# Copyright (c) OpenMMLab. All rights reserved. import copy import os import random from typing import Dict, List import mmcv import numpy as np import pytest import torch from mmdeploy.codebase import import_codebase from mmdeploy.utils import Backend, Codebase from mmdeploy.utils.config_utils import get_ir_config from mmdeploy.utils.test import (WrapModel, check_backend, get_model_outputs, get_rewrite_outputs) import_codebase(Codebase.MMDET) def seed_everything(seed=1029): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) np.random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) # if you are using multi-GPU. torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.backends.cudnn.enabled = False def convert_to_list(rewrite_output: Dict, output_names: List[str]) -> List: """Converts output from a dictionary to a list. The new list will contain only those output values, whose names are in list 'output_names'. """ outputs = [ value for name, value in rewrite_output.items() if name in output_names ] return outputs def get_anchor_head_model(): """AnchorHead Config.""" test_cfg = mmcv.Config( dict( deploy_nms_pre=0, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)) from mmdet.models.dense_heads import AnchorHead model = AnchorHead(num_classes=4, in_channels=1, test_cfg=test_cfg) model.requires_grad_(False) return model def get_ssd_head_model(): """SSDHead Config.""" test_cfg = mmcv.Config( dict( nms_pre=1000, nms=dict(type='nms', iou_threshold=0.45), min_bbox_size=0, score_thr=0.02, max_per_img=200)) from mmdet.models import SSDHead model = SSDHead( in_channels=(96, 1280, 512, 256, 256, 128), num_classes=4, use_depthwise=True, norm_cfg=dict(type='BN', eps=0.001, momentum=0.03), act_cfg=dict(type='ReLU6'), init_cfg=dict(type='Normal', layer='Conv2d', std=0.001), anchor_generator=dict( type='SSDAnchorGenerator', scale_major=False, strides=[16, 32, 64, 107, 160, 320], ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]], min_sizes=[48, 100, 150, 202, 253, 304], max_sizes=[100, 150, 202, 253, 304, 320]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[0.1, 0.1, 0.2, 0.2]), test_cfg=test_cfg) model.requires_grad_(False) return model def get_fcos_head_model(): """FCOS Head Config.""" test_cfg = mmcv.Config( dict( deploy_nms_pre=0, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)) from mmdet.models.dense_heads import FCOSHead model = FCOSHead(num_classes=4, in_channels=1, test_cfg=test_cfg) model.requires_grad_(False) return model def get_focus_backbone_model(): """Backbone Focus Config.""" from mmdet.models.backbones.csp_darknet import Focus model = Focus(3, 32) model.requires_grad_(False) return model def get_l2norm_forward_model(): """L2Norm Neck Config.""" from mmdet.models.necks.ssd_neck import L2Norm model = L2Norm(16) torch.nn.init.uniform_(model.weight) model.requires_grad_(False) return model def get_rpn_head_model(): """RPN Head Config.""" test_cfg = mmcv.Config( dict( deploy_nms_pre=0, nms_pre=0, max_per_img=100, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0)) from mmdet.models.dense_heads import RPNHead model = RPNHead(in_channels=1, test_cfg=test_cfg) model.requires_grad_(False) return model def get_reppoints_head_model(): """Reppoints Head Config.""" test_cfg = mmcv.Config( dict( deploy_nms_pre=0, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)) from mmdet.models.dense_heads import RepPointsHead model = RepPointsHead(num_classes=4, in_channels=1, test_cfg=test_cfg) model.requires_grad_(False) return model def get_single_roi_extractor(): """SingleRoIExtractor Config.""" from mmdet.models.roi_heads import SingleRoIExtractor roi_layer = dict(type='RoIAlign', output_size=7, sampling_ratio=2) out_channels = 1 featmap_strides = [4, 8, 16, 32] model = SingleRoIExtractor(roi_layer, out_channels, featmap_strides).eval() return model def get_gfl_head_model(): test_cfg = mmcv.Config( dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.6), max_per_img=100)) anchor_generator = dict( type='AnchorGenerator', scales_per_octave=1, octave_base_scale=8, ratios=[1.0], strides=[8, 16, 32, 64, 128]) from mmdet.models.dense_heads import GFLHead model = GFLHead( num_classes=3, in_channels=256, reg_max=3, test_cfg=test_cfg, anchor_generator=anchor_generator) model.requires_grad_(False) return model @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME, Backend.NCNN]) def test_focus_forward(backend_type): check_backend(backend_type) focus_model = get_focus_backbone_model() focus_model.cpu().eval() s = 128 seed_everything(1234) x = torch.rand(1, 3, s, s) model_outputs = [focus_model.forward(x)] wrapped_model = WrapModel(focus_model, 'forward') rewrite_inputs = { 'x': x, } deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(input_shape=None))) rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) for