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import numpy as np
import pandas as pd
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from dataloading.dataloader2D import NiftiSegmentationDataset
import yaml
from pathlib import Path
import matplotlib.pyplot as plt
from sklearn.calibration import calibration_curve
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.preprocessing import label_binarize
from odelia_breast_mri.scripts.main_predict import evaluate
import torchvision.transforms.functional as TF
from dataloading.collate_function import custom_collate
from models.swinunetr import SwinUNETRMultiTask
def tta_transforms(x):
"""
Apply test-time augmentations to a single batch tensor.
Input: x (B, C, H, W)
Returns a list of augmented versions.
"""
return [
x, # original
TF.hflip(x),
TF.vflip(x),
TF.rotate(x, 90),
TF.rotate(x, 180),
TF.rotate(x, 270),
TF.hflip(TF.vflip(x)), # h+v flip
]
def plot_multiclass_reliability(probs, labels, class_names=None, n_bins=10):
"""
Plot calibration curves for each class in a multiclass problem.
Args:
probs: numpy array of shape (n_samples, n_classes) with predicted probabilities.
labels: numpy array of shape (n_samples,) with integer class labels.
class_names: list of class names (optional).
n_bins: number of bins for calibration curve.
"""
n_classes = probs.shape[1]
if class_names is None:
class_names = [f"Class {i}" for i in range(n_classes)]
plt.figure(figsize=(8, 8))
for i in range(n_classes):
# Binary labels: is this class or not
binarized_labels = (labels == i).astype(int)
fraction_of_positives, mean_predicted_value = calibration_curve(
binarized_labels, probs[:, i], n_bins=n_bins, strategy='uniform'
)
plt.plot(
mean_predicted_value, fraction_of_positives, "s-",
label=f"{class_names[i]}"
)
plt.plot([0, 1], [0, 1], "k:", label="Perfectly calibrated")
plt.title("Multiclass Reliability Diagram")
plt.xlabel("Mean Predicted Probability")
plt.ylabel("Fraction of Positives")
plt.legend()
plt.grid()
# plt.show()
plt.savefig('/workspace/ClassifierSegmenter/results/multiclass_reliability_diagram.png')
# ----------------------
# Load Config & Set Up
# ----------------------
with open("/workspace/ClassifierSegmenter/config2d.yaml", "r") as f:
config = yaml.safe_load(f)
device = torch.device(config["device"] if torch.cuda.is_available() else "cpu")
# ----------------------
# Load Model
# ----------------------
checkpoint_paths = [
"/workspace/Classifier/checkpoints/final/model1/best_score_model.pth",
"/workspace/Classifier/checkpoints/final/model2/best_score_model.pth",
"/workspace/Classifier/checkpoints/final/model3/best_score_model.pth",
"/workspace/Classifier/checkpoints/final/model4/best_score_model.pth",
"/workspace/Classifier/checkpoints/final/model5/best_score_model.pth",
"/workspace/Classifier/checkpoints/final/model6/best_score_model.pth",
"/workspace/Classifier/checkpoints/final/model7/best_score_model.pth",
"/workspace/Classifier/checkpoints/final/model8/best_score_model.pth",
"/workspace/Classifier/checkpoints/final/model9/best_score_model.pth",
"/workspace/Classifier/checkpoints/final/model10/best_score_model.pth"
]
models = []
for i, ckpt_path in enumerate(checkpoint_paths, start=1):
model = SwinUNETRMultiTask(
img_size=(256, 256),
in_channels=4,
out_seg_channels=2,
out_cls_classes=3
).to(device)
if ckpt_path is not None:
state = torch.load(ckpt_path, map_location=device, weights_only=True)
model.load_state_dict(state)
model.to(device)
model.eval()
models.append(model)
# ----------------------
# Load Test Dataset
# ----------------------
test_dataset = NiftiSegmentationDataset(config["csv_file_test"], channel_keys=config["channel_keys"], augment=False)
test_loader = DataLoader(test_dataset, batch_size=config["batch_size"], collate_fn=custom_collate, shuffle=False)
metrics = {}
# ----------------------
# Inference + Metrics
# ----------------------
all_preds, all_probs, all_targets = [], [], []
all_patient_ids = []
with torch.no_grad():
if not config["tta"]:
for batch in tqdm(test_loader, desc="Running Test Inference"):
x = batch['image'].to(device)
y = batch['cls_label'].to(device)
