# Copyright (c) OpenMMLab. All rights reserved. import copy import os import random import tempfile from typing import Dict, List import mmengine import numpy as np import pytest import torch from packaging import version try: from torch.testing import assert_close as torch_assert_close except Exception: from torch.testing import assert_allclose as torch_assert_close from mmengine import Config from mmengine.config import ConfigDict from mmdeploy.codebase import import_codebase from mmdeploy.core.rewriters.rewriter_manager import RewriterContext from mmdeploy.utils import Backend, Codebase from mmdeploy.utils.test import (WrapFunction, WrapModel, backend_checker, check_backend, get_model_outputs, get_onnx_model, get_rewrite_outputs) try: import_codebase(Codebase.MMDET) except ImportError: pytest.skip(f'{Codebase.MMDET} is not installed.', allow_module_level=True) @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME]) @pytest.mark.parametrize('add_ctr_clamp', [True, False]) @pytest.mark.parametrize('clip_border,max_shape', [(False, None), (True, torch.tensor([100, 200]))]) def test_delta2bbox(backend_type: Backend, add_ctr_clamp: bool, clip_border: bool, max_shape: tuple): check_backend(backend_type) deploy_cfg = Config( dict( onnx_config=dict(output_names=None, input_shape=None), backend_config=dict(type=backend_type.value, model_inputs=None), codebase_config=dict(type='mmdet', task='ObjectDetection'))) # wrap function to enable rewrite def delta2bbox(*args, **kwargs): import mmdet return mmdet.models.task_modules.coders.delta_xywh_bbox_coder. \ delta2bbox(*args, **kwargs) rois = torch.rand(5, 4) deltas = torch.rand(5, 4) original_outputs = delta2bbox(rois, deltas, add_ctr_clamp=add_ctr_clamp) # wrap function to nn.Module, enable torch.onnx.export wrapped_func = WrapFunction(delta2bbox, add_ctr_clamp=add_ctr_clamp) rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_func, model_inputs={ 'rois': rois.unsqueeze(0), 'deltas': deltas.unsqueeze(0) }, deploy_cfg=deploy_cfg) if is_backend_output: model_output = original_outputs.squeeze().cpu().numpy() rewrite_output = rewrite_outputs[0].squeeze().cpu().numpy() assert np.allclose( model_output, rewrite_output, rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME]) def test_tblr2bbox(backend_type: Backend): check_backend(backend_type) deploy_cfg = Config( dict( onnx_config=dict(output_names=None, input_shape=None), backend_config=dict(type=backend_type.value, model_inputs=None), codebase_config=dict(type='mmdet', task='ObjectDetection'))) # wrap function to enable rewrite def tblr2bboxes(*args, **kwargs): import mmdet return mmdet.models.task_modules.coders.tblr_bbox_coder.tblr2bboxes( *args, **kwargs) priors = torch.rand(1, 5, 4) tblr = torch.rand(1, 5, 4) original_outputs = tblr2bboxes(priors, tblr) # wrap function to nn.Module, enable torch.onnx.export wrapped_func = WrapFunction(tblr2bboxes) rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_func, model_inputs={ 'priors': priors, 'tblr': tblr }, deploy_cfg=deploy_cfg) if is_backend_output: model_output = original_outputs.squeeze().cpu().numpy() rewrite_output = rewrite_outputs[0].squeeze() assert np.allclose( model_output, rewrite_output, rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME]) def test__distancepointbboxcoder__decode(backend_type: Backend): check_backend(backend_type) deploy_cfg = Config( dict( onnx_config=dict(output_names=None, input_shape=None), backend_config=dict(type=backend_type.value, model_inputs=None), codebase_config=dict(type='mmdet', task='ObjectDetection'))) from mmdet.models.task_modules.coders.distance_point_bbox_coder import \ DistancePointBBoxCoder coder = DistancePointBBoxCoder() # wrap function to enable rewrite wrapped_model = WrapModel(coder, 'decode') points = torch.rand(3, 2) pred_bboxes = torch.rand(3, 4) original_outputs = coder.decode(points, pred_bboxes) rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs={ 'points': points, 'pred_bboxes': pred_bboxes }, deploy_cfg=deploy_cfg) if is_backend_output: model_output = original_outputs.squeeze().cpu().numpy() rewrite_output = rewrite_outputs[0].squeeze() assert