v1.0
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rmaphoh
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# --------------------------------------------------------
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# References:
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# DeiT: https://github.com/facebookresearch/deit
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# BEiT: https://github.com/microsoft/unilm/tree/master/beit
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# --------------------------------------------------------
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import math
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import sys
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import csv
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import os
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.data import Mixup
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from timm.utils import accuracy
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from typing import Iterable, Optional
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import util.misc as misc
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import util.lr_sched as lr_sched
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from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, average_precision_score,multilabel_confusion_matrix
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from pycm import *
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import matplotlib.pyplot as plt
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import numpy as np
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def misc_measures(confusion_matrix):
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acc = []
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sensitivity = []
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specificity = []
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precision = []
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G = []
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F1_score_2 = []
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mcc_ = []
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for i in range(1, confusion_matrix.shape[0]):
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cm1=confusion_matrix[i]
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acc.append(1.*(cm1[0,0]+cm1[1,1])/np.sum(cm1))
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sensitivity_ = 1.*cm1[1,1]/(cm1[1,0]+cm1[1,1])
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sensitivity.append(sensitivity_)
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specificity_ = 1.*cm1[0,0]/(cm1[0,1]+cm1[0,0])
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specificity.append(specificity_)
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precision_ = 1.*cm1[1,1]/(cm1[1,1]+cm1[0,1])
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precision.append(precision_)
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G.append(np.sqrt(sensitivity_*specificity_))
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F1_score_2.append(2*precision_*sensitivity_/(precision_+sensitivity_))
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mcc = (cm1[0,0]*cm1[1,1]-cm1[0,1]*cm1[1,0])/np.sqrt((cm1[0,0]+cm1[0,1])*(cm1[0,0]+cm1[1,0])*(cm1[1,1]+cm1[1,0])*(cm1[1,1]+cm1[0,1]))
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mcc_.append(mcc)
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acc = np.array(acc).mean()
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sensitivity = np.array(sensitivity).mean()
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specificity = np.array(specificity).mean()
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precision = np.array(precision).mean()
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G = np.array(G).mean()
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F1_score_2 = np.array(F1_score_2).mean()
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mcc_ = np.array(mcc_).mean()
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return acc, sensitivity, specificity, precision, G, F1_score_2, mcc_
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def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
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data_loader: Iterable, optimizer: torch.optim.Optimizer,
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device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
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mixup_fn: Optional[Mixup] = None, log_writer=None,
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args=None):
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model.train(True)
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metric_logger = misc.MetricLogger(delimiter=" ")
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metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
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header = 'Epoch: [{}]'.format(epoch)
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print_freq = 20
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accum_iter = args.accum_iter
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optimizer.zero_grad()
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if log_writer is not None:
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print('log_dir: {}'.format(log_writer.log_dir))
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for data_iter_step, (samples, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
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# we use a per iteration (instead of per epoch) lr scheduler
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if data_iter_step % accum_iter == 0:
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lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)
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samples = samples.to(device, non_blocking=True)
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targets = targets.to(device, non_blocking=True)
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if mixup_fn is not None:
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samples, targets = mixup_fn(samples, targets)
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with torch.cuda.amp.autocast():
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outputs = model(samples)
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loss = criterion(outputs, targets)
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loss_value = loss.item()
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if not math.isfinite(loss_value):
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print("Loss is {}, stopping training".format(loss_value))
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sys.exit(1)
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loss /= accum_iter
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loss_scaler(loss, optimizer, clip_grad=max_norm,
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parameters=model.parameters(), create_graph=False,
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update_grad=(data_iter_step + 1) % accum_iter == 0)
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if (data_iter_step + 1) % accum_iter == 0:
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optimizer.zero_grad()
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torch.cuda.synchronize()
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metric_logger.update(loss=loss_value)
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min_lr = 10.
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max_lr = 0.
