用MMCls训练手势模型
import os
import json
import mmcv
import time
from mmcv import Config
from mmdet.apis import inference_detector, init_detector, show_result_pyplot, train_detector
from mmdet.models import build_detector
from mmdet.datasets import build_dataset
from mmcv.runner import load_checkpoint
from tqdm import tqdm
import warnings
import torch
import numpy as np
import cv2 warnings.filterwarnings("ignore") class MMDetection:
def sota():
pypath = os.path.abspath(__file__)
father = os.path.dirname(pypath)
models = os.path.join(father, 'models')
sota_model = []
for i in os.listdir(models):
if i[0] != '_':
sota_model.append(i)
return sota_model def __init__(self,
backbone='FasterRCNN',
num_classes=-1,
dataset_path=None
): # 获取外部运行py的绝对路径
self.cwd = os.path.dirname(os.getcwd())
# 获取当前文件的绝对路径
self.file_dirname = os.path.dirname(os.path.abspath(__file__))
self.save_fold = None
self.is_sample = False
self.config = os.path.join(
self.file_dirname, 'models', 'FasterRCNN/FasterRCNN.py')
self.checkpoint = os.path.join(
self.file_dirname, 'models', '/FasterRCNN/FasterRCNN.pth') self.backbone = backbone
backbone_path = os.path.join(
self.file_dirname, 'models', self.backbone)
ckpt_cfg_list = list(os.listdir(backbone_path))
for item in ckpt_cfg_list:
if item[-1] == 'y' and item[0] != '_': # pip包修改1
self.config = os.path.join(backbone_path, item)
elif item[-1] == 'h':
self.checkpoint = os.path.join(backbone_path, item)
else:
# print("Warning!!! There is an unrecognized file in the backbone folder.")
pass self.cfg = Config.fromfile(self.config) self.dataset_path = dataset_path
self.lr = None
self.backbonedict = {
"FasterRCNN": os.path.join(self.file_dirname, 'models', 'FasterRCNN/FasterRCNN.py'),
"Yolov3": os.path.join(self.file_dirname, 'models', 'Yolov3/Yolov3.py'),
"SSD_Lite":os.path.join(self.file_dirname, 'models', 'SSD_Lite/SSD_Lite.py'),
# "Mask_RCNN":os.path.join(self.file_dirname, 'models', 'Mask_RCNN/Mask_RCNN.py'),
# 下略
}
self.num_classes = num_classes
self.chinese_res = None
self.is_sample = False def train(self, random_seed=0, save_fold=None, distributed=False, validate=True, device='cpu',
metric='bbox', save_best='bbox_mAP', optimizer="SGD", epochs=100, lr=0.001, weight_decay=0.001,
Frozen_stages=1,
checkpoint=None, batch_size=None): # 加载网络模型的配置文件
self.cfg = Config.fromfile(self.backbonedict[self.backbone]) # 如果外部不指定save_fold
if not self.save_fold:
# 如果外部也没有传入save_fold，我们使用默认路径
if not save_fold:
self.save_fold = os.path.join(
self.cwd, 'checkpoints/det_model')
# 如果外部传入save_fold，我们使用传入值
else:
self.save_fold = save_fold self.cfg.model.backbone.frozen_stages = Frozen_stages if self.num_classes != -1:
if "RCNN" not in self.backbone: # 单阶段
self.cfg.model.bbox_head.num_classes =self.num_classes
elif self.backbone == "FasterRCNN": # rcnn系列 双阶段
self.cfg.model.roi_head.bbox_head.num_classes = self.num_classes
elif self.backbone == "Mask_RCNN":
self.cfg.model.roi_head.bbox_head.num_classes = self.num_classes
self.cfg.model.roi_head.mask_head.num_classes = self.num_classes self.load_dataset(self.dataset_path)
# 添加需要进行检测的类名
if self.backbone in ["Yolov3"]:
self.cfg.classes = self.get_classes(self.cfg.data.train.dataset.ann_file)
else:
self.cfg.classes = self.get_classes(self.cfg.data.train.ann_file) # 分别为训练、测试、验证添加类名
if self.backbone in ["Yolov3"]:
self.cfg.data.train.dataset.classes = self.cfg.classes
else:
self.cfg.data.train.classes = self.cfg.classes
self.cfg.data.test.classes = self.cfg.classes
self.cfg.data.val.classes = self.cfg.classes # 进行
self.cfg.work_dir = self.save_fold
# 创建工作目录
mmcv.mkdir_or_exist(os.path.abspath(self.cfg.work_dir))
# 创建分类器
datasets = [build_dataset(self.cfg.data.train)]
model = build_detector(self.cfg.model, train_cfg=self.cfg.get(
'train_cfg'), test_cfg=self.cfg.get('test_cfg'))
