Initial commit

configs/_base_/models/rpn_r50-caffe-c4.py

+# model settings
+model = dict(
+    type='RPN',
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[103.530, 116.280, 123.675],
+        std=[1.0, 1.0, 1.0],
+        bgr_to_rgb=False,
+        pad_size_divisor=32),
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=3,
+        strides=(1, 2, 2),
+        dilations=(1, 1, 1),
+        out_indices=(2, ),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    neck=None,
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=1024,
+        feat_channels=1024,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[2, 4, 8, 16, 32],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[16]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=12000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0)))

configs/_base_/models/rpn_r50_fpn.py

+# model settings
+model = dict(
+    type='RPN',
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True,
+        pad_size_divisor=32),
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0)))

configs/_base_/models/ssd300.py

+# model settings
+input_size = 300
+model = dict(
+    type='SingleStageDetector',
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[1, 1, 1],
+        bgr_to_rgb=True,
+        pad_size_divisor=1),
+    backbone=dict(
+        type='SSDVGG',
+        depth=16,
+        with_last_pool=False,
+        ceil_mode=True,
+        out_indices=(3, 4),
+        out_feature_indices=(22, 34),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://vgg16_caffe')),
+    neck=dict(
+        type='SSDNeck',
+        in_channels=(512, 1024),
+        out_channels=(512, 1024, 512, 256, 256, 256),
+        level_strides=(2, 2, 1, 1),
+        level_paddings=(1, 1, 0, 0),
+        l2_norm_scale=20),
+    bbox_head=dict(
+        type='SSDHead',
+        in_channels=(512, 1024, 512, 256, 256, 256),
+        num_classes=80,
+        anchor_generator=dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            input_size=input_size,
+            basesize_ratio_range=(0.15, 0.9),
+            strides=[8, 16, 32, 64, 100, 300],
+            ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2])),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0.,
+            ignore_iof_thr=-1,
+            gt_max_assign_all=False),
+        sampler=dict(type='PseudoSampler'),
+        smoothl1_beta=1.,
+        allowed_border=-1,
+        pos_weight=-1,
+        neg_pos_ratio=3,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        nms=dict(type='nms', iou_threshold=0.45),
+        min_bbox_size=0,
+        score_thr=0.02,
+        max_per_img=200))
+cudnn_benchmark = True

configs/_base_/schedules/schedule_1x.py

+# training schedule for 1x
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=12,
+        by_epoch=True,
+        milestones=[8, 11],
+        gamma=0.1)
+]
+
+# optimizer
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)

configs/_base_/schedules/schedule_20e.py

+# training schedule for 20e
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=20, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=20,
+        by_epoch=True,
+        milestones=[16, 19],
+        gamma=0.1)
+]
+
+# optimizer
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)

configs/_base_/schedules/schedule_2x.py

+# training schedule for 2x
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=24,
+        by_epoch=True,
+        milestones=[16, 22],
+        gamma=0.1)
+]
+
+# optimizer
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)

dataset-index.yml

+openxlab: true
+voc2007:
+  dataset: OpenDataLab/PASCAL_VOC2007
+  download_root: data
+  data_root: data
+  script: tools/dataset_converters/scripts/preprocess_voc2007.sh
+
+voc2012:
+  dataset: OpenDataLab/PASCAL_VOC2012
+  download_root: data
+  data_root: data
+  script: tools/dataset_converters/scripts/preprocess_voc2012.sh
+
+coco2017:
+  dataset: OpenDataLab/COCO_2017
+  download_root: data
+  data_root: data/coco
+  script: tools/dataset_converters/scripts/preprocess_coco2017.sh

demo/create_result_gif.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import mmcv
+import numpy as np
+from mmengine.utils import scandir
+
+try:
+    import imageio
+except ImportError:
+    imageio = None
+
+
+# TODO verify after refactoring analyze_results.py
+def parse_args():
+    parser = argparse.ArgumentParser(description='Create GIF for demo')
+    parser.add_argument(
+        'image_dir',
+        help='directory where result '
+        'images save path generated by ‘analyze_results.py’')
+    parser.add_argument(
+        '--out',
+        type=str,
+        default='result.gif',
+        help='gif path where will be saved')
+    args = parser.parse_args()
+    return args
+
+
+def _generate_batch_data(sampler, batch_size):
+    batch = []
+    for idx in sampler:
+        batch.append(idx)
+        if len(batch) == batch_size:
+            yield batch
+            batch = []
+    if len(batch) > 0:
+        yield batch
+
+
+def create_gif(frames, gif_name, duration=2):
+    """Create gif through imageio.
+
+    Args:
+        frames (list[ndarray]): Image frames
+        gif_name (str): Saved gif name
+        duration (int): Display interval (s),
+            Default: 2
+    """
+    if imageio is None:
+        raise RuntimeError('imageio is not installed,'
+                           'Please use “pip install imageio” to install')
+    imageio.mimsave(gif_name, frames, 'GIF', duration=duration)
+
+
+def create_frame_by_matplotlib(image_dir,
+                               nrows=1,
+                               fig_size=(300, 300),
+                               font_size=15):
+    """Create gif frame image through matplotlib.
+
+    Args:
+        image_dir (str): Root directory of result images
+        nrows (int): Number of rows displayed, Default: 1
+        fig_size (tuple): Figure size of the pyplot figure.
+           Default: (300, 300)
+        font_size (int): Font size of texts. Default: 15
+
+    Returns:
+        list[ndarray]: image frames
+    """
+
+    result_dir_names = os.listdir(image_dir)
+    assert len(result_dir_names) == 2
+    # Longer length has higher priority
+    result_dir_names.reverse()
+
+    images_list = []
+    for dir_names in result_dir_names:
+        images_list.append(scandir(osp.join(image_dir, dir_names)))
+
+    frames = []
+    for paths in _generate_batch_data(zip(*images_list), nrows):
+
+        fig, axes = plt.subplots(nrows=nrows, ncols=2)
+        fig.suptitle('Good/bad case selected according '
+                     'to the COCO mAP of the single image')
+
+        det_patch = mpatches.Patch(color='salmon', label='prediction')
+        gt_patch = mpatches.Patch(color='royalblue', label='ground truth')
+        # bbox_to_anchor may need to be finetuned
+        plt.legend(
+            handles=[det_patch, gt_patch],
+            bbox_to_anchor=(1, -0.18),
+            loc='lower right',
+            borderaxespad=0.)
+
+        if nrows == 1:
+            axes = [axes]
+
+        dpi = fig.get_dpi()
+        # set fig size and margin
+        fig.set_size_inches(
+            (fig_size[0] * 2 + fig_size[0] // 20) / dpi,
+            (fig_size[1] * nrows + fig_size[1] // 3) / dpi,
+        )
+
+        fig.tight_layout()
+        # set subplot margin
+        plt.subplots_adjust(
+            hspace=.05,
+            wspace=0.05,
+            left=0.02,
+            right=0.98,
+            bottom=0.02,
+            top=0.98)
+
+        for i, (path_tuple, ax_tuple) in enumerate(zip(paths, axes)):
+            image_path_left = osp.join(
+                osp.join(image_dir, result_dir_names[0], path_tuple[0]))
+            image_path_right = osp.join(
+                osp.join(image_dir, result_dir_names[1], path_tuple[1]))
+            image_left = mmcv.imread(image_path_left)
+            image_left = mmcv.rgb2bgr(image_left)
+            image_right = mmcv.imread(image_path_right)
+            image_right = mmcv.rgb2bgr(image_right)
+
+            if i == 0:
+                ax_tuple[0].set_title(
+                    result_dir_names[0], fontdict={'size': font_size})
+                ax_tuple[1].set_title(
+                    result_dir_names[1], fontdict={'size': font_size})
+            ax_tuple[0].imshow(
+                image_left, extent=(0, *fig_size, 0), interpolation='bilinear')
+            ax_tuple[0].axis('off')
+            ax_tuple[1].imshow(
+                image_right,
+                extent=(0, *fig_size, 0),
+                interpolation='bilinear')
+            ax_tuple[1].axis('off')
+
+        canvas = fig.canvas
+        s, (width, height) = canvas.print_to_buffer()
+        buffer = np.frombuffer(s, dtype='uint8')
+        img_rgba = buffer.reshape(height, width, 4)
+        rgb, alpha = np.split(img_rgba, [3], axis=2)
+        img = rgb.astype('uint8')
+
+        frames.append(img)
+
+    return frames
+
+
+def main():
+    args = parse_args()
+    frames = create_frame_by_matplotlib(args.image_dir)
+    create_gif(frames, args.out)
+
+
+if __name__ == '__main__':
+    main()