np.allclose( model_output, rewrite_output, rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None @backend_checker(Backend.ONNXRUNTIME) @pytest.mark.parametrize('pre_top_k', [-1, 1000]) def test_multiclass_nms_with_keep_top_k(pre_top_k): backend_type = 'onnxruntime' from mmdeploy.mmcv.ops import multiclass_nms max_output_boxes_per_class = 20 keep_top_k = 15 deploy_cfg = Config( dict( onnx_config=dict( output_names=None, input_shape=None, dynamic_axes=dict( boxes={ 0: 'batch_size', 1: 'num_boxes' }, scores={ 0: 'batch_size', 1: 'num_boxes', 2: 'num_classes' }, ), ), backend_config=dict(type=backend_type), codebase_config=dict( type='mmdet', task='ObjectDetection', post_processing=dict( score_threshold=0.05, iou_threshold=0.5, max_output_boxes_per_class=max_output_boxes_per_class, pre_top_k=pre_top_k, keep_top_k=keep_top_k, background_label_id=-1, )))) num_classes = 5 num_boxes = 2 batch_size = 1 export_boxes = torch.rand(batch_size, num_boxes, 4) export_scores = torch.ones(batch_size, num_boxes, num_classes) model_inputs = {'boxes': export_boxes, 'scores': export_scores} wrapped_func = WrapFunction( multiclass_nms, nms_type='nms', max_output_boxes_per_class=max_output_boxes_per_class, keep_top_k=keep_top_k) onnx_model_path = get_onnx_model( wrapped_func, model_inputs=model_inputs, deploy_cfg=deploy_cfg) num_boxes = 100 test_boxes = torch.rand(batch_size, num_boxes, 4) test_scores = torch.ones(batch_size, num_boxes, num_classes) model_inputs = {'boxes': test_boxes, 'scores': test_scores} import mmdeploy.backend.onnxruntime as ort_apis backend_model = ort_apis.ORTWrapper(onnx_model_path, 'cpu', None) output = backend_model.forward(model_inputs) output = backend_model.output_to_list(output) dets = output[0] # Subtract 1 dim since we pad the tensors assert dets.shape[1] - 1 < keep_top_k, \ 'multiclass_nms returned more values than "keep_top_k"\n' \ f'dets.shape: {dets.shape}\n' \ f'keep_top_k: {keep_top_k}' @backend_checker(Backend.TENSORRT) def test__anchorgenerator__single_level_grid_priors(): backend_type = 'tensorrt' import onnx from mmdet.models.task_modules.prior_generators.anchor_generator import \ AnchorGenerator import mmdeploy.codebase.mmdet.models.task_modules.prior_generators.anchor # noqa from mmdeploy.apis.onnx import export generator = AnchorGenerator( scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4]) def single_level_grid_priors(input): return generator.single_level_grid_priors(input.shape[2:], 0, input.dtype, input.device) x = torch.rand(1, 3, 4, 4) wrapped_func = WrapFunction(single_level_grid_priors) output = wrapped_func(x) # test forward with RewriterContext({}, backend_type): wrap_output = wrapped_func(x) torch_assert_close(output, wrap_output) onnx_prefix = tempfile.NamedTemporaryFile().name export( wrapped_func, x, onnx_prefix, backend=backend_type, input_names=['input'], output_names=['output'], dynamic_axes=dict(input={ 2: 'h', 3: 'w' })) onnx_model = onnx.load(onnx_prefix + '.onnx') find_trt_grid_priors = False for n in onnx_model.graph.node: if n.op_type == 'GridPriorsTRT': find_trt_grid_priors = True assert find_trt_grid_priors 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 = 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 = 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_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 = 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 = 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_detrhead_model(): """DETR head Config.""" from mmdet.registry import MODELS model = MODELS.build( dict( type='DETRHead', num_classes=4, embed_dims=4, loss_cls=dict( type='CrossEntropyLoss', bg_cls_weight=0.1, use_sigmoid=False, loss_weight=1.0, class_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=5.0), loss_iou=dict(type='GIoULoss', loss_weight=2.0))) 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 = 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 = 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_assert_close( 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 = 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) @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME, Backend.NCNN]) def test_predict_by_feat_of_rpn_head(backend_type: