deep-learning-with-pytorch/p2ch12/training.py

import argparse
import datetime
import os
import sys

import numpy as np

from torch.utils.tensorboard import SummaryWriter

import torch
import torch.nn as nn
from torch.optim import SGD
from torch.utils.data import DataLoader

from util.util import enumerateWithEstimate
from .dsets import LunaDataset
from util.logconf import logging
from .model import LunaModel

log = logging.getLogger(__name__)
# log.setLevel(logging.WARN)
log.setLevel(logging.INFO)
# log.setLevel(logging.DEBUG)

# Used for computeBatchLoss and logMetrics to index into metrics_t/metrics_a
METRICS_LABEL_NDX=0
METRICS_PRED_NDX=1
METRICS_LOSS_NDX=2
METRICS_SIZE = 3

class LunaTrainingApp:
    def __init__(self, sys_argv=None):
        if sys_argv is None:
            sys_argv = sys.argv[1:]

        parser = argparse.ArgumentParser()
        parser.add_argument('--batch-size',
            help='Batch size to use for training',
            default=32,
            type=int,
        )
        parser.add_argument('--num-workers',
            help='Number of worker processes for background data loading',
            default=8,
            type=int,
        )
        parser.add_argument('--epochs',
            help='Number of epochs to train for',
            default=1,
            type=int,
        )
        parser.add_argument('--balanced',
            help="Balance the training data to half positive, half negative.",
            action='store_true',
            default=False,
        )
        parser.add_argument('--augmented',
            help="Augment the training data.",
            action='store_true',
            default=False,
        )
        parser.add_argument('--augment-flip',
            help="Augment the training data by randomly flipping the data left-right, up-down, and front-back.",
            action='store_true',
            default=False,
        )
        parser.add_argument('--augment-offset',
            help="Augment the training data by randomly offsetting the data slightly along the X and Y axes.",
            action='store_true',
            default=False,
        )
        parser.add_argument('--augment-scale',
            help="Augment the training data by randomly increasing or decreasing the size of the candidate.",
            action='store_true',
            default=False,
        )
        parser.add_argument('--augment-rotate',
            help="Augment the training data by randomly rotating the data around the head-foot axis.",
            action='store_true',
            default=False,
        )
        parser.add_argument('--augment-noise',
            help="Augment the training data by randomly adding noise to the data.",
            action='store_true',
            default=False,
        )

        parser.add_argument('--tb-prefix',
            default='p2ch12',
            help="Data prefix to use for Tensorboard run. Defaults to chapter.",
        )
        parser.add_argument('comment',
            help="Comment suffix for Tensorboard run.",
            nargs='?',
            default='dlwpt',
        )

        self.cli_args = parser.parse_args(sys_argv)
        self.time_str = datetime.datetime.now().strftime('%Y-%m-%d_%H.%M.%S')

        self.trn_writer = None
        self.val_writer = None
        self.totalTrainingSamples_count = 0

        self.augmentation_dict = {}
        if self.cli_args.augmented or self.cli_args.augment_flip:
            self.augmentation_dict['flip'] = True
        if self.cli_args.augmented or self.cli_args.augment_offset:
            self.augmentation_dict['offset'] = 0.1
        if self.cli_args.augmented or self.cli_args.augment_scale:
            self.augmentation_dict['scale'] = 0.2
        if self.cli_args.augmented or self.cli_args.augment_rotate:
            self.augmentation_dict['rotate'] = True
        if self.cli_args.augmented or self.cli_args.augment_noise:
            self.augmentation_dict['noise'] = 25.0

        self.use_cuda = torch.cuda.is_available()
        self.device = torch.device("cuda" if self.use_cuda else "cpu")

        self.model = self.initModel()
        self.optimizer = self.initOptimizer()


    def initModel(self):
        model = LunaModel()
        if self.use_cuda:
            log.info("Using CUDA; {} devices.".format(torch.cuda.device_count()))
            if torch.cuda.device_count() > 1:
                model = nn.DataParallel(model)
            model = model.to(self.device)
        return model

    def initOptimizer(self):
        return SGD(self.model.parameters(), lr=0.001, momentum=0.99)
        # return Adam(self.model.parameters())

    def initTrainDl(self):
        train_ds = LunaDataset(
            val_stride=10,
            isValSet_bool=False,
            ratio_int=int(self.cli_args.balanced),
            augmentation_dict=self.augmentation_dict,
        )

        batch_size = self.cli_args.batch_size
        if self.use_cuda:
            batch_size *= torch.cuda.device_count()

        train_dl = DataLoader(
            train_ds,
            batch_size=batch_size,
            num_workers=self.cli_args.num_workers,
            pin_memory=self.use_cuda,
        )