model_output, rewrite_output in zip(model_outputs[0], rewrite_outputs): model_output = model_output.squeeze() rewrite_output = rewrite_output.squeeze() torch.testing.assert_allclose( model_output, rewrite_output, rtol=1e-03, atol=1e-05) @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME]) def test_l2norm_forward(backend_type): check_backend(backend_type) l2norm_neck = get_l2norm_forward_model() l2norm_neck.cpu().eval() s = 128 deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(input_shape=None))) seed_everything(1234) feat = torch.rand(1, 16, s, s) model_outputs = [l2norm_neck.forward(feat)] wrapped_model = WrapModel(l2norm_neck, 'forward') rewrite_inputs = { 'x': feat, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) if is_backend_output: for model_output, rewrite_output in zip(model_outputs[0], rewrite_outputs[0]): model_output = model_output.squeeze().cpu().numpy() rewrite_output = rewrite_output.squeeze() assert np.allclose( model_output, rewrite_output, rtol=1e-03, atol=1e-05) else: for model_output, rewrite_output in zip(model_outputs[0], rewrite_outputs[0]): model_output = model_output.squeeze().cpu().numpy() rewrite_output = rewrite_output.squeeze() assert np.allclose( model_output[0], rewrite_output, rtol=1e-03, atol=1e-05) def test_get_bboxes_of_fcos_head_ncnn(): backend_type = Backend.NCNN check_backend(backend_type) fcos_head = get_fcos_head_model() fcos_head.cpu().eval() s = 128 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['detection_output'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', model_type='ncnn_end2end', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=200, pre_top_k=5000, keep_top_k=100, background_label_id=-1, )))) # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16), # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2). # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16), # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2) seed_everything(1234) cls_score = [ torch.rand(1, fcos_head.num_classes, pow(2, i), pow(2, i)) for i in range(5, 0, -1) ] seed_everything(5678) bboxes = [torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)] seed_everything(9101) centernesses = [ torch.rand(1, 1, pow(2, i), pow(2, i)) for i in range(5, 0, -1) ] # to get outputs of onnx model after rewrite img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( fcos_head, 'get_bboxes', img_metas=img_metas, with_nms=True) rewrite_inputs = { 'cls_scores': cls_score, 'bbox_preds': bboxes, 'centernesses': centernesses } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) # output should be of shape [1, N, 6] if is_backend_output: assert rewrite_outputs[0].shape[-1] == 6 else: assert rewrite_outputs.shape[-1] == 6 @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME, Backend.NCNN]) def test_get_bboxes_of_rpn_head(backend_type: Backend): check_backend(backend_type) head = get_rpn_head_model() head.cpu().eval() s = 4 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['dets'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=200, pre_top_k=5000, keep_top_k=100, background_label_id=-1, )))) # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16), # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2). # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16), # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2) seed_everything(1234) cls_score = [ torch.rand(1, 9, pow(2, i), pow(2, i)) for i in range(5, 0, -1) ] seed_everything(5678) bboxes = [torch.rand(1, 36, pow(2, i), pow(2, i)) for i in range(5, 0, -1)] # to get outputs of onnx model after rewrite img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( head, 'get_bboxes', img_metas=img_metas, with_nms=True) rewrite_inputs = { 'cls_scores': cls_score, 'bbox_preds': bboxes, } # do not run with ncnn backend run_with_backend = False if backend_type in [Backend.NCNN] else True rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg, run_with_backend=run_with_backend) assert rewrite_outputs is not None @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME]) def test_get_bboxes_of_gfl_head(backend_type): check_backend(backend_type) head = get_gfl_head_model() head.cpu().eval() s = 4 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['dets'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', model_type='ncnn_end2end', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=200, pre_top_k=5000, keep_top_k=100, background_label_id=-1, )))) seed_everything(1234) cls_score = [ torch.rand(1, 3, pow(2, i), pow(2, i)) for i in range(5, 0, -1) ] seed_everything(5678) bboxes = [torch.rand(1, 16, pow(2, i), pow(2, i)) for i in range(5, 0, -1)] # to get outputs of onnx model after rewrite