demo/demo_multi_model.py

+# Copyright (c) OpenMMLab. All rights reserved.
+"""Support for multi-model fusion, and currently only the Weighted Box Fusion
+(WBF) fusion method is supported.
+
+References: https://github.com/ZFTurbo/Weighted-Boxes-Fusion
+
+Example:
+
+     python demo/demo_multi_model.py demo/demo.jpg \
+         ./configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py \
+         ./configs/retinanet/retinanet_r50-caffe_fpn_1x_coco.py \
+         --checkpoints \
+         https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth \  # noqa
+         https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth \
+         --weights 1 2
+"""
+
+import argparse
+import os.path as osp
+
+import mmcv
+import mmengine
+from mmengine.fileio import isdir, join_path, list_dir_or_file
+from mmengine.logging import print_log
+from mmengine.structures import InstanceData
+
+from mmdet.apis import DetInferencer
+from mmdet.models.utils import weighted_boxes_fusion
+from mmdet.registry import VISUALIZERS
+from mmdet.structures import DetDataSample
+
+IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif',
+                  '.tiff', '.webp')
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='MMDetection multi-model inference demo')
+    parser.add_argument(
+        'inputs', type=str, help='Input image file or folder path.')
+    parser.add_argument(
+        'config',
+        type=str,
+        nargs='*',
+        help='Config file(s), support receive multiple files')
+    parser.add_argument(
+        '--checkpoints',
+        type=str,
+        nargs='*',
+        help='Checkpoint file(s), support receive multiple files, '
+        'remember to correspond to the above config',
+    )
+    parser.add_argument(
+        '--weights',
+        type=float,
+        nargs='*',
+        default=None,
+        help='weights for each model, remember to '
+        'correspond to the above config')
+    parser.add_argument(
+        '--fusion-iou-thr',
+        type=float,
+        default=0.55,
+        help='IoU value for boxes to be a match in wbf')
+    parser.add_argument(
+        '--skip-box-thr',
+        type=float,
+        default=0.0,
+        help='exclude boxes with score lower than this variable in wbf')
+    parser.add_argument(
+        '--conf-type',
+        type=str,
+        default='avg',  # avg, max, box_and_model_avg, absent_model_aware_avg
+        help='how to calculate confidence in weighted boxes in wbf')
+    parser.add_argument(
+        '--out-dir',
+        type=str,
+        default='outputs',
+        help='Output directory of images or prediction results.')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--pred-score-thr',
+        type=float,
+        default=0.3,
+        help='bbox score threshold')
+    parser.add_argument(
+        '--batch-size', type=int, default=1, help='Inference batch size.')
+    parser.add_argument(
+        '--show',
+        action='store_true',
+        help='Display the image in a popup window.')
+    parser.add_argument(
+        '--no-save-vis',
+        action='store_true',
+        help='Do not save detection vis results')
+    parser.add_argument(
+        '--no-save-pred',
+        action='store_true',
+        help='Do not save detection json results')
+    parser.add_argument(
+        '--palette',
+        default='none',
+        choices=['coco', 'voc', 'citys', 'random', 'none'],
+        help='Color palette used for visualization')
+
+    args = parser.parse_args()
+
+    if args.no_save_vis and args.no_save_pred:
+        args.out_dir = ''
+
+    return args
+
+
+def main():
+    args = parse_args()
+
+    results = []
+    cfg_visualizer = None
+    dataset_meta = None
+
+    inputs = []
+    filename_list = []
+    if isdir(args.inputs):
+        dir = list_dir_or_file(
+            args.inputs, list_dir=False, suffix=IMG_EXTENSIONS)
+        for filename in dir:
+            img = mmcv.imread(join_path(args.inputs, filename))
+            inputs.append(img)
+            filename_list.append(filename)
+    else:
+        img = mmcv.imread(args.inputs)
+        inputs.append(img)
+        img_name = osp.basename(args.inputs)
+        filename_list.append(img_name)
+
+    for i, (config,
+            checkpoint) in enumerate(zip(args.config, args.checkpoints)):
+        inferencer = DetInferencer(
+            config, checkpoint, device=args.device, palette=args.palette)
+
+        result_raw = inferencer(
+            inputs=inputs,
+            batch_size=args.batch_size,
+            no_save_vis=True,
+            pred_score_thr=args.pred_score_thr)
+
+        if i == 0:
+            cfg_visualizer = inferencer.cfg.visualizer
+            dataset_meta = inferencer.model.dataset_meta
+            results = [{
+                'bboxes_list': [],
+                'scores_list': [],
+                'labels_list': []
+            } for _ in range(len(result_raw['predictions']))]
+
+        for res, raw in zip(results, result_raw['predictions']):
+            res['bboxes_list'].append(raw['bboxes'])
+            res['scores_list'].append(raw['scores'])
+            res['labels_list'].append(raw['labels'])
+
+    visualizer = VISUALIZERS.build(cfg_visualizer)
+    visualizer.dataset_meta = dataset_meta
+
+    for i in range(len(results)):
+        bboxes, scores, labels = weighted_boxes_fusion(
+            results[i]['bboxes_list'],
+            results[i]['scores_list'],
+            results[i]['labels_list'],
+            weights=args.weights,
+            iou_thr=args.fusion_iou_thr,
+            skip_box_thr=args.skip_box_thr,
+            conf_type=args.conf_type)
+
+        pred_instances = InstanceData()
+        pred_instances.bboxes = bboxes
+        pred_instances.scores = scores
+        pred_instances.labels = labels
+
+        fusion_result = DetDataSample(pred_instances=pred_instances)
+
+        img_name = filename_list[i]
+
+        if not args.no_save_pred:
+            out_json_path = (
+                args.out_dir + '/preds/' + img_name.split('.')[0] + '.json')
+            mmengine.dump(
+                {
+                    'labels': labels.tolist(),
+                    'scores': scores.tolist(),
+                    'bboxes': bboxes.tolist()
+                }, out_json_path)
+
+        out_file = osp.join(args.out_dir, 'vis',
+                            img_name) if not args.no_save_vis else None
+
+        visualizer.add_datasample(
+            img_name,
+            inputs[i][..., ::-1],
+            data_sample=fusion_result,
+            show=args.show,
+            draw_gt=False,
+            wait_time=0,
+            pred_score_thr=args.pred_score_thr,
+            out_file=out_file)
+
+    if not args.no_save_vis:
+        print_log(f'results have been saved at {args.out_dir}')
+
+
+if __name__ == '__main__':
+    main()