        return train_dl

    def initValDl(self):
        val_ds = LunaDataset(
            val_stride=10,
            isValSet_bool=True,
        )

        batch_size = self.cli_args.batch_size
        if self.use_cuda:
            batch_size *= torch.cuda.device_count()

        val_dl = DataLoader(
            val_ds,
            batch_size=batch_size,
            num_workers=self.cli_args.num_workers,
            pin_memory=self.use_cuda,
        )

        return val_dl

    def initTensorboardWriters(self):
        if self.trn_writer is None:
            log_dir = os.path.join('runs', self.cli_args.tb_prefix, self.time_str)

            self.trn_writer = SummaryWriter(
                log_dir=log_dir + '-trn_cls-' + self.cli_args.comment)
            self.val_writer = SummaryWriter(
                log_dir=log_dir + '-val_cls-' + self.cli_args.comment)


    def main(self):
        log.info("Starting {}, {}".format(type(self).__name__, self.cli_args))

        train_dl = self.initTrainDl()
        val_dl = self.initValDl()

        for epoch_ndx in range(1, self.cli_args.epochs + 1):

            log.info("Epoch {} of {}, {}/{} batches of size {}*{}".format(
                epoch_ndx,
                self.cli_args.epochs,
                len(train_dl),
                len(val_dl),
                self.cli_args.batch_size,
                (torch.cuda.device_count() if self.use_cuda else 1),
            ))

            trnMetrics_t = self.doTraining(epoch_ndx, train_dl)
            self.logMetrics(epoch_ndx, 'trn', trnMetrics_t)

            valMetrics_t = self.doValidation(epoch_ndx, val_dl)
            self.logMetrics(epoch_ndx, 'val', valMetrics_t)

        if hasattr(self, 'trn_writer'):
            self.trn_writer.close()
            self.val_writer.close()


    def doTraining(self, epoch_ndx, train_dl):
        self.model.train()
        train_dl.dataset.shuffleSamples()
        trnMetrics_g = torch.zeros(
            METRICS_SIZE,
            len(train_dl.dataset),
            device=self.device,
        )

        batch_iter = enumerateWithEstimate(
            train_dl,
            "E{} Training".format(epoch_ndx),
            start_ndx=train_dl.num_workers,
        )
        for batch_ndx, batch_tup in batch_iter:
            self.optimizer.zero_grad()

            loss_var = self.computeBatchLoss(
                batch_ndx,
                batch_tup,
                train_dl.batch_size,
                trnMetrics_g,
            )

            loss_var.backward()
            self.optimizer.step()

        self.totalTrainingSamples_count += len(train_dl.dataset)

        return trnMetrics_g.to('cpu')


    def doValidation(self, epoch_ndx, val_dl):
        with torch.no_grad():
            self.model.eval()
            valMetrics_g = torch.zeros(
                METRICS_SIZE,
                len(val_dl.dataset),
                device=self.device,
            )

            batch_iter = enumerateWithEstimate(
                val_dl,
                "E{} Validation ".format(epoch_ndx),
                start_ndx=val_dl.num_workers,
            )
            for batch_ndx, batch_tup in batch_iter:
                self.computeBatchLoss(
                    batch_ndx,
                    batch_tup,
                    val_dl.batch_size,
                    valMetrics_g,
                )

        return valMetrics_g.to('cpu')



    def computeBatchLoss(self, batch_ndx, batch_tup, batch_size, metrics_g):
        input_t, label_t, _series_list, _center_list = batch_tup

        input_g = input_t.to(self.device, non_blocking=True)
        label_g = label_t.to(self.device, non_blocking=True)

        logits_g, probability_g = self.model(input_g)

        loss_func = nn.CrossEntropyLoss(reduction='none')
        loss_g = loss_func(
            logits_g,
            label_g[:,1],
        )
        start_ndx = batch_ndx * batch_size
        end_ndx = start_ndx + label_t.size(0)

        metrics_g[METRICS_LABEL_NDX, start_ndx:end_ndx] = label_g[:,1]
        metrics_g[METRICS_PRED_NDX, start_ndx:end_ndx] = probability_g[:,1]
        metrics_g[METRICS_LOSS_NDX, start_ndx:end_ndx] = loss_g

        return loss_g.mean()


    def logMetrics(
            self,
            epoch_ndx,
            mode_str,
            metrics_t,
            classificationThreshold=0.5,
    ):
        self.initTensorboardWriters()
        log.info("E{} {}".format(
            epoch_ndx,
            type(self).__name__,
        ))

        negLabel_mask = metrics_t[METRICS_LABEL_NDX] <= classificationThreshold
        negPred_mask = metrics_t[METRICS_PRED_NDX] <= classificationThreshold

