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test_reset.py
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test_reset.py
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import argparse
import os
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from data_isic import DataLoaderISIC
from sklearn.metrics import roc_auc_score, balanced_accuracy_score, confusion_matrix, roc_curve
from reset import ReSeT
parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", type=int, default=256)
parser.add_argument("--train_epochs", type=int, default=150)
args = parser.parse_args()
def main():
model_name = "CNN"
dim_input = sum(['feature' in col for col in pd.read_csv(f"features/{model_name}_val.csv").columns])
num_outputs = 1
emb_dim = 20
dim_output = 2
n_vect = 50
val_gen = DataLoaderISIC(
f"features/{model_name}_val.csv",
"GroundTruth.csv",
batch_size=args.batch_size,
n_vect=n_vect
)
test_gen = DataLoaderISIC(
f"features/{model_name}_test.csv",
"GroundTruth.csv",
batch_size=args.batch_size,
n_vect=n_vect
)
class_weights = torch.tensor([0.02, 0.98]).cuda()
criterion = nn.CrossEntropyLoss(class_weights)
model = ReSeT(dim_input, num_outputs, emb_dim, dim_output)
model = nn.DataParallel(model)
model = model.cuda()
model.load_state_dict(torch.load(f"models/{model_name}/{model_name}.pth"))
model.eval()
losses, total, correct, true_labels, predicted_probs = [], 0, 0, [], []
base_fpr = np.linspace(0, 1, 101)
for imgs, lbls in val_gen.test_data():
imgs = torch.Tensor(imgs).cuda()
lbls = torch.Tensor(lbls).long().cuda()
preds = model(imgs)
zero_rows_mask = torch.all(imgs == 0, dim=2)
non_zero_rows_mask = ~zero_rows_mask
preds = preds[non_zero_rows_mask]
lbls = lbls[non_zero_rows_mask]
loss = criterion(preds.view(-1, 2), lbls.view(-1))
losses.append(loss.item())
total += lbls.view(-1).shape[0]
correct += (preds.view(-1, 2).argmax(dim=1) == lbls.view(-1)).sum().item()
true_labels += lbls.view(-1).cpu().numpy().tolist()
predicted_probs += torch.softmax(preds.view(-1, 2), dim=1)[:, 1].cpu().detach().numpy().tolist()
avg_loss, avg_acc = np.mean(losses), correct / total
auc = roc_auc_score(true_labels, predicted_probs)
best_bacc = 0
best_thresh = 0
thresholds = np.linspace(0, 1, 5)
for thresh in thresholds:
balanced_acc = balanced_accuracy_score(true_labels, (np.array(predicted_probs) > thresh).astype(int))
if balanced_acc > best_bacc:
best_bacc = balanced_acc
best_thresh = thresh
print(
f"val loss {avg_loss:.3f} val acc {avg_acc:.3f} val AUC {auc:.3f}"
f" val balanced acc {best_bacc:.3f} best threshold {best_thresh:.3f}")
losses, total, correct, true_labels, predicted_probs = [], 0, 0, [], []
for imgs, lbls in test_gen.test_data():
imgs = torch.Tensor(imgs).cuda()
lbls = torch.Tensor(lbls).long().cuda()
preds = model(imgs)
zero_rows_mask = torch.all(imgs == 0, dim=2)
non_zero_rows_mask = ~zero_rows_mask
preds = preds[non_zero_rows_mask]
lbls = lbls[non_zero_rows_mask]
loss = criterion(preds.view(-1, 2), lbls.view(-1))
losses.append(loss.item())
total += lbls.view(-1).shape[0]
correct += (preds.view(-1, 2).argmax(dim=1) == lbls.view(-1)).sum().item()
true_labels += lbls.view(-1).cpu().numpy().tolist()
predicted_probs += torch.softmax(preds.view(-1, 2), dim=1)[:, 1].cpu().detach().numpy().tolist()
avg_loss, avg_acc = np.mean(losses), correct / total
if not os.path.exists("predictions"):
os.makedirs("predictions")
np.savetxt(f"predictions/{model_name}", np.array(predicted_probs), delimiter=",")
binary_predictions = (np.array(predicted_probs) > best_thresh).astype(int)
conf_matrix = confusion_matrix(true_labels, binary_predictions)
auc = roc_auc_score(true_labels, predicted_probs)
bacc = balanced_accuracy_score(true_labels, binary_predictions)
fpr, tpr, thresholds = roc_curve(true_labels, predicted_probs)
tpr = np.interp(base_fpr, fpr, tpr)
tpr[0] = 0.0
print(
f"test loss {avg_loss:.3f} test acc {avg_acc:.3f} test AUC {auc:.3f}"
f" test balanced acc {bacc:.3f}")
if __name__ == "__main__":
main()