deep-learning-with-pytorch/p2ch13/training.py

import argparse
import datetime
import hashlib
import os
import shutil
import socket
import sys

import numpy as np
from torch.utils.tensorboard import SummaryWriter

import torch
import torch.nn as nn
import torch.optim

from torch.optim import SGD, Adam
from torch.utils.data import DataLoader

from util.util import enumerateWithEstimate
from .dsets import Luna2dSegmentationDataset, TrainingLuna2dSegmentationDataset, getCt
from util.logconf import logging
from .model import UNetWrapper, SegmentationAugmentation

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

# Used for computeClassificationLoss and logMetrics to index into metrics_t/metrics_a
# METRICS_LABEL_NDX = 0
METRICS_LOSS_NDX = 1
# METRICS_FN_LOSS_NDX = 2
# METRICS_ALL_LOSS_NDX = 3

# METRICS_PTP_NDX = 4
# METRICS_PFN_NDX = 5
# METRICS_MFP_NDX = 6
METRICS_TP_NDX = 7
METRICS_FN_NDX = 8
METRICS_FP_NDX = 9

METRICS_SIZE = 10

class SegmentationTrainingApp:
    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=16,
            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('--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='p2ch13',
            help="Data prefix to use for Tensorboard run. Defaults to chapter.",
        )

        parser.add_argument('comment',
            help="Comment suffix for Tensorboard run.",
            nargs='?',
            default='none',
        )

        self.cli_args = parser.parse_args(sys_argv)
        self.time_str = datetime.datetime.now().strftime('%Y-%m-%d_%H.%M.%S')
        self.totalTrainingSamples_count = 0
        self.trn_writer = None
        self.val_writer = None

        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'] = 0.03
        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.segmentation_model, self.augmentation_model = self.initModel()
        self.optimizer = self.initOptimizer()


    def initModel(self):
        segmentation_model = UNetWrapper(
            in_channels=7,
            n_classes=1,
            depth=3,
            wf=4,
            padding=True,
            batch_norm=True,
            up_mode='upconv',
        )

        augmentation_model = SegmentationAugmentation(**self.augmentation_dict)

        if self.use_cuda:
            log.info("Using CUDA; {} devices.".format(torch.cuda.device_count()))
            if torch.cuda.device_count() > 1:
                segmentation_model = nn.DataParallel(segmentation_model)
                augmentation_model = nn.DataParallel(augmentation_model)
            segmentation_model = segmentation_model.to(self.device)
            augmentation_model = augmentation_model.to(self.device)

        return segmentation_model, augmentation_model

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


    def initTrainDl(self):
        train_ds = TrainingLuna2dSegmentationDataset(
            val_stride=10,
            isValSet_bool=False,
            contextSlices_count=3,
        )

        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 = Luna2dSegmentationDataset(
            val_stride=10,
            isValSet_bool=True,
            contextSlices_count=3,
        )

        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_seg_' + self.cli_args.comment)
            self.val_writer = SummaryWriter(
                log_dir=log_dir + '_val_seg_' + 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()

        best_score = 0.0
        self.validation_cadence = 5
        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)

            if epoch_ndx == 1 or epoch_ndx % self.validation_cadence == 0:
                # if validation is wanted
                valMetrics_t = self.doValidation(epoch_ndx, val_dl)
                score = self.logMetrics(epoch_ndx, 'val', valMetrics_t)
                best_score = max(score, best_score)

                self.saveModel('seg', epoch_ndx, score == best_score)

                self.logImages(epoch_ndx, 'trn', train_dl)
                self.logImages(epoch_ndx, 'val', val_dl)

        self.trn_writer.close()
        self.val_writer.close()

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

        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 += trnMetrics_g.size(1)

        return trnMetrics_g.to('cpu')

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

            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,
                         classificationThreshold=0.5):
        input_t, label_t, series_list, _slice_ndx_list = batch_tup

