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更换文档检测模型

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liuyebo
2024-08-27 14:42:45 +08:00
parent aea6f19951
commit 1514e09c40
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paddle_detection/deploy/end2end_ppyoloe/README.md Normal file
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# Export ONNX Model
## Download pretrain paddle models
* [ppyoloe-s](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams)
* [ppyoloe-m](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams)
* [ppyoloe-l](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams)
* [ppyoloe-x](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams)
* [ppyoloe-s-400e](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_400e_coco.pdparams)
## Export paddle model for deploying
```shell
python ./tools/export_model.py \
-c configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml \
-o weights=ppyoloe_crn_s_300e_coco.pdparams \
trt=True \
exclude_nms=True \
TestReader.inputs_def.image_shape=[3,640,640] \
--output_dir ./
# if you want to try ppyoloe-s-400e model
python ./tools/export_model.py \
-c configs/ppyoloe/ppyoloe_crn_s_400e_coco.yml \
-o weights=ppyoloe_crn_s_400e_coco.pdparams \
trt=True \
exclude_nms=True \
TestReader.inputs_def.image_shape=[3,640,640] \
--output_dir ./
```
## Check requirements
```shell
pip install onnx>=1.10.0
pip install paddle2onnx
pip install onnx-simplifier
pip install onnx-graphsurgeon --index-url https://pypi.ngc.nvidia.com
# if use cuda-python infer, please install it
pip install cuda-python
# if use cupy infer, please install it
pip install cupy-cuda117 # cuda110-cuda117 are all available
```
## Export script
```shell
python ./deploy/end2end_ppyoloe/end2end.py \
--model-dir ppyoloe_crn_s_300e_coco \
--save-file ppyoloe_crn_s_300e_coco.onnx \
--opset 11 \
--batch-size 1 \
--topk-all 100 \
--iou-thres 0.6 \
--conf-thres 0.4
# if you want to try ppyoloe-s-400e model
python ./deploy/end2end_ppyoloe/end2end.py \
--model-dir ppyoloe_crn_s_400e_coco \
--save-file ppyoloe_crn_s_400e_coco.onnx \
--opset 11 \
--batch-size 1 \
--topk-all 100 \
--iou-thres 0.6 \
--conf-thres 0.4
```
#### Description of all arguments
- `--model-dir` : the path of ppyoloe export dir.
- `--save-file` : the path of export onnx.
- `--opset` : onnx opset version.
- `--img-size` : image size for exporting ppyoloe.
- `--batch-size` : batch size for exporting ppyoloe.
- `--topk-all` : topk objects for every image.
- `--iou-thres` : iou threshold for NMS algorithm.
- `--conf-thres` : confidence threshold for NMS algorithm.
### TensorRT backend (TensorRT version>= 8.0.0)
#### TensorRT engine export
``` shell
/path/to/trtexec \
--onnx=ppyoloe_crn_s_300e_coco.onnx \
--saveEngine=ppyoloe_crn_s_300e_coco.engine \
--fp16 # if export TensorRT fp16 model
# if you want to try ppyoloe-s-400e model
/path/to/trtexec \
--onnx=ppyoloe_crn_s_400e_coco.onnx \
--saveEngine=ppyoloe_crn_s_400e_coco.engine \
--fp16 # if export TensorRT fp16 model
```
#### TensorRT image infer
``` shell
# cuda-python infer script
python ./deploy/end2end_ppyoloe/cuda-python.py ppyoloe_crn_s_300e_coco.engine
# cupy infer script
python ./deploy/end2end_ppyoloe/cupy-python.py ppyoloe_crn_s_300e_coco.engine
# if you want to try ppyoloe-s-400e model
python ./deploy/end2end_ppyoloe/cuda-python.py ppyoloe_crn_s_400e_coco.engine
# or
python ./deploy/end2end_ppyoloe/cuda-python.py ppyoloe_crn_s_400e_coco.engine
```

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paddle_detection/deploy/end2end_ppyoloe/cuda-python.py Normal file
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import sys
import requests
import cv2
import random
import time
import numpy as np
import tensorrt as trt
from cuda import cudart
from pathlib import Path
from collections import OrderedDict, namedtuple
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleup=True, stride=32):
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
# Compute padding
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return im, r, (dw, dh)
w = Path(sys.argv[1])
assert w.exists() and w.suffix in ('.engine', '.plan'), 'Wrong engine path'
names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush']
colors = {name: [random.randint(0, 255) for _ in range(3)] for i, name in enumerate(names)}
url = 'https://oneflow-static.oss-cn-beijing.aliyuncs.com/tripleMu/image1.jpg'
file = requests.get(url)
img = cv2.imdecode(np.frombuffer(file.content, np.uint8), 1)
_, stream = cudart.cudaStreamCreate()
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 3, 1, 1)
std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 3, 1, 1)
# Infer TensorRT Engine
Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
