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 import CLAM, CLAM_Simple
from models.model_attention_mil import MIL_Attention_fc
from models.model_histogram import MIL_fc_Histogram
import pdb
import os
import pandas as pd
from utils.utils import *
from utils.core_utils import EarlyStopping, Accuracy_Logger
from utils.file_utils import save_pkl, load_pkl
from sklearn.metrics import roc_auc_score, roc_curve, auc
import h5py
from models.resnet_custom import resnet50_baseline
import math
from sklearn.preprocessing import label_binarize
def initiate_model(args, ckpt_path=None):
print('Init Model')
model_dict = {"dropout": args.drop_out, 'n_classes': args.n_classes}
if args.model_size is not None and args.model_type in ['clam', 'attention_mil', 'clam_simple']:
model_dict.update({"size_arg": args.model_size})
if args.model_type =='clam':
model = CLAM(**model_dict)
elif args.model_type =='clam_simple':
model = CLAM_Simple(**model_dict)
elif args.model_type == 'attention_mil':
model = MIL_Attention_fc(**model_dict)
elif args.model_type == 'histogram_mil':
model = MIL_fc_Histogram(**model_dict)
else: # args.model_type == 'mil'
if args.n_classes > 2:
model = MIL_fc_mc(**model_dict)
else:
model = MIL_fc(**model_dict)
#model.relocate()
print_network(model)
if ckpt_path is not None:
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
#%------------
# if ckpt_path is not None:
# 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, args, ckpt_path):
model = initiate_model(args, ckpt_path)
print('Init Loaders')
loader = get_simple_loader(dataset)
patient_results, test_error, auc, aucs, df, _ = summary(model, loader, args)
print('test_error: ', test_error)
print('auc: ', auc)
for cls_idx in range(len(aucs)):
print('class {} auc: {}'.format(cls_idx, aucs[cls_idx]))
return model, patient_results, test_error, auc, aucs, df
def infer(dataset, args, ckpt_path, class_labels):
model = initiate_model(args, ckpt_path)
df = infer_dataset(model, dataset, args, class_labels)
return model, df
# Code taken from pytorch/examples for evaluating topk classification on on ImageNet
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(1.0 / batch_size))
return res
def summary(model, loader, args):
acc_logger = Accuracy_Logger(n_classes=args.n_classes)
model.eval()
test_loss = 0.
test_error = 0.
all_probs = np.zeros((len(loader), args.n_classes))
all_labels = np.zeros(len(loader))
all_preds = np.zeros(len(loader))
if not args.patient_level:
slide_ids = loader.dataset.slide_data['slide_id']
patient_results = {}
for batch_idx, (data, label) in enumerate(loader):
data, label = data.to(device), label.to(device)
slide_id = slide_ids.iloc[batch_idx]
with torch.no_grad():
logits, Y_prob, Y_hat, _, results_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
else:
case_ids = loader.dataset.slide_data['case_id']
patient_results = {}
for batch_idx, (data, label) in enumerate(loader):
data, label = data.to(device), label.to(device)
case_id = case_ids.iloc[batch_idx]
with torch.no_grad():
logits, Y_prob, Y_hat, _, results_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({case_id: {'case_id': np.array(case_id), 'prob': probs, 'label': label.item()}})
error = calculate_error(Y_hat, label)
test_error += error
del data
test_error /= len(loader)
if args.n_classes > 2:
# pdb.set_trace()
acc1, acc3 = accuracy(torch.from_numpy(all_probs), torch.from_numpy(all_labels), topk=(1, 3))
print('top1 acc: {:.3f}, top3 acc: {:.3f}'.format(acc1.item(), acc3.item()))
if len(np.unique(all_labels)) == 1:
auc_score = -1
aucs = []
else:
if args.n_classes == 2:
auc_score = roc_auc_score(all_labels, all_probs[:, 1])
aucs = []
else:
aucs = []
binary_labels = label_binarize(all_labels, classes=[i for i in range(args.n_classes)])
for class_idx in range(args.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 args.micro_average:
binary_labels = label_binarize(all_labels, classes=[i for i in range(args.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))
if not args.patient_level:
results_dict = {'slide_id': slide_ids, 'Y': all_labels, 'Y_hat': all_preds}
