How to use test_arguments method in pytest-bdd

Best Python code snippet using pytest-bdd_python

_test.py

Source:_test.py

1# -*- coding:utf-8 -*-2#3# Created by Drogo Zhang4#5# On 2018-10-0967import numpy as np8import torch as t9import torch.nn.functional as F10from torch.autograd import Variable11import config as cfg12from model import RCNN13import utils14from utils import reg_to_bbox, non_maximum_suppression, evaluate, get_count_dataframe_by_confusion_matrix, denorm15from utils import first_write, lb_reg_to_bbox16from arguments import TestAruguments1718word_embedding_dim = cfg.WORD_EMBEDDING_DIM19classes_num = cfg.CLASSES_NUM20th_score = cfg.TH_SCORE212223def load_model(test_arguments):24    rcnn = RCNN(test_arguments.pos_loss_method, test_arguments.loss_weight_lambda).cuda()25    rcnn.load_state_dict(t.load(test_arguments.model_path))26    rcnn.eval()  # dropout rate = 027    return rcnn282930def load_test_sentence(test_sentence_npz_path):31    npz = np.load(test_sentence_npz_path)32    test_sentences = npz['test_sentences']33    test_sentence_info = npz['test_sentence_info']34    test_roi = npz['test_roi']3536    test_sentences = t.Tensor(test_sentences).cuda()3738    return test_sentences, test_sentence_info, test_roi394041def _test_one_sentence(test_arguments, sentence, rois, rcnn, fold_index, this_sentence_len, info):42    roi_num = rois.shape[0]43    ridx = np.zeros(roi_num).astype(int)44    pred_cls_score, pred_tbbox = rcnn(sentence, rois, ridx)45    pred_cls_score = F.softmax(pred_cls_score, dim=1).data.cpu().numpy()  # softmax score for each row46    pred_tbbox = pred_tbbox.data.cpu().numpy()47    if test_arguments.normalize:48        pred_tbbox = denorm(pred_tbbox, fold_index, test_arguments)49    if test_arguments.dx_compute_method == "left_boundary":50        pred_bbox = lb_reg_to_bbox(this_sentence_len, pred_tbbox, rois)51    else:52        pred_bbox = reg_to_bbox(this_sentence_len, pred_tbbox, rois)5354    result_bbox = []55    result_cls = []56    drop_bbox = []57    drop_cls = []58    for c in range(1, classes_num + 1):59        c_cls_score = pred_cls_score[:, c]60        c_bboxs = pred_bbox[:, :, c].T61        if c == 4 and max(c_cls_score) >= test_arguments.score_threshold:62            wait = True63        boxes, _boxes = non_maximum_suppression(c_cls_score, c_bboxs, iou_threshold=test_arguments.th_nms_iou,64                                                score_threshold=test_arguments.score_threshold, info=info)65        result_bbox.extend(boxes)66        drop_bbox.extend(_boxes)67        result_cls.extend([c] * len(boxes))  # print the predict result of this sentence.68        drop_cls.extend([c] * len(_boxes))69    if len(result_cls) == 0:  # if the sentence without detecting anything!, we will lower the score criterion70        drop_bbox = []71        drop_cls = []72        for c in range(1, classes_num + 1):73            c_sc = pred_cls_score[:, c]74            c_bboxs = pred_bbox[:, :, c].T75            boxes, _boxes = non_maximum_suppression(c_sc, c_bboxs, iou_threshold=test_arguments.th_nms_iou,76                                                    score_threshold=test_arguments.score_threshold / 6, info=info)77            result_bbox.extend(boxes)78            result_cls.extend([c] * len(boxes))79            drop_bbox.extend(_boxes)80            drop_cls.extend([c] * len(_boxes))81        if len(result_bbox) > 1:82            wait = True83        result_bbox = result_bbox[:1]84        result_cls = result_cls[:1]85        drop_bbox += result_bbox[1:]86        drop_cls += result_cls[1:]8788    return np.array(result_bbox), np.array(result_cls), np.array(drop_bbox), np.array(drop_cls)899091def _test_epoch(test_arguments, fold_index):92    rcnn = load_model(test_arguments)93    test_sentences, test_sentence_info, test_roi = load_test_sentence(test_arguments.test_sentence_npz_path)9495    sentence_num = test_sentences.size(0)9697    perm = np.random.permutation(sentence_num)9899    for i in range(sentence_num):100        pi = perm[i]101        sentence = Variable(test_sentences[pi:pi + 1]).cuda()102        # sentence_real_len =103104        info = test_sentence_info[pi]105106        idxs = info['roi_ids']107        this_sentence_len = len(info['gt_str'].split(" "))108        rois = test_roi[idxs]109110        result_bbox, result_cls, drop_bbox, drop_cls = _test_one_sentence(test_arguments, sentence, rois, rcnn,111                                                                          fold_index,112                                                                          this_sentence_len, info=info)113114        evaluate(result_bbox, result_cls, drop_bbox, drop_cls, info, test_arguments.confusion_matrix,115                 test_arguments.th_iou_p)116117    return118119120def _test_k_fold(test_arguments, all_csv_result):121    for model_epoch in range(test_arguments.min_test_epoch, test_arguments.max_test_epoch + 1):122        test_arguments.initialize_confusion_matrix()123        for fold_index in range(test_arguments.fold_k):124            test_arguments.model_path = test_arguments.get_model_path(fold_index, model_epoch)125            test_arguments.test_sentence_npz_path = test_arguments.get_test_npz_path(fold_index)126            _test_epoch(test_arguments, fold_index)127128        test_arguments.confusion_matrix = test_arguments.confusion_matrix.astype(np.int32).tolist()129        test_arguments.data_frame = get_count_dataframe_by_confusion_matrix(test_arguments.confusion_matrix)130        print(utils.nms_mis_hit)131        utils.nms_mis_hit = [0] * 7132        test_arguments.write_one_result(all_csv_result)133        print(test_arguments.data_frame)134    return135136137def main():138    pos_loss_method = "mse"  # "mse"139    th_train_iou = 0.6  # 0.8140    norm = True  # Flase141    min_test_epoch = 40142    max_test_epoch = 40143    loss_weight_lambda = 1.0144    prevent_overfitting_method = "Dropout"  # "L2 Regu" # "Dropout"145    partial_l2 = False146    dx_compute_method = "centre"  # "left_boundary"  # "centre"147148    test_arguments = TestAruguments(norm, pos_loss_method, th_train_iou, min_test_epoch, max_test_epoch,149                                    dx_compute_method=dx_compute_method,150                                    loss_weight_lambda=loss_weight_lambda, partial_l2_penalty=partial_l2,151                                    prevent_overfitting_method=prevent_overfitting_method)152    test_arguments.show_arguments()153    write_result_path = test_arguments.get_write_result_path()154155    all_csv_result = open(write_result_path, "w")156    first_write(all_csv_result)157    th_nms_iou_ls = [0.01]158    th_iou_p_ls = [0.6, 0.8, 1]  # [0.8]  # [0.6, 0.8, 1]  # [1],159    for th_iou_p in th_iou_p_ls:160        for th_nms_iou in th_nms_iou_ls:161            test_arguments.th_nms_iou = th_nms_iou162            test_arguments.th_iou_p = th_iou_p163            _test_k_fold(test_arguments, all_csv_result)164165166if __name__ == '__main__':
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