demo/image_demo.py

+# Copyright (c) OpenMMLab. All rights reserved.
+"""Image Demo.
+
+This script adopts a new infenence class, currently supports image path,
+np.array and folder input formats, and will support video and webcam
+in the future.
+
+Example:
+    Save visualizations and predictions results::
+
+        python demo/image_demo.py demo/demo.jpg rtmdet-s
+
+        python demo/image_demo.py demo/demo.jpg \
+        configs/rtmdet/rtmdet_s_8xb32-300e_coco.py \
+        --weights rtmdet_s_8xb32-300e_coco_20220905_161602-387a891e.pth
+
+        python demo/image_demo.py demo/demo.jpg \
+        glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365 --texts bench
+
+        python demo/image_demo.py demo/demo.jpg \
+        glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365 --texts 'bench . car .'
+
+        python demo/image_demo.py demo/demo.jpg \
+        glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365
+        --texts 'bench . car .' -c
+
+        python demo/image_demo.py demo/demo.jpg \
+        glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365 \
+        --texts 'There are a lot of cars here.'
+
+        python demo/image_demo.py demo/demo.jpg \
+        glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365 \
+        --texts '$: coco'
+
+        python demo/image_demo.py demo/demo.jpg \
+        glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365 \
+        --texts '$: lvis' --pred-score-thr 0.7 \
+        --palette random --chunked-size 80
+
+        python demo/image_demo.py demo/demo.jpg \
+        grounding_dino_swin-t_pretrain_obj365_goldg_cap4m \
+        --texts '$: lvis' --pred-score-thr 0.4 \
+        --palette random --chunked-size 80
+
+        python demo/image_demo.py demo/demo.jpg \
+        grounding_dino_swin-t_pretrain_obj365_goldg_cap4m \
+        --texts "a red car in the upper right corner" \
+        --tokens-positive -1
+
+    Visualize prediction results::
+
+        python demo/image_demo.py demo/demo.jpg rtmdet-ins-s --show
+
+        python demo/image_demo.py demo/demo.jpg rtmdet-ins_s_8xb32-300e_coco \
+        --show
+"""
+
+import ast
+from argparse import ArgumentParser
+
+from mmengine.logging import print_log
+
+from mmdet.apis import DetInferencer
+from mmdet.evaluation import get_classes
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument(
+        'inputs', type=str, help='Input image file or folder path.')
+    parser.add_argument(
+        'model',
+        type=str,
+        help='Config or checkpoint .pth file or the model name '
+        'and alias defined in metafile. The model configuration '
+        'file will try to read from .pth if the parameter is '
+        'a .pth weights file.')
+    parser.add_argument('--weights', default=None, help='Checkpoint file')
+    parser.add_argument(
+        '--out-dir',
+        type=str,
+        default='outputs',
+        help='Output directory of images or prediction results.')
+    # Once you input a format similar to $: xxx, it indicates that
+    # the prompt is based on the dataset class name.
+    # support $: coco, $: voc, $: cityscapes, $: lvis, $: imagenet_det.
+    # detail to `mmdet/evaluation/functional/class_names.py`
+    parser.add_argument(
+        '--texts', help='text prompt, such as "bench . car .", "$: coco"')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--pred-score-thr',
+        type=float,
+        default=0.3,
+        help='bbox score threshold')
+    parser.add_argument(
+        '--batch-size', type=int, default=1, help='Inference batch size.')
+    parser.add_argument(
+        '--show',
+        action='store_true',
+        help='Display the image in a popup window.')
+    parser.add_argument(
+        '--no-save-vis',
+        action='store_true',
+        help='Do not save detection vis results')
+    parser.add_argument(
+        '--no-save-pred',
+        action='store_true',
+        help='Do not save detection json results')
+    parser.add_argument(
+        '--print-result',
+        action='store_true',
+        help='Whether to print the results.')
+    parser.add_argument(
+        '--palette',
+        default='none',
+        choices=['coco', 'voc', 'citys', 'random', 'none'],
+        help='Color palette used for visualization')
+    # only for GLIP and Grounding DINO
+    parser.add_argument(
+        '--custom-entities',
+        '-c',
+        action='store_true',
+        help='Whether to customize entity names? '
+        'If so, the input text should be '
+        '"cls_name1 . cls_name2 . cls_name3 ." format')
+    parser.add_argument(
+        '--chunked-size',
+        '-s',
+        type=int,
+        default=-1,
+        help='If the number of categories is very large, '
+        'you can specify this parameter to truncate multiple predictions.')
+    # only for Grounding DINO
+    parser.add_argument(
+        '--tokens-positive',
+        '-p',
+        type=str,
+        help='Used to specify which locations in the input text are of '
+        'interest to the user. -1 indicates that no area is of interest, '
+        'None indicates ignoring this parameter. '
+        'The two-dimensional array represents the start and end positions.')
+
+    call_args = vars(parser.parse_args())
+
+    if call_args['no_save_vis'] and call_args['no_save_pred']:
+        call_args['out_dir'] = ''
+
+    if call_args['model'].endswith('.pth'):
+        print_log('The model is a weight file, automatically '
+                  'assign the model to --weights')
+        call_args['weights'] = call_args['model']
+        call_args['model'] = None
+
+    if call_args['texts'] is not None:
+        if call_args['texts'].startswith('$:'):
+            dataset_name = call_args['texts'][3:].strip()
+            class_names = get_classes(dataset_name)
+            call_args['texts'] = [tuple(class_names)]
+
+    if call_args['tokens_positive'] is not None:
+        call_args['tokens_positive'] = ast.literal_eval(
+            call_args['tokens_positive'])
+
+    init_kws = ['model', 'weights', 'device', 'palette']
+    init_args = {}
+    for init_kw in init_kws:
+        init_args[init_kw] = call_args.pop(init_kw)
+
+    return init_args, call_args
+
+
+def main():
+    init_args, call_args = parse_args()
+    # TODO: Video and Webcam are currently not supported and
+    #  may consume too much memory if your input folder has a lot of images.
+    #  We will be optimized later.
+    inferencer = DetInferencer(**init_args)
+
+    chunked_size = call_args.pop('chunked_size')
+    inferencer.model.test_cfg.chunked_size = chunked_size
+
+    inferencer(**call_args)
+
+    if call_args['out_dir'] != '' and not (call_args['no_save_vis']
+                                           and call_args['no_save_pred']):
+        print_log(f'results have been saved at {call_args["out_dir"]}')
+
+
+if __name__ == '__main__':
+    main()

