ignite version': ignite.__version__,
            'cuda version': torch.version.cuda,
            'device name': torch.cuda.get_device_name(0)
        })

        lf = FastaiLRFinder()
        to_save = {'model': model, 'optimizer': optimizer}
        with lf.attach(trainer,
                       to_save,
                       end_lr=end_lr,
                       diverge_th=diverge_th) as trainer_with_lr_finder:
            trainer_with_lr_finder.run(train_evaluator_loader)

        lf_log = pd.DataFrame(lf.get_results())

        lf_log.to_csv(f'./temp/lf_log_{exp_name}.csv', index=False)
        mlflow_logger.log_artifact(f'./temp/lf_log_{exp_name}.csv')

        fig, ax = plt.subplots()

        ax.plot(lf_log.lr[:-1], lf_log.loss[:-1])
        ax.set_xscale('log')
        ax.set_xlabel('Learning rate')
        ax.set_ylabel('Loss')
        ax.set_title(f'Suggestion from finder: {lf.lr_suggestion()}')
        fig.savefig(f'./temp/lr_finder_{exp_name}.png', dpi=600)
        plt.close(fig)

        mlflow_logger.log_artifact(f'./temp/lr_finder_{exp_name}.png')


def run(batch_size=64, lr=3e-4, alpha=0.1, model_name='resnet', epoch=50,
        pretrain=True, fold_idx=0, device=torch.device('cpu'),
        exp_name='dataset_xxx', run_name='model_xxx', seed=42):
    """Run the experiment with a given setting.

    Args:
        batch_size (int): Batch size.
        lr (float): The value of the learning rate, possibly from lrfinder.
        alpha (float): The value of weight decay (a.k.a. regularization).
        model_name (str): The name of the backbone.
        epoch (int): The number of training epochs.
        pretrain (bool): Whether load the weights in pretrained models.
        fold_idx (int): The index of the training/validation set.
        device (torch.device or str): The device to load the models.
        exp_name (str): The name of the experiments with a format of
            dataset+xxx, which defines the experiment name inside MLflow.
        run_name (str): The name of the run with a format of
            [model_name]_linear_regressor, which defines the run name inside
            MLflow.
        seed (int): The number of the random seed to ensure the reproducibility.

    Returns:
        None: The evolution of training loss and evaluation loss are saved to
            MLflow.

    """

    np.random.seed(seed)
    torch.manual_seed(seed)

    df_path = Path('../DL_lung_0.5.csv')
    im_path = Path(f'{str(Path.home())}/Key_DL/')

    df_split = pd.read_csv('../DL_lung_0.5_idx_split.csv',
                           index_col=0)

    custom_tranform = get_tranform(mean=(0.389, 0.389, 0.389),
                                   std=(0.240, 0.240, 0.240),
                                   bbox=True)

    train_loader, train_evaluator_loader, valid_loader, test_loader =  \
        get_augm_data_loaders_in_file(df_path, im_path, df_split,
                                      train_batch_size=batch_size,
                                      valid_batch_size=128,
                                      custom_tranform=custom_tranform,
                                      datasetclass=AugmBBoxDatasetAll,
                                      fname_col='File_name',
                                      n_fold=fold_idx, augm=True, 
                                      albu_format=True)

    model = get_model(model_name, pretrain=pretrain, output_dim=4)
    model = model.to(device)

    optimizer = torch.optim.Adam(model.parameters(), lr=lr,
                                 weight_decay=alpha)
    # step_scheduler = StepLR(optimizer, step_size=int(epoch/2), gamma=0.1)
    # scheduler = LRScheduler(step_scheduler)

    def train_output_transform(x, y, y_pred, loss):
        return {'both': (y_pred, y),
                'x': (y_pred[:, 0], y[:, 0]),
                'y': (y_pred[:, 1], y[:, 1]),
                'w': (y_pred[:, 2], y[:, 2]),
                'h': (y_pred[:, 3], y[:, 3]),
                'loss': loss.item()
                }

    trainer = create_supervised_trainer(model, optimizer,
                                        nn.MSELoss(),
                                        # nn.L1Loss(),
                                        device=device,
                                        prepare_batch=prepare_batch,
                                        output_transform=train_output_transform
                                        )