# print("checkpoint", checkpoint)
if not checkpoint:
model.init_weights()
else:
checkpoint = os.path.abspath(checkpoint) # pip修改2
load_checkpoint(model, checkpoint, map_location=torch.device(device)) model.CLASSES = self.cfg.classes
if optimizer == 'Adam':
self.cfg.optimizer = dict(type='Adam', lr=lr, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.0001)
elif optimizer == 'Adagrad':
self.cfg.optimizer = dict(type='Adagrad', lr=lr, lr_decay=0)
# 根据输入参数更新config文件
self.cfg.optimizer.lr = lr # 学习率
self.cfg.optimizer.type = optimizer # 优化器
self.cfg.optimizer.weight_decay = weight_decay # 优化器的衰减权重
self.cfg.evaluation.metric = metric # 验证指标
# self.cfg.evaluation.save_best = save_best
self.cfg.runner.max_epochs = epochs # 最大的训练轮次 # 设置每 5 个训练批次输出一次日志
# self.cfg.log_config.interval = 1
self.cfg.gpu_ids = range(1) self.cfg.seed = random_seed
if batch_size is not None:
self.cfg.data.samples_per_gpu = batch_size
train_detector(
model,
datasets,
self.cfg,
distributed=distributed,
validate=validate,
timestamp=time.strftime('%Y%m%d_%H%M%S', time.localtime()),
meta=dict()
) def print_result(self, res=None):
if self.is_sample == True:
print("示例检测结果如下：")
sample_result = r"[{'类别标签': 0, '置信度': 1.0, '坐标': {'x': 26, 'y': 81, 'w': 497, 'h': 414}},{'类别标签': 1, '置信度': 1.0, '坐标': {'x1': 250, 'y1': 103, 'x2': 494, 'y2': 341}}]"
print(sample_result)
else:
print("检测结果如下：")
print(self.chinese_res)
return self.chinese_res def load_checkpoint(self, checkpoint=None, device='cpu',
rpn_threshold=0.7, rcnn_threshold=0.7):
print("========= begin inference ==========")
if self.num_classes != -1 and self.backbone not in ["Yolov3", "SSD_Lite"]:
self.cfg.model.roi_head.bbox_head.num_classes = self.num_classes if checkpoint:
# 加载数据集及配置文件的路径
# self.load_dataset(self.dataset_path)
# 修正检测的目标
# self.cfg.classes = self.get_class(class_path)
self.cfg.classes = torch.load(checkpoint, map_location=torch.device('cpu'))['meta']['CLASSES']
self.cfg.data.train.classes = self.cfg.classes
self.cfg.data.test.classes = self.cfg.classes
self.cfg.data.val.classes = self.cfg.classes
if "RCNN" not in self.backbone: # 单阶段
self.cfg.model.bbox_head.num_classes = len(self.cfg.classes)
else: # rcnn系列 双阶段
self.cfg.model.roi_head.bbox_head.num_classes = len(self.cfg.classes)
# self.cfg.model.roi_head.bbox_head.num_classes = len(self.cfg.classes)
self.infer_model = init_detector(self.cfg, checkpoint, device=device)
self.infer_model.CLASSES = self.cfg.classes
else:
self.infer_model = init_detector(self.cfg, self.checkpoint, device=device)
if self.backbone not in ["Yolov3", "SSD_Lite"]: self.infer_model.test_cfg.rpn.nms.iou_threshold = 1 - rpn_threshold
if self.backbone not in ["Yolov3", "SSD_Lite"]: self.infer_model.test_cfg.rcnn.nms.iou_threshold = 1 - rcnn_threshold def fast_inference(self, image, show=False, save_fold='det_result'):
img_array = mmcv.imread(image)
try:
self.infer_model
except:
print("请先使用load_checkpoint()方法加载权重！")
return
result = inference_detector(self.infer_model, img_array) # 此处的model和外面的无关,纯局部变量
self.infer_model.show_result(image, result, show=show,
out_file=os.path.join(save_fold, os.path.split(image)[1]))
chinese_res = []
for i in range(len(result)):
for j in range(result[i].shape[0]):
tmp = {}
tmp['类别标签'] = i
tmp['置信度'] = result[i][j][4]
tmp['坐标'] = {"x": int(result[i][j][0]), "y": int(
result[i][j][1]), 'w': int(result[i][j][2]), 'h': int(result[i][j][3])}
# img.append(tmp)
chinese_res.append(tmp)
# print(chinese_res)
self.chinese_res = chinese_res
# print("========= finish inference ==========")
return result def inference(self, device='cpu',
checkpoint=None,
image=None,
show=True,
rpn_threshold=0.7,
rcnn_threshold=0.7,
save_fold='det_result',
):