Backend): check_backend(backend_type) head = get_rpn_head_model() head.cpu().eval() s = 4 batch_img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['dets'] deploy_cfg = 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 batch_img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( head, 'predict_by_feat', batch_img_metas=batch_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_predict_by_feat_of_gfl_head(backend_type): check_backend(backend_type) head = get_gfl_head_model() head.cpu().eval() s = 4 batch_img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['dets'] deploy_cfg = 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 batch_img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( head, 'predict_by_feat', batch_img_metas=batch_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 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 = 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, export_postprocess_mask=False, )))) model_cfg = Config(dict(model=mmengine.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, device='cpu', palette='coco') img = torch.randn(1, 3, 64, 64) from mmdet.structures import DetDataSample data_sample = DetDataSample(metainfo=dict(img_shape=(800, 1216, 3))) rewrite_inputs = {'batch_inputs': img} wrapped_model = WrapModel(model, 'forward', data_samples=[data_sample]) rewrite_outputs, _ = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) assert rewrite_outputs is not None @pytest.mark.parametrize('backend', [Backend.ONNXRUNTIME]) @pytest.mark.skipif( reason='mha only support torch greater than 1.10.0', condition=version.parse(torch.__version__) < version.parse('1.10.0')) @pytest.mark.parametrize( 'model_cfg_path', ['tests/test_codebase/test_mmdet/data/detr_model.json']) def test_predict_of_detr_detector(model_cfg_path, backend): # Skip test when torch.__version__ < 1.10.0 # See https://github.com/open-mmlab/mmdeploy/discussions/1434 check_backend(backend) deploy_cfg = 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, export_postprocess_mask=False, )))) model_cfg = Config(dict(model=mmengine.load(model_cfg_path))) from mmdet.apis import init_detector model = init_detector(model_cfg, None, device='cpu', palette='coco') img = torch.randn(1, 3, 64, 64) from mmdet.structures import DetDataSample data_sample = DetDataSample(metainfo=dict(batch_input_shape=(64, 64))) rewrite_inputs = {'batch_inputs': img} wrapped_model = WrapModel(model, 'predict', data_samples=[data_sample]) rewrite_outputs, _ = get_rewrite_outputs( wrapped_model=wrapped_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 = 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 test_single_roi_extractor__ascend(): check_backend(Backend.ASCEND) # create wrap function from mmdeploy.utils.test import WrapFunction single_roi_extractor = get_single_roi_extractor() out_channels = single_roi_extractor.out_channels def single_roi_extractor_func(feat0, feat1, feat2, feat3, rois): return single_roi_extractor([feat0, feat1, feat2, feat3], rois) single_roi_extractor_wrapper = WrapFunction(single_roi_extractor_func) # generate data seed_everything(1234) 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]]) # create config input_names = ['feat0', 'feat1', 'feat2', 'feat3', 'rois'] output_names = ['roi_feat'] model_inputs = dict(zip(input_names, feats + [rois])) deploy_cfg = mmengine.Config( dict( backend_config=dict( type=Backend.ASCEND.value, model_inputs=[ dict( input_shapes=dict( feat0=feats[0].shape, feat1=feats[1].shape, feat2=feats[2].shape, feat3=feats[3].shape, rois=rois.shape)) ]), onnx_config=dict( input_names=input_names, output_names=output_names, input_shape=None), codebase_config=dict( type='mmdet', task='ObjectDetection', ))) # get torch output model_outputs = get_model_outputs(single_roi_extractor_wrapper, 'forward', model_inputs) # get backend output backend_outputs, _ = get_rewrite_outputs( wrapped_model=single_roi_extractor_wrapper, 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 model_output.shape == backend_output.shape 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': 1, 'sampling_ratio': 0 }, 'out_channels': 4, 'featmap_strides': [4, 8, 16, 32] } mask_head = { 