else:
results_dict = {'case_id': case_ids, 'Y': all_labels, 'Y_hat': all_preds}
for c in range(args.n_classes):
results_dict.update({'p_{}'.format(c): all_probs[:,c]})
df = pd.DataFrame(results_dict)
if args.patient_level:
df = df.drop_duplicates(subset=['case_id'])
return patient_results, test_error, auc_score, aucs, df, acc_logger
def infer_dataset(model, dataset, args, class_labels, k=3):
model.eval()
all_probs = np.zeros((len(dataset), k))
all_preds = np.zeros((len(dataset), k))
all_preds_str = np.full((len(dataset), k), ' ', dtype=object)
slide_ids = dataset.slide_data
for batch_idx, data in enumerate(dataset):
data = data.to(device)
with torch.no_grad():
logits, Y_prob, Y_hat, _, results_dict = model(data)
probs, ids = torch.topk(Y_prob, k)
probs = probs.cpu().numpy()
ids = ids.cpu().numpy()
all_probs[batch_idx] = probs
all_preds[batch_idx] = ids
all_preds_str[batch_idx] = np.array(class_labels)[ids]
del data
results_dict = {'slide_id': slide_ids}
for c in range(k):
results_dict.update({'Pred_{}'.format(c): all_preds_str[:, c]})
results_dict.update({'p_{}'.format(c): all_probs[:, c]})
df = pd.DataFrame(results_dict)
return df
# def infer_dataset(model, dataset, args, class_labels, k=3):
# model.eval()
# all_probs = np.zeros((len(dataset), args.n_classes))
# all_preds = np.zeros(len(dataset))
# all_str_preds = np.full(len(dataset), ' ', dtype=object)
# slide_ids = dataset.slide_data
# for batch_idx, data in enumerate(dataset):
# data = data.to(device)
# with torch.no_grad():
# logits, Y_prob, Y_hat, _, results_dict = model(data)
# probs = Y_prob.cpu().numpy()
# all_probs[batch_idx] = probs
# all_preds[batch_idx] = Y_hat.item()
# all_str_preds[batch_idx] = class_labels[Y_hat.item()]
# del data
# results_dict = {'slide_id': slide_ids, 'Prediction': all_str_preds, 'Y_hat': all_preds}
# for c in range(args.n_classes):
# results_dict.update({'p_{}_{}'.format(c, class_labels[c]): all_probs[:,c]})
# df = pd.DataFrame(results_dict)
# return df
def compute_features(dataset, args, ckpt_path, save_dir, model=None, feature_dim=512):
if model is None:
model = initiate_model(args, ckpt_path)
names = dataset.get_list(np.arange(len(dataset))).values
file_path = os.path.join(save_dir, 'features.h5')
initialize_features_hdf5_file(file_path, len(dataset), feature_dim=feature_dim, names=names)
for i in range(len(dataset)):
print("Progress: {}/{}".format(i, len(dataset)))
save_features(dataset, i, model, args, file_path)
def save_features(dataset, idx, model, args, save_file_path):
name = dataset.get_list(idx)
print(name)
features, label = dataset[idx]
features = features.to(device)
with torch.no_grad():
if type(model) == CLAM:
_, Y_prob, Y_hat, _, results_dict = model(features, instance_eval=False, return_features=True)
bag_feat = results_dict['features'][Y_hat.item()]
else:
_, Y_prob, Y_hat, _, results_dict = model(features, return_features=True)
bag_feat = results_dict['features']
del features
Y_hat = Y_hat.item()
Y_prob = Y_prob.view(-1).cpu().numpy()
bag_feat = bag_feat.view(1, -1).cpu().numpy()
with h5py.File(save_file_path, 'r+') as file:
print('label', label)
file['features'][idx, :] = bag_feat
file['label'][idx] = label
file['Y_hat'][idx] = Y_hat
file['Y_prob'][idx] = Y_prob[Y_hat]
def initialize_features_hdf5_file(file_path, length, feature_dim=512, names = None):
file = h5py.File(file_path, "w")
dset = file.create_dataset('features',
shape=(length, feature_dim), chunks=(1, feature_dim), dtype=np.float32)
# if names is not None:
# names = np.array(names, dtype='S')
# dset.attrs['names'] = names
if names is not None:
dt = h5py.string_dtype()
label_dset = file.create_dataset('names',
shape=(length, ), chunks=(1, ), dtype=dt)
file['names'][:] = names
label_dset = file.create_dataset('label',
shape=(length, ), chunks=(1, ), dtype=np.int32)
pred_dset = file.create_dataset('Y_hat',
shape=(length, ), chunks=(1, ), dtype=np.int32)
prob_dset = file.create_dataset('Y_prob',
shape=(length, ), chunks=(1, ), dtype=np.float32)
file.close()
return file_path
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