1189 lines
40 KiB
Python
1189 lines
40 KiB
Python
# 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.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_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_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
|
|
|
|
|
|
@pytest.mark.parametrize('backend_type', [Backend.NCNN])
|
|
def test_get_bboxes_of_fcos_head(backend_type: Backend):
|
|
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 = ['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=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 pytorch model
|
|
model_inputs = {
|
|
'cls_scores': cls_score,
|
|
'bbox_preds': bboxes,
|
|
'centernesses': centernesses,
|
|
'img_metas': img_metas
|
|
}
|
|
model_outputs = get_model_outputs(fcos_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(
|
|
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)
|
|
if is_backend_output:
|
|
if isinstance(rewrite_outputs, dict):
|
|
rewrite_outputs = [
|
|
value for name, value in rewrite_outputs.items()
|
|
if name in 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.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,
|
|
}
|
|
rewrite_outputs, is_backend_output = get_rewrite_outputs(
|
|
wrapped_model=wrapped_model,
|
|
model_inputs=rewrite_inputs,
|
|
deploy_cfg=deploy_cfg)
|
|
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 = 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]
|
|
}
|
|
model_outputs = get_model_outputs(cascade_roi_head, 'simple_test',
|
|
model_inputs)
|
|
processed_model_outputs = []
|
|
outputs = model_outputs[0]
|
|
for output in outputs:
|
|
if output.shape == (0, 5):
|
|
processed_model_outputs.append(np.zeros((1, 5)))
|
|
else:
|
|
processed_model_outputs.append(output)
|
|
processed_model_outputs = np.array(processed_model_outputs).squeeze()
|
|
processed_model_outputs = processed_model_outputs[None, :, :]
|
|
|
|
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)
|
|
|
|
if isinstance(backend_outputs, (list, tuple)) and \
|
|
backend_outputs[0].shape == (1, 0, 5):
|
|
processed_backend_outputs = torch.zeros((1, 80, 5))
|
|
else:
|
|
processed_backend_outputs = backend_outputs
|
|
|
|
model_output = processed_model_outputs
|
|
backend_output = [
|
|
out.detach().cpu().numpy() for out in processed_backend_outputs
|
|
]
|
|
assert np.allclose(model_output, backend_output, rtol=1e-03, atol=1e-05)
|
|
|
|
|
|
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]
|
|
expected_bbox_results = np.zeros((1, 80, 5))
|
|
expected_segm_results = -np.ones((1, 80))
|
|
assert np.allclose(
|
|
expected_bbox_results, bbox_results, rtol=1e-03,
|
|
atol=1e-05), 'bbox_results do not match.'
|
|
assert np.allclose(
|
|
expected_segm_results, segm_results, rtol=1e-03,
|
|
atol=1e-05), 'segm_results do not match.'
|
|
|
|
|
|
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 = ['outout']
|
|
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 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
|
|
|
|
|
|
@pytest.mark.parametrize('backend_type',
|
|
[Backend.ONNXRUNTIME, Backend.NCNN, Backend.OPENVINO])
|
|
def test_base_dense_head_get_bboxes(backend_type: Backend):
|
|
"""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)
|
|
}]
|
|
|
|
if backend_type != Backend.NCNN:
|
|
output_names = ['dets', 'labels']
|
|
else:
|
|
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',
|
|
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 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
|
|
|
|
|
|
@pytest.mark.parametrize('backend_type', [Backend.NCNN])
|
|
def test_ssd_head_get_bboxes(backend_type: Backend):
|
|
"""Test get_bboxes rewrite of anchor head."""
|
|
check_backend(backend_type)
|
|
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']
|
|
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,
|
|
))))
|
|
|
|
# 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 pytorch model
|
|
model_inputs = {
|
|
'cls_scores': cls_score,
|
|
'bbox_preds': bboxes,
|
|
'img_metas': img_metas
|
|
}
|
|
model_outputs = get_model_outputs(ssd_head, 'get_bboxes', model_inputs)
|
|
|
|
# to get outputs of onnx model after rewrite
|
|
img_metas[0]['img_shape'] = torch.tensor([s, s], dtype=torch.int32)
|
|
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)
|
|
|
|
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
|
|
assert np.allclose(
|
|
model_output[:rewrite_output.shape[0]],
|
|
rewrite_output,
|
|
rtol=1e-03,
|
|
atol=1e-05)
|
|
else:
|
|
assert rewrite_outputs is not None
|