img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( head, 'get_bboxes', img_metas=img_metas, with_nms=True) rewrite_inputs = { 'cls_scores': cls_score, 'bbox_preds': bboxes, } # do not run with ncnn backend run_with_backend = False if backend_type in [Backend.NCNN] else True rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg, run_with_backend=run_with_backend) assert rewrite_outputs is not None @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME]) def test_forward_of_gfl_head(backend_type): check_backend(backend_type) head = get_gfl_head_model() head.cpu().eval() deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(input_shape=None))) feats = [torch.rand(1, 256, pow(2, i), pow(2, i)) for i in range(5, 0, -1)] model_outputs = [head.forward(feats)] wrapped_model = WrapModel(head, 'forward') rewrite_inputs = { 'feats': feats, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) model_outputs[0] = [*model_outputs[0][0], *model_outputs[0][1]] for model_output, rewrite_output in zip(model_outputs[0], rewrite_outputs[0]): model_output = model_output.squeeze().cpu().numpy() rewrite_output = rewrite_output.squeeze() assert np.allclose( model_output, rewrite_output, rtol=1e-03, atol=1e-05) def _replace_r50_with_r18(model): """Replace ResNet50 with ResNet18 in config.""" model = copy.deepcopy(model) if model.backbone.type == 'ResNet': model.backbone.depth = 18 model.backbone.base_channels = 2 model.neck.in_channels = [2, 4, 8, 16] return model @pytest.mark.parametrize('backend', [Backend.ONNXRUNTIME]) @pytest.mark.parametrize('model_cfg_path', [ 'tests/test_codebase/test_mmdet/data/single_stage_model.json', 'tests/test_codebase/test_mmdet/data/mask_model.json' ]) def test_forward_of_base_detector(model_cfg_path, backend): check_backend(backend) deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend.value), onnx_config=dict( output_names=['dets', 'labels'], input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=200, pre_top_k=-1, keep_top_k=100, background_label_id=-1, )))) model_cfg = mmcv.Config(dict(model=mmcv.load(model_cfg_path))) model_cfg.model = _replace_r50_with_r18(model_cfg.model) from mmdet.apis import init_detector model = init_detector(model_cfg, None, 'cpu') img = torch.randn(1, 3, 64, 64) rewrite_inputs = {'img': img} rewrite_outputs, _ = get_rewrite_outputs( wrapped_model=model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) assert rewrite_outputs is not None @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME, Backend.OPENVINO]) def test_single_roi_extractor(backend_type: Backend): check_backend(backend_type) single_roi_extractor = get_single_roi_extractor() output_names = ['roi_feat'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', ))) seed_everything(1234) out_channels = single_roi_extractor.out_channels feats = [ torch.rand((1, out_channels, 200, 336)), torch.rand((1, out_channels, 100, 168)), torch.rand((1, out_channels, 50, 84)), torch.rand((1, out_channels, 25, 42)), ] seed_everything(5678) rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]]) model_inputs = { 'feats': feats, 'rois': rois, } model_outputs = get_model_outputs(single_roi_extractor, 'forward', model_inputs) backend_outputs, _ = get_rewrite_outputs( wrapped_model=single_roi_extractor, model_inputs=model_inputs, deploy_cfg=deploy_cfg) if isinstance(backend_outputs, dict): backend_outputs = backend_outputs.values() for model_output, backend_output in zip(model_outputs[0], backend_outputs): model_output = model_output.squeeze().cpu().numpy() backend_output = backend_output.squeeze() assert np.allclose( model_output, backend_output, rtol=1e-03, atol=1e-05) def get_cascade_roi_head(is_instance_seg=False): """CascadeRoIHead Config.""" num_stages = 3 stage_loss_weights = [1, 0.5, 0.25] bbox_roi_extractor = { 'type': 'SingleRoIExtractor', 'roi_layer': { 'type': 'RoIAlign', 'output_size': 7, 'sampling_ratio': 0 }, 'out_channels': 64, 'featmap_strides': [4, 8, 16, 32] } all_target_stds = [[0.1, 0.1, 0.2, 0.2], [0.05, 0.05, 0.1, 0.1], [0.033, 0.033, 0.067, 0.067]] bbox_head = [{ 'type': 'Shared2FCBBoxHead', 'in_channels': 64, 'fc_out_channels': 1024, 'roi_feat_size': 7, 'num_classes': 80, 'bbox_coder': { 'type': 'DeltaXYWHBBoxCoder', 'target_means': [0.0, 0.0, 0.0, 0.0], 'target_stds': target_stds }, 'reg_class_agnostic': True, 'loss_cls': { 'type': 'CrossEntropyLoss', 'use_sigmoid': False, 'loss_weight': 1.0 }, 'loss_bbox': { 'type': 'SmoothL1Loss', 'beta': 1.0, 'loss_weight': 1.0 } } for target_stds in all_target_stds] mask_roi_extractor = { 'type': 'SingleRoIExtractor', 'roi_layer': { 'type': 'RoIAlign', 'output_size': 14, 'sampling_ratio': 0 }, 'out_channels': 64, 'featmap_strides': [4, 8, 16, 32] } mask_head = { 'type': 'FCNMaskHead', 'num_convs': 4, 'in_channels': 64, 