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evaluate_score_fusion_all_data.py
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import argparse
import os
import yaml
import numpy as np
import time
import pickle
from tqdm import tqdm
import torch
import torch.nn.functional as F
from utils.load_dataset import load_dataset
from models.load_model import load_model
from utils.data_utils import class_maps
from sklearn.metrics import confusion_matrix, classification_report, ConfusionMatrixDisplay, accuracy_score
def main(config):
all_classes = ['contrast-A2C', 'contrast-A3C', 'contrast-A4C', 'contrast-PLAX',
'noncontrast-A2C', 'noncontrast-A3C', 'noncontrast-A4C', 'noncontrast-A5C',
'noncontrast-PLAX','noncontrast-PLAX-AV', 'noncontrast-PSAX-AV', 'noncontrast-PSAX-PM',
'noncontrast-RV','noncontrast-SC','noncontrast-SC-IVC']
camus_config = 'resnet_mcdo_camus_2_class'
with open('/config/' + camus_config + '.yaml') as f:
camus_config = yaml.load(f, yaml.FullLoader)
camus_config['DATASET'] = 'CAMUS_feat'
camus_config['WEIGHTED'] = config['WEIGHTED']
mstr_config = 'resnet_mcdo_mstr_11_class'
with open('/config/' + mstr_config + '.yaml') as f:
mstr_config = yaml.load(f, yaml.FullLoader)
mstr_config['DATASET'] = 'MSTR_feat'
mstr_config['WEIGHTED'] = config['WEIGHTED']
wase_config = 'resnet_mcdo_wase_8_class'
with open('/config/' + wase_config + '.yaml') as f:
wase_config = yaml.load(f, yaml.FullLoader)
wase_config['DATASET'] = 'WASE_feat'
wase_config['WEIGHTED'] = config['WEIGHTED']
stg_config = 'resnet_mcdo_stg_4_class'
with open('/config/' + stg_config + '.yaml') as f:
stg_config = yaml.load(f, yaml.FullLoader)
stg_config['DATASET'] = 'STG_feat'
stg_config['WEIGHTED'] = config['WEIGHTED']
mahi_config = camus_config.copy()
mahi_config['DATASET'] = 'MAHI_feat'
mahi_config['AVC_CLASSES'] = all_classes
uoc_config = mahi_config.copy()
uoc_config['DATASET'] = 'UOC_feat'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('connected to device: {}'.format(device))
model = load_model(config, device)
model.load_state_dict(torch.load(os.path.join('', 'results', config['RUN_ID'], 'best_acc.pt')))
Y_pred_testset1 = []
Y_true_testset1 = []
Y_pred_testset2 = []
Y_true_testset2 = []
if config['PRED_CLASS_MAP_ID'] is not None:
pred_class_map = class_maps(config['PRED_CLASS_MAP_ID'])
config['AVC_CLASSES'] = all_classes
for dataset_id, dataset_config in enumerate([wase_config, camus_config, mstr_config, stg_config, mahi_config, uoc_config]):
print('Evaluating {}'.format(dataset_config['DATASET']))
class_map = class_maps(dataset_id)
(_, _, _), (_, _, test_dataset) = load_dataset(dataset_config)
save_folder = os.path.join('', "results", config['RUN_ID'], 'evaluation_' + dataset_config['DATASET'])
try:
os.mkdir(save_folder)
except Exception as e:
print('Warning: ', e)
Y_pred_all = np.zeros(len(test_dataset), dtype=np.int32)
Y_true_all = np.zeros(len(test_dataset), dtype=np.int32)
intermediate_all = []
avg_time = []
model.eval()
with torch.no_grad():
for i in tqdm(range(len(test_dataset))):
X, Y = test_dataset[i]
if len(X) == 3:
nmd, features, logits = X
nmd, features, logits = nmd.unsqueeze(0).to(device), features.unsqueeze(0).to(device), logits.unsqueeze(0).to(device)
start = time.time()
output = model(nmd, features, logits)
end = time.time()
elif len(X) == 2:
features, logits = X
features, logits = features.unsqueeze(0).to(device), logits.unsqueeze(0).to(device)
start = time.time()
output = model(features, logits)
end = time.time()
else:
print('Error.. length of X: ', len(X))
avg_time.append(end - start)
