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Copy pathutils.py
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executable file
·166 lines (136 loc) · 6.1 KB
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from matplotlib import pyplot as plt
import torch
import torchmetrics
from torchmetrics.classification import MulticlassAccuracy
# 計算Accuracy, Recall, Precision
def cal_metrics(outputs, targets, num_classes, device):
outputs = torch.softmax(outputs, dim=1)
targets = torch.argmax(targets, dim=1)
accuracy = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes).to(device)
accuracy_no_avg = MulticlassAccuracy(num_classes=num_classes, average=None).to(device)
precision = torchmetrics.Precision(task="multiclass", average='none', num_classes=num_classes).to(device)
recall = torchmetrics.Recall(task="multiclass", average='none', num_classes=num_classes).to(device)
accuracy.update(outputs, targets)
accuracy_no_avg.update(outputs, targets)
precision.update(outputs, targets)
recall.update(outputs, targets)
acc = accuracy.compute()
acc_no_avg = accuracy_no_avg.compute()
pre = precision.compute()
rec = recall.compute()
acc_0 = acc_no_avg[0]
acc_1 = acc_no_avg[1]
rec_0 = rec[0]
rec_1 = rec[1]
pre_0 = pre[0]
pre_1 = pre[1]
prec_macro = torchmetrics.Precision(task="multiclass", average='macro', num_classes=num_classes).to(device)
rec_macro = torchmetrics.Recall(task="multiclass", average='macro', num_classes=num_classes).to(device)
prec = prec_macro(outputs,targets)
rec = rec_macro(outputs,targets)
return acc, acc_0, acc_1, rec, prec, rec_0, rec_1, pre_0, pre_1
# 繪製訓練與測試模型結果
def draw_pics(record, n, current_save_dir, file_name):
n = n+1
plt.figure(figsize=(15, 10))
## Training ##
# loss
plt.subplot(4, 2, 1)
plt.plot(record['train']['loss'])
plt.axvline(len(record['train']['loss']) - n, color='red', linestyle='--', label='early stopping')
plt.title('Training Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
# acc
plt.subplot(4, 2, 3)
plt.plot(record['train']['acc'], color='black', label='accuracy')
plt.axvline(len(record['train']['acc']) - n, color='red', linestyle='--', label='early stopping')
plt.title('Training accuracy')
plt.xlabel('Epoch')
plt.ylabel('accuracy')
plt.legend(loc='upper right')
# acc of classes
plt.subplot(4, 2, 5)
plt.plot(record['train']['acc_0'], color='purple', label='A')
plt.plot(record['train']['acc_1'], color='blue', label='B')
plt.axvline(len(record['train']['acc_0']) - n, color='red', linestyle='--', label='early stopping')
plt.title('Training acc. of each class')
plt.xlabel('Epoch')
plt.ylabel('accuracy')
plt.legend(loc='upper right')
# rec of classes
plt.subplot(4, 2, 7)
plt.plot(record['train']['rec_0'], color='purple', label='A')
plt.plot(record['train']['rec_1'], color='blue', label='B')
plt.axvline(len(record['train']['rec_0']) - n, color='red', linestyle='--', label='early stopping')
plt.title('Training recall')
plt.xlabel('Epoch')
plt.ylabel('recall')
plt.legend(loc='upper right')
## Testing ##
# loss
plt.subplot(4, 2, 2)
plt.plot(record['test']['loss'])
plt.axvline(len(record['test']['loss']) - n, color='red', linestyle='--', label='early stopping')
plt.title('Testing Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
# acc
plt.subplot(4, 2, 4)
plt.plot(record['test']['acc'], color='black', label='accuracy')
plt.axvline(len(record['test']['acc']) - n, color='red', linestyle='--', label='early stopping')
plt.title('Testing accuracy')
plt.xlabel('Epoch')
plt.ylabel('accuracy')
plt.legend(loc='upper right')
# acc of classes
plt.subplot(4, 2, 6)
plt.plot(record['test']['acc_0'], color='purple', label='A')
plt.plot(record['test']['acc_1'], color='blue', label='B')
plt.axvline(len(record['test']['acc_0']) - n, color='red', linestyle='--', label='early stopping')
plt.title('Testing acc. of each class')
plt.xlabel('Epoch')
plt.ylabel('accuracy')
plt.legend(loc='upper right')
# rec of classes
plt.subplot(4, 2, 8)
plt.plot(record['test']['rec_0'], color='purple', label='A')
plt.plot(record['test']['rec_1'], color='blue', label='B')
plt.axvline(len(record['test']['rec_0']) - n, color='red', linestyle='--', label='early stopping')
plt.title('Testing recall')
plt.xlabel('Epoch')
plt.ylabel('recall')
plt.legend(loc='upper right')
plt.tight_layout()
plt.savefig(current_save_dir+"/"+file_name+".png")
def initial_record():
record = {'train':{}, 'test':{}}
metrics = ['loss', 'acc', 'rec', 'prec', 'rec_0', 'rec_1', 'pre_0', 'pre_1', 'acc_0', 'acc_1']
for j in record.keys():
for m in metrics:
record[j][m] = []
return record
# 取得EarlyStopping前最後一次模型測試結果
def get_the_last_record(record):
result = initial_record()
result['train']['loss'].append(record['train']['loss'][-1])
result['train']['acc'].append(record['train']['acc'][-1])
result['train']['rec'].append(record['train']['rec'][-1])
result['train']['prec'].append(record['train']['prec'][-1])
result['train']['rec_0'].append(record['train']['rec_0'][-1])
result['train']['rec_1'].append(record['train']['rec_1'][-1])
result['train']['pre_0'].append(record['train']['pre_0'][-1])
result['train']['pre_1'].append(record['train']['pre_1'][-1])
result['train']['acc_0'].append(record['train']['acc_0'][-1])
result['train']['acc_1'].append(record['train']['acc_1'][-1])
result['test']['loss'].append(record['test']['loss'][-1])
result['test']['acc'].append(record['test']['acc'][-1])
result['test']['rec'].append(record['test']['rec'][-1])
result['test']['prec'].append(record['test']['prec'][-1])
result['test']['rec_0'].append(record['test']['rec_0'][-1])
result['test']['rec_1'].append(record['test']['rec_1'][-1])
result['test']['pre_0'].append(record['test']['pre_0'][-1])
result['test']['pre_1'].append(record['test']['pre_1'][-1])
result['test']['acc_0'].append(record['test']['acc_0'][-1])
result['test']['acc_1'].append(record['test']['acc_1'][-1])
return result