deep-learning-with-pytorch/p2ch14/training.py

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

import numpy as np
from matplotlib import pyplot

from torch.utils.tensorboard import SummaryWriter

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

import p2ch14.dsets
import p2ch14.model

from util.util import enumerateWithEstimate
from util.logconf import logging


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_PRED_P_NDX=2
METRICS_LOSS_NDX=3
METRICS_SIZE = 4

class ClassificationTrainingApp:
    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=24,
            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('--dataset',
            help="What to dataset to feed the model.",
            action='store',
            default='LunaDataset',
        )
        parser.add_argument('--model',
            help="What to model class name to use.",
            action='store',
            default='LunaModel',
        )
        parser.add_argument('--malignant',
            help="Train the model to classify nodules as benign or malignant.",
            action='store_true',
            default=False,
        )
        parser.add_argument('--finetune',
            help="Start finetuning from this model.",
            default='',
        )
        parser.add_argument('--finetune-depth',
            help="Number of blocks (counted from the head) to include in finetuning",
            type=int,
            default=1,
        )
        parser.add_argument('--tb-prefix',
            default='p2ch14',
            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 True:
        # 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_cls = getattr(p2ch14.model, self.cli_args.model)
        model = model_cls()

        if self.cli_args.finetune:
            d = torch.load(self.cli_args.finetune, map_location='cpu')
            model_blocks = [
                n for n, subm in model.named_children()
                if len(list(subm.parameters())) > 0
            ]
            finetune_blocks = model_blocks[-self.cli_args.finetune_depth:]
            log.info(f"finetuning from {self.cli_args.finetune}, blocks {' '.join(finetune_blocks)}")
            model.load_state_dict(
                {
                    k: v for k,v in d['model_state'].items()
                    if k.split('.')[0] not in model_blocks[-1]
                },
                strict=False,
            )
            for n, p in model.named_parameters():
                if n.split('.')[0] not in finetune_blocks:
                    p.requires_grad_(False)
        if self.use_cuda:
            log.info("Using CUDA with {} 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):
        lr = 0.003 if self.cli_args.finetune else 0.001
        return SGD(self.model.parameters(), lr=lr, weight_decay=1e-4)
        #return Adam(self.model.parameters(), lr=3e-4)

    def initTrainDl(self):
        ds_cls = getattr(p2ch14.dsets, self.cli_args.dataset)

        train_ds = ds_cls(
            val_stride=10,
            isValSet_bool=False,
            ratio_int=1,
        )

        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):
        ds_cls = getattr(p2ch14.dsets, self.cli_args.dataset)

        val_ds = ds_cls(
            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()

        best_score = 0.0
        validation_cadence = 5 if not self.cli_args.finetune else 1
        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 % validation_cadence == 0:
                valMetrics_t = self.doValidation(epoch_ndx, val_dl)
                score = self.logMetrics(epoch_ndx, 'val', valMetrics_t)
                best_score = max(score, best_score)

                # TODO: this 'cls' will need to change for the malignant classifier
                self.saveModel('cls', epoch_ndx, score == best_score)


        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,
                augment=True
            )

            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,
                    augment=False
                )

        return valMetrics_g.to('cpu')



    def computeBatchLoss(self, batch_ndx, batch_tup, batch_size, metrics_g,
                         augment=True):
        input_t, label_t, index_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)
        index_g = index_t.to(self.device, non_blocking=True)


        if augment:
            input_g = p2ch14.model.augment3d(input_g)

        logits_g, probability_g = self.model(input_g)

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

        _, predLabel_g = torch.max(probability_g, dim=1, keepdim=False,
                                   out=None)

        # log.debug(index_g)

        metrics_g[METRICS_LABEL_NDX, start_ndx:end_ndx] = index_g
        metrics_g[METRICS_PRED_NDX, start_ndx:end_ndx] = predLabel_g
        # metrics_g[METRICS_PRED_N_NDX, start_ndx:end_ndx] = probability_g[:,0]
        metrics_g[METRICS_PRED_P_NDX, start_ndx:end_ndx] = probability_g[:,1]
        # metrics_g[METRICS_PRED_M_NDX, start_ndx:end_ndx] = probability_g[:,2]
        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__,
        ))

        if self.cli_args.dataset == 'MalignantLunaDataset':
            pos = 'mal'
            neg = 'ben'
        else:
            pos = 'pos'
            neg = 'neg'


        negLabel_mask = metrics_t[METRICS_LABEL_NDX] == 0
        negPred_mask = metrics_t[METRICS_PRED_NDX] == 0

        posLabel_mask = ~negLabel_mask
        posPred_mask = ~negPred_mask

        # benLabel_mask = metrics_t[METRICS_LABEL_NDX] == 1
        # benPred_mask = metrics_t[METRICS_PRED_NDX] == 1
        #
        # malLabel_mask = metrics_t[METRICS_LABEL_NDX] == 2
        # malPred_mask = metrics_t[METRICS_PRED_NDX] == 2