# self.cfg.classes = self.get_class(class_path)
# self.num_classes = len(self.get_class(class_path))
# if self.num_classes != -1:
# if "RCNN" not in self.backbone: # 单阶段
# self.cfg.model.bbox_head.num_classes =self.num_classes
# elif self.backbone == "FasterRCNN": # rcnn系列 双阶段
# self.cfg.model.roi_head.bbox_head.num_classes = self.num_classes
# elif self.backbone == "Mask_RCNN":
# self.cfg.model.roi_head.bbox_head.num_classes = self.num_classes
# self.cfg.model.roi_head.mask_head.num_classes = self.num_classes if image == None:
self.is_sample = True
sample_return = """
[array([[ 26.547777 , 81.55447 , 497.37015 , 414.4934 ,
1.0]], dtype=float32),
array([[2.5098564e+02, 1.0334784e+02, 4.9422855e+02, 3.4187744e+02,
1.0], dtype=float32)]
"""
return sample_return
self.is_sample = False
print("========= begin inference ==========") if self.num_classes != -1 and self.backbone not in ["Yolov3", "SSD_Lite"] :
self.cfg.model.roi_head.bbox_head.num_classes = self.num_classes if checkpoint:
# 加载数据集及配置文件的路径
# self.load_dataset(self.dataset_path)
# 修正检测的目标
self.cfg.classes = torch.load(checkpoint, map_location=torch.device('cpu'))['meta']['CLASSES'] self.num_classes = len(self.cfg.classes)
if self.num_classes != -1:
if "RCNN" not in self.backbone: # 单阶段
self.cfg.model.bbox_head.num_classes =self.num_classes
elif self.backbone == "FasterRCNN": # rcnn系列 双阶段
self.cfg.model.roi_head.bbox_head.num_classes = self.num_classes
elif self.backbone == "Mask_RCNN":
self.cfg.model.roi_head.bbox_head.num_classes = self.num_classes
self.cfg.model.roi_head.mask_head.num_classes = self.num_classes
self.cfg.data.train.classes = self.cfg.classes
self.cfg.data.test.classes = self.cfg.classes
self.cfg.data.val.classes = self.cfg.classes
if self.backbone not in ["Yolov3", "SSD_Lite"]:
self.cfg.model.roi_head.bbox_head.num_classes = len(self.cfg.classes)
model = init_detector(self.cfg, checkpoint, device=device)
model.CLASSES = self.cfg.classes
else:
model = init_detector(self.cfg, self.checkpoint, device=device)
# model = build_detector(self.cfg.model, train_cfg=self.cfg.get(
# 'train_cfg'), test_cfg=self.cfg.get('test_cfg'))
# if not checkpoint:
# model.init_weights()
# else:
# checkpoint = os.path.abspath(checkpoint) # pip修改2
# load_checkpoint(model, checkpoint, map_location=torch.device(device)) if self.backbone not in ["Yolov3", "SSD_Lite"]: model.test_cfg.rpn.nms.iou_threshold = 1 - rpn_threshold
if self.backbone not in ["Yolov3", "SSD_Lite"]: model.test_cfg.rcnn.nms.iou_threshold = 1 - rcnn_threshold results = []
if (image[-1] != '/'):
img_array = mmcv.imread(image)
result = inference_detector(
model, img_array) # 此处的model和外面的无关,纯局部变量
if show == True:
show_result_pyplot(model, image, result)
model.show_result(image, result, show=show, out_file=os.path.join(save_fold, os.path.split(image)[1]))
chinese_res = []
for i in range(len(result)):
for j in range(result[i].shape[0]):
tmp = {}
tmp['类别标签'] = i
tmp['置信度'] = result[i][j][4]
tmp['坐标'] = {"x1": int(result[i][j][0]), "y1": int(
result[i][j][1]), 'x2': int(result[i][j][2]), 'y2': int(result[i][j][3])}
# img.append(tmp)
chinese_res.append(tmp)
# print(chinese_res)
self.chinese_res = chinese_res
print("========= finish inference ==========")
return result
else:
img_dir = image
mmcv.mkdir_or_exist(os.path.abspath(save_fold))
chinese_results = []
for i, img in enumerate(tqdm(os.listdir(img_dir))):
result = inference_detector(
model, img_dir + img) # 此处的model和外面的无关,纯局部变量
model.show_result(img_dir + img, result,
out_file=os.path.join(save_fold, img))
chinese_res = []
for i in range(len(result)):
for j in range(result[i].shape[0]):
tmp = {}
tmp['类别标签'] = i
tmp['置信度'] = result[i][j][4]
tmp['坐标'] = {"x1": int(result[i][j][0]), "y1": int(
result[i][j][1]), 'x2': int(result[i][j][2]), 'y2': int(result[i][j][3])}
# img.append(tmp)
chinese_res.append(tmp)
chinese_results.append(chinese_res)
results.append(result)
