def debug_train()

in detic/modeling/debug.py [0:0]

• 63 lines of code
• 10 McCabe index (conditional complexity)

def debug_train(
    images, gt_instances, flattened_hms, reg_targets, labels, pos_inds,
    shapes_per_level, locations, strides):
    '''
    images: N x 3 x H x W
    flattened_hms: LNHiWi x C
    shapes_per_level: L x 2 [(H_i, W_i)]
    locations: LNHiWi x 2
    '''
    reg_inds = torch.nonzero(
        reg_targets.max(dim=1)[0] > 0).squeeze(1)
    N = len(images)
    images = _imagelist_to_tensor(images)
    repeated_locations = [torch.cat([loc] * N, dim=0) \
        for loc in locations]
    locations = torch.cat(repeated_locations, dim=0)
    gt_hms = _decompose_level(flattened_hms, shapes_per_level, N)
    masks = flattened_hms.new_zeros((flattened_hms.shape[0], 1))
    masks[pos_inds] = 1
    masks = _decompose_level(masks, shapes_per_level, N)
    for i in range(len(images)):
        image = images[i].detach().cpu().numpy().transpose(1, 2, 0)
        color_maps = []
        for l in range(len(gt_hms)):
            color_map = _get_color_image(
                gt_hms[l][i].detach().cpu().numpy())
            color_maps.append(color_map)
            cv2.imshow('gthm_{}'.format(l), color_map)
        blend = _blend_image_heatmaps(image.copy(), color_maps)
        if gt_instances is not None:
            bboxes = gt_instances[i].gt_boxes.tensor
            for j in range(len(bboxes)):
                bbox = bboxes[j]
                cv2.rectangle(
                    blend, 
                    (int(bbox[0]), int(bbox[1])),
                    (int(bbox[2]), int(bbox[3])),
                    (0, 0, 255), 3, cv2.LINE_AA)
    
        for j in range(len(pos_inds)):
            image_id, l = _ind2il(pos_inds[j], shapes_per_level, N)
            if image_id != i:
                continue
            loc = locations[pos_inds[j]]
            cv2.drawMarker(
                blend, (int(loc[0]), int(loc[1])), (0, 255, 255),
                markerSize=(l + 1) * 16)
        
        for j in range(len(reg_inds)):
            image_id, l = _ind2il(reg_inds[j], shapes_per_level, N)
            if image_id != i:
                continue
            ltrb = reg_targets[reg_inds[j]]
            ltrb *= strides[l]
            loc = locations[reg_inds[j]]
            bbox = [(loc[0] - ltrb[0]), (loc[1] - ltrb[1]),
                    (loc[0] + ltrb[2]), (loc[1] + ltrb[3])]
            cv2.rectangle(
                blend, 
                (int(bbox[0]), int(bbox[1])),
                (int(bbox[2]), int(bbox[3])),
                (255, 0, 0), 1, cv2.LINE_AA)  
            cv2.circle(blend, (int(loc[0]), int(loc[1])), 2, (255, 0, 0), -1)

        cv2.imshow('blend', blend)
        cv2.waitKey()