'type': 'FCNMaskHead', 'num_convs': 4, 'in_channels': 4, 'conv_out_channels': 4, 'num_classes': 80, 'loss_mask': { 'type': 'CrossEntropyLoss', 'use_mask': True, 'loss_weight': 1.0 } } test_cfg = Config( dict( score_thr=0.05, nms=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]]]) batch_img_metas = { 'img_shape': torch.tensor([800, 1216]), 'ori_shape': torch.tensor([800, 1216]), 'scale_factor': torch.tensor([1, 1, 1, 1]) } output_names = ['results'] deploy_cfg = 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)))) rcnn_test_cfg = ConfigDict( dict( score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)) model_inputs = {'x': x, 'rpn_results_list': [proposals]} wrapped_model = WrapModel( cascade_roi_head, 'predict_bbox', batch_img_metas=[batch_img_metas], # rpn_results_list=[proposals], rcnn_test_cfg=rcnn_test_cfg) 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 = 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_predict_by_feat_of_fovea_head(backend_type: Backend): check_backend(backend_type) fovea_head = get_fovea_head_model() fovea_head.cpu().eval() s = 128 batch_img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['dets', 'labels'] deploy_cfg = 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, 'batch_img_metas': batch_img_metas } model_outputs = get_model_outputs(fovea_head, 'predict_by_feat', model_inputs) # to get outputs of onnx model after rewrite batch_img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( fovea_head, 'predict_by_feat', batch_img_metas=batch_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: for i in range(len(model_outputs)): assert np.allclose( model_outputs[i].bboxes, rewrite_outputs[0][i, :, :4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].scores, rewrite_outputs[0][i, :, 4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].labels, rewrite_outputs[1][i], 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, 4, 200, 304)), torch.rand((1, 4, 100, 152)), torch.rand((1, 4, 50, 76)), torch.rand((1, 4, 25, 38)), ] proposals = [ torch.tensor([[[587.8285, 52.1405, 886.2484, 341.5644, 0.5]]]), torch.tensor([[[0]]], dtype=torch.long) ] batch_img_metas = { 'img_shape': torch.tensor([800, 1216]), 'ori_shape': torch.tensor([800, 1216]), 'scale_factor': torch.tensor([1, 1, 1, 1]) } output_names = ['dets', 'labels', 'masks'] deploy_cfg = 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, export_postprocess_mask=False)))) model_inputs = {'x': x} wrapped_model = WrapModel( cascade_roi_head, 'predict_mask', batch_img_metas=[batch_img_metas], results_list=proposals) backend_outputs, _ = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=model_inputs, deploy_cfg=deploy_cfg) dets = backend_outputs[0] labels = backend_outputs[1] masks = backend_outputs[2] assert dets is not None assert labels is not None assert masks is not None def get_yolov3_head_model(): """yolov3 Head Config.""" test_cfg = 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=10)) 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_predict_by_feat(backend_type): """Test predict_by_feat rewrite of yolov3 head.""" check_backend(backend_type) yolov3_head = get_yolov3_head_model() yolov3_head.cpu().eval() s = 128 batch_img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['dets', 'labels'] deploy_cfg = 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, 'batch_img_metas': batch_img_metas} model_outputs = get_model_outputs(yolov3_head, 'predict_by_feat', model_inputs) # to get outputs of onnx model after rewrite wrapped_model = WrapModel( yolov3_head, 'predict_by_feat', batch_img_metas=batch_img_metas) 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: for i in range(len(model_outputs)): assert np.allclose( model_outputs[i].bboxes, rewrite_outputs[0][i, :, :4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].scores, rewrite_outputs[0][i, :, 4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].labels, rewrite_outputs[1][i], rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None def test_yolov3_head_predict_by_feat_ncnn(): """Test predict_by_feat rewrite of yolov3 head.""" backend_type = Backend.NCNN check_backend(backend_type) yolov3_head = get_yolov3_head_model() yolov3_head.cpu().eval() s = 128 batch_img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['detection_output'] deploy_cfg = 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, 'predict_by_feat', batch_img_metas=batch_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_centernet_head_model(): """CenterNet Head Config.""" test_cfg = Config(dict(topk=100, local_maximum_kernel=3, max_per_img=100)) from mmdet.models.dense_heads import CenterNetHead model = CenterNetHead(8, 8, 4, test_cfg=test_cfg) model.requires_grad_(False) return model @pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME]) def test_centernet_head_predict_by_feat(backend_type: Backend): """Test predict_by_feat rewrite of CenterNetHead.""" check_backend(backend_type) centernet_head = get_centernet_head_model() centernet_head.cpu().eval() s = 128 batch_img_metas = [{ 'border': np.array([11., 99., 11., 99.], dtype=np.float32), 'img_shape': (s, s), 'batch_input_shape': (s, s) }] output_names = ['dets', 'labels'] deploy_cfg = 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) center_heatmap_preds = [ torch.rand(1, centernet_head.num_classes, s // 4, s // 4) ] seed_everything(5678) wh_preds = [torch.rand(1, 2, s // 4, s // 4)] seed_everything(9101) offset_preds = [torch.rand(1, 2, s // 4, s // 4)] # to get outputs of pytorch model model_inputs = { 'center_heatmap_preds': center_heatmap_preds, 'wh_preds': wh_preds, 'offset_preds': offset_preds, 'batch_img_metas': batch_img_metas, 'with_nms': False } model_outputs = get_model_outputs(centernet_head, 'predict_by_feat', model_inputs) # to get outputs of onnx model after rewrite wrapped_model = WrapModel( centernet_head, 'predict_by_feat', batch_img_metas=batch_img_metas) rewrite_inputs = { 'center_heatmap_preds': center_heatmap_preds, 'wh_preds': wh_preds, 'offset_preds': offset_preds, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) if is_backend_output: # hard code to make two tensors with the same shape # rewrite and original codes applied different nms strategy min_shape = min(model_outputs[0].bboxes.shape[0], rewrite_outputs[0].shape[1], 5) for i in range(len(model_outputs)): border = batch_img_metas[i]['border'] rewrite_outputs[0][i, :, 0] -= border[2] rewrite_outputs[0][i, :, 1] -= border[0] rewrite_outputs[0][i, :, 2] -= border[2] rewrite_outputs[0][i, :, 3] -= border[0] assert np.allclose( model_outputs[i].bboxes[:min_shape], rewrite_outputs[0][i, :min_shape, :4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].scores[:min_shape], rewrite_outputs[0][i, :min_shape, 4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].labels[:min_shape], rewrite_outputs[1][i, :min_shape], rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None def get_yolox_head_model(): """YOLOX Head Config.""" test_cfg = 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_predict_by_feat(backend_type: Backend): """Test predict_by_feat rewrite of YOLOXHead.""" check_backend(backend_type) yolox_head = get_yolox_head_model() yolox_head.cpu().eval() s = 128 batch_img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['dets', 'labels'] deploy_cfg = 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, 2 * pow(2, i), 2 * pow(2, i)) for i in range(3, 0, -1) ] seed_everything(5678) bbox_preds = [ torch.rand(1, 4, 2 * pow(2, i), 2 * pow(2, i)) for i in range(3, 0, -1) ] seed_everything(9101) objectnesses = [ torch.rand(1, 1, 2 * pow(2, i), 2 * 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, 'batch_img_metas': batch_img_metas, 'with_nms': True } model_outputs = get_model_outputs(yolox_head, 'predict_by_feat', model_inputs) # to get outputs of onnx model after rewrite wrapped_model = WrapModel( yolox_head, 'predict_by_feat', batch_img_metas=batch_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: # hard code to make two tensors with the same shape # rewrite and original codes applied different nms strategy min_shape = min(model_outputs[0].bboxes.shape[0], rewrite_outputs[0].shape[1], 5) for i in range(len(model_outputs)): assert np.allclose( model_outputs[i].bboxes[:min_shape], rewrite_outputs[0][i, :min_shape, :4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].scores[:min_shape], rewrite_outputs[0][i, :min_shape, 