demo/inference_demo.ipynb

+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gCMycQ_2U8SA"
+      },
+      "source": [
+        "<div align=\"center\">\n",
+        "  <img src=\"https://github.com/open-mmlab/mmdetection/raw/3.x/resources/mmdet-logo.png\" width=\"600\"/>\n",
+        "  <div>&nbsp;</div>\n",
+        "  <div align=\"center\">\n",
+        "    <b><font size=\"5\">OpenMMLab website</font></b>\n",
+        "    <sup>\n",
+        "      <a href=\"https://openmmlab.com\">\n",
+        "        <i><font size=\"4\">HOT</font></i>\n",
+        "      </a>\n",
+        "    </sup>\n",
+        "    &nbsp;&nbsp;&nbsp;&nbsp;\n",
+        "    <b><font size=\"5\">OpenMMLab platform</font></b>\n",
+        "    <sup>\n",
+        "      <a href=\"https://platform.openmmlab.com\">\n",
+        "        <i><font size=\"4\">TRY IT OUT</font></i>\n",
+        "      </a>\n",
+        "    </sup>\n",
+        "  </div>\n",
+        "  <div>&nbsp;</div>\n",
+        "\n",
+        "<a href=\"https://colab.research.google.com/github/open-mmlab/mmdetection/blob/dev-3.x/demo/inference_demo.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "\n",
+        "[![PyPI](https://img.shields.io/pypi/v/mmdet)](https://pypi.org/project/mmdet)\n",
+        "[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection.readthedocs.io/en/latest/)\n",
+        "[![badge](https://github.com/open-mmlab/mmdetection/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection/actions)\n",
+        "[![codecov](https://codecov.io/gh/open-mmlab/mmdetection/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection)\n",
+        "[![license](https://img.shields.io/github/license/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/blob/master/LICENSE)\n",
+        "[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)\n",
+        "[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)\n",
+        "\n",
+        "[📘Documentation](https://mmdetection.readthedocs.io/en/3.x/) |\n",
+        "[🛠️Installation](https://mmdetection.readthedocs.io/en/3.x/get_started.html) |\n",
+        "[👀Model Zoo](https://mmdetection.readthedocs.io/en/3.x/model_zoo.html) |\n",
+        "[🆕Update News](https://mmdetection.readthedocs.io/en/3.x/notes/changelog.html) |\n",
+        "[🚀Ongoing Projects](https://github.com/open-mmlab/mmdetection/projects) |\n",
+        "[🤔Reporting Issues](https://github.com/open-mmlab/mmdetection/issues/new/choose)\n",
+        "\n",
+        "</div>\n",
+        "\n",
+        "<div align=\"center\">\n",
+        "  <a href=\"https://openmmlab.medium.com/\" style=\"text-decoration:none;\">\n",
+        "    <img src=\"https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png\" width=\"3%\" alt=\"\" /></a>\n",
+        "  <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n",
+        "  <a href=\"https://discord.com/channels/1037617289144569886/1046608014234370059\" style=\"text-decoration:none;\">\n",
+        "    <img src=\"https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png\" width=\"3%\" alt=\"\" /></a>\n",
+        "  <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n",
+        "  <a href=\"https://twitter.com/OpenMMLab\" style=\"text-decoration:none;\">\n",
+        "    <img src=\"https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png\" width=\"3%\" alt=\"\" /></a>\n",
+        "  <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n",
+        "  <a href=\"https://www.youtube.com/openmmlab\" style=\"text-decoration:none;\">\n",
+        "    <img src=\"https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png\" width=\"3%\" alt=\"\" /></a>\n",
+        "  <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n",
+        "  <a href=\"https://space.bilibili.com/1293512903\" style=\"text-decoration:none;\">\n",
+        "    <img src=\"https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png\" width=\"3%\" alt=\"\" /></a>\n",
+        "  <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n",
+        "  <a href=\"https://www.zhihu.com/people/openmmlab\" style=\"text-decoration:none;\">\n",
+        "    <img src=\"https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png\" width=\"3%\" alt=\"\" /></a>\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aGYwt_UjIrqp"
+      },
+      "source": [
+        "# Inferencer\n",
+        "\n",
+        "In this tutorial, you will learn how to perform inference with a MMDetection `DetInferencer`.\n",
+        "\n",
+        "Let's start!\n",
+        "\n",
+        "```{note}\n",
+        "The commands in this tutorial are mainly for Colab.\n",
+        "You can click the button above, `Open in Colab`, to run this notebook in Colab.\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tJxJHruNLb7Y"
+      },
+      "source": [
+        "## Install MMDetection"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wi4LPmsR66sy",
+        "outputId": "0077a9a3-0183-4002-fe7a-2a12f020cf69"
+      },
+      "outputs": [],
+      "source": [
+        "# Check nvcc version\n",
+        "!nvcc -V\n",
+        "# Check GCC version\n",
+        "!gcc --version"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gkGnB9WyHSXB",
+        "outputId": "5945fe0b-13a5-4f1b-dff9-8beb1df67ab0"
+      },
+      "outputs": [],
+      "source": [
+        "# install dependencies\n",
+        "%pip install -U openmim\n",
+        "!mim install \"mmengine>=0.7.0\"\n",
+        "!mim install \"mmcv>=2.0.0rc4\"\n",
+        "\n",
+        "# Install mmdetection\n",
+        "!rm -rf mmdetection\n",
+        "!git clone https://github.com/open-mmlab/mmdetection.git -b dev-3.x\n",
+        "%cd mmdetection\n",
+        "\n",
+        "%pip install -e ."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_YeUiqAoCaoV",
+        "outputId": "06e4c803-ac46-49e6-b8fa-1a85c23fa482"
+      },
+      "outputs": [],
+      "source": [
+        "from mmengine.utils import get_git_hash\n",
+        "from mmengine.utils.dl_utils import collect_env as collect_base_env\n",
+        "\n",
+        "import mmdet\n",
+        "\n",
+        "\n",
+        "def collect_env():\n",
+        "    \"\"\"Collect the information of the running environments.\"\"\"\n",
+        "    env_info = collect_base_env()\n",
+        "    env_info['MMDetection'] = f'{mmdet.__version__}+{get_git_hash()[:7]}'\n",
+        "    return env_info\n",
+        "\n",
+        "\n",
+        "if __name__ == '__main__':\n",
+        "    for name, val in collect_env().items():\n",
+        "        print(f'{name}: {val}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fLgFRMtP91ue"
+      },
+      "source": [
+        "## `DetInferencer`\n",
+        "\n",
+        "### Basic Usage\n",
+        "\n",
+        "We use the high-level API `DetInferencer` implemented in the MMDetection. This API is created to ease the inference process. The details of the codes can be found [here](https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/apis/det_inferencer.py)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "6fa2cda48fda43f9bf53a0f533392eba",
+            "0226fedc26044ab2abdccc4fcbe226f8"
+          ]
+        },
+        "id": "WJHpC402p2w9",
+        "outputId": "c2326326-d198-4fce-ec0e-a9cc2e35ba09"
+      },
+      "outputs": [],
+      "source": [
+        "from mmdet.apis import DetInferencer\n",
+        "\n",
+        "# Initialize the DetInferencer\n",
+        "inferencer = DetInferencer('rtmdet_tiny_8xb32-300e_coco')\n",
+        "\n",
+        "# Perform inference\n",
+        "inferencer('demo/demo.jpg', out_dir='./output')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 444
+        },
+        "id": "34JfPWRRSlNh",
+        "outputId": "8eec8bc4-4824-47ac-b10f-41538422fb28"
+      },
+      "outputs": [],
+      "source": [
+        "# Show the output image\n",
+        "from PIL import Image\n",
+        "Image.open('./output/vis/demo.jpg')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-53WPeyBqRHe"
+      },
+      "source": [
+        "### Initialization\n",
+        "\n",
+        "Each Inferencer must be initialized with a model. You can also choose the inference device during initialization.\n",
+        "\n",
