未验证 提交 45427e89 编写于 作者: T TeslaZhao 提交者: GitHub

Merge branch 'develop' into encryption

......@@ -24,7 +24,6 @@ You can get images in two ways:
```
## Image description
......@@ -40,3 +39,13 @@ Runtime images cannot be used for compilation.
| GPU (cuda10.0-cudnn7) development | CentOS7 | latest-cuda10.0-cudnn7-devel | [Dockerfile.cuda10.0-cudnn7.devel](../tools/Dockerfile.cuda10.0-cudnn7.devel) |
| CPU development (Used to compile packages on Ubuntu) | CentOS6 | <None> | [Dockerfile.centos6.devel](../tools/Dockerfile.centos6.devel) |
| GPU (cuda9.0-cudnn7) development (Used to compile packages on Ubuntu) | CentOS6 | <None> | [Dockerfile.centos6.cuda9.0-cudnn7.devel](../tools/Dockerfile.centos6.cuda9.0-cudnn7.devel) |
## Requirements for running CUDA containers
Running a CUDA container requires a machine with at least one CUDA-capable GPU and a driver compatible with the CUDA toolkit version you are using.
The machine running the CUDA container **only requires the NVIDIA driver**, the CUDA toolkit doesn't have to be installed.
For the relationship between CUDA toolkit version, Driver version and GPU architecture, please refer to [nvidia-docker wiki](https://github.com/NVIDIA/nvidia-docker/wiki/CUDA).
......@@ -22,7 +22,6 @@
```shell
docker build -t <image-name>:<images-tag> .
```
......@@ -40,3 +39,13 @@
| GPU (cuda10.0-cudnn7) 开发镜像 | CentOS7 | latest-cuda10.0-cudnn7-devel | [Dockerfile.cuda10.0-cudnn7.devel](../tools/Dockerfile.cuda10.0-cudnn7.devel) |
| CPU 开发镜像 (用于编译 Ubuntu 包) | CentOS6 | <无> | [Dockerfile.centos6.devel](../tools/Dockerfile.centos6.devel) |
| GPU (cuda9.0-cudnn7) 开发镜像 (用于编译 Ubuntu 包) | CentOS6 | <无> | [Dockerfile.centos6.cuda9.0-cudnn7.devel](../tools/Dockerfile.centos6.cuda9.0-cudnn7.devel) |
## 运行CUDA容器的要求
运行CUDA容器需要至少具有一个支持CUDA的GPU以及与您所使用的CUDA工具包版本兼容的驱动程序。
运行CUDA容器的机器**只需要相应的NVIDIA驱动程序**,而CUDA工具包不是必要的。
相关CUDA工具包版本、驱动版本和GPU架构的关系请参阅 [nvidia-docker wiki](https://github.com/NVIDIA/nvidia-docker/wiki/CUDA)
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_serving_client import Client
from paddle_serving_app.reader import *
import numpy as np
preprocess = Sequential([
File2Image(), BGR2RGB(), Div(255.0),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], False),
Resize(800, 1333), Transpose((2, 0, 1)), PadStride(32)
])
postprocess = RCNNPostprocess("label_list.txt", "output")
client = Client()
client.load_client_config("serving_client/serving_client_conf.prototxt")
client.connect(['127.0.0.1:9292'])
im = preprocess('000000570688.jpg')
fetch_map = client.predict(
feed={
"image": im,
"im_info": np.array(list(im.shape[1:]) + [1.0]),
"im_shape": np.array(list(im.shape[1:]) + [1.0])
},
fetch=["multiclass_nms_0.tmp_0"],
batch=True)
fetch_map["image"] = '000000570688.jpg'
print(fetch_map)
postprocess(fetch_map)
print(fetch_map)
......@@ -29,13 +29,11 @@ from paddle_serving_app.reader import ChineseBertReader
from paddle_serving_app.reader import *
import numpy as np
args = benchmark_args()
def single_func(idx, resource):
img="./000000570688.jpg"
img = "./000000570688.jpg"
profile_flags = False
latency_flags = False
if os.getenv("FLAGS_profile_client"):
......@@ -67,10 +65,12 @@ def single_func(idx, resource):
for bi in range(args.batch_size):
print("1111batch")
print(bi)
feed_batch.append({"image": im,
feed_batch.append({
"image": im,
"im_info": np.array(list(im.shape[1:]) + [1.0]),
"im_shape": np.array(list(im.shape[1:]) + [1.0])})
# im = preprocess(img)
"im_shape": np.array(list(im.shape[1:]) + [1.0])
})
# im = preprocess(img)
b_end = time.time()
if profile_flags:
......@@ -81,8 +81,7 @@ def single_func(idx, resource):
int(round(b_end * 1000000))))
#result = client.predict(feed=feed_batch, fetch=fetch)
fetch_map = client.predict(
feed=feed_batch,
fetch=["multiclass_nms"])
feed=feed_batch, fetch=["multiclass_nms"])
fetch_map["image"] = img
postprocess(fetch_map)
......@@ -102,13 +101,12 @@ def single_func(idx, resource):
if __name__ == '__main__':
multi_thread_runner = MultiThreadRunner()
endpoint_list = [
"127.0.0.1:7777"
]
endpoint_list = ["127.0.0.1:7777"]
turns = 10
start = time.time()
result = multi_thread_runner.run(
single_func, args.thread, {"endpoint": endpoint_list,"turns": turns})
single_func, args.thread, {"endpoint": endpoint_list,
"turns": turns})
end = time.time()
total_cost = end - start
......