'conv_out_channels': 64, 'num_classes': 80, 'loss_mask': { 'type': 'CrossEntropyLoss', 'use_mask': True, 'loss_weight': 1.0 } } test_cfg = mmcv.Config( dict( score_thr=0.05, nms=mmcv.Config(dict(type='nms', iou_threshold=0.5)), max_per_img=100, mask_thr_binary=0.5)) args = [num_stages, stage_loss_weights, bbox_roi_extractor, bbox_head] kwargs = {'test_cfg': test_cfg} if is_instance_seg: args += [mask_roi_extractor, mask_head] from mmdet.models.roi_heads import CascadeRoIHead model = CascadeRoIHead(*args, **kwargs).eval() return model @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME, Backend.OPENVINO]) def test_cascade_roi_head(backend_type: Backend): check_backend(backend_type) cascade_roi_head = get_cascade_roi_head() seed_everything(1234) x = [ torch.rand((1, 64, 200, 304)), torch.rand((1, 64, 100, 152)), torch.rand((1, 64, 50, 76)), torch.rand((1, 64, 25, 38)), ] proposals = torch.tensor([[587.8285, 52.1405, 886.2484, 341.5644, 0.5]]) img_metas = { 'img_shape': torch.tensor([800, 1216]), 'ori_shape': torch.tensor([800, 1216]), 'scale_factor': torch.tensor([1, 1, 1, 1]) } model_inputs = { 'x': x, 'proposal_list': [proposals], 'img_metas': [img_metas] } output_names = ['results'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=200, pre_top_k=-1, keep_top_k=100, background_label_id=-1)))) model_inputs = {'x': x, 'proposals': proposals.unsqueeze(0)} wrapped_model = WrapModel( cascade_roi_head, 'simple_test', img_metas=[img_metas]) backend_outputs, _ = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=model_inputs, deploy_cfg=deploy_cfg) assert backend_outputs is not None def get_fovea_head_model(): """FoveaHead Config.""" test_cfg = mmcv.Config( dict( deploy_nms_pre=0, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)) from mmdet.models.dense_heads import FoveaHead model = FoveaHead(num_classes=4, in_channels=1, test_cfg=test_cfg) model.requires_grad_(False) return model @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME, Backend.OPENVINO]) def test_get_bboxes_of_fovea_head(backend_type: Backend): check_backend(backend_type) fovea_head = get_fovea_head_model() fovea_head.cpu().eval() s = 128 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['dets', 'labels'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=200, pre_top_k=-1, keep_top_k=100, background_label_id=-1, )))) # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16), # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2). # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16), # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2) seed_everything(1234) cls_score = [ torch.rand(1, fovea_head.num_classes, pow(2, i), pow(2, i)) for i in range(5, 0, -1) ] seed_everything(5678) bboxes = [torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)] model_inputs = { 'cls_scores': cls_score, 'bbox_preds': bboxes, 'img_metas': img_metas } model_outputs = get_model_outputs(fovea_head, 'get_bboxes', model_inputs) # to get outputs of onnx model after rewrite img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel(fovea_head, 'get_bboxes', img_metas=img_metas) rewrite_inputs = { 'cls_scores': cls_score, 'bbox_preds': bboxes, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) if is_backend_output: if isinstance(rewrite_outputs, dict): rewrite_outputs = convert_to_list(rewrite_outputs, output_names) for model_output, rewrite_output in zip(model_outputs[0], rewrite_outputs): model_output = model_output.squeeze().cpu().numpy() rewrite_output = rewrite_output.squeeze() # hard code to make two tensors with the same shape # rewrite and original codes applied different nms strategy assert np.allclose( model_output[:rewrite_output.shape[0]], rewrite_output, rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None @pytest.mark.parametrize('backend_type', [Backend.OPENVINO]) def test_cascade_roi_head_with_mask(backend_type: Backend): check_backend(backend_type) cascade_roi_head = get_cascade_roi_head(is_instance_seg=True) seed_everything(1234) x = [ torch.rand((1, 64, 200, 304)), torch.rand((1, 64, 100, 152)), torch.rand((1, 64, 50, 76)), torch.rand((1, 64, 25, 38)), ] proposals = torch.tensor([[587.8285, 52.1405, 886.2484, 341.5644, 0.5]]) img_metas = { 'img_shape': torch.tensor([800, 1216]), 'ori_shape': torch.tensor([800, 1216]), 'scale_factor': torch.tensor([1, 1, 1, 1]) } output_names = ['bbox_results', 'segm_results'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=200, pre_top_k=-1, keep_top_k=100, background_label_id=-1)))) model_inputs = {'x': x, 'proposals': proposals.unsqueeze(0)} wrapped_model = WrapModel( cascade_roi_head, 'simple_test', img_metas=[img_metas]) backend_outputs, _ = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=model_inputs, deploy_cfg=deploy_cfg) bbox_results = backend_outputs[0] segm_results = backend_outputs[1] assert bbox_results is not None assert segm_results is not None def get_yolov3_head_model(): """yolov3 Head Config.""" test_cfg = mmcv.Config( dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, conf_thr=0.005, nms=dict(type='nms', iou_threshold=0.45), max_per_img=100)) from mmdet.models.dense_heads import YOLOV3Head model = YOLOV3Head( num_classes=4, in_channels=[16, 8, 4], out_channels=[32, 16, 8], test_cfg=test_cfg) model.requires_grad_(False) return model @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME, Backend.OPENVINO]) def test_yolov3_head_get_bboxes(backend_type): """Test get_bboxes rewrite of yolov3 head.""" check_backend(backend_type) yolov3_head = get_yolov3_head_model() yolov3_head.cpu().eval() s = 128 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['dets', 'labels'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', post_processing=dict( score_threshold=0.05, iou_threshold=0.45, confidence_threshold=0.005, max_output_boxes_per_class=200, pre_top_k=-1, keep_top_k=100, background_label_id=-1, )))) seed_everything(1234) pred_maps = [ torch.rand(1, 27, 5, 5), torch.rand(1, 27, 10, 10), torch.rand(1, 27, 20, 20) ] # to get outputs of pytorch model model_inputs = {'pred_maps': pred_maps, 'img_metas': img_metas} model_outputs = get_model_outputs(yolov3_head, 'get_bboxes', model_inputs) # to get outputs of onnx model after rewrite wrapped_model = WrapModel( yolov3_head, 'get_bboxes', img_metas=img_metas, with_nms=True) rewrite_inputs = { 'pred_maps': pred_maps, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) if is_backend_output: if isinstance(rewrite_outputs, dict): rewrite_outputs = convert_to_list(rewrite_outputs, output_names) for model_output, rewrite_output in zip(model_outputs[0], rewrite_outputs): model_output = model_output.squeeze().cpu().numpy() rewrite_output = rewrite_output.squeeze() # hard code to make two tensors with the same shape # rewrite and original codes applied different nms strategy assert np.allclose( model_output[:rewrite_output.shape[0]], rewrite_output, rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None def test_yolov3_head_get_bboxes_ncnn(): """Test get_bboxes rewrite of yolov3 head.""" backend_type = Backend.NCNN check_backend(backend_type) yolov3_head = get_yolov3_head_model() yolov3_head.cpu().eval() s = 128 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['detection_output'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', model_type='ncnn_end2end', task='ObjectDetection', post_processing=dict( score_threshold=0.05, iou_threshold=0.45, confidence_threshold=0.005, max_output_boxes_per_class=200, pre_top_k=-1, keep_top_k=10, background_label_id=-1, )))) seed_everything(1234) pred_maps = [ torch.rand(1, 27, 5, 5), torch.rand(1, 27, 10, 10), torch.rand(1, 27, 20, 20) ] # to get outputs of onnx model after rewrite wrapped_model = WrapModel( yolov3_head, 'get_bboxes', img_metas=img_metas[0], with_nms=True) rewrite_inputs = { 'pred_maps': pred_maps, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) # output should be of shape [1, N, 6] if is_backend_output: assert rewrite_outputs[0].shape[-1] == 6 else: assert rewrite_outputs.shape[-1] == 6 def get_yolox_head_model(): """YOLOX Head Config.""" test_cfg = mmcv.Config( dict( deploy_nms_pre=0, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)) from mmdet.models.dense_heads import YOLOXHead model = YOLOXHead(num_classes=4, in_channels=1, test_cfg=test_cfg) model.requires_grad_(False) return model @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME, Backend.OPENVINO]) def test_yolox_head_get_bboxes(backend_type: Backend): """Test get_bboxes rewrite of YOLOXHead.""" check_backend(backend_type) yolox_head = get_yolox_head_model() yolox_head.cpu().eval() s = 128 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['dets', 'labels'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=20, pre_top_k=-1, keep_top_k=10, background_label_id=-1, )))) seed_everything(1234) cls_scores = [ torch.rand(1, yolox_head.num_classes, pow(2, i), pow(2, i)) for i in range(3, 0, -1) ] seed_everything(5678) bbox_preds = [ torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(3, 0, -1) ] seed_everything(9101) objectnesses = [ torch.rand(1, 1, pow(2, i), pow(2, i)) for i in range(3, 0, -1) ] # to get outputs of pytorch model model_inputs = { 'cls_scores': cls_scores, 'bbox_preds': bbox_preds, 'objectnesses': objectnesses, 'img_metas': img_metas } model_outputs = get_model_outputs(yolox_head, 'get_bboxes', model_inputs) # to get outputs of onnx model after rewrite wrapped_model = WrapModel( yolox_head, 'get_bboxes', img_metas=img_metas, with_nms=True) rewrite_inputs = { 'cls_scores': cls_scores, 