+        "#### Model Initialization\n",
+        "\n",
+        "- To infer with MMDetection's pre-trained model, passing its name to the argument `model` can work. The weights will be automatically downloaded and loaded from OpenMMLab's model zoo."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bbMu3IPtv-cX",
+        "outputId": "2bceb594-06c8-4c18-e8c6-b1816b0acb23"
+      },
+      "outputs": [],
+      "source": [
+        "inferencer = DetInferencer(model='rtmdet_tiny_8xb32-300e_coco')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AwKtnol3TQlM"
+      },
+      "source": [
+        "There is a very easy to list all model names in MMDetection."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3kYfK3ssTIQE",
+        "outputId": "88c4fbb9-bb92-42af-baaa-14cee5b5bdc1"
+      },
+      "outputs": [],
+      "source": [
+        "# models is a list of model names, and them will print automatically\n",
+        "models = DetInferencer.list_models('mmdet')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G-25HR9HTZvr"
+      },
+      "source": [
+        "You can load another weight by passing its path/url to `weights`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "j4doHX4exvS1",
+        "outputId": "54ac0be2-835f-4390-aa0e-3be5236d8cc9"
+      },
+      "outputs": [],
+      "source": [
+        "!mkdir ./checkpoints\n",
+        "!mim download mmdet --config rtmdet_tiny_8xb32-300e_coco --dest ./checkpoints"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8LQB4EC-Tako",
+        "outputId": "2cc0960e-d5b5-4c3a-8a0c-cec23989f6a0"
+      },
+      "outputs": [],
+      "source": [
+        "# Setup a checkpoint file to load\n",
+        "checkpoint = './checkpoints/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth'\n",
+        "\n",
+        "# Initialize the DetInferencer\n",
+        "inferencer = DetInferencer(model='rtmdet_tiny_8xb32-300e_coco', weights=checkpoint)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Atft9tjcwgeD"
+      },
+      "source": [
+        "- To load custom config and weight, you can pass the path to the config file to `model` and the path to the weight to `weights`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eukDD4Rzwp9P",
+        "outputId": "0a34392c-0544-4a90-c844-7628d184efc0"
+      },
+      "outputs": [],
+      "source": [
+        "# Choose to use a config\n",
+        "config_path = './configs/rtmdet/rtmdet_tiny_8xb32-300e_coco.py'\n",
+        "\n",
+        "# Setup a checkpoint file to load\n",
+        "checkpoint = './checkpoints/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth'\n",
+        "\n",
+        "# Initialize the DetInferencer\n",
+        "inferencer = DetInferencer(model=config_path, weights=checkpoint)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FC1je9iiTuMS"
+      },
+      "source": [
+        "- By default, [MMEngine](https://github.com/open-mmlab/mmengine/) dumps config to the weight. If you have a weight trained on MMEngine, you can also pass the path to the weight file to `weights` without specifying `model`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Kenyo80RTx63",
+        "outputId": "46fe8219-d1a7-4e45-b5a1-b6d21c30be42"
+      },
+      "outputs": [],
+      "source": [
+        "# It will raise an error if the config file cannot be found in the weight. Currently, within the MMDetection model repository, only the weights of ddq-detr-4scale_r50 can be loaded in this manner.\n",
+        "inferencer = DetInferencer(weights='https://download.openmmlab.com/mmdetection/v3.0/ddq/ddq-detr-4scale_r50_8xb2-12e_coco/ddq-detr-4scale_r50_8xb2-12e_coco_20230809_170711-42528127.pth')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b-AlYOw4T3AO"
+      },
+      "source": [
+        "- Passing config file to `model` without specifying `weight` will result in a randomly initialized model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "quFQ8abYT6As"
+      },
+      "source": [
+        "### Device\n",
+        "\n",
+        "Each Inferencer instance is bound to a device.\n",
+        "By default, the best device is automatically decided by [MMEngine](https://github.com/open-mmlab/mmengine/). You can also alter the device by specifying the `device` argument. For example, you can use the following code to create an Inferencer on GPU 0."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wi6DRpsQPEmV",
+        "outputId": "9eac2017-cce6-491a-ef51-3e7e2560f107"
+      },
+      "outputs": [],
+      "source": [
+        "inferencer = DetInferencer(model='rtmdet_tiny_8xb32-300e_coco', device='cuda:0')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h3pgIACHUXEv"
+      },
+      "source": [
+        "To create an Inferencer on CPU:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JsAotaiRUXWH",
+        "outputId": "531b1cb0-e986-4e0a-91c9-6d3ad65544e7"
+      },
+      "outputs": [],
+      "source": [
+        "inferencer = DetInferencer(model='rtmdet_tiny_8xb32-300e_coco', device='cpu')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0a4Zw5plUisX"
+      },
+      "source": [
+        "### Inference\n",
+        "\n",
+        "Once the Inferencer is initialized, you can directly pass in the raw data to be inferred and get the inference results from return values.\n",
+        "\n",
+        "#### Input\n",
+        "\n",
+        "Input can be either of these types:\n",
+        "\n",
+        "- str: Path/URL to the image."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "2abd7eef6f1f4b9c865a466b3dd5ef24",
+            "8951ec1ee7164f7ca7239a37e80e98ea"
+          ]
+        },
+        "id": "C4McAmYdUnCL",
+        "outputId": "50bea3e2-a912-497e-cee9-26109dccdc12"
+      },
+      "outputs": [],
+      "source": [
+        "inferencer = DetInferencer(model='rtmdet_tiny_8xb32-300e_coco', device='cuda:0')\n",
+        "inferencer('demo/demo.jpg')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3G_TPKrMUp2T"
+      },
+      "source": [
+        "- array: Image in numpy array. It should be in BGR order."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "59bfd22c751f4ed4baefa466e7653315",
+            "0164804ae2f842fe8d2a4c5414c4a0c2"
+          ]
+        },
+        "id": "-M1qGlfaUpha",
+        "outputId": "5a06cfe8-e056-4d56-c8e9-489e8f6633a0"
+      },
+      "outputs": [],
+      "source": [
+        "import mmcv\n",
+        "array = mmcv.imread('demo/demo.jpg')\n",
+        "inferencer(array)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I45B_CtzUuh2"
+      },
+      "source": [
+        "- list: A list of basic types above. Each element in the list will be processed separately."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "a64a6eb038c44236b80579b2bfc4b8e3",
+            "eef25a0509854f98883395a2c0fc2134",
+            "f87f0b153b0342ad99dcd320a1302c92",
+            "f6634888109048069b6844e9f9b4ec13"
+          ]
+        },
+        "id": "k1IXIWXHUwKP",
+        "outputId": "0af73b0b-d703-4cbc-91ad-052f0b521d50"
+      },
+      "outputs": [],
+      "source": [
+        "inferencer(['tests/data/color.jpg', 'tests/data/gray.jpg'])\n",
+        "# You can even mix the types\n",
+        "inferencer(['tests/data/color.jpg', array])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hUGrTtxrVBAS"
+      },
+      "source": [
+        "- str: Path to the directory. All images in the directory will be processed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "07ed8efcd87a40059af36f0c43ef5147",
+            "69ce7e58e27f4e1186ab0afcb99d37c3"
+          ]
+        },
+        "id": "JWK10ZD6VDDE",
+        "outputId": "91418597-d9ea-4613-b141-16bc8bcc8caf"
+      },
+      "outputs": [],
+      "source": [
+        "inferencer('tests/data/')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BQxEVr2pVGen"
+      },
+      "source": [
+        "### Output\n",
+        "\n",
+        "By default, each `Inferencer` returns the prediction results in a dictionary format.\n",
+        "\n",
+        "- `visualization` contains the visualized predictions.\n",
+        "\n",
+        "- `predictions` contains the predictions results in a json-serializable format. But it's an empty list by default unless `return_vis=True`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 306,