4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].labels[:min_shape], rewrite_outputs[1][i, :min_shape], rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None def test_yolox_head_predict_by_feat_ncnn(): """Test predict_by_feat 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 batch_img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['detection_output'] deploy_cfg = 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, 'predict_by_feat', batch_img_metas=batch_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_deploy_cfg(backend_type: Backend, ir_type: str): return 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_predict_by_feat(backend_type: Backend, ir_type: str): """Test predict_by_feat rewrite of base dense head.""" check_backend(backend_type) anchor_head = get_anchor_head_model() anchor_head.cpu().eval() s = 128 batch_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) # 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, 'batch_img_metas': batch_img_metas } model_outputs = get_model_outputs(anchor_head, 'predict_by_feat', model_inputs) # to get outputs of onnx model after rewrite batch_img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( anchor_head, 'predict_by_feat', batch_img_metas=batch_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: # hard code to make two tensors with the same shape # rewrite and original codes applied different topk strategy # rewrite and original codes applied different nms strategy min_shape = min(model_outputs[0].bboxes.shape[0], rewrite_outputs[0].shape[1], 5) for i in range(len(model_outputs)): assert np.allclose( model_outputs[i].bboxes[:min_shape], rewrite_outputs[0][i, :min_shape, :4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].scores[:min_shape], rewrite_outputs[0][i, :min_shape, 4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].labels[:min_shape], rewrite_outputs[1][i, :min_shape], rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None def test_base_dense_head_predict_by_feat__ncnn(): """Test predict_by_feat 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 batch_img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['output'] deploy_cfg = 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 batch_img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( anchor_head, 'predict_by_feat', batch_img_metas=batch_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 @backend_checker(Backend.RKNN) def test_base_dense_head_get_bboxes__rknn(): """Test get_bboxes rewrite of ssd head for rknn.""" 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 deploy_cfg = mmengine.Config( dict( backend_config=dict(type=Backend.RKNN.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='rknn', 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'] = [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, run_with_backend=False) # output should be of shape [1, N, 4] assert rewrite_outputs[0].shape[-1] == 4 @pytest.mark.parametrize('backend_type, ir_type', [(Backend.OPENVINO, 'onnx')]) def test_reppoints_head_predict_by_feat(backend_type: Backend, ir_type: str): """Test predict_by_feat rewrite of base dense head.""" check_backend(backend_type) dense_head = get_reppoints_head_model() dense_head.cpu().eval() s = 128 batch_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) # 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, 'batch_img_metas': batch_img_metas } model_outputs = get_model_outputs(dense_head, 'predict_by_feat', model_inputs) # to get outputs of onnx model after rewrite batch_img_metas[0]['img_shape'] = torch.Tensor([s, s]) wrapped_model = WrapModel( dense_head, 'predict_by_feat', batch_img_metas=batch_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: # hard code to make two tensors with the same shape # rewrite and original codes applied different topk strategy # rewrite and original codes applied different nms strategy min_shape = min(model_outputs[0].bboxes.shape[0], rewrite_outputs[0].shape[1], 5) for i in range(len(model_outputs)): assert np.allclose( model_outputs[i].bboxes[:min_shape], rewrite_outputs[0][i, :min_shape, :4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].scores[:min_shape], rewrite_outputs[0][i, :min_shape, 