        # benLabel_mask = ~malLabel_mask & posLabel_mask
        # benPred_mask = ~malPred_mask & posLabel_mask

        neg_count = int(negLabel_mask.sum())
        pos_count = int(posLabel_mask.sum())
        # ben_count = int(benLabel_mask.sum())
        # mal_count = int(malLabel_mask.sum())

        neg_correct = int((negLabel_mask & negPred_mask).sum())
        pos_correct = int((posLabel_mask & posPred_mask).sum())
        # ben_correct = int((benLabel_mask & benPred_mask).sum())
        # mal_correct = int((malLabel_mask & malPred_mask).sum())

        trueNeg_count = neg_correct
        truePos_count = pos_correct

        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['loss/ben'] = metrics_t[METRICS_LOSS_NDX, benLabel_mask].mean()
        # metrics_dict['loss/mal'] = metrics_t[METRICS_LOSS_NDX, malLabel_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
        # metrics_dict['correct/ben'] = (ben_correct) / ben_count * 100
        # metrics_dict['correct/mal'] = (mal_correct) / mal_count * 100

        precision = metrics_dict['pr/precision'] = \
            truePos_count / np.float64(truePos_count + falsePos_count)
        recall    = metrics_dict['pr/recall'] = \
            truePos_count / np.float64(truePos_count + falseNeg_count)

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

        threshold = torch.linspace(1, 0)
        tpr = (metrics_t[None, METRICS_PRED_P_NDX, posLabel_mask] >= threshold[:, None]).sum(1).float() / pos_count
        fpr = (metrics_t[None, METRICS_PRED_P_NDX, negLabel_mask] >= threshold[:, None]).sum(1).float() / neg_count
        fp_diff = fpr[1:]-fpr[:-1]
        tp_avg  = (tpr[1:]+tpr[:-1])/2
        auc = (fp_diff * tp_avg).sum()
        metrics_dict['auc'] = auc

        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, "
                 + "{auc:.4f} auc"
            ).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,
            )
        )
        # log.info(
        #     ("E{} {:8} {loss/ben:.4f} loss, "
        #          + "{correct/ben:-5.1f}% correct ({ben_correct:} of {ben_count:})"
        #     ).format(
        #         epoch_ndx,
        #         mode_str + '_ben',
        #         ben_correct=ben_correct,
        #         ben_count=ben_count,
        #         **metrics_dict,
        #     )
        # )
        # log.info(
        #     ("E{} {:8} {loss/mal:.4f} loss, "
        #          + "{correct/mal:-5.1f}% correct ({mal_correct:} of {mal_count:})"
        #     ).format(
        #         epoch_ndx,
        #         mode_str + '_mal',
        #         mal_correct=mal_correct,
        #         mal_count=mal_count,
        #         **metrics_dict,
        #     )
        # )
        writer = getattr(self, mode_str + '_writer')

        for key, value in metrics_dict.items():
            key = key.replace('pos', pos)
            key = key.replace('neg', neg)
            writer.add_scalar(key, value, self.totalTrainingSamples_count)

        fig = pyplot.figure()
        pyplot.plot(fpr, tpr)
        writer.add_figure('roc', fig, self.totalTrainingSamples_count)

        writer.add_scalar('auc', auc, self.totalTrainingSamples_count)
# # tag::logMetrics_writer_prcurve[]
#        writer.add_pr_curve(
#            'pr',
#            metrics_t[METRICS_LABEL_NDX],
#            metrics_t[METRICS_PRED_P_NDX],
#            self.totalTrainingSamples_count,
#        )
# # end::logMetrics_writer_prcurve[]

        bins = np.linspace(0, 1)

        writer.add_histogram(
            'label_neg',
            metrics_t[METRICS_PRED_P_NDX, negLabel_mask],
            self.totalTrainingSamples_count,
            bins=bins
        )
        writer.add_histogram(
            'label_pos',
            metrics_t[METRICS_PRED_P_NDX, posLabel_mask],
            self.totalTrainingSamples_count,
            bins=bins
        )

        if not self.cli_args.malignant:
            score = metrics_dict['pr/f1_score']
        else:
            score = metrics_dict['auc']

        return score

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

        state = {
            '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.debug("Saved model params to {}".format(file_path))

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

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

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

    # 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__':
    ClassificationTrainingApp().main()