+          "referenced_widgets": [
+            "95674a6baa1842d2981fe60b31ab6cad",
+            "7e62816d1f6c441fb98c1f8e942fff1d"
+          ]
+        },
+        "id": "m6a8T4goU8Sq",
+        "outputId": "6f74098f-a3d3-4897-c58f-68fae88889af"
+      },
+      "outputs": [],
+      "source": [
+        "# Show the structure of result dict\n",
+        "from rich.pretty import pprint\n",
+        "\n",
+        "result = inferencer('demo/demo.jpg')\n",
+        "pprint(result, max_length=4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a93hFT0jVkrR"
+      },
+      "source": [
+        "If you wish to get the raw outputs from the model, you can set `return_datasamples` to `True` to get the original `DataSample`, which will be stored in `predictions`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "060f510b5bda498583d7212060bb528c",
+            "1bb724cb12c240a18f651dd99842e5b0"
+          ]
+        },
+        "id": "U5DFI7QAVbnP",
+        "outputId": "effaf3ec-2476-4b64-dcbd-802a18a26479"
+      },
+      "outputs": [],
+      "source": [
+        "result = inferencer('demo/demo.jpg', return_datasamples=True)\n",
+        "pprint(result, max_length=4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JHdcUnGzVsk1"
+      },
+      "source": [
+        "#### Dumping Results\n",
+        "\n",
+        "Apart from obtaining predictions from the return value, you can also export the predictions/visualizations to files by setting `out_dir` and `no_save_pred`/`no_save_vis` arguments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "38083c2f29604d1d9a7dcf9845dfbf33",
+            "54cdfe55e0f04df9ab844961a089fe2f"
+          ]
+        },
+        "id": "0dr-ixmfVtng",
+        "outputId": "af22d458-9aed-41e2-f675-e017a0cb588b"
+      },
+      "outputs": [],
+      "source": [
+        "inferencer('demo/demo.jpg', out_dir='outputs/', no_save_pred=False)"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.13"
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+                "data": {
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Inference <span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span>  <span style=\"color: #008080; text-decoration-color: #008080\"> </span>\n</pre>\n",
+                  "text/plain": "Inference \u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m  \u001b[36m \u001b[0m\n"
+                },
+                "metadata": {},
+                "output_type": "display_data"
+              }
+            ]
+          }
+        },
+        "07ed8efcd87a40059af36f0c43ef5147": {
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+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_69ce7e58e27f4e1186ab0afcb99d37c3",
+            "msg_id": "",
+            "outputs": [
+              {
+                "data": {
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Inference <span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span> <span style=\"color: #800080; text-decoration-color: #800080\">13.5 it/s</span> <span style=\"color: #008080; text-decoration-color: #008080\"> </span>\n</pre>\n",
+                  "text/plain": "Inference \u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m \u001b[35m13.5 it/s\u001b[0m \u001b[36m \u001b[0m\n"
+                },
+                "metadata": {},
+                "output_type": "display_data"
+              }
+            ]
+          }
+        },
+        "1bb724cb12c240a18f651dd99842e5b0": {
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+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
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+            "align_items": null,
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+            "border": null,
+            "bottom": null,
+            "display": null,
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+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
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+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
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+        },
+        "2abd7eef6f1f4b9c865a466b3dd5ef24": {
+          "model_module": "@jupyter-widgets/output",
+          "model_module_version": "1.0.0",
+          "model_name": "OutputModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_8951ec1ee7164f7ca7239a37e80e98ea",
+            "msg_id": "",
+            "outputs": [
+              {
+                "data": {
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Inference <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span>  <span style=\"color: #008080; text-decoration-color: #008080\"> </span>\n</pre>\n",
+                  "text/plain": "Inference \u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m  \u001b[36m \u001b[0m\n"
+                },
+                "metadata": {},
+                "output_type": "display_data"
+              }
+            ]
+          }
+        },
+        "38083c2f29604d1d9a7dcf9845dfbf33": {
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+          "model_module_version": "1.0.0",
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+          "state": {
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+                  "text/plain": "Inference \u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m  \u001b[36m \u001b[0m\n"
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+                  "text/plain": "Inference \u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m  \u001b[36m \u001b[0m\n"
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+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Inference <span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span>  <span style=\"color: #008080; text-decoration-color: #008080\"> </span>\n</pre>\n",
+                  "text/plain": "Inference \u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m  \u001b[36m \u001b[0m\n"
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+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Inference <span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span>  <span style=\"color: #008080; text-decoration-color: #008080\"> </span>\n</pre>\n",
+                  "text/plain": "Inference \u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m  \u001b[36m \u001b[0m\n"
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+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Inference <span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span> <span style=\"color: #800080; text-decoration-color: #800080\">9.7 it/s</span> <span style=\"color: #008080; text-decoration-color: #008080\"> </span>\n</pre>\n",
+                  "text/plain": "Inference \u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m \u001b[35m9.7 it/s\u001b[0m \u001b[36m \u001b[0m\n"
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+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Inference <span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #f92672; text-decoration-color: #f92672\">━</span><span style=\"color: #f42670; text-decoration-color: #f42670\">━</span><span style=\"color: #e6276c; text-decoration-color: #e6276c\">━</span><span style=\"color: #d12a66; text-decoration-color: #d12a66\">━</span><span style=\"color: #b72c5e; text-decoration-color: #b72c5e\">━</span><span style=\"color: #993056; text-decoration-color: #993056\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━</span><span style=\"color: #3e393b; text-decoration-color: #3e393b\">━</span><span style=\"color: #4c383f; text-decoration-color: #4c383f\">━</span><span style=\"color: #613545; text-decoration-color: #613545\">━</span><span style=\"color: #7b334d; text-decoration-color: #7b334d\">━</span> <span style=\"color: #800080; text-decoration-color: #800080\">9.0 it/s</span> <span style=\"color: #008080; text-decoration-color: #008080\"> </span>\n</pre>\n",
+                  "text/plain": "Inference \u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m \u001b[35m9.0 it/s\u001b[0m \u001b[36m \u001b[0m\n"
+                },
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+  "nbformat_minor": 0
+}