self.chinese_res = chinese_results
print("========= finish inference ==========")
return results def load_dataset(self, path):
self.dataset_path = path # 数据集修正为coco格式
if self.backbone in ["Yolov3"]:
self.cfg.data.train.dataset.img_prefix = os.path.join(self.dataset_path, 'images/train/')
self.cfg.data.train.dataset.ann_file = os.path.join(self.dataset_path, 'annotations/train.json')
else:
self.cfg.data.train.img_prefix = os.path.join(self.dataset_path, 'images/train/')
self.cfg.data.train.ann_file = os.path.join(self.dataset_path, 'annotations/train.json') self.cfg.data.val.img_prefix = os.path.join(self.dataset_path, 'images/test/')
self.cfg.data.val.ann_file = os.path.join(self.dataset_path, 'annotations/valid.json') self.cfg.data.test.img_prefix = os.path.join(self.dataset_path, 'images/test/')
self.cfg.data.test.ann_file = os.path.join(self.dataset_path, 'annotations/valid.json') def get_class(self, class_path):
classes = []
with open(class_path, 'r') as f:
for name in f:
classes.append(name.strip('\n'))
return classes def get_classes(self, annotation_file):
classes = ()
with open(annotation_file, 'r') as f:
dataset = json.load(f)
# categories = dataset["categories"]
if 'categories' in dataset:
for cat in dataset['categories']:
classes = classes + (cat['name'],)
return classes def convert(self, checkpoint=None, backend="ONNX", out_file="convert_model.onnx",device='cpu'):
import os.path as osp
from mmdet.core.export import build_model_from_cfg ashape = self.cfg.test_pipeline[1].img_scale
if len(ashape) == 1:
input_shape = (1, 3, ashape[0], ashape[0])
elif len(ashape) == 2:
input_shape = (
1,
3,
) + tuple(ashape)
else:
raise ValueError('invalid input shape')
self.cfg.model.pretrained = None
if self.backbone not in ["Yolov3", "SSD_Lite"] :
self.cfg.model.roi_head.bbox_head.num_classes = self.num_classes
else:
self.cfg.model.bbox_head.num_classes = self.num_classes # build the model and load checkpoint
# detector = build_detector(self.cfg.model)
# model = build_model_from_cfg(self.config, checkpoint) if checkpoint:
# 加载数据集及配置文件的路径
# self.load_dataset(self.dataset_path)
# 修正检测的目标
self.cfg.classes = torch.load(checkpoint, map_location=torch.device('cpu'))['meta']['CLASSES']
self.num_classes = len(self.cfg.classes)
if self.num_classes != -1:
if "RCNN" not in self.backbone: # 单阶段
self.cfg.model.bbox_head.num_classes =self.num_classes
elif self.backbone == "FasterRCNN": # rcnn系列 双阶段
self.cfg.model.roi_head.bbox_head.num_classes = self.num_classes
elif self.backbone == "Mask_RCNN":
self.cfg.model.roi_head.bbox_head.num_classes = self.num_classes
self.cfg.model.roi_head.mask_head.num_classes = self.num_classes
self.cfg.data.train.classes = self.cfg.classes
self.cfg.data.test.classes = self.cfg.classes
self.cfg.data.val.classes = self.cfg.classes
if self.backbone not in ["Yolov3", "SSD_Lite"]:
self.cfg.model.roi_head.bbox_head.num_classes = len(self.cfg.classes)
model = init_detector(self.cfg, checkpoint, device=device)
model.CLASSES = self.cfg.classes
else:
model = init_detector(self.cfg, self.checkpoint, device=device)
if self.backbone not in ["Yolov3", "SSD_Lite"]: model.test_cfg.rpn.nms.iou_threshold = 0.3 # 1 - rpn_threshold
if self.backbone not in ["Yolov3", "SSD_Lite"]: model.test_cfg.rcnn.nms.iou_threshold = 0.3 # 1 - rcnn_threshold #detector = build_detector(self.cfg.model, test_cfg=self.cfg.get('test_cfg'))
# detector.CLASSES = self.num_classes
normalize_cfg = parse_normalize_cfg(self.cfg.test_pipeline)
input_img = osp.join(osp.dirname(__file__), './demo/demo.jpg')
if backend == "ONNX" or backend == 'onnx':
pytorch2onnx(
# detector,
model,
input_img,
input_shape,
normalize_cfg,
show=False,
output_file=out_file,
verify=False,
test_img=None,
do_simplify=False)
else:
print("Sorry, we only suport ONNX up to now.")