'bbox_preds': bbox_preds, 'objectnesses': objectnesses, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) if is_backend_output: if isinstance(rewrite_outputs, dict): rewrite_outputs = convert_to_list(rewrite_outputs, output_names) for model_output, rewrite_output in zip(model_outputs[0], rewrite_outputs): model_output = model_output.squeeze().cpu().numpy() rewrite_output = rewrite_output.squeeze().cpu().numpy() # hard code to make two tensors with the same shape # rewrite and original codes applied different nms strategy min_shape = min(model_output.shape[0], rewrite_output.shape[0], 5) assert np.allclose( model_output[:min_shape], rewrite_output[:min_shape], rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None def test_yolox_head_get_bboxes_ncnn(): """Test get_bboxes rewrite of yolox head for ncnn.""" backend_type = Backend.NCNN check_backend(backend_type) yolox_head = get_yolox_head_model() yolox_head.cpu().eval() s = 128 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['detection_output'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=20, pre_top_k=5000, keep_top_k=10, background_label_id=0, )))) seed_everything(1234) cls_scores = [ torch.rand(1, yolox_head.num_classes, pow(2, i), pow(2, i)) for i in range(3, 0, -1) ] seed_everything(5678) bbox_preds = [ torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(3, 0, -1) ] seed_everything(9101) objectnesses = [ torch.rand(1, 1, pow(2, i), pow(2, i)) for i in range(3, 0, -1) ] # to get outputs of onnx model after rewrite wrapped_model = WrapModel(yolox_head, 'get_bboxes', img_metas=img_metas) rewrite_inputs = { 'cls_scores': cls_scores, 'bbox_preds': bbox_preds, 'objectnesses': objectnesses, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) # output should be of shape [1, N, 6] if is_backend_output: assert rewrite_outputs[0].shape[-1] == 6 else: assert rewrite_outputs.shape[-1] == 6 def get_vfnet_head_model(): """VFNet Head Config.""" test_cfg = mmcv.Config( dict( deploy_nms_pre=0, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)) from mmdet.models.dense_heads import VFNetHead model = VFNetHead(num_classes=4, in_channels=1, test_cfg=test_cfg) model.requires_grad_(False) model.cpu().eval() return model @pytest.mark.parametrize('backend_type', [Backend.OPENVINO, Backend.ONNXRUNTIME]) def test_get_bboxes_of_vfnet_head(backend_type: Backend): """Test get_bboxes rewrite of VFNet head.""" check_backend(backend_type) vfnet_head = get_vfnet_head_model() vfnet_head.cpu().eval() s = 16 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['dets', 'labels'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=200, pre_top_k=-1, keep_top_k=100, background_label_id=-1, )))) seed_everything(1234) cls_score = [ torch.rand(1, vfnet_head.num_classes, pow(2, i), pow(2, i)) for i in range(5, 0, -1) ] seed_everything(5678) bboxes = [torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)] seed_everything(9101) model_inputs = { 'cls_scores': cls_score, 'bbox_preds': bboxes, 'img_metas': img_metas } model_outputs = get_model_outputs(vfnet_head, 'get_bboxes', model_inputs) img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( vfnet_head, 'get_bboxes', img_metas=img_metas, with_nms=True) rewrite_inputs = {'cls_scores': cls_score, 'bbox_preds': bboxes} rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) if is_backend_output: if isinstance(rewrite_outputs, dict): rewrite_outputs = convert_to_list(rewrite_outputs, output_names) for model_output, rewrite_output in zip(model_outputs[0], rewrite_outputs): model_output = model_output.squeeze().cpu().numpy() rewrite_output = rewrite_output.squeeze() min_shape = min(model_output.shape[0], rewrite_output.shape[0]) assert np.allclose( model_output[:min_shape], rewrite_output[:min_shape], rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None def get_deploy_cfg(backend_type: Backend, ir_type: str): return mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict( type=ir_type, output_names=['dets', 'labels'], input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=200, pre_top_k=5000, keep_top_k=100, background_label_id=-1, )))) @pytest.mark.parametrize('backend_type, ir_type', [(Backend.ONNXRUNTIME, 'onnx'), (Backend.OPENVINO, 'onnx'), (Backend.TORCHSCRIPT, 'torchscript')]) def test_base_dense_head_get_bboxes(backend_type: Backend, ir_type: str): """Test get_bboxes rewrite of base dense head.""" check_backend(backend_type) anchor_head = get_anchor_head_model() anchor_head.cpu().eval() s = 128 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] deploy_cfg = get_deploy_cfg(backend_type, ir_type) output_names = get_ir_config(deploy_cfg).get('output_names', None) # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16), # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2). # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16), # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2) seed_everything(1234) cls_score = [ torch.rand(1, 36, pow(2, i), pow(2, i)) for i in range(5, 0, -1) ] seed_everything(5678) bboxes = [torch.rand(1, 36, pow(2, i), pow(2, i)) for i in range(5, 0, -1)] # to get outputs of pytorch model model_inputs = { 'cls_scores': cls_score, 'bbox_preds': bboxes, 'img_metas': img_metas } model_outputs = get_model_outputs(anchor_head, 'get_bboxes', model_inputs) # to get outputs of onnx model after rewrite img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( anchor_head, 'get_bboxes', img_metas=img_metas, with_nms=True) rewrite_inputs = { 'cls_scores': cls_score, 'bbox_preds': bboxes, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) if is_backend_output: if isinstance(rewrite_outputs, dict): rewrite_outputs = convert_to_list(rewrite_outputs, output_names) for model_output, rewrite_output in zip(model_outputs[0], rewrite_outputs): model_output = model_output.squeeze().cpu().numpy() rewrite_output = rewrite_output.squeeze() # hard code to make two tensors with the same shape # rewrite and original codes applied different nms strategy assert np.allclose( model_output[:rewrite_output.shape[0]], rewrite_output, rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None def test_base_dense_head_get_bboxes__ncnn(): """Test get_bboxes rewrite of base dense head.""" backend_type = Backend.NCNN check_backend(backend_type) anchor_head = get_anchor_head_model() anchor_head.cpu().eval() s = 128 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['output'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict(output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', model_type='ncnn_end2end', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=200, pre_top_k=5000, keep_top_k=100, background_label_id=-1, )))) # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16), # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2). # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16), # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2) seed_everything(1234) cls_score = [ torch.rand(1, 36, pow(2, i), pow(2, i)) for i in range(5, 0, -1) ] seed_everything(5678) bboxes = [torch.rand(1, 36, pow(2, i), pow(2, i)) for i in range(5, 0, -1)] # to get outputs of onnx model after rewrite img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( anchor_head, 'get_bboxes', img_metas=img_metas, with_nms=True) rewrite_inputs = { 'cls_scores': cls_score, 'bbox_preds': bboxes, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) # output should be of shape [1, N, 6] if is_backend_output: rewrite_outputs = rewrite_outputs[0] assert rewrite_outputs.shape[-1] == 6 @pytest.mark.parametrize('is_dynamic', [True, False]) def test_ssd_head_get_bboxes__ncnn(is_dynamic: bool): """Test get_bboxes rewrite of ssd head for ncnn.""" check_backend(Backend.NCNN) ssd_head = get_ssd_head_model() ssd_head.cpu().eval() s = 128 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['output'] input_names = [] for i in range(6): input_names.append('cls_scores_' + str(i)) input_names.append('bbox_preds_' + str(i)) dynamic_axes = None if is_dynamic: dynamic_axes = { output_names[0]: { 1: 'num_dets', } } for input_name in input_names: dynamic_axes[input_name] = {2: 'height', 3: 'width'} deploy_cfg = mmcv.Config( dict( backend_config=dict(type=Backend.NCNN.value), onnx_config=dict( input_names=input_names, output_names=output_names, input_shape=None, dynamic_axes=dynamic_axes), codebase_config=dict( type='mmdet', task='ObjectDetection', model_type='ncnn_end2end', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=200, pre_top_k=5000, keep_top_k=100, background_label_id=-1, )))) # For the ssd_head: # the cls_score's size: (1, 30, 20, 20), (1, 30, 10, 10), # (1, 30, 5, 5), (1, 30, 3, 3), (1, 30, 2, 2), (1, 30, 1, 1) # the bboxes's size: (1, 24, 20, 20), (1, 24, 10, 10), # (1, 24, 5, 5), (1, 24, 3, 3), (1, 24, 2, 2), (1, 24, 1, 1) feat_shape = [20, 10, 5, 3, 2, 1] num_prior = 6 seed_everything(1234) cls_score = [ torch.rand(1, 30, feat_shape[i], feat_shape[i]) for i in range(num_prior) ] seed_everything(5678) bboxes = [ torch.rand(1, 24, feat_shape[i], feat_shape[i]) for i in range(num_prior) ] # to get outputs of onnx model after rewrite img_metas[0]['img_shape'] = torch.tensor([s, s]) if is_dynamic else [s, s] wrapped_model = WrapModel( ssd_head, 'get_bboxes', img_metas=img_metas, with_nms=True) rewrite_inputs = { 'cls_scores': cls_score, 'bbox_preds': bboxes, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) # output should be of shape [1, N, 6] if is_backend_output: rewrite_outputs = rewrite_outputs[0] assert rewrite_outputs.shape[-1] == 6 @pytest.mark.parametrize('backend_type, ir_type', [(Backend.OPENVINO, 'onnx')]) def test_reppoints_head_get_bboxes(backend_type: Backend, ir_type: str): """Test get_bboxes rewrite of base dense head.""" check_backend(backend_type) dense_head = get_reppoints_head_model() dense_head.cpu().eval() s = 128 img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] deploy_cfg = get_deploy_cfg(backend_type, ir_type) output_names = get_ir_config(deploy_cfg).get('output_names', None) # the cls_score's size: (1, 4, 32, 32), (1, 4, 16, 16), # (1, 4, 8, 8), (1, 4, 4, 4), (1, 4, 2, 2). # the bboxes's size: (1, 4, 32, 32), (1, 4, 16, 16), # (1, 4, 8, 8), (1, 4, 4, 4), (1, 4, 2, 2) seed_everything(1234) cls_score = [ torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1) ] seed_everything(5678) bboxes = [torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)] # to get outputs of pytorch model model_inputs = { 'cls_scores': cls_score, 'bbox_preds': bboxes, 'img_metas': img_metas } model_outputs = get_model_outputs(dense_head, 'get_bboxes', model_inputs) # to get outputs of onnx model after rewrite img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( dense_head, 'get_bboxes', img_metas=img_metas, with_nms=True) rewrite_inputs = { 'cls_scores': cls_score, 'bbox_preds': bboxes, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) if is_backend_output: if isinstance(rewrite_outputs, dict): rewrite_outputs = convert_to_list(rewrite_outputs, output_names) for model_output, rewrite_output in zip(model_outputs[0], rewrite_outputs): model_output = model_output.squeeze().cpu().numpy() rewrite_output = rewrite_output.squeeze() # hard code to make two tensors with the same shape # rewrite and original codes applied different nms strategy assert np.allclose( model_output[:rewrite_output.shape[0]], rewrite_output, rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None @pytest.mark.parametrize('backend_type, ir_type', [(Backend.OPENVINO, 'onnx')]) def test_reppoints_head_points2bbox(backend_type: Backend, ir_type: str): """Test get_bboxes rewrite of base dense head.""" check_backend(backend_type) dense_head = get_reppoints_head_model() dense_head.cpu().eval() output_names = ['output'] deploy_cfg = mmcv.Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict( input_shape=None, input_names=['pts'], output_names=output_names))) # the cls_score's size: (1, 4, 32, 32), (1, 4, 16, 16), # (1, 4, 8, 8), (1, 4, 4, 4), (1, 4, 2, 2). # the bboxes's size: (1, 4, 32, 32), (1, 4, 16, 16), # (1, 4, 8, 8), (1, 4, 4, 4), (1, 4, 2, 2) seed_everything(1234) pts = torch.rand(1, 18, 16, 16) # to get outputs of onnx model after rewrite wrapped_model = WrapModel(dense_head, 'points2bbox', y_first=True) rewrite_inputs = {'pts': pts} _ = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) @pytest.mark.skipif( reason='Only support GPU test', condition=not torch.cuda.is_available()) @pytest.mark.parametrize('backend_type', [(Backend.TENSORRT)]) def test_windows_msa(backend_type: Backend): check_backend(backend_type) from mmdet.models.backbones.swin import WindowMSA model = WindowMSA(96, 3, (7, 7)) model.cuda().eval() output_names = ['output'] deploy_cfg = mmcv.Config( dict( backend_config=dict( type=backend_type.value, common_config=dict(fp16_mode=True, max_workspace_size=1 << 20), model_inputs=[ dict( input_shapes=dict( x=dict( min_shape=[12, 49, 96], opt_shape=[12, 49, 96], max_shape=[12, 49, 96]), mask=dict( min_shape=[12, 49, 49], opt_shape=[12, 49, 49], max_shape=[12, 49, 49]))) ]), onnx_config=dict( input_shape=None, input_names=['x', 'mask'], output_names=output_names))) x = torch.randn([12, 49, 96]).cuda() mask = torch.randn([12, 49, 49]).cuda() wrapped_model = WrapModel(model, 'forward') rewrite_inputs = {'x': x, 'mask': mask} _ = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) @pytest.mark.skipif( reason='Only support GPU test', condition=not torch.cuda.is_available()) @pytest.mark.parametrize('backend_type', [(Backend.TENSORRT)]) def test_shift_windows_msa(backend_type: Backend): check_backend(backend_type) from mmdet.models.backbones.swin import ShiftWindowMSA model = ShiftWindowMSA(96, 3, 7) model.cuda().eval() output_names = ['output'] deploy_cfg = mmcv.Config( dict( backend_config=dict( type=backend_type.value, model_inputs=[ dict( input_shapes=dict( query=dict( min_shape=[1, 60800, 96], opt_shape=[1, 60800, 96], max_shape=[1, 60800, 96]))) ]), onnx_config=dict( input_shape=None, input_names=['query'], output_names=output_names))) query = torch.randn([1, 60800, 96]).cuda() hw_shape = (torch.tensor(200), torch.tensor(304)) wrapped_model = WrapModel(model, 'forward') rewrite_inputs = {'query': query, 'hw_shape': hw_shape} _ = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg, run_with_backend=False)