tf.reshape(inputs[1], [-1, REGION_SIZES[1], REGION_SIZES[1], REGION_SIZES[1], 1]), tf.reshape(inputs[2], [-1, REGION_SIZES[2], REGION_SIZES[2], REGION_SIZES[2], 1]) ] if "bn_files" in dir(): bn_params = [ np.load(bn_files[0]), np.load(bn_files[1]), np.load(bn_files[2]) ] else: bn_params = [None, None, None] outputs0, variables0, _ = tft.volume_bndo_flbias_l5_20( inputs_reshape[0], dropout_rate=0.0, batch_normalization_statistic=False, bn_params=bn_params[0]) outputs1, variables1, _ = tft.volume_bndo_flbias_l5_30( inputs_reshape[1], dropout_rate=0.0, batch_normalization_statistic=False, bn_params=bn_params[1]) outputs2, variables2, _ = tft.volume_bndo_flbias_l6_40( inputs_reshape[2], dropout_rate=0.0, batch_normalization_statistic=False, bn_params=bn_params[2]) if FUSION_MODE == 'vote': predictions = [ outputs0['sm_out'], outputs1['sm_out'], outputs2['sm_out']
] real_label = tf.placeholder(tf.float32, [None, 2]) #bn_params = np.load(net_init_path + "/batch_normalization_statistic.npy") positive_confidence = aug_proportion / float(aug_proportion + np_proportion) if "bn_files" in dir(): bn_params = [ np.load(bn_files[0]), np.load(bn_files[1]), np.load(bn_files[2]) ] else: bn_params = [None, None, None] net_outs0, variables0, _ = tft.volume_bndo_flbias_l5_20( volumes_reshape[0], False, positive_confidence, dropout_rate=0.0, batch_normalization_statistic=False, bn_params=bn_params[0]) net_outs1, variables1, _ = tft.volume_bndo_flbias_l5_30( volumes_reshape[1], False, positive_confidence, dropout_rate=0.0, batch_normalization_statistic=False, bn_params=bn_params[1]) net_outs2, variables2, _ = tft.volume_bndo_flbias_l6_40( volumes_reshape[2], False, positive_confidence, dropout_rate=0.0,