with open(out_file.replace(".onnx", ".py"), "w+") as f:
tp = str(self.cfg.test_pipeline).replace("},","},\n\t")
# if class_path != None:
# classes_list = self.get_class(class_path)
classes_list = torch.load(checkpoint, map_location=torch.device('cpu'))['meta']['CLASSES'] gen0 = """
import onnxruntime as rt
import BaseData
import numpy as np
import cv2 cap = cv2.VideoCapture(0)
ret_flag,image = cap.read()
cap.release()
"""
gen_sz = """
image = cv2.resize(image,(sz_h,sz_w))
tag =
"""
gen1 = """
sess = rt.InferenceSession('
"""
gen2 = """', None)
input_name = sess.get_inputs()[0].name
output_names = [o.name for o in sess.get_outputs()]
dt = BaseData.ImageData(image, backbone="
""" gen3 = """")
input_data = dt.to_tensor()
outputs = sess.run(output_names, {input_name: input_data}) boxes = outputs[0]
labels = outputs[1][0]
img_height, img_width = image.shape[:2]
size = min([img_height, img_width]) * 0.001
text_thickness = int(min([img_height, img_width]) * 0.001) idx = 0
for box in zip(boxes[0]):
x1, y1, x2, y2, score = box[0]
label = tag[labels[idx]]
idx = idx + 1
caption = f'{label}{int(score * 100)}%'
if score >= 0.15:
(tw, th), _ = cv2.getTextSize(text=caption, fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=size, thickness=text_thickness)
th = int(th * 1.2)
cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2)
cv2.putText(image, caption, (int(x1), int(y1)),
cv2.FONT_HERSHEY_SIMPLEX, size, (255, 255, 255), text_thickness, cv2.LINE_AA) cv2.imwrite("result.jpg", image)
"""
ashape = self.cfg.test_pipeline[1].img_scale
# if class_path != None:
gen = gen0.strip("\n") + '\n' + gen_sz.replace('sz_h',str(ashape[0])).replace('sz_w',str(ashape[1])).strip('\n') + str(classes_list)+ "\n" + gen1.strip("\n") + out_file + gen2.strip("\n") + str(self.backbone) + gen3
# else:
# gen = gen0.strip("tag = \n") + "\n\n" + gen1.strip("\n")+out_file+ gen2.strip("\n") + str(self.backbone) + gen3.replace("tag[labels[idx]]", "labels[idx]")
f.write(gen) def parse_normalize_cfg(test_pipeline):
transforms = None
for pipeline in test_pipeline:
if 'transforms' in pipeline:
transforms = pipeline['transforms']
break
assert transforms is not None, 'Failed to find `transforms`'
norm_config_li = [_ for _ in transforms if _['type'] == 'Normalize']
assert len(norm_config_li) == 1, '`norm_config` should only have one'
norm_config = norm_config_li[0]
return norm_config def pytorch2onnx(model,
input_img,
input_shape,
normalize_cfg,
opset_version=11,
show=False,
output_file='tmp.onnx',
verify=False,
test_img=None,
do_simplify=False,
dynamic_export=None,
skip_postprocess=False):
from mmdet.core.export import build_model_from_cfg, preprocess_example_input
from mmdet.core.export.model_wrappers import ONNXRuntimeDetector
from functools import partial
from mmcv import Config, DictAction
import onnx
input_config = {
'input_shape': input_shape,
'input_path': input_img,
'normalize_cfg': normalize_cfg
}
# prepare input
one_img, one_meta = preprocess_example_input(input_config)
img_list, img_meta_list = [one_img], [[one_meta]] if skip_postprocess:
warnings.warn('Not all models support export onnx without post '
'process, especially two stage detectors!')