    # trainer.add_event_handler(Events.EPOCH_COMPLETED, scheduler)

    pbar = ProgressBar(persist=True)
    pbar.attach(trainer,
                output_transform=lambda out: {'batch loss': out['loss']})

    # evaluators
    def eva_output_tranform(x, y, y_pred):
        return {'both': (y_pred, y),
                'x': (y_pred[:, 0], y[:, 0]),
                'y': (y_pred[:, 1], y[:, 1]),
                'w': (y_pred[:, 2], y[:, 2]),
                'h': (y_pred[:, 3], y[:, 3]),
                }

    val_metrics = {
                   'mse': MeanSquaredError(output_transform=lambda out: out['both']),
                   'r2score_x': R2Score(output_transform=lambda out: out['x']),
                   'r2score_y': R2Score(output_transform=lambda out: out['y']),
                   'r2score_w': R2Score(output_transform=lambda out: out['w']),
                   'r2score_h': R2Score(output_transform=lambda out: out['h'])
                   }

    # add metrics to trainer, evaluating based on a moving model / moving data
    for name, metric in val_metrics.items():
        metric.attach(trainer, name)

    # evaluate the model on the training dataset
    train_evaluator = create_supervised_evaluator(model, metrics=val_metrics,
                                                  device=device,
                                                  prepare_batch=prepare_batch,
                                                  output_transform=eva_output_tranform
                                                  )

    pbar.attach(train_evaluator)

    evaluator = create_supervised_evaluator(model, metrics=val_metrics,
                                            device=device,
                                            prepare_batch=prepare_batch,
                                            output_transform=eva_output_tranform
                                            )
    pbar.attach(evaluator)

    eos_x_train = EpochOutputStore(output_transform=lambda out: out['x'])
    eos_y_train = EpochOutputStore(output_transform=lambda out: out['y'])
    eos_w_train = EpochOutputStore(output_transform=lambda out: out['w'])
    eos_h_train = EpochOutputStore(output_transform=lambda out: out['h'])
    eos_x_train.attach(train_evaluator)
    eos_y_train.attach(train_evaluator)
    eos_w_train.attach(train_evaluator)
    eos_h_train.attach(train_evaluator)

    eos_x_val = EpochOutputStore(output_transform=lambda out: out['x'])
    eos_y_val = EpochOutputStore(output_transform=lambda out: out['y'])
    eos_w_val = EpochOutputStore(output_transform=lambda out: out['w'])
    eos_h_val = EpochOutputStore(output_transform=lambda out: out['h'])
    eos_x_val.attach(evaluator)
    eos_y_val.attach(evaluator)
    eos_w_val.attach(evaluator)
    eos_h_val.attach(evaluator)

    mlflow.set_experiment(exp_name)
    with mlflow.start_run(run_name=run_name):

        mlflow_logger = MLflowLogger()

        mlflow_logger.log_params({
            'seed': seed,
            'batch_size': batch_size,
            'num_epoch': epoch,
            'model': model_name,
            'weight_decay': alpha,
            'fold_index': fold_idx,
            'pytorch version': torch.__version__,
            'ignite version': ignite.__version__,
            'cuda version': torch.version.cuda,
            'device name': torch.cuda.get_device_name(0)
        })

        # handlers for evaluator
        # note, this actually calls the evaluator
        @trainer.on(Events.EPOCH_COMPLETED)
        def log_validation_results(engine):
            evaluator.run(valid_loader)
            metrics = evaluator.state.metrics
            pbar.log_message(f"Validation Results "
                             f"- Epoch: {engine.state.epoch} "
                             f"- Mean Square Error: {metrics['mse']:.4f} "
                             f"- R squared of x: {metrics['r2score_x']:.2f}"
                             f"- R squared of y: {metrics['r2score_y']:.2f}"
                             f"- R squared of w: {metrics['r2score_w']:.2f}"
                             f"- R squared of h: {metrics['r2score_h']:.2f}"
                             )
            log_metrics = {f'validation {k}': v for k, v in metrics.items()}
            mlflow_logger.log_metrics(log_metrics, step=engine.state.epoch)

        temp_name = f'temp_{uuid.uuid4()}'

        def score_function(engine):
            return -engine.state.metrics['mse']

        to_save = {'model': model,
                   # 'optimizer': optimizer
                   }
        handler = CheckPointAfter(start_epoch=int(0.9 * epoch),
                                  to_save=to_save,
                                  save_handler=DiskSaver(f'./{temp_name}',
                                                         create_dir=True),
                                  n_saved=2,
                                  filename_prefix='best',
                                  score_function=score_function,
                                  score_name="val_mse",
                                  global_step_transform=global_step_from_engine(trainer))

        evaluator.add_event_handler(Events.COMPLETED, handler)