logger = trt.Logger(trt.Logger.ERROR)
trt.init_libnvinfer_plugins(logger, namespace="")
with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
bindings = OrderedDict()
fp16 = False # default updated below
for index in range(model.num_bindings):
name = model.get_binding_name(index)
dtype = trt.nptype(model.get_binding_dtype(index))
shape = tuple(model.get_binding_shape(index))
data = np.empty(shape, dtype=np.dtype(dtype))
_, data_ptr = cudart.cudaMallocAsync(data.nbytes, stream)
bindings[name] = Binding(name, dtype, shape, data, data_ptr)
if model.binding_is_input(index) and dtype == np.float16:
fp16 = True
binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
context = model.create_execution_context()
image = img.copy()
image, ratio, dwdh = letterbox(image, auto=False)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_copy = image.copy()
image = image.transpose((2, 0, 1))
image = np.expand_dims(image, 0)
image = np.ascontiguousarray(image)
im = image.astype(np.float32)
im /= 255
im -= mean
im /= std
_, image_ptr = cudart.cudaMallocAsync(im.nbytes, stream)
cudart.cudaMemcpyAsync(image_ptr, im.ctypes.data, im.nbytes,
cudart.cudaMemcpyKind.cudaMemcpyHostToDevice, stream)
# warmup for 10 times
for _ in range(10):
tmp = np.random.randn(1, 3, 640, 640).astype(np.float32)
_, tmp_ptr = cudart.cudaMallocAsync(tmp.nbytes, stream)
binding_addrs['image'] = tmp_ptr
context.execute_v2(list(binding_addrs.values()))
start = time.perf_counter()
binding_addrs['image'] = image_ptr
context.execute_v2(list(binding_addrs.values()))
print(f'Cost {(time.perf_counter() - start) * 1000}ms')
nums = bindings['num_dets'].data
boxes = bindings['det_boxes'].data
scores = bindings['det_scores'].data
classes = bindings['det_classes'].data
cudart.cudaMemcpyAsync(nums.ctypes.data,
bindings['num_dets'].ptr,
nums.nbytes,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost,
stream)
cudart.cudaMemcpyAsync(boxes.ctypes.data,
bindings['det_boxes'].ptr,
boxes.nbytes,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost,
stream)
cudart.cudaMemcpyAsync(scores.ctypes.data,
bindings['det_scores'].ptr,
scores.nbytes,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost,
stream)
cudart.cudaMemcpyAsync(classes.ctypes.data,
bindings['det_classes'].ptr,
classes.data.nbytes,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost,
stream)
cudart.cudaStreamSynchronize(stream)
cudart.cudaStreamDestroy(stream)
for i in binding_addrs.values():
cudart.cudaFree(i)
num = int(nums[0][0])
box_img = boxes[0, :num].round().astype(np.int32)
score_img = scores[0, :num]
clss_img = classes[0, :num]
for i, (box, score, clss) in enumerate(zip(box_img, score_img, clss_img)):
name = names[int(clss)]
color = colors[name]
cv2.rectangle(image_copy, box[:2].tolist(), box[2:].tolist(), color, 2)
cv2.putText(image_copy, name, (int(box[0]), int(box[1]) - 2), cv2.FONT_HERSHEY_SIMPLEX,
0.75, [225, 255, 255], thickness=2)
cv2.imshow('Result', cv2.cvtColor(image_copy, cv2.COLOR_RGB2BGR))
cv2.waitKey(0)

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import sys
import requests
import cv2
import random
import time
import numpy as np
import cupy as cp
import tensorrt as trt
from PIL import Image
from collections import OrderedDict, namedtuple
from pathlib import Path
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleup=True, stride=32):
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
# Compute padding
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return im, r, (dw, dh)
names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush']
colors = {name: [random.randint(0, 255) for _ in range(3)] for i, name in enumerate(names)}
url = 'https://oneflow-static.oss-cn-beijing.aliyuncs.com/tripleMu/image1.jpg'
file = requests.get(url)
img = cv2.imdecode(np.frombuffer(file.content, np.uint8), 1)
w = Path(sys.argv[1])
assert w.exists() and w.suffix in ('.engine', '.plan'), 'Wrong engine path'
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 3, 1, 1)
std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 3, 1, 1)
mean = cp.asarray(mean)
std = cp.asarray(std)
# Infer TensorRT Engine
Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
logger = trt.Logger(trt.Logger.INFO)
trt.init_libnvinfer_plugins(logger, namespace="")
with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
model = runtime.deserialize_cuda_engine(f.read())
bindings = OrderedDict()
fp16 = False # default updated below
for index in range(model.num_bindings):
name = model.get_binding_name(index)
dtype = trt.nptype(model.get_binding_dtype(index))
shape = tuple(model.get_binding_shape(index))
data = cp.empty(shape, dtype=cp.dtype(dtype))
bindings[name] = Binding(name, dtype, shape, data, int(data.data.ptr))
