2124 lines
70 KiB
Python
2124 lines
70 KiB
Python
# 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
|
|
|
|
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_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 base dense 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
|