4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].labels[:min_shape], rewrite_outputs[1][i, :min_shape], 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 predict_by_feat rewrite of base dense head.""" check_backend(backend_type) dense_head = get_reppoints_head_model() dense_head.cpu().eval() output_names = ['output'] deploy_cfg = 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 = 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 = 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) @pytest.mark.skipif( reason='Only support GPU test', condition=not torch.cuda.is_available()) @pytest.mark.parametrize('backend_type', [(Backend.TENSORRT)]) def test_mlvl_point_generator__single_level_grid_priors__tensorrt( backend_type: Backend): check_backend(backend_type) from mmdet.models.task_modules.prior_generators import MlvlPointGenerator model = MlvlPointGenerator([8, 16, 32]) output_names = ['output'] deploy_cfg = Config( dict( backend_config=dict(type=backend_type.value), onnx_config=dict( input_shape=None, input_names=['featmap_size', 'level_idx'], output_names=output_names))) featmap_size = torch.tensor([80, 80]) with_stride = True wrapped_model = WrapModel( model, 'single_level_grid_priors', with_stride=with_stride) rewrite_inputs = { 'featmap_size': featmap_size, 'level_idx': torch.tensor(0) } _ = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg, run_with_backend=False) @pytest.mark.parametrize('backend_type, ir_type', [(Backend.ONNXRUNTIME, 'onnx')]) def test_detrhead__predict_by_feat(backend_type: Backend, ir_type: str): """Test predict_by_feat rewrite of detr head.""" check_backend(backend_type) dense_head = get_detrhead_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) seed_everything(1234) cls_score = [torch.rand(1, 100, 5) for i in range(5, 0, -1)] seed_everything(5678) bboxes = [torch.rand(1, 100, 4) 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( dense_head, 'predict_by_feat', batch_img_metas=img_metas) rewrite_inputs = { 'all_cls_scores_list': cls_score, 'all_bbox_preds_list': bboxes, } rewrite_outputs, _ = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg, run_with_backend=False) assert rewrite_outputs is not None def get_solo_head_model(): test_cfg = Config( dict( nms_pre=500, score_thr=0.1, mask_thr=0.5, filter_thr=0.05, kernel='gaussian', # gaussian/linear sigma=2.0, max_per_img=100)) from mmdet.models.dense_heads import SOLOHead model = SOLOHead(4, 32, feat_channels=32, test_cfg=test_cfg) model.requires_grad_(False) return model @pytest.mark.parametrize('backend_type', [Backend.OPENVINO]) def test_solo_head_predict_by_feat(backend_type: Backend): """Test predict_by_feat rewrite of solo head.""" check_backend(backend_type) solo_head = get_solo_head_model() s = 128 solo_head.cpu().eval() batch_img_metas = [{'img_shape': (s, s, 3), 'ori_shape': (s, s, 3)}] output_names = ['dets', 'labels', 'masks'] deploy_cfg = 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, export_postprocess_mask=True)))) seed_everything(1234) num_grids = [24, 20, 16, 12, 8] mask_preds = [ torch.rand(1, num_grid**2, s // 4, s // 4) for num_grid in num_grids ] seed_everything(5678) cls_scores = [ torch.rand(1, solo_head.num_classes, num_grid, num_grid) for num_grid in num_grids ] # to get outputs of pytorch model model_inputs = { 'mlvl_mask_preds': mask_preds, 'mlvl_cls_scores': cls_scores, 'batch_img_metas': batch_img_metas, } model_outputs = get_model_outputs(solo_head, 'predict_by_feat', model_inputs) wrapped_model = WrapModel( solo_head, 'predict_by_feat', batch_img_metas=batch_img_metas) rewrite_inputs = { 'mlvl_mask_preds': mask_preds, 'mlvl_cls_scores': cls_scores, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) if is_backend_output: # hard code to make two tensors with the same shape # rewrite and original codes applied different nms strategy min_shape = min(model_outputs[0].bboxes.shape[0], rewrite_outputs[0].shape[1], 5) for i in range(len(model_outputs)): assert np.allclose( model_outputs[i].scores[:min_shape], rewrite_outputs[0][i, :min_shape, 