rmaphoh
149 lines
6.9 KiB
Python
149 lines
6.9 KiB
Python
import os
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import csv
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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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import numpy as np
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import matplotlib.pyplot as plt
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from typing import Iterable, Optional
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from timm.data import Mixup
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from timm.utils import accuracy
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from sklearn.metrics import (
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accuracy_score, roc_auc_score, f1_score, average_precision_score,
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hamming_loss, jaccard_score, recall_score, precision_score, cohen_kappa_score
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)
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from pycm import ConfusionMatrix
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import util.misc as misc
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import util.lr_sched as lr_sched
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def train_one_epoch(
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model: torch.nn.Module,
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criterion: torch.nn.Module,
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data_loader: Iterable,
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optimizer: torch.optim.Optimizer,
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device: torch.device,
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epoch: int,
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loss_scaler,
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max_norm: float = 0,
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mixup_fn: Optional[Mixup] = None,
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log_writer=None,
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args=None
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):
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"""Train the model for one epoch."""
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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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print_freq, accum_iter = 20, args.accum_iter
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optimizer.zero_grad()
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if log_writer:
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print(f'log_dir: {log_writer.log_dir}')
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for data_iter_step, (samples, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, f'Epoch: [{epoch}]')):
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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, targets = samples.to(device, non_blocking=True), targets.to(device, non_blocking=True)
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if mixup_fn:
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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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loss /= accum_iter
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loss_scaler(loss, optimizer, clip_grad=max_norm, 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/train', loss_value_reduce, epoch_1000x)
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log_writer.add_scalar('lr', max_lr, epoch_1000x)
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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, args, epoch, mode, num_class, log_writer):
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"""Evaluate the model."""
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criterion = nn.CrossEntropyLoss()
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metric_logger = misc.MetricLogger(delimiter=" ")
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os.makedirs(os.path.join(args.output_dir, args.task), exist_ok=True)
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model.eval()
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true_onehot, pred_onehot, true_labels, pred_labels, pred_softmax = [], [], [], [], []
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for batch in metric_logger.log_every(data_loader, 10, f'{mode}:'):
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images, target = batch[0].to(device, non_blocking=True), batch[-1].to(device, non_blocking=True)
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target_onehot = F.one_hot(target.to(torch.int64), num_classes=num_class)
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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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output_ = nn.Softmax(dim=1)(output)
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output_label = output_.argmax(dim=1)
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output_onehot = F.one_hot(output_label.to(torch.int64), num_classes=num_class)
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metric_logger.update(loss=loss.item())
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true_onehot.extend(target_onehot.cpu().numpy())
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pred_onehot.extend(output_onehot.detach().cpu().numpy())
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true_labels.extend(target.cpu().numpy())
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pred_labels.extend(output_label.detach().cpu().numpy())
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pred_softmax.extend(output_.detach().cpu().numpy())
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accuracy = accuracy_score(true_labels, pred_labels)
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hamming = hamming_loss(true_onehot, pred_onehot)
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jaccard = jaccard_score(true_onehot, pred_onehot, average='macro')
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average_precision = average_precision_score(true_onehot, pred_softmax, average='macro')
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kappa = cohen_kappa_score(true_labels, pred_labels)
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f1 = f1_score(true_onehot, pred_onehot, zero_division=0, average='macro')
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roc_auc = roc_auc_score(true_onehot, pred_softmax, multi_class='ovr', average='macro')
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precision = precision_score(true_onehot, pred_onehot, zero_division=0, average='macro')
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recall = recall_score(true_onehot, pred_onehot, zero_division=0, average='macro')
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score = (f1 + roc_auc + kappa) / 3
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if log_writer:
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for metric_name, value in zip(['accuracy', 'f1', 'roc_auc', 'hamming', 'jaccard', 'precision', 'recall', 'average_precision', 'kappa', 'score'],
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[accuracy, f1, roc_auc, hamming, jaccard, precision, recall, average_precision, kappa, score]):
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log_writer.add_scalar(f'perf/{metric_name}', value, epoch)
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print(f'val loss: {metric_logger.meters["loss"].global_avg}')
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print(f'Accuracy: {accuracy:.4f}, F1 Score: {f1:.4f}, ROC AUC: {roc_auc:.4f}, Hamming Loss: {hamming:.4f},\n'
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f' Jaccard Score: {jaccard:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f},\n'
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f' Average Precision: {average_precision:.4f}, Kappa: {kappa:.4f}, Score: {score:.4f}')
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metric_logger.synchronize_between_processes()
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results_path = os.path.join(args.output_dir, args.task, f'metrics_{mode}.csv')
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file_exists = os.path.isfile(results_path)
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with open(results_path, 'a', newline='', encoding='utf8') as cfa:
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wf = csv.writer(cfa)
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if not file_exists:
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wf.writerow(['val_loss', 'accuracy', 'f1', 'roc_auc', 'hamming', 'jaccard', 'precision', 'recall', 'average_precision', 'kappa'])
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wf.writerow([metric_logger.meters["loss"].global_avg, accuracy, f1, roc_auc, hamming, jaccard, precision, recall, average_precision, kappa])
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if mode == 'test':
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cm = ConfusionMatrix(actual_vector=true_labels, predict_vector=pred_labels)
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cm.plot(cmap=plt.cm.Blues, number_label=True, normalized=True, plot_lib="matplotlib")
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plt.savefig(os.path.join(args.output_dir, args.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()}, score
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