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for group in optimizer.param_groups:
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min_lr = min(min_lr, group["lr"])
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max_lr = max(max_lr, group["lr"])
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metric_logger.update(lr=max_lr)
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loss_value_reduce = misc.all_reduce_mean(loss_value)
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if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
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""" We use epoch_1000x as the x-axis in tensorboard.
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This calibrates different curves when batch size changes.
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"""
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epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
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log_writer.add_scalar('loss', loss_value_reduce, epoch_1000x)
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log_writer.add_scalar('lr', max_lr, epoch_1000x)
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# gather the stats from all processes
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metric_logger.synchronize_between_processes()
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print("Averaged stats:", metric_logger)
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return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
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@torch.no_grad()
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def evaluate(data_loader, model, device, task, epoch, mode, num_class):
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criterion = torch.nn.CrossEntropyLoss()
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metric_logger = misc.MetricLogger(delimiter=" ")
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header = 'Test:'
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if not os.path.exists(task):
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os.makedirs(task)
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prediction_decode_list = []
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prediction_list = []
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true_label_decode_list = []
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true_label_onehot_list = []
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# switch to evaluation mode
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model.eval()
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for batch in metric_logger.log_every(data_loader, 10, header):
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images = batch[0]
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target = batch[-1]
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images = images.to(device, non_blocking=True)
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target = target.to(device, non_blocking=True)
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true_label=F.one_hot(target.to(torch.int64), num_classes=num_class)
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# compute output
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with torch.cuda.amp.autocast():
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output = model(images)
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loss = criterion(output, target)
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prediction_softmax = nn.Softmax(dim=1)(output)
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_,prediction_decode = torch.max(prediction_softmax, 1)
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_,true_label_decode = torch.max(true_label, 1)
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prediction_decode_list.extend(prediction_decode.cpu().detach().numpy())
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true_label_decode_list.extend(true_label_decode.cpu().detach().numpy())
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true_label_onehot_list.extend(true_label.cpu().detach().numpy())
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prediction_list.extend(prediction_softmax.cpu().detach().numpy())
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acc1,_ = accuracy(output, target, topk=(1,2))
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batch_size = images.shape[0]
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metric_logger.update(loss=loss.item())
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metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
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# gather the stats from all processes
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true_label_decode_list = np.array(true_label_decode_list)
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prediction_decode_list = np.array(prediction_decode_list)
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confusion_matrix = multilabel_confusion_matrix(true_label_decode_list, prediction_decode_list,labels=[i for i in range(num_class)])
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acc, sensitivity, specificity, precision, G, F1, mcc = misc_measures(confusion_matrix)
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auc_roc = roc_auc_score(true_label_onehot_list, prediction_list,multi_class='ovr',average='macro')
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auc_pr = average_precision_score(true_label_onehot_list, prediction_list,average='macro')
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metric_logger.synchronize_between_processes()
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print('Sklearn Metrics - Acc: {:.4f} AUC-roc: {:.4f} AUC-pr: {:.4f} F1-score: {:.4f} MCC: {:.4f}'.format(acc, auc_roc, auc_pr, F1, mcc))
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results_path = task+'_metrics_{}.csv'.format(mode)
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with open(results_path,mode='a',newline='',encoding='utf8') as cfa:
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wf = csv.writer(cfa)
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data2=[[acc,sensitivity,specificity,precision,auc_roc,auc_pr,F1,mcc,metric_logger.loss]]
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for i in data2:
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wf.writerow(i)
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if mode=='test':
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cm = ConfusionMatrix(actual_vector=true_label_decode_list, predict_vector=prediction_decode_list)
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cm.plot(cmap=plt.cm.Blues,number_label=True,normalized=True,plot_lib="matplotlib")
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plt.savefig(task+'confusion_matrix_test.jpg',dpi=600,bbox_inches ='tight')
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return {k: meter.global_avg for k, meter in metric_logger.meters.items()},auc_roc
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