        posLabel_mask = ~negLabel_mask
        posPred_mask = ~negPred_mask

        neg_count = int(negLabel_mask.sum())
        pos_count = int(posLabel_mask.sum())

        trueNeg_count = neg_correct = int((negLabel_mask & negPred_mask).sum())
        truePos_count = pos_correct = int((posLabel_mask & posPred_mask).sum())

        falsePos_count = neg_count - neg_correct
        falseNeg_count = pos_count - pos_correct

        metrics_dict = {}
        metrics_dict['loss/all'] = metrics_t[METRICS_LOSS_NDX].mean()
        metrics_dict['loss/neg'] = metrics_t[METRICS_LOSS_NDX, negLabel_mask].mean()
        metrics_dict['loss/pos'] = metrics_t[METRICS_LOSS_NDX, posLabel_mask].mean()

        metrics_dict['correct/all'] = (pos_correct + neg_correct) / metrics_t.shape[1] * 100
        metrics_dict['correct/neg'] = (neg_correct) / neg_count * 100
        metrics_dict['correct/pos'] = (pos_correct) / pos_count * 100

        precision = metrics_dict['pr/precision'] = \
            truePos_count / np.float32(truePos_count + falsePos_count)
        recall    = metrics_dict['pr/recall'] = \
            truePos_count / np.float32(truePos_count + falseNeg_count)

        metrics_dict['pr/f1_score'] = \
            2 * (precision * recall) / (precision + recall)

        log.info(
            ("E{} {:8} {loss/all:.4f} loss, "
                 + "{correct/all:-5.1f}% correct, "
                 + "{pr/precision:.4f} precision, "
                 + "{pr/recall:.4f} recall, "
                 + "{pr/f1_score:.4f} f1 score"
            ).format(
                epoch_ndx,
                mode_str,
                **metrics_dict,
            )
        )
        log.info(
            ("E{} {:8} {loss/neg:.4f} loss, "
                 + "{correct/neg:-5.1f}% correct ({neg_correct:} of {neg_count:})"
            ).format(
                epoch_ndx,
                mode_str + '_neg',
                neg_correct=neg_correct,
                neg_count=neg_count,
                **metrics_dict,
            )
        )
        log.info(
            ("E{} {:8} {loss/pos:.4f} loss, "
                 + "{correct/pos:-5.1f}% correct ({pos_correct:} of {pos_count:})"
            ).format(
                epoch_ndx,
                mode_str + '_pos',
                pos_correct=pos_correct,
                pos_count=pos_count,
                **metrics_dict,
            )
        )
        writer = getattr(self, mode_str + '_writer')

        for key, value in metrics_dict.items():
            writer.add_scalar(key, value, self.totalTrainingSamples_count)

        writer.add_pr_curve(
            'pr',
            metrics_t[METRICS_LABEL_NDX],
            metrics_t[METRICS_PRED_NDX],
            self.totalTrainingSamples_count,
        )

        bins = [x/50.0 for x in range(51)]

        negHist_mask = negLabel_mask & (metrics_t[METRICS_PRED_NDX] > 0.01)
        posHist_mask = posLabel_mask & (metrics_t[METRICS_PRED_NDX] < 0.99)

        if negHist_mask.any():
            writer.add_histogram(
                'is_neg',
                metrics_t[METRICS_PRED_NDX, negHist_mask],
                self.totalTrainingSamples_count,
                bins=bins,
            )
        if posHist_mask.any():
            writer.add_histogram(
                'is_pos',
                metrics_t[METRICS_PRED_NDX, posHist_mask],
                self.totalTrainingSamples_count,
                bins=bins,
            )

        # score = 1 \
        #     + metrics_dict['pr/f1_score'] \
        #     - metrics_dict['loss/mal'] * 0.01 \
        #     - metrics_dict['loss/all'] * 0.0001
        #
        # return score

    # def logModelMetrics(self, model):
    #     writer = getattr(self, 'trn_writer')
    #
    #     model = getattr(model, 'module', model)
    #
    #     for name, param in model.named_parameters():
    #         if param.requires_grad:
    #             min_data = float(param.data.min())
    #             max_data = float(param.data.max())
    #             max_extent = max(abs(min_data), abs(max_data))
    #
    #             # bins = [x/50*max_extent for x in range(-50, 51)]
    #
    #             try:
    #                 writer.add_histogram(
    #                     name.rsplit('.', 1)[-1] + '/' + name,
    #                     param.data.cpu().numpy(),
    #                     # metrics_a[METRICS_PRED_NDX, negHist_mask],
    #                     self.totalTrainingSamples_count,
    #                     # bins=bins,
    #                 )
    #             except Exception as e:
    #                 log.error([min_data, max_data])
    #                 raise


if __name__ == '__main__':
    LunaTrainingApp().main()