demo/large_image_demo.py

+# Copyright (c) OpenMMLab. All rights reserved.
+"""Perform MMDET inference on large images (as satellite imagery) as:
+
+```shell
+wget -P checkpoint https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth # noqa: E501, E261.
+
+python demo/large_image_demo.py \
+    demo/large_image.jpg \
+    configs/faster_rcnn/faster-rcnn_r101_fpn_2x_coco.py \
+    checkpoint/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth
+```
+"""
+
+import os
+import random
+from argparse import ArgumentParser
+from pathlib import Path
+
+import mmcv
+import numpy as np
+from mmengine.config import Config, ConfigDict
+from mmengine.logging import print_log
+from mmengine.utils import ProgressBar
+
+from mmdet.apis import inference_detector, init_detector
+
+try:
+    from sahi.slicing import slice_image
+except ImportError:
+    raise ImportError('Please run "pip install -U sahi" '
+                      'to install sahi first for large image inference.')
+
+from mmdet.registry import VISUALIZERS
+from mmdet.utils.large_image import merge_results_by_nms, shift_predictions
+from mmdet.utils.misc import get_file_list
+
+
+def parse_args():
+    parser = ArgumentParser(
+        description='Perform MMDET inference on large images.')
+    parser.add_argument(
+        'img', help='Image path, include image file, dir and URL.')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--out-dir', default='./output', help='Path to output file')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--show', action='store_true', help='Show the detection results')
+    parser.add_argument(
+        '--tta',
+        action='store_true',
+        help='Whether to use test time augmentation')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.3, help='Bbox score threshold')
+    parser.add_argument(
+        '--patch-size', type=int, default=640, help='The size of patches')
+    parser.add_argument(
+        '--patch-overlap-ratio',
+        type=float,
+        default=0.25,
+        help='Ratio of overlap between two patches')
+    parser.add_argument(
+        '--merge-iou-thr',
+        type=float,
+        default=0.25,
+        help='IoU threshould for merging results')
+    parser.add_argument(
+        '--merge-nms-type',
+        type=str,
+        default='nms',
+        help='NMS type for merging results')
+    parser.add_argument(
+        '--batch-size',
+        type=int,
+        default=1,
+        help='Batch size, must greater than or equal to 1')
+    parser.add_argument(
+        '--debug',
+        action='store_true',
+        help='Export debug results before merging')
+    parser.add_argument(
+        '--save-patch',
+        action='store_true',
+        help='Save the results of each patch. '
+        'The `--debug` must be enabled.')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    config = args.config
+
+    if isinstance(config, (str, Path)):
+        config = Config.fromfile(config)
+    elif not isinstance(config, Config):
+        raise TypeError('config must be a filename or Config object, '
+                        f'but got {type(config)}')
+    if 'init_cfg' in config.model.backbone:
+        config.model.backbone.init_cfg = None
+
+    if args.tta:
+        assert 'tta_model' in config, 'Cannot find ``tta_model`` in config.' \
+                                      " Can't use tta !"
+        assert 'tta_pipeline' in config, 'Cannot find ``tta_pipeline`` ' \
+                                         "in config. Can't use tta !"
+        config.model = ConfigDict(**config.tta_model, module=config.model)
+        test_data_cfg = config.test_dataloader.dataset
+        while 'dataset' in test_data_cfg:
+            test_data_cfg = test_data_cfg['dataset']
+
+        test_data_cfg.pipeline = config.tta_pipeline
+
+    # TODO: TTA mode will error if cfg_options is not set.
+    #  This is an mmdet issue and needs to be fixed later.
+    # build the model from a config file and a checkpoint file
+    model = init_detector(
+        config, args.checkpoint, device=args.device, cfg_options={})
+
+    if not os.path.exists(args.out_dir) and not args.show:
+        os.mkdir(args.out_dir)
+
+    # init visualizer
+    visualizer = VISUALIZERS.build(model.cfg.visualizer)
+    visualizer.dataset_meta = model.dataset_meta
+
+    # get file list
+    files, source_type = get_file_list(args.img)
+
+    # start detector inference
+    print(f'Performing inference on {len(files)} images.... '
+          'This may take a while.')
+    progress_bar = ProgressBar(len(files))
+    for file in files:
+        # read image
+        img = mmcv.imread(file)
+
+        # arrange slices
+        height, width = img.shape[:2]
+        sliced_image_object = slice_image(
+            img,
+            slice_height=args.patch_size,
+            slice_width=args.patch_size,
+            auto_slice_resolution=False,
+            overlap_height_ratio=args.patch_overlap_ratio,
+            overlap_width_ratio=args.patch_overlap_ratio,
+        )
+        # perform sliced inference
+        slice_results = []
+        start = 0
+        while True:
+            # prepare batch slices
+            end = min(start + args.batch_size, len(sliced_image_object))
+            images = []
+            for sliced_image in sliced_image_object.images[start:end]:
+                images.append(sliced_image)
+
+            # forward the model
+            slice_results.extend(inference_detector(model, images))
+
+            if end >= len(sliced_image_object):
+                break
+            start += args.batch_size
+
+        if source_type['is_dir']:
+            filename = os.path.relpath(file, args.img).replace('/', '_')
+        else:
+            filename = os.path.basename(file)
+
+        img = mmcv.imconvert(img, 'bgr', 'rgb')
+        out_file = None if args.show else os.path.join(args.out_dir, filename)
+
+        # export debug images
+        if args.debug:
+            # export sliced image results
+            name, suffix = os.path.splitext(filename)
+
+            shifted_instances = shift_predictions(
+                slice_results,
+                sliced_image_object.starting_pixels,
+                src_image_shape=(height, width))
+            merged_result = slice_results[0].clone()
+            merged_result.pred_instances = shifted_instances
+
+            debug_file_name = name + '_debug' + suffix
+            debug_out_file = None if args.show else os.path.join(
+                args.out_dir, debug_file_name)
+            visualizer.set_image(img.copy())
+
+            debug_grids = []
+            for starting_point in sliced_image_object.starting_pixels:
+                start_point_x = starting_point[0]
+                start_point_y = starting_point[1]
+                end_point_x = start_point_x + args.patch_size
+                end_point_y = start_point_y + args.patch_size
+                debug_grids.append(
+                    [start_point_x, start_point_y, end_point_x, end_point_y])
+            debug_grids = np.array(debug_grids)
+            debug_grids[:, 0::2] = np.clip(debug_grids[:, 0::2], 1,
+                                           img.shape[1] - 1)
+            debug_grids[:, 1::2] = np.clip(debug_grids[:, 1::2], 1,
+                                           img.shape[0] - 1)
+
+            palette = np.random.randint(0, 256, size=(len(debug_grids), 3))
+            palette = [tuple(c) for c in palette]
+            line_styles = random.choices(['-', '-.', ':'], k=len(debug_grids))
+            visualizer.draw_bboxes(
+                debug_grids,
+                edge_colors=palette,
+                alpha=1,
+                line_styles=line_styles)
+            visualizer.draw_bboxes(
+                debug_grids, face_colors=palette, alpha=0.15)
+
+            visualizer.draw_texts(
+                list(range(len(debug_grids))),
+                debug_grids[:, :2] + 5,
+                colors='w')
+
+            visualizer.add_datasample(
+                debug_file_name,
+                visualizer.get_image(),
+                data_sample=merged_result,
+                draw_gt=False,
+                show=args.show,
+                wait_time=0,
+                out_file=debug_out_file,
+                pred_score_thr=args.score_thr,
+            )
+
+            if args.save_patch:
+                debug_patch_out_dir = os.path.join(args.out_dir,
+                                                   f'{name}_patch')
+                for i, slice_result in enumerate(slice_results):
+                    patch_out_file = os.path.join(
+                        debug_patch_out_dir,
+                        f'{filename}_slice_{i}_result.jpg')
+                    image = mmcv.imconvert(sliced_image_object.images[i],
+                                           'bgr', 'rgb')
+
+                    visualizer.add_datasample(
+                        'patch_result',
+                        image,
+                        data_sample=slice_result,
+                        draw_gt=False,
+                        show=False,
+                        wait_time=0,
+                        out_file=patch_out_file,
+                        pred_score_thr=args.score_thr,
+                    )
+
+        image_result = merge_results_by_nms(
+            slice_results,
+            sliced_image_object.starting_pixels,
+            src_image_shape=(height, width),
+            nms_cfg={
+                'type': args.merge_nms_type,
+                'iou_threshold': args.merge_iou_thr
+            })
+
+        visualizer.add_datasample(
+            filename,
+            img,
+            data_sample=image_result,
+            draw_gt=False,
+            show=args.show,
+            wait_time=0,
+            out_file=out_file,
+            pred_score_thr=args.score_thr,
+        )
+        progress_bar.update()
+
+    if not args.show or (args.debug and args.save_patch):
+        print_log(
+            f'\nResults have been saved at {os.path.abspath(args.out_dir)}')
+
+
+if __name__ == '__main__':
+    main()

