Merge pull request #1421 from felixhjh/develop

modify detection examples preprocess

python/examples/cascade_rcnn/README.md

@@ -15,7 +15,7 @@ python3 -m paddle_serving_server.serve --model serving_server --port 9292 --gpu_
 
 ### Perform prediction
 ```
-python3 test_client.py 
+python3 test_client.py 000000570688.jpg
 ```
 
 Image with bounding boxes and json result would be saved in `output` folder.

python/examples/cascade_rcnn/README_CN.md

@@ -15,7 +15,7 @@ python3 -m paddle_serving_server.serve --model serving_server --port 9292 --gpu_
 
 ### 执行预测
 ```
-python3 test_client.py
+python3 test_client.py 000000570688.jpg
 ```
 
 客户端已经为图片做好了后处理，在`output`文件夹下存放各个框的json格式信息还有后处理结果图片。

python/examples/cascade_rcnn/test_client.py

@@ -17,27 +17,30 @@
 from paddle_serving_client import Client
 from paddle_serving_app.reader import *
 import cv2
-preprocess = Sequential([
-    File2Image(), BGR2RGB(), Resize(
-        (608, 608), interpolation=cv2.INTER_LINEAR), Div(255.0), Transpose(
-            (2, 0, 1))
+
+preprocess = DetectionSequential([
+        DetectionFile2Image(),
+        DetectionResize((800, 1333), True, interpolation=2), 
+        DetectionNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True),
+        DetectionTranspose((2,0,1)),
+        DetectionPadStride(32)
 ])
 
-postprocess = RCNNPostprocess("label_list.txt", "output", [608, 608])
+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')
+im, im_info = preprocess(sys.argv[1])
 fetch_map = client.predict(
     feed={
         "image": im,
         "im_shape": np.array(list(im.shape[1:])).reshape(-1),
-        "scale_factor": np.array([1.0, 1.0]).reshape(-1),
+        "scale_factor": im_info['scale_factor'],
     },
     fetch=["save_infer_model/scale_0.tmp_1"],
     batch=False)
 print(fetch_map)
-fetch_map["image"] = '000000570688.jpg'
+fetch_map["image"] = sys.argv[1]
 postprocess(fetch_map)

python/examples/detection/faster_rcnn_hrnetv2p_w18_1x/README.md

@@ -9,7 +9,7 @@ wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/fast
 
 ### Start the service
 ```
-tar xf faster_rcnn_hrnetv2p_w18_1x.tar
+tar xf faster_rcnn_hrnetv2p_w18_1x.tar.gz
 python3 -m paddle_serving_server.serve --model serving_server --port 9494 --gpu_ids 0
 ```
 

python/examples/detection/faster_rcnn_hrnetv2p_w18_1x/README_CN.md

@@ -10,7 +10,7 @@ wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/fast
 
 ### 启动服务
 ```
-tar xf faster_rcnn_hrnetv2p_w18_1x.tar
+tar xf faster_rcnn_hrnetv2p_w18_1x.tar.gz
 python3 -m paddle_serving_server.serve --model serving_server --port 9494 --gpu_ids 0
 ```
 该模型支持TensorRT，如果想要更快的预测速度，可以开启`--use_trt`选项,但此时需要额外设置子图的TRT变长最大最小最优shape.

python/examples/detection/faster_rcnn_hrnetv2p_w18_1x/test_client.py

@@ -17,24 +17,27 @@
 from paddle_serving_client import Client
 from paddle_serving_app.reader import *
 import cv2
-preprocess = Sequential([
-    File2Image(), BGR2RGB(), Resize(
-        (608, 608), interpolation=cv2.INTER_LINEAR), Div(255.0), Transpose(
-            (2, 0, 1))
+
+preprocess = DetectionSequential([
+       DetectionFile2Image(),
+       DetectionResize((800, 1333), True, interpolation=2), 
+       DetectionNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True),
+       DetectionTranspose((2,0,1)),
+       DetectionPadStride(32)
 ])
 