background
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
......@@ -53,7 +53,9 @@ class OCRService(WebService):
self.ori_h, self.ori_w, _ = im.shape
det_img = self.det_preprocess(im)
_, self.new_h, self.new_w = det_img.shape
return {"image": det_img[np.newaxis, :].copy()}, ["concat_1.tmp_0"]
return {
"image": det_img[np.newaxis, :].copy()
}, ["concat_1.tmp_0"], True
def postprocess(self, feed={}, fetch=[], fetch_map=None):
det_out = fetch_map["concat_1.tmp_0"]
......
......@@ -54,7 +54,7 @@ class OCRService(WebService):
det_img = self.det_preprocess(im)
_, self.new_h, self.new_w = det_img.shape
print(det_img)
return {"image": det_img}, ["concat_1.tmp_0"]
return {"image": det_img}, ["concat_1.tmp_0"], False
def postprocess(self, feed={}, fetch=[], fetch_map=None):
det_out = fetch_map["concat_1.tmp_0"]
......
......@@ -42,10 +42,9 @@ class OCRService(WebService):
self.det_client = LocalPredictor()
if sys.argv[1] == 'gpu':
self.det_client.load_model_config(
det_model_config, gpu=True, profile=False)
det_model_config, use_gpu=True, gpu_id=1)
elif sys.argv[1] == 'cpu':
self.det_client.load_model_config(
det_model_config, gpu=False, profile=False)
self.det_client.load_model_config(det_model_config)
self.ocr_reader = OCRReader()
def preprocess(self, feed=[], fetch=[]):
......@@ -58,7 +57,7 @@ class OCRService(WebService):
det_img = det_img[np.newaxis, :]
det_img = det_img.copy()
det_out = self.det_client.predict(
feed={"image": det_img}, fetch=["concat_1.tmp_0"])
feed={"image": det_img}, fetch=["concat_1.tmp_0"], batch=True)
filter_func = FilterBoxes(10, 10)
post_func = DBPostProcess({
"thresh": 0.3,
......@@ -91,7 +90,7 @@ class OCRService(WebService):
imgs[id] = norm_img
feed = {"image": imgs.copy()}
fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
return feed, fetch
return feed, fetch, True
def postprocess(self, feed={}, fetch=[], fetch_map=None):
rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
......@@ -107,7 +106,8 @@ ocr_service.load_model_config("ocr_rec_model")
ocr_service.prepare_server(workdir="workdir", port=9292)
ocr_service.init_det_debugger(det_model_config="ocr_det_model")
if sys.argv[1] == 'gpu':
ocr_service.run_debugger_service(gpu=True)
ocr_service.set_gpus("2")
ocr_service.run_debugger_service()
elif sys.argv[1] == 'cpu':
ocr_service.run_debugger_service()
ocr_service.run_web_service()
......@@ -36,4 +36,5 @@ for img_file in os.listdir(test_img_dir):
image = cv2_to_base64(image_data1)
data = {"feed": [{"image": image}], "fetch": ["res"]}
r = requests.post(url=url, headers=headers, data=json.dumps(data))
print(r)
print(r.json())
......@@ -50,7 +50,7 @@ class OCRService(WebService):
ori_h, ori_w, _ = im.shape
det_img = self.det_preprocess(im)
det_out = self.det_client.predict(
feed={"image": det_img}, fetch=["concat_1.tmp_0"])
feed={"image": det_img}, fetch=["concat_1.tmp_0"], batch=False)
_, new_h, new_w = det_img.shape
filter_func = FilterBoxes(10, 10)
post_func = DBPostProcess({
......@@ -77,10 +77,10 @@ class OCRService(WebService):
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for img in img_list:
norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
feed = {"image": norm_img}
feed_list.append(feed)
feed_list.append(norm_img[np.newaxis, :])
feed_batch = {"image": np.concatenate(feed_list, axis=0)}
fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
return feed_list, fetch
return feed_batch, fetch, True
def postprocess(self, feed={}, fetch=[], fetch_map=None):
rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
......