if model.binding_is_input(index) and dtype == np.float16:
fp16 = True
binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
context = model.create_execution_context()
image = img.copy()
image, ratio, dwdh = letterbox(image, auto=False)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_copy = image.copy()
image = image.transpose((2, 0, 1))
image = np.expand_dims(image, 0)
image = np.ascontiguousarray(image)
im = cp.asarray(image)
im = im.astype(cp.float32)
im /= 255
im -= mean
im /= std
# warmup for 10 times
for _ in range(10):
tmp = cp.random.randn(1, 3, 640, 640).astype(cp.float32)
binding_addrs['image'] = int(tmp.data.ptr)
context.execute_v2(list(binding_addrs.values()))
start = time.perf_counter()
binding_addrs['image'] = int(im.data.ptr)
context.execute_v2(list(binding_addrs.values()))
print(f'Cost {(time.perf_counter() - start) * 1000}ms')
nums = bindings['num_dets'].data
boxes = bindings['det_boxes'].data
scores = bindings['det_scores'].data
classes = bindings['det_classes'].data
num = int(nums[0][0])
box_img = boxes[0, :num].round().astype(cp.int32)
score_img = scores[0, :num]
clss_img = classes[0, :num]
for i, (box, score, clss) in enumerate(zip(box_img, score_img, clss_img)):
name = names[int(clss)]
color = colors[name]
cv2.rectangle(image_copy, box[:2].tolist(), box[2:].tolist(), color, 2)
cv2.putText(image_copy, name, (int(box[0]), int(box[1]) - 2), cv2.FONT_HERSHEY_SIMPLEX,
0.75, [225, 255, 255], thickness=2)
cv2.imshow('Result', cv2.cvtColor(image_copy, cv2.COLOR_RGB2BGR))
cv2.waitKey(0)

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import argparse
import onnx
import onnx_graphsurgeon as gs
import numpy as np
from pathlib import Path
from paddle2onnx.legacy.command import program2onnx
from collections import OrderedDict
def main(opt):
model_dir = Path(opt.model_dir)
save_file = Path(opt.save_file)
assert model_dir.exists() and model_dir.is_dir()
if save_file.is_dir():
save_file = (save_file / model_dir.stem).with_suffix('.onnx')
elif save_file.is_file() and save_file.suffix != '.onnx':
save_file = save_file.with_suffix('.onnx')
input_shape_dict = {'image': [opt.batch_size, 3, *opt.img_size],
'scale_factor': [opt.batch_size, 2]}
program2onnx(str(model_dir), str(save_file),
'model.pdmodel', 'model.pdiparams',
opt.opset, input_shape_dict=input_shape_dict)
onnx_model = onnx.load(save_file)
try:
import onnxsim
onnx_model, check = onnxsim.simplify(onnx_model)
assert check, 'assert check failed'
except Exception as e:
print(f'Simplifier failure: {e}')
onnx.checker.check_model(onnx_model)
graph = gs.import_onnx(onnx_model)
graph.fold_constants()
graph.cleanup().toposort()
mul = concat = None
for node in graph.nodes:
if node.op == 'Div' and node.i(0).op == 'Mul':
mul = node.i(0)
if node.op == 'Concat' and node.o().op == 'Reshape' and node.o().o().op == 'ReduceSum':
concat = node
assert mul.outputs[0].shape[1] == concat.outputs[0].shape[2], 'Something wrong in outputs shape'
anchors = mul.outputs[0].shape[1]
classes = concat.outputs[0].shape[1]
scores = gs.Variable(name='scores', shape=[opt.batch_size, anchors, classes], dtype=np.float32)
graph.layer(op='Transpose', name='lastTranspose',
inputs=[concat.outputs[0]],
outputs=[scores],
attrs=OrderedDict(perm=[0, 2, 1]))
graph.inputs = [graph.inputs[0]]
attrs = OrderedDict(
plugin_version="1",
background_class=-1,
max_output_boxes=opt.topk_all,
score_threshold=opt.conf_thres,
iou_threshold=opt.iou_thres,
score_activation=False,
box_coding=0, )
outputs = [gs.Variable("num_dets", np.int32, [opt.batch_size, 1]),
gs.Variable("det_boxes", np.float32, [opt.batch_size, opt.topk_all, 4]),
gs.Variable("det_scores", np.float32, [opt.batch_size, opt.topk_all]),
gs.Variable("det_classes", np.int32, [opt.batch_size, opt.topk_all])]
graph.layer(op='EfficientNMS_TRT', name="batched_nms",
inputs=[mul.outputs[0], scores],
outputs=outputs,
attrs=attrs)
graph.outputs = outputs
graph.cleanup().toposort()
onnx.save(gs.export_onnx(graph), save_file)
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--model-dir', type=str,
default=None,
help='paddle static model')
parser.add_argument('--save-file', type=str,
default=None,
help='onnx model save path')
parser.add_argument('--opset', type=int, default=11, help='opset version')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--topk-all', type=int, default=100, help='topk objects for every images')
parser.add_argument('--iou-thres', type=float, default=0.45, help='iou threshold for NMS')
parser.add_argument('--conf-thres', type=float, default=0.25, help='conf threshold for NMS')
opt = parser.parse_args()
opt.img_size *= 2 if len(opt.img_size) == 1 else 1
return opt
if __name__ == '__main__':
opt = parse_opt()
main(opt)