-postprocess = RCNNPostprocess("label_list.txt", "output", [608, 608])
+postprocess = RCNNPostprocess("label_list.txt", "output")
 client = Client()
 
 client.load_client_config("serving_client/serving_client_conf.prototxt")
 client.connect(['127.0.0.1:9494'])
 
-im = preprocess(sys.argv[1])
+im, im_info = preprocess(sys.argv[1])
 fetch_map = client.predict(
     feed={
         "image": im,
         "im_shape": np.array(list(im.shape[1:])).reshape(-1),
-        "scale_factor": np.array([1.0, 1.0]).reshape(-1),
+        "scale_factor": im_info['scale_factor'],
     },
     fetch=["save_infer_model/scale_0.tmp_1"],
     batch=False)

python/examples/detection/faster_rcnn_r50_fpn_1x_coco/test_client.py

@@ -12,15 +12,19 @@
 # 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 sys
 import numpy as np
+from paddle_serving_client import Client
+from paddle_serving_app.reader import *
+import cv2
 
-preprocess = Sequential([
-    File2Image(), BGR2RGB(), Div(255.0),
-    Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], False),
-    Resize(640, 640), Transpose((2, 0, 1))
+preprocess = DetectionSequential([
+        DetectionFile2Image(),
+        DetectionNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True),
+        DetectionResize(
+        (800, 1333), True, interpolation=cv2.INTER_LINEAR), 
+        DetectionTranspose((2,0,1)),
+        DetectionPadStride(128)
 ])
 
 postprocess = RCNNPostprocess("label_list.txt", "output")
@@ -29,15 +33,14 @@
 client.load_client_config("serving_client/serving_client_conf.prototxt")
 client.connect(['127.0.0.1:9494'])
 
-im = preprocess(sys.argv[1])
+im, im_info = preprocess(sys.argv[1])
 fetch_map = client.predict(
     feed={
         "image": im,
         "im_shape": np.array(list(im.shape[1:])).reshape(-1),
-        "scale_factor": np.array([1.0, 1.0]).reshape(-1),
+        "scale_factor": im_info['scale_factor'],
     },
     fetch=["save_infer_model/scale_0.tmp_1"],
     batch=False)
-print(fetch_map)
 fetch_map["image"] = sys.argv[1]
 postprocess(fetch_map)

python/examples/detection/fcos_dcn_r50_fpn_1x_coco/test_client.py

@@ -17,23 +17,27 @@
 from paddle_serving_client import Client
 from paddle_serving_app.reader import *
 import cv2
-preprocess = Sequential([
-    File2Image(), BGR2RGB(), Resize(
-        (608, 608), interpolation=cv2.INTER_LINEAR), Div(255.0), Transpose(
-            (2, 0, 1))
+
+preprocess = DetectionSequential([
+        DetectionFile2Image(),
+        DetectionNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True),
+        DetectionResize(
+        (800, 1333), True, interpolation=cv2.INTER_LINEAR), 
+        DetectionTranspose((2,0,1)),
+        DetectionPadStride(128)
 ])
 
-postprocess = RCNNPostprocess("label_list.txt", "output", [608, 608])
+postprocess = RCNNPostprocess("label_list.txt", "output")
 client = Client()
 
 client.load_client_config("serving_client/serving_client_conf.prototxt")
 client.connect(['127.0.0.1:9494'])
 
-im = preprocess(sys.argv[1])
+im, im_info = preprocess(sys.argv[1])
 fetch_map = client.predict(
     feed={
         "image": im,
-        "scale_factor": np.array([1.0, 1.0]).reshape(-1),
+        "scale_factor": im_info['scale_factor'],
     },
     fetch=["save_infer_model/scale_0.tmp_1"],
     batch=False)

python/examples/detection/ppyolo_r50vd_dcn_1x_coco/test_client.py

@@ -17,27 +17,29 @@
 from paddle_serving_client import Client
 from paddle_serving_app.reader import *
 import cv2
-preprocess = Sequential([
-    File2Image(), BGR2RGB(), Resize(
-        (608, 608), interpolation=cv2.INTER_LINEAR), Div(255.0), Transpose(
-            (2, 0, 1))
+
+preprocess = DetectionSequential([
+        DetectionFile2Image(),
+        DetectionNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True),
+        DetectionResize(
+        (608, 608), False, interpolation=2), 
+        DetectionTranspose((2,0,1))
 ])
 