model.forward = model.forward_dummy
torch.onnx.export(
model,
one_img,
output_file,
input_names=['input'],
export_params=True,
keep_initializers_as_inputs=True,
do_constant_folding=True,
verbose=show,
opset_version=opset_version) print(f'Successfully exported ONNX model without '
f'post process: {output_file}')
return # replace original forward function
origin_forward = model.forward
model.forward = partial(
model.forward,
img_metas=img_meta_list,
return_loss=False,
rescale=False) output_names = ['dets', 'labels']
if model.with_mask:
output_names.append('masks')
input_name = 'input'
dynamic_axes = None
if dynamic_export:
dynamic_axes = {
input_name: {
0: 'batch',
2: 'height',
3: 'width'
},
'dets': {
0: 'batch',
1: 'num_dets',
},
'labels': {
0: 'batch',
1: 'num_dets',
},
}
if model.with_mask:
dynamic_axes['masks'] = {0: 'batch', 1: 'num_dets'} torch.onnx.export(
model,
img_list,
output_file,
input_names=[input_name],
output_names=output_names,
export_params=True,
keep_initializers_as_inputs=True,
do_constant_folding=True,
verbose=show,
opset_version=opset_version,
dynamic_axes=dynamic_axes) model.forward = origin_forward if do_simplify:
import onnxsim from mmdet import digit_version min_required_version = '0.4.0'
assert digit_version(onnxsim.__version__) >= digit_version(
min_required_version
), f'Requires to install onnxsim>={min_required_version}' model_opt, check_ok = onnxsim.simplify(output_file)
if check_ok:
onnx.save(model_opt, output_file)
print(f'Successfully simplified ONNX model: {output_file}')
else:
warnings.warn('Failed to simplify ONNX model.')
print(f'Successfully exported ONNX model: {output_file}') if verify:
# check by onnx
onnx_model = onnx.load(output_file)
onnx.checker.check_model(onnx_model)
#print(model.CLASSES)
# wrap onnx model
onnx_model = ONNXRuntimeDetector(output_file, model.CLASSES, 0)
if dynamic_export:
# scale up to test dynamic shape
h, w = [int((_ * 1.5) // 32 * 32) for _ in input_shape[2:]]
h, w = min(1344, h), min(1344, w)
input_config['input_shape'] = (1, 3, h, w) if test_img is None:
input_config['input_path'] = input_img # prepare input once again
one_img, one_meta = preprocess_example_input(input_config)
img_list, img_meta_list = [one_img], [[one_meta]] # get pytorch output
with torch.no_grad():
pytorch_results = model(
img_list,
img_metas=img_meta_list,
return_loss=False,
rescale=True)[0] img_list = [_.cuda().contiguous() for _ in img_list]
if dynamic_export:
img_list = img_list + [_.flip(-1).contiguous() for _ in img_list]
img_meta_list = img_meta_list * 2
# get onnx output
onnx_results = onnx_model(
img_list, img_metas=img_meta_list, return_loss=False)[0]
# visualize predictions
score_thr = 0.3
if show:
out_file_ort, out_file_pt = None, None
else:
out_file_ort, out_file_pt = 'show-ort.png', 'show-pt.png' show_img = one_meta['show_img']
model.show_result(
show_img,
pytorch_results,
score_thr=score_thr,
show=True,
win_name='PyTorch',
out_file=out_file_pt)
onnx_model.show_result(
show_img,
onnx_results,
score_thr=score_thr,
show=True,
win_name='ONNXRuntime',
out_file=out_file_ort) # compare a part of result
if model.with_mask:
compare_pairs = list(zip(onnx_results, pytorch_results))
else:
compare_pairs = [(onnx_results, pytorch_results)]
err_msg = 'The numerical values are different between Pytorch' + \
' and ONNX, but it does not necessarily mean the' + \
' exported ONNX model is problematic.'
# check the numerical value
for onnx_res, pytorch_res in compare_pairs:
for o_res, p_res in zip(onnx_res, pytorch_res):
np.testing.assert_allclose(
o_res, p_res, rtol=1e-03, atol=1e-05, err_msg=err_msg)
print('The numerical values are the same between Pytorch and ONNX')
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