......@@ -52,7 +52,7 @@ class OCRService(WebService):
imgs[i] = norm_img
feed = {"image": imgs.copy()}
fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
return feed, fetch
return feed, fetch, True
def postprocess(self, feed={}, fetch=[], fetch_map=None):
rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
......
......@@ -51,10 +51,17 @@ class OCRService(WebService):
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for img in img_list:
norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
feed = {"image": norm_img}
feed_list.append(feed)
#feed = {"image": norm_img}
feed_list.append(norm_img)
if len(feed_list) == 1:
feed_batch = {
"image": np.concatenate(
feed_list, axis=0)[np.newaxis, :]
}
else:
feed_batch = {"image": np.concatenate(feed_list, axis=0)}
fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
return feed_list, fetch
return feed_batch, fetch, True
def postprocess(self, feed={}, fetch=[], fetch_map=None):
rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
......
......@@ -199,7 +199,7 @@ class WebService(object):
from paddle_serving_app.local_predict import LocalPredictor
self.client = LocalPredictor()
self.client.load_model_config(
"{}".format(self.model_config), gpu=False, profile=False)
"{}".format(self.model_config), use_gpu=False)
def run_web_service(self):
print("This API will be deprecated later. Please do not use it")
......
......@@ -260,7 +260,7 @@ class WebService(object):
from paddle_serving_app.local_predict import LocalPredictor
self.client = LocalPredictor()
self.client.load_model_config(
"{}".format(self.model_config), gpu=gpu, profile=False)
"{}".format(self.model_config), use_gpu=True, gpu_id=self.gpus[0])
def run_web_service(self):
print("This API will be deprecated later. Please do not use it")
......
......@@ -43,8 +43,8 @@ if '${PACK}' == 'ON':
copy_lib()
REQUIRED_PACKAGES = [
'six >= 1.10.0', 'protobuf >= 3.11.0', 'numpy >= 1.12', 'grpcio >= 1.28.1',
'grpcio-tools >= 1.28.1', 'requests'
'six >= 1.10.0', 'protobuf >= 3.11.0', 'numpy >= 1.12', 'grpcio <= 1.33.2',
'grpcio-tools <= 1.33.2'
]
......
......@@ -28,7 +28,7 @@ max_version, mid_version, min_version = util.python_version()
util.gen_pipeline_code("paddle_serving_server")
REQUIRED_PACKAGES = [
'six >= 1.10.0', 'protobuf >= 3.11.0', 'grpcio >= 1.28.1', 'grpcio-tools >= 1.28.1',
'six >= 1.10.0', 'protobuf >= 3.11.0', 'grpcio <= 1.33.2', 'grpcio-tools <= 1.33.2',
'paddle_serving_client', 'flask >= 1.1.1', 'paddle_serving_app', 'func_timeout', 'pyyaml'
]
......
......@@ -30,7 +30,7 @@ max_version, mid_version, min_version = util.python_version()
util.gen_pipeline_code("paddle_serving_server_gpu")
REQUIRED_PACKAGES = [
'six >= 1.10.0', 'protobuf >= 3.11.0', 'grpcio >= 1.28.1', 'grpcio-tools >= 1.28.1',
'six >= 1.10.0', 'protobuf >= 3.11.0', 'grpcio <= 1.33.2', 'grpcio-tools <= 1.33.2',
'paddle_serving_client', 'flask >= 1.1.1', 'paddle_serving_app', 'func_timeout', 'pyyaml'
]
......
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