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eval_core.py
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eval_core.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.model_mil import MIL_fc, MIL_fc_mc
# from models.model_clam_v3 import CLAM_SB, CLAM_MB
from models.model_clam import CLAM_SB, CLAM_MB
import pdb
import os
import pandas as pd
from utils.utils import get_simple_loader, get_split_loader, print_network, get_optim, calculate_error
from train_core import Accuracy_Logger
from sklearn.metrics import roc_auc_score, roc_curve, auc
from sklearn.preprocessing import label_binarize
import matplotlib.pyplot as plt
def initiate_model(ckpt_path, dropout=True, n_classes=2, model_size='small', model_type='clam_sb',
feature_extract_model_name=''):
if model_type == 'clam_sb':
model = CLAM_SB(dropout=dropout, n_classes=n_classes, size_arg=model_size,
feature_extract_model_name=feature_extract_model_name)
elif model_type == 'clam_mb':
model = CLAM_MB(dropout=dropout, n_classes=n_classes, size_arg=model_size,
feature_extract_model_name=feature_extract_model_name)
else: # args.model_type == 'mil'
if n_classes > 2:
model = MIL_fc_mc(dropout=dropout, n_classes=n_classes, size_arg=model_size)
else:
model = MIL_fc(dropout=dropout, n_classes=n_classes, size_arg=model_size)
print_network(model)
ckpt = torch.load(ckpt_path)
ckpt_clean = {}
for key in ckpt.keys():
if 'instance_loss_fn' in key:
continue
ckpt_clean.update({key.replace('.module', ''): ckpt[key]})
model.load_state_dict(ckpt_clean, strict=True)
model.relocate()
model.eval()
return model
def eval(
dataset, ckpt_path, dropout=True,
n_classes=2, model_size='small',
model_type='clam_sb', device=None, micro_average=False,
feature_extract_model_name=''):
model = initiate_model(
ckpt_path, dropout=dropout, n_classes=n_classes,
model_size=model_size, model_type=model_type,
feature_extract_model_name=feature_extract_model_name)
print('Init Loaders')
loader = get_simple_loader(dataset, device=device)
patient_results, test_error, auc, df, _ = summary(
model, loader, n_classes=n_classes, device=device, micro_average=micro_average)
print('test_error: ', test_error)
print('auc: ', auc)
return model, patient_results, test_error, auc, df
def summary(model, loader, n_classes=2, device=None, micro_average=False):
acc_logger = Accuracy_Logger(n_classes=n_classes)
model.eval()
test_loss = 0.
test_error = 0.
all_probs = np.zeros((len(loader), n_classes))
all_labels = np.zeros(len(loader))
all_preds = np.zeros(len(loader))
slide_ids = loader.dataset.slide_data['slide_id']
patient_results = {}
for batch_idx, (data, label, neg_feature, pos_feature) in enumerate(loader):
data, label = data.to(device), label.to(device)
slide_id = slide_ids[batch_idx]
with torch.no_grad():
# logits, Y_prob, Y_hat, _, results_dict = model(data)
logits, Y_prob, Y_hat, A_raw, A, A_sigmoid, \
y_prob_instance, instance_dict, = model(data)
acc_logger.log(Y_hat, label)
probs = Y_prob.cpu().numpy()
all_probs[batch_idx] = probs
all_labels[batch_idx] = label.item()
all_preds[batch_idx] = Y_hat.item()
patient_results.update({slide_id: {'slide_id': np.array(slide_id), 'prob': probs, 'label': label.item()}})
error = calculate_error(Y_hat, label)
test_error += error
del data
test_error /= len(loader)
aucs = []
if len(np.unique(all_labels)) == 1:
auc_score = -1
else:
if n_classes == 2:
auc_score = roc_auc_score(all_labels, all_probs[:, 1])
else:
binary_labels = label_binarize(all_labels, classes=[i for i in range(n_classes)])
for class_idx in range(n_classes):
if class_idx in all_labels:
fpr, tpr, _ = roc_curve(binary_labels[:, class_idx], all_probs[:, class_idx])
aucs.append(auc(fpr, tpr))
else:
aucs.append(float('nan'))
if micro_average:
binary_labels = label_binarize(all_labels, classes=[i for i in range(n_classes)])
fpr, tpr, _ = roc_curve(binary_labels.ravel(), all_probs.ravel())
auc_score = auc(fpr, tpr)
else:
auc_score = np.nanmean(np.array(aucs))
results_dict = {'slide_id': slide_ids, 'labels': all_labels, 'Y_hat': all_preds}
for c in range(n_classes):
results_dict.update({'p_{}'.format(c): all_probs[:,c]})
df = pd.DataFrame(results_dict)
return patient_results, test_error, auc_score, df, acc_logger