# some of the models output a single integer rather than a logit/softmax as the final view prediction.
if isinstance(output, int):
Y_pred = output
else:
Y_logits, intermediate = output
Y_softmax = F.softmax(Y_logits, dim=1).cpu().numpy()
Y_pred = np.argmax(Y_softmax, axis=1)[0]
# the original view classifers do not predict 15 classes.
if Y_softmax.shape[1] < 15:
Y_pred = pred_class_map[int(Y_pred)]
intermediate_all.append(intermediate + [Y])
Y_pred_all[i] = Y_pred
Y_true_all[i] = Y
if dataset_config['DATASET'] in ['WASE_feat', 'CAMUS_feat', 'MSTR_feat', 'STG_feat']:
Y_pred_testset1.append(Y_pred_all)
Y_true_testset1.append(Y_true_all)
if dataset_config['DATASET'] in ['MAHI_feat', 'UOC_feat']:
Y_pred_testset2.append(Y_pred_all)
Y_true_testset2.append(Y_true_all)
clf_report = classification_report(Y_true_all, Y_pred_all, labels=list(range(len(config['AVC_CLASSES']))),
target_names=config['AVC_CLASSES'], digits=4, output_dict=False, zero_division='warn')
print(clf_report)
results = {'clf_report': clf_report, 'speed': avg_time}
print('Saving predictions and scores to {}'.format(save_folder))
with open(save_folder + '/scores.pkl', 'wb') as f:
pickle.dump(results, f)
with open(save_folder+'/predictions.pkl', 'wb') as f:
pickle.dump([Y_true_all, Y_pred_all], f)
with open(save_folder + '/logits.pkl', 'wb') as f:
pickle.dump(intermediate_all, f)
print('Evaluating all Test set 1 (WASE, CAMUS, MSTR, STG)...')
Y_pred_testset1_all = np.concatenate(Y_pred_testset1, axis=0)
Y_true_testset1_all = np.concatenate(Y_true_testset1, axis=0)
clf_report = classification_report(Y_true_testset1_all, Y_pred_testset1_all, labels=list(range(len(config['AVC_CLASSES']))),
target_names=config['AVC_CLASSES'], digits=4, output_dict=False,
zero_division='warn')
print(clf_report)
save_folder = os.path.join('', "results", config['RUN_ID'], 'evaluation_wase_camus_mstr_stg')
try:
os.mkdir(save_folder)
except Exception as e:
print('Warning: ', e)
results = {'clf_report': clf_report}
print('Saving predictions and scores to {}'.format(save_folder))
with open(save_folder + '/scores.pkl', 'wb') as f:
pickle.dump(results, f)
with open(save_folder + '/predictions.pkl', 'wb') as f:
pickle.dump([Y_pred_testset1_all, Y_true_testset1_all], f)
print('Evaluating all Test set 2 (MAHI, UOC)...')
Y_pred_testset2_all = np.concatenate(Y_pred_testset2, axis=0)
Y_true_testset2_all = np.concatenate(Y_true_testset2, axis=0)
clf_report = classification_report(Y_true_testset2_all, Y_pred_testset2_all,
labels=list(range(len(config['AVC_CLASSES']))),
target_names=config['AVC_CLASSES'], digits=4, output_dict=False,
zero_division='warn')
print(clf_report)
save_folder = os.path.join('', "results", config['RUN_ID'], 'evaluation_mahi_uoc')
try:
os.mkdir(save_folder)
except Exception as e:
print('Warning: ', e)
results = {'clf_report': clf_report}
print('Saving predictions and scores to {}'.format(save_folder))
with open(save_folder + '/scores.pkl', 'wb') as f:
pickle.dump(results, f)
with open(save_folder + '/predictions.pkl', 'wb') as f:
pickle.dump([Y_pred_testset2_all, Y_true_testset2_all], f)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--DATA_ROOT', type=str)
parser.add_argument('--RUN_ID', type=str)
parser.add_argument('--CONFIG', type=str)
config = parser.parse_args()
cmd_config = vars(config)
# load model and training configs
with open('/config/' + cmd_config['CONFIG'] + '.yaml') as f:
yaml_config = yaml.load(f, yaml.FullLoader)
config = yaml_config
config.update(cmd_config) # command line args overide yaml
print('config: ', config)
main(config)