demo/mot_demo.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import os.path as osp
+import tempfile
+from argparse import ArgumentParser
+
+import mmcv
+import mmengine
+from mmengine.registry import init_default_scope
+
+from mmdet.apis import inference_mot, init_track_model
+from mmdet.registry import VISUALIZERS
+
+IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png')
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument(
+        'inputs', type=str, help='Input image file or folder path.')
+    parser.add_argument('config', help='config file')
+    parser.add_argument('--checkpoint', help='checkpoint file')
+    parser.add_argument('--detector', help='det checkpoint file')
+    parser.add_argument('--reid', help='reid checkpoint file')
+    parser.add_argument(
+        '--device', default='cuda:0', help='device used for inference')
+    parser.add_argument(
+        '--score-thr',
+        type=float,
+        default=0.0,
+        help='The threshold of score to filter bboxes.')
+    parser.add_argument(
+        '--out', help='output video file (mp4 format) or folder')
+    parser.add_argument(
+        '--show',
+        action='store_true',
+        help='whether show the results on the fly')
+    parser.add_argument('--fps', help='FPS of the output video')
+    args = parser.parse_args()
+    return args
+
+
+def main(args):
+    assert args.out or args.show
+    # load images
+    if osp.isdir(args.inputs):
+        imgs = sorted(
+            filter(lambda x: x.endswith(IMG_EXTENSIONS),
+                   os.listdir(args.inputs)),
+            key=lambda x: int(x.split('.')[0]))
+        in_video = False
+    else:
+        imgs = mmcv.VideoReader(args.inputs)
+        in_video = True
+
+    # define output
+    out_video = False
+    if args.out is not None:
+        if args.out.endswith('.mp4'):
+            out_video = True
+            out_dir = tempfile.TemporaryDirectory()
+            out_path = out_dir.name
+            _out = args.out.rsplit(os.sep, 1)
+            if len(_out) > 1:
+                os.makedirs(_out[0], exist_ok=True)
+        else:
+            out_path = args.out
+            os.makedirs(out_path, exist_ok=True)
+
+    fps = args.fps
+    if args.show or out_video:
+        if fps is None and in_video:
+            fps = imgs.fps
+        if not fps:
+            raise ValueError('Please set the FPS for the output video.')
+        fps = int(fps)
+
+    init_default_scope('mmdet')
+
+    # build the model from a config file and a checkpoint file
+    model = init_track_model(
+        args.config,
+        args.checkpoint,
+        args.detector,
+        args.reid,
+        device=args.device)
+
+    # build the visualizer
+    visualizer = VISUALIZERS.build(model.cfg.visualizer)
+    visualizer.dataset_meta = model.dataset_meta
+
+    prog_bar = mmengine.ProgressBar(len(imgs))
+    # test and show/save the images
+    for i, img in enumerate(imgs):
+        if isinstance(img, str):
+            img_path = osp.join(args.inputs, img)
+            img = mmcv.imread(img_path)
+        # result [TrackDataSample]
+        result = inference_mot(model, img, frame_id=i, video_len=len(imgs))
+        if args.out is not None:
+            if in_video or out_video:
+                out_file = osp.join(out_path, f'{i:06d}.jpg')
+            else:
+                out_file = osp.join(out_path, img.rsplit(os.sep, 1)[-1])
+        else:
+            out_file = None
+
+        # show the results
+        visualizer.add_datasample(
+            'mot',
+            img[..., ::-1],
+            data_sample=result[0],
+            show=args.show,
+            draw_gt=False,
+            out_file=out_file,
+            wait_time=float(1 / int(fps)) if fps else 0,
+            pred_score_thr=args.score_thr,
+            step=i)
+
+        prog_bar.update()
+
+    if args.out and out_video:
+        print(f'making the output video at {args.out} with a FPS of {fps}')
+        mmcv.frames2video(out_path, args.out, fps=fps, fourcc='mp4v')
+        out_dir.cleanup()
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)

demo/video_demo.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import cv2
+import mmcv
+from mmcv.transforms import Compose
+from mmengine.utils import track_iter_progress
+
+from mmdet.apis import inference_detector, init_detector
+from mmdet.registry import VISUALIZERS
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='MMDetection video demo')
+    parser.add_argument('video', help='Video file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.3, help='Bbox score threshold')
+    parser.add_argument('--out', type=str, help='Output video file')
+    parser.add_argument('--show', action='store_true', help='Show video')
+    parser.add_argument(
+        '--wait-time',
+        type=float,
+        default=1,
+        help='The interval of show (s), 0 is block')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    assert args.out or args.show, \
+        ('Please specify at least one operation (save/show the '
+         'video) with the argument "--out" or "--show"')
+
+    # build the model from a config file and a checkpoint file
+    model = init_detector(args.config, args.checkpoint, device=args.device)
+
+    # build test pipeline
+    model.cfg.test_dataloader.dataset.pipeline[
+        0].type = 'mmdet.LoadImageFromNDArray'
+    test_pipeline = Compose(model.cfg.test_dataloader.dataset.pipeline)
+
+    # init visualizer
+    visualizer = VISUALIZERS.build(model.cfg.visualizer)
+    # the dataset_meta is loaded from the checkpoint and
+    # then pass to the model in init_detector
+    visualizer.dataset_meta = model.dataset_meta
+
+    video_reader = mmcv.VideoReader(args.video)
+    video_writer = None
+    if args.out:
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        video_writer = cv2.VideoWriter(
+            args.out, fourcc, video_reader.fps,
+            (video_reader.width, video_reader.height))
+
+    for frame in track_iter_progress((video_reader, len(video_reader))):
+        result = inference_detector(model, frame, test_pipeline=test_pipeline)
+        visualizer.add_datasample(
+            name='video',
+            image=frame,
+            data_sample=result,
+            draw_gt=False,
+            show=False,
+            pred_score_thr=args.score_thr)
+        frame = visualizer.get_image()
+
+        if args.show:
+            cv2.namedWindow('video', 0)
+            mmcv.imshow(frame, 'video', args.wait_time)
+        if args.out:
+            video_writer.write(frame)
+
+    if video_writer:
+        video_writer.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    main()