mask = batch['mask'].to(device) if batch['mask'] is not None else None
has_mask = batch['has_mask'].to(device) if batch['has_mask'] is not None else None
with torch.autocast(device_type='cuda'):
# Collect logits from all models
logits_list = []
for model in models:
_, out, _ = model(x)
logits_list.append(out)
# Average probabilities across models
probs_list = [torch.softmax(logits, dim=1) for logits in logits_list]
probs = torch.stack(probs_list, dim=0).mean(dim=0)
preds = torch.argmax(probs, dim=1)
all_preds.append(preds.cpu())
all_probs.append(probs.cpu())
all_targets.append(y.cpu())
all_patient_ids.extend(batch['patient_id'])
else:
for batch in tqdm(test_loader, desc="Running Test Inference w/ TTA"):
x = batch['image'].to(device)
y = batch['cls_label'].to(device)
mask = batch['mask'].to(device) if batch['mask'] is not None else None
has_mask = batch['has_mask'].to(device) if batch['has_mask'] is not None else None
tta_versions = tta_transforms(x) # list of augmented batches
# Combine TTA with ensemble: collect logits for each model and each augmentation
model_logits = []
for model in models:
all_logits = []
for aug_x in tta_versions:
with torch.autocast(device_type='cuda'):
_, logits, _ = model(aug_x)
all_logits.append(logits)
stacked_logits = torch.stack(all_logits, dim=0).mean(dim=0)
model_logits.append(stacked_logits)
# Average across models
mean_logits = torch.stack(model_logits, dim=0).mean(dim=0)
probs = torch.softmax(mean_logits, dim=1)
preds = torch.argmax(probs, dim=1)
all_preds.append(preds.cpu())
all_probs.append(probs.cpu())
all_targets.append(y.cpu())
all_patient_ids.extend(batch['patient_id'])
# Concatenate everything
all_preds = torch.cat(all_preds)
all_probs = torch.cat(all_probs)
all_targets = torch.cat(all_targets)
# ----------------------
# Evaluate MACRO metrics
# ----------------------
accuracy = (all_preds == all_targets).sum().item() / len(all_targets)
auc, sensitivity, specificity = evaluate(
all_targets,
all_preds,
all_probs,
path_out=Path('/workspace/ClassifierSegmenter/results')
)
print("\n✅ MACRO Results")
print(f"Accuracy: {accuracy:.4f}")
print(f"AUC: {auc:.4f}")
print(f"Sensitivity: {sensitivity:.4f}")
print(f"Specificity: {specificity:.4f}")
plot_multiclass_reliability(
all_probs.numpy(),
all_targets.numpy(),
class_names=['no lesion', 'benign', 'malignant'],
n_bins=10
)
# ----------------------
# Evaluate MICRO metrics
# ----------------------
y_true_hot = label_binarize(all_targets.numpy().astype(str), classes=['0', '1', '2'])
fpr, tpr, thresholds = roc_curve(y_true_hot.ravel(), all_probs.numpy().ravel(), drop_intermediate=False)
roc_auc = roc_auc_score(y_true_hot, all_probs.numpy(), average="micro")
roc_auc_macro = roc_auc_score(y_true_hot, all_probs.numpy(), average="macro")
# Sensitivity at 90% specificity
specificity_threshold = 0.90
fpr_threshold = 1 - specificity_threshold
sensitivity_at_90_specificity = np.interp(fpr_threshold, fpr, tpr)
# Specificity at 90% sensitivity
sensitivity_threshold = 0.90
fpr_at_90_sensitivity = np.interp(sensitivity_threshold, tpr, fpr)
specificity_at_90_sensitivity = 1 - fpr_at_90_sensitivity
amalgamated_results = [roc_auc, specificity_at_90_sensitivity, sensitivity_at_90_specificity]
averaged_results = np.mean(amalgamated_results)
metrics["results"] = {
"AUC": roc_auc,
"AUC macro": roc_auc_macro,
"Specificity": specificity_at_90_sensitivity,
"Sensitivity": sensitivity_at_90_specificity,
"Score": averaged_results
}
print("\n✅ MICRO Results")
print(f"AUC: {roc_auc:.4f}")
print(f"Sensitivity: {sensitivity_at_90_specificity:.4f}")
print(f"Specificity: {specificity_at_90_sensitivity:.4f}")
print(f"Score: {averaged_results:.4f}")
# Save results
results_df = pd.DataFrame({
"PatientID": all_patient_ids,
"TrueLabel": all_targets.numpy(),
"PredLabel": all_preds.numpy(),
**{f"Prob_Class_{i}": all_probs[:, i].numpy() for i in range(3)}
})
results_df.to_csv("/workspace/ClassifierSegmenter/results/test_predictions.csv", index=False)
print("\n📁 Saved test predictions to 'results/test_predictions.csv'")
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