-postprocess = RCNNPostprocess("label_list.txt", "output", [608, 608])
+postprocess = RCNNPostprocess("label_list.txt", "output")
 client = Client()
 
 client.load_client_config("serving_client/serving_client_conf.prototxt")
 client.connect(['127.0.0.1:9494'])
 
-im = preprocess(sys.argv[1])
+im, im_info = preprocess(sys.argv[1])
 fetch_map = client.predict(
     feed={
         "image": im,
         "im_shape": np.array(list(im.shape[1:])).reshape(-1),
-        "scale_factor": np.array([1.0, 1.0]).reshape(-1),
+        "scale_factor": im_info['scale_factor'],
     },
     fetch=["save_infer_model/scale_0.tmp_1"],
     batch=False)
-print(fetch_map)
 fetch_map["image"] = sys.argv[1]
 postprocess(fetch_map)

python/examples/detection/ssd_vgg16_300_240e_voc/label_list.txt

@@ -1,80 +1,20 @@
-person
+aeroplane
 bicycle
-car
-motorcycle
-airplane
-bus
-train
-truck
-boat
-traffic light
-fire hydrant
-stop sign
-parking meter
-bench
 bird
+boat
+bottle
+bus
+car
 cat
+chair
+cow
+diningtable
 dog
 horse
+motorbike
+person
+pottedplant
 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
+sofa
+train
+tvmonitor

python/examples/detection/ssd_vgg16_300_240e_voc/test_client.py

@@ -17,23 +17,27 @@
 from paddle_serving_client import Client
 from paddle_serving_app.reader import *
 import cv2
-preprocess = Sequential([
-    File2Image(), BGR2RGB(), Resize(
-        (608, 608), interpolation=cv2.INTER_LINEAR), Div(255.0), Transpose(
-            (2, 0, 1))
+
+preprocess = DetectionSequential([
+        DetectionFile2Image(),
+        DetectionResize(
+        (300, 300), False, interpolation=cv2.INTER_LINEAR), 
+        DetectionNormalize([104.0, 117.0, 123.0], [1.0, 1.0, 1.0], False),
+        DetectionTranspose((2,0,1)),
 ])
 
-postprocess = RCNNPostprocess("label_list.txt", "output", [608, 608])
+postprocess = RCNNPostprocess("label_list.txt", "output")
 client = Client()
 
 client.load_client_config("serving_client/serving_client_conf.prototxt")
 client.connect(['127.0.0.1:9494'])
 
-im = preprocess(sys.argv[1])
+im, im_info = preprocess(sys.argv[1])
 fetch_map = client.predict(
     feed={
         "image": im,
-        "scale_factor": np.array([1.0, 1.0]).reshape(-1),
+        "im_shape": np.array(list(im.shape[1:])).reshape(-1),
+        "scale_factor": im_info['scale_factor'],
     },
     fetch=["save_infer_model/scale_0.tmp_1"],
     batch=False)

python/examples/detection/ttfnet_darknet53_1x_coco/README.md

@@ -4,7 +4,7 @@
 
 ### Get Model
 ```
-wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/ttfnet_darknet53_1x_coco.tar
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/ttfnet_darknet53_1x_coco.tar
 ```
 
 ### Start the service

python/examples/detection/ttfnet_darknet53_1x_coco/README_CN.md

@@ -4,7 +4,7 @@
 
 ## 获得模型
 ```
-wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/ttfnet_darknet53_1x_coco.tar
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/ttfnet_darknet53_1x_coco.tar
 ```