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

        if self.segmentation_model.training and self.augmentation_dict:
            input_g, label_g = self.augmentation_model(input_g, label_g)

        prediction_g = self.segmentation_model(input_g)

        diceLoss_g = self.diceLoss(prediction_g, label_g)
        fnLoss_g = self.diceLoss(prediction_g * label_g, label_g)

        start_ndx = batch_ndx * batch_size
        end_ndx = start_ndx + input_t.size(0)

        with torch.no_grad():
            predictionBool_g = (prediction_g[:, 0:1]
                                > classificationThreshold).to(torch.float32)

            tp = (     predictionBool_g *  label_g).sum(dim=[1,2,3])
            fn = ((1 - predictionBool_g) *  label_g).sum(dim=[1,2,3])
            fp = (     predictionBool_g * (~label_g)).sum(dim=[1,2,3])

            metrics_g[METRICS_LOSS_NDX, start_ndx:end_ndx] = diceLoss_g
            metrics_g[METRICS_TP_NDX, start_ndx:end_ndx] = tp
            metrics_g[METRICS_FN_NDX, start_ndx:end_ndx] = fn
            metrics_g[METRICS_FP_NDX, start_ndx:end_ndx] = fp

        return diceLoss_g.mean() + fnLoss_g.mean() * 8

    def diceLoss(self, prediction_g, label_g, epsilon=1):
        diceLabel_g = label_g.sum(dim=[1,2,3])
        dicePrediction_g = prediction_g.sum(dim=[1,2,3])
        diceCorrect_g = (prediction_g * label_g).sum(dim=[1,2,3])

        diceRatio_g = (2 * diceCorrect_g + epsilon) \
            / (dicePrediction_g + diceLabel_g + epsilon)

        return 1 - diceRatio_g


    def logImages(self, epoch_ndx, mode_str, dl):
        self.segmentation_model.eval()

        images = sorted(dl.dataset.series_list)[:12]
        for series_ndx, series_uid in enumerate(images):
            ct = getCt(series_uid)

            for slice_ndx in range(6):
                ct_ndx = slice_ndx * (ct.hu_a.shape[0] - 1) // 5
                sample_tup = dl.dataset.getitem_fullSlice(series_uid, ct_ndx)

                ct_t, label_t, series_uid, ct_ndx = sample_tup

                input_g = ct_t.to(self.device).unsqueeze(0)
                label_g = pos_g = label_t.to(self.device).unsqueeze(0)

                prediction_g = self.segmentation_model(input_g)[0]
                prediction_a = prediction_g.to('cpu').detach().numpy()[0] > 0.5
                label_a = label_g.cpu().numpy()[0][0] > 0.5

                ct_t[:-1,:,:] /= 2000
                ct_t[:-1,:,:] += 0.5

                ctSlice_a = ct_t[dl.dataset.contextSlices_count].numpy()

                image_a = np.zeros((512, 512, 3), dtype=np.float32)
                image_a[:,:,:] = ctSlice_a.reshape((512,512,1))
                image_a[:,:,0] += prediction_a & (1 - label_a)
                image_a[:,:,0] += (1 - prediction_a) & label_a
                image_a[:,:,1] += ((1 - prediction_a) & label_a) * 0.5

                image_a[:,:,1] += prediction_a & label_a
                image_a *= 0.5
                image_a.clip(0, 1, image_a)

                writer = getattr(self, mode_str + '_writer')
                writer.add_image(
                    f'{mode_str}/{series_ndx}_prediction_{slice_ndx}',
                    image_a,
                    self.totalTrainingSamples_count,
                    dataformats='HWC',
                )

                if epoch_ndx == 1:
                    image_a = np.zeros((512, 512, 3), dtype=np.float32)
                    image_a[:,:,:] = ctSlice_a.reshape((512,512,1))
                    # image_a[:,:,0] += (1 - label_a) & lung_a # Red
                    image_a[:,:,1] += label_a  # Green
                    # image_a[:,:,2] += neg_a  # Blue

                    image_a *= 0.5
                    image_a[image_a < 0] = 0
                    image_a[image_a > 1] = 1
                    writer.add_image(
                        '{}/{}_label_{}'.format(
                            mode_str,
                            series_ndx,
                            slice_ndx,
                        ),
                        image_a,
                        self.totalTrainingSamples_count,
                        dataformats='HWC',
                    )
                # This flush prevents TB from getting confused about which
                # data item belongs where.
                writer.flush()