        # handlers for trainer
        @trainer.on(Events.EPOCH_COMPLETED(every=10))
        def log_training_results(engine):
            train_evaluator.run(train_evaluator_loader)
            metrics = train_evaluator.state.metrics
            pbar.log_message(f"Evaluation Training Set "
                             f"- Epoch: {engine.state.epoch} "
                             f"- Mean Square Error: {metrics['mse']:.4f} "
                             f"- R squared of x: {metrics['r2score_x']:.2f}"
                             f"- R squared of y: {metrics['r2score_y']:.2f}"
                             f"- R squared of w: {metrics['r2score_w']:.2f}"
                             f"- R squared of h: {metrics['r2score_h']:.2f}"
                             )

        def log_plots(engine, label='valid'):
            train_hist_x_p, train_hist_x = eos_x_train.get_output(to_numpy=True)
            train_hist_y_p, train_hist_y = eos_y_train.get_output(to_numpy=True)
            train_hist_w_p, train_hist_w = eos_w_train.get_output(to_numpy=True)
            train_hist_h_p, train_hist_h = eos_h_train.get_output(to_numpy=True)
            val_hist_x_p, val_hist_x = eos_x_val.get_output(to_numpy=True)
            val_hist_y_p, val_hist_y = eos_y_val.get_output(to_numpy=True)
            val_hist_w_p, val_hist_w = eos_w_val.get_output(to_numpy=True)
            val_hist_h_p, val_hist_h = eos_h_val.get_output(to_numpy=True)

            # joint plot for predicted location values
            joint_plot_xy(train_hist_x_p, train_hist_y_p,
                          val_hist_x_p, val_hist_y_p,
                          dp=temp_name, n_epoch=engine.state.epoch,
                          label=f'pred_{label}')

            # residual for x
            residual_plot(train_hist_x, train_hist_x_p,
                          val_hist_x, val_hist_x_p, dp=temp_name,
                          n_epoch=engine.state.epoch, label=f'x_{label}')

            # residual for y
            residual_plot(train_hist_y, train_hist_y_p,
                          val_hist_y, val_hist_y_p, dp=temp_name,
                          n_epoch=engine.state.epoch, label=f'y_{label}')

            # residual for w
            residual_plot(train_hist_w, train_hist_w_p,
                          val_hist_w, val_hist_w_p, dp=temp_name,
                          n_epoch=engine.state.epoch, label=f'w_{label}')

            # residual for h
            residual_plot(train_hist_h, train_hist_h_p,
                          val_hist_h, val_hist_h_p, dp=temp_name,
                          n_epoch=engine.state.epoch, label=f'h_{label}')

        trainer.add_event_handler(Events.EPOCH_COMPLETED(every=10),
                                  log_plots, label='valid')

        @trainer.on(Events.COMPLETED)
        def log_plot(engine):
            _, train_hist_x = eos_x_train.get_output(to_numpy=True)
            _, train_hist_y = eos_y_train.get_output(to_numpy=True)
            _, train_hist_w = eos_w_train.get_output(to_numpy=True)
            _, train_hist_h = eos_h_train.get_output(to_numpy=True)
            _, val_hist_x = eos_x_val.get_output(to_numpy=True)
            _, val_hist_y = eos_y_val.get_output(to_numpy=True)
            _, val_hist_w = eos_w_val.get_output(to_numpy=True)
            _, val_hist_h = eos_h_val.get_output(to_numpy=True)

            joint_plot_xy(train_hist_x, train_hist_y, val_hist_x, val_hist_y,
                          dp=temp_name, n_epoch=engine.state.epoch, label='true'
                          )

