def run(self, c=5, param=100, n_fold_cv=10, kernel='RBF'):
'''
Run tests with given parameters on currently loaded data
@param c: SVM classifier parameter
@param param: kernel parameter
@param n_fold_cv: n-fold cross-validation parameter
@param kernel: used kernel - possibilities in src/kernels.py
@return: classification results
'''
data = Data(dbfile=None, n_fold_cv=n_fold_cv)
k = str2kernel[kernel](param=param)
C = c
self.svm = SVM(kernel=k, C=C)
correct_sum = 0
test_len = 0
res = {}
res['true_positive'] = 0.0
res['true_negative'] = 0.0
res['false_positive'] = 0.0
res['false_negative'] = 0.0
for i in xrange(n_fold_cv):
X1, Y1, X2, Y2 = data.get(i)
self._logger.info('Training SVM... (i={0})'.format(str(i)))
self.svm.train(X1, Y1)
if self.svm.model_exists:
# predict
Y_predict = self.svm.predict(X2)
self._logger.info('using {0} of {1} support vectors'.format(
self.svm.lm_count, self.svm.all_lm_count))
# calculate tp, fp, tn, fn
test_len = len(Y_predict)
Y_predict_P = Y_predict[:(test_len/2)]
Y_predict_N = Y_predict[(test_len/2):]
Y2_P = Y2[:(test_len/2)]
Y2_N = Y2[(test_len/2):]
tp = np.sum(Y_predict_P == Y2_P)
fn = np.sum(Y_predict_P != Y2_P)
tn = np.sum(Y_predict_N == Y2_N)
fp = np.sum(Y_predict_N != Y2_N)
res['true_positive'] += tp / float(n_fold_cv)
res['false_positive'] += fp / float(n_fold_cv)
res['true_negative'] += tn / float(n_fold_cv)
res['false_negative'] += fn / float(n_fold_cv)
# this iteration result
self._logger.info('tp: {0}, fp: {1}, tn: {2}, fn :{3}'.format(
str(tp), str(fp), str(tn), str(fn)))
# print and return results
self._calculate_results(res)
return res
test_model_path = r'E:\PycharmProjects\crowdcount_8\best_models\model_20190318_DPLNet\trancos\pool_16_3.83\saved_models_trancos\trancos_18.h5'
# test_model_path = './saved_models_trancos/trancos_9.h5'
original_dataset_name = 'trancos'
test_data_config = dict()
test_data_config['tran1Resize1_test'] = test_flag.copy()
elif is_ucf_qnrf:
test_model_path = r'E:\PycharmProjects\crowdcount_8\best_models\model_20190318_DPLNet\ucf_qnrf\pool_4_112.33_205.77\saved_models_ucf_qnrf\ucf_qnrf_26_21617.h5'
original_dataset_name = 'ucf_qnrf'
test_data_config = dict()
test_data_config['ucfQnrf1Resize1024_test'] = test_flag.copy()
# load data
all_data = Data(test_data_config)
all_data = all_data.get()
net = CrowdCount()
network.load_net(test_model_path, net)
net.cuda()
net.eval()
# log_info = []
save_path = './test_output'
make_path(save_path)
make_path(os.path.join(save_path, 'ground_truth_map'))
make_path(os.path.join(save_path, 'estimate_map'))
make_path(os.path.join(save_path, 'estimate_raw_map'))
'roi'] = '/media/dell/OS/data/mall/formatted_trainval_15_4/mall_patches_1_resize_05_rgb/val_roi'
test_path.append(blob)
data_path['test'] = test_path
if random_seed is not None:
np.random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed_all(random_seed)
# load data
data = Data(data_path,
shuffle=True,
random_seed=random_seed,
pre_load=is_pre_load_data,
is_label=False)
data = data.get()
save_path = './info'
if not os.path.exists(save_path):
os.mkdir(save_path)
excel_book = excel.Workbook()
excel_sheet = excel_book.active
excel_sheet.title = 'Image'
excel_sheet['A1'] = 'filename'
excel_sheet['B1'] = 'gt max density'
excel_sheet['C1'] = 'gt mean density'
row_count = 1
train_data = data['train']