4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].labels[:min_shape], rewrite_outputs[1][i, :min_shape], rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None def get_rtmdet_head_model(): from mmdet.models.dense_heads import RTMDetHead from mmdet.models.task_modules.prior_generators.point_generator import \ MlvlPointGenerator test_cfg = Config( dict( deploy_nms_pre=0, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.6), max_per_img=100)) model = RTMDetHead(1, 64) model.prior_generator = MlvlPointGenerator([8, 4, 2]) model.test_cfg = test_cfg model.requires_grad_(False) return model def test_rtmdet_head_predict_by_feat_ncnn(): """Test predict_by_feat rewrite of yolov3 head.""" backend_type = Backend.NCNN check_backend(backend_type) rtmdet_head = get_rtmdet_head_model() rtmdet_head.cpu().eval() s = 320 batch_img_metas = [{ 'scale_factor': np.ones(4), 'pad_shape': (s, s, 3), 'img_shape': (s, s, 3) }] output_names = ['detection_output'] deploy_cfg = 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) cls_scores = [ torch.rand(1, 1, 40, 40), torch.rand(1, 1, 20, 20), torch.rand(1, 1, 10, 10) ] bbox_preds = [ torch.rand(1, 4, 40, 40), torch.rand(1, 4, 20, 20), torch.rand(1, 4, 10, 10) ] # to get outputs of onnx model after rewrite wrapped_model = WrapModel( rtmdet_head, 'predict_by_feat', batch_img_metas=batch_img_metas, with_nms=True) rewrite_inputs = {'cls_scores': cls_scores, 'bbox_preds': bbox_preds} rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg, run_with_backend=False) # 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_solov2_head_model(): """solov2 Head Config.""" test_cfg = Config( dict( nms_pre=500, score_thr=0.1, mask_thr=0.5, filter_thr=0.05, kernel='gaussian', # gaussian/linear sigma=2.0, max_per_img=100)) mask_feature_head_cfg = Config( dict( feat_channels=16, start_level=0, end_level=3, out_channels=32, mask_stride=4, norm_cfg=dict( type='GN', num_groups=4, ))) from mmdet.models.dense_heads import SOLOV2Head model = SOLOV2Head( num_classes=4, in_channels=32, feat_channels=64, mask_feature_head=mask_feature_head_cfg, test_cfg=test_cfg) model.requires_grad_(False) return model @pytest.mark.parametrize('backend_type', [Backend.OPENVINO]) def test_solov2_head_predict_by_feat(backend_type): """Test predict_by_feat rewrite of solov2 head.""" check_backend(backend_type) solov2_head = get_solov2_head_model() solov2_head.cpu().eval() s = 128 batch_img_metas = [{'img_shape': (s, s, 3), 'ori_shape': (s, s, 3)}] output_names = ['dets', 'labels', 'masks'] deploy_cfg = 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, max_output_boxes_per_class=20, pre_top_k=-1, keep_top_k=10, background_label_id=-1, export_postprocess_mask=True)))) seed_everything(1234) num_grids = [24, 20, 16, 12, 8] kernel_preds = [ torch.rand(1, solov2_head.mask_feature_head.out_channels, num_grid, num_grid) for num_grid in num_grids ] cls_scores = [ torch.rand(1, solov2_head.num_classes, num_grid, num_grid) for num_grid in num_grids ] mask_feats = torch.rand(1, solov2_head.mask_feature_head.out_channels, s // 4, s // 4) # to get outputs of pytorch model model_inputs = { 'mlvl_kernel_preds': kernel_preds, 'mlvl_cls_scores': cls_scores, 'mask_feats': mask_feats, 'batch_img_metas': batch_img_metas, } model_outputs = get_model_outputs(solov2_head, 'predict_by_feat', model_inputs) # to get outputs of onnx model after rewrite wrapped_model = WrapModel( solov2_head, 'predict_by_feat', batch_img_metas=batch_img_metas) rewrite_inputs = { 'mlvl_kernel_preds': kernel_preds, 'mlvl_cls_scores': cls_scores, 'mask_feats': mask_feats, } rewrite_outputs, is_backend_output = get_rewrite_outputs( wrapped_model=wrapped_model, model_inputs=rewrite_inputs, deploy_cfg=deploy_cfg) if is_backend_output: # hard code to make two tensors with the same shape # rewrite and original codes applied different nms strategy min_shape = min(model_outputs[0].bboxes.shape[0], rewrite_outputs[0].shape[1], 5) for i in range(len(model_outputs)): assert np.allclose( model_outputs[i].scores[:min_shape], rewrite_outputs[0][i, :min_shape, 4], rtol=1e-03, atol=1e-05) assert np.allclose( model_outputs[i].labels[:min_shape], rewrite_outputs[1][i, :min_shape], rtol=1e-03, atol=1e-05) else: assert rewrite_outputs is not None