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

        metrics_a = metrics_t.detach().numpy()
        sum_a = metrics_a.sum(axis=1)
        assert np.isfinite(metrics_a).all()

        allLabel_count = sum_a[METRICS_TP_NDX] + sum_a[METRICS_FN_NDX]

        metrics_dict = {}
        metrics_dict['loss/all'] = metrics_a[METRICS_LOSS_NDX].mean()

        metrics_dict['percent_all/tp'] = \
            sum_a[METRICS_TP_NDX] / (allLabel_count or 1) * 100
        metrics_dict['percent_all/fn'] = \
            sum_a[METRICS_FN_NDX] / (allLabel_count or 1) * 100
        metrics_dict['percent_all/fp'] = \
            sum_a[METRICS_FP_NDX] / (allLabel_count or 1) * 100


        precision = metrics_dict['pr/precision'] = sum_a[METRICS_TP_NDX] \
            / ((sum_a[METRICS_TP_NDX] + sum_a[METRICS_FP_NDX]) or 1)
        recall    = metrics_dict['pr/recall']    = sum_a[METRICS_TP_NDX] \
            / ((sum_a[METRICS_TP_NDX] + sum_a[METRICS_FN_NDX]) or 1)

        metrics_dict['pr/f1_score'] = 2 * (precision * recall) \
            / ((precision + recall) or 1)

        log.info(("E{} {:8} "
                 + "{loss/all:.4f} loss, "
                 + "{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/all:.4f} loss, "
                  + "{percent_all/tp:-5.1f}% tp, {percent_all/fn:-5.1f}% fn, {percent_all/fp:-9.1f}% fp"
        ).format(
            epoch_ndx,
            mode_str + '_all',
            **metrics_dict,
        ))

        self.initTensorboardWriters()
        writer = getattr(self, mode_str + '_writer')

        prefix_str = 'seg_'

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

        writer.flush()

        score = metrics_dict['pr/recall']

        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)]
    #
    #             writer.add_histogram(
    #                 name.rsplit('.', 1)[-1] + '/' + name,
    #                 param.data.cpu().numpy(),
    #                 # metrics_a[METRICS_PRED_NDX, negHist_mask],
    #                 self.totalTrainingSamples_count,
    #                 # bins=bins,
    #             )
    #
    #             # print name, param.data

    def saveModel(self, type_str, epoch_ndx, isBest=False):
        file_path = os.path.join(
            'data-unversioned',
            'part2',
            'models',
            self.cli_args.tb_prefix,
            '{}_{}_{}.{}.state'.format(
                type_str,
                self.time_str,
                self.cli_args.comment,
                self.totalTrainingSamples_count,
            )
        )

        os.makedirs(os.path.dirname(file_path), mode=0o755, exist_ok=True)

        model = self.segmentation_model
        if isinstance(model, torch.nn.DataParallel):
            model = model.module

        state = {
            'sys_argv': sys.argv,
            'time': str(datetime.datetime.now()),
            'model_state': model.state_dict(),
            'model_name': type(model).__name__,
            'optimizer_state' : self.optimizer.state_dict(),
            'optimizer_name': type(self.optimizer).__name__,
            'epoch': epoch_ndx,
            'totalTrainingSamples_count': self.totalTrainingSamples_count,
        }
        torch.save(state, file_path)

        log.info("Saved model params to {}".format(file_path))

        if isBest:
            best_path = os.path.join(
                'data-unversioned', 'part2', 'models',
                self.cli_args.tb_prefix,
                f'{type_str}_{self.time_str}_{self.cli_args.comment}.best.state')
            shutil.copyfile(file_path, best_path)

            log.info("Saved model params to {}".format(best_path))

        with open(file_path, 'rb') as f:
            log.info("SHA1: " + hashlib.sha1(f.read()).hexdigest())


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