            joint_plot_xy(train_hist_w, train_hist_h, val_hist_w, val_hist_h,
                          dp=temp_name, n_epoch=engine.state.epoch,
                          label='hw_true'
                          )

        def final_evaluation(engine):
            to_load = to_save
            last_checkpoint_fp = f'./{temp_name}/{handler.last_checkpoint}'
            checkpoint = torch.load(last_checkpoint_fp, map_location=device)
            CheckPointAfter.load_objects(to_load=to_load, checkpoint=checkpoint)
            logger.info('The best model on validation is reloaded for '
                        'evaluation on the test set')
            evaluator.run(test_loader)
            metrics = evaluator.state.metrics
            pbar.log_message(f"Testing Results "
                             f"- Epoch: {engine.state.epoch} "
                             f"- Mean Square Error: {metrics['mse']:.4f} "
                             f"- R squared of x: {metrics['r2score_x']:.2f}"
                             f"- R squared of y: {metrics['r2score_y']:.2f}"
                             f"- R squared of w: {metrics['r2score_w']:.2f}"
                             f"- R squared of h: {metrics['r2score_h']:.2f}"
                             )
            log_metrics = {f'test {k}': v for k, v in metrics.items()}
            mlflow_logger.log_metrics(log_metrics, step=engine.state.epoch)

        trainer.add_event_handler(Events.COMPLETED, final_evaluation)
        trainer.add_event_handler(Events.COMPLETED, log_plots, label='test')

        @trainer.on(Events.COMPLETED)
        def save_model_to_mlflow(engine):
            mlflow_logger.log_artifacts(f'./{temp_name}/')
            try:
                shutil.rmtree(temp_name)
            except FileNotFoundError:
                logger.warning('Temp drectory not found!')
                raise

        # log training loss at each iteration
        mlflow_logger.attach_output_handler(trainer,
                                            event_name=Events.ITERATION_COMPLETED,
                                            tag='training',
                                            output_transform=lambda out: {
                                                'batch_loss': out['loss']}
                                            )

        # setup `global_step_transform=global_step_from_engine(trainer)` to
        # take the epoch of the `trainer` instead of `train_evaluator`.
        mlflow_logger.attach_output_handler(trainer,
                                            event_name=Events.EPOCH_COMPLETED,
                                            tag='training',
                                            metric_names=['mse',
                                                          'r2score_x',
                                                          'r2score_y',
                                                          'r2score_w',
                                                          'r2score_h',
                                                          ]
                                            )

        # Attach the logger to the trainer to log optimizer's parameters,
        # e.g. learning rate at each iteration
        mlflow_logger.attach_opt_params_handler(trainer,
                                                event_name=Events.ITERATION_STARTED,
                                                optimizer=optimizer,
                                                param_name='lr'
                                                )

        _ = trainer.run(train_loader, max_epochs=epoch)


if __name__ == '__main__':
    sd = 42
    dc = torch.device('cuda:3' if torch.cuda.is_available() else 'cpu')

    # fold = 0
    bs = 128
    pt = True
    n_epoch = 100
    exp = 'DL_lung_final'
    m_name = 'resnet'

    # r_name = f'{m_name}_lrf'
    # lr_finder(batch_size=bs, alpha=1e-4, end_lr=10, diverge_th=5,
    #           model_name=m_name, pretrain=pt, fold_idx=fold, device=dc,
    #           exp_name=exp, run_name=r_name, seed=sd)

    lrt = 1e-4  # this is from lr finder

    r_name = f'{m_name}_linear_regressor_albu'

    # for a in [1e-5, 1e-4, 1e-3]:
    #     run(batch_size=bs, lr=lrt, alpha=a, model_name=m_name,
    #         pretrain=pt, epoch=n_epoch, fold_idx=fold, device=dc,
    #         exp_name=exp, run_name=r_name, seed=sd)
    #     gc.collect()

    for f in range(5):
        for a in [1e-5, 1e-4, 1e-3]:
            run(batch_size=bs, lr=lrt, alpha=a, model_name=m_name,
                pretrain=pt, epoch=n_epoch, fold_idx=f, device=dc,
                exp_name=exp, run_name=r_name, seed=sd)
            gc.collect()