cos_dcn_r50_fpn_1x_coco和ppyolo_r50vd_dcn_1x_coco 框偏移bug

python/examples/detection/fcos_dcn_r50_fpn_1x_coco/README.md

@@ -16,5 +16,5 @@ This model support TensorRT, if you want a faster inference, please use `--use_t
 
 ### Perform prediction
 ```
-python3 test_client.py 000000570688.jpg
+python3 test_client.py 000000014439.jpg
 ```

python/examples/detection/fcos_dcn_r50_fpn_1x_coco/README_CN.md

@@ -18,5 +18,5 @@ python3 -m paddle_serving_server.serve --model serving_server --port 9494 --gpu_
 
 ### 执行预测
 ```
-python3 test_client.py 000000570688.jpg
+python3 test_client.py 000000014439.jpg
 ```

python/examples/detection/fcos_dcn_r50_fpn_1x_coco/test_client.py

@@ -12,18 +12,18 @@
 # 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))
+    File2Image(), BGR2RGB(), Resize(
+        (608, 608), interpolation=cv2.INTER_LINEAR), Div(255.0), Transpose(
+            (2, 0, 1))
 ])
 
-postprocess = RCNNPostprocess("label_list.txt", "output")
+postprocess = RCNNPostprocess("label_list.txt", "output", [608, 608])
 client = Client()
 
 client.load_client_config("serving_client/serving_client_conf.prototxt")
@@ -38,3 +38,5 @@ fetch_map = client.predict(
     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/README.md

@@ -16,5 +16,5 @@ This model support TensorRT, if you want a faster inference, please use `--use_t
 
 ### Perform prediction
 ```
-python3 test_client.py 000000570688.jpg
+python3 test_client.py 000000014439.jpg
 ```

python/examples/detection/ssd_vgg16_300_240e_voc/README_CN.md

@@ -18,5 +18,5 @@ python3 -m paddle_serving_server.serve --model serving_server --port 9494 --gpu_
 
 ### 执行预测
 ```
-python3 test_client.py 000000570688.jpg
+python3 test_client.py 000000014439.jpg
 ```

python/examples/detection/ssd_vgg16_300_240e_voc/test_client.py

@@ -12,18 +12,18 @@
 # 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(),
-    Normalize([123.675, 116.28, 103.53], [58.395, 57.12, 57.375], False),
-    Resize((512, 512)), Transpose((2, 0, 1))
+    File2Image(), BGR2RGB(), Resize(
+        (608, 608), interpolation=cv2.INTER_LINEAR), Div(255.0), Transpose(
+            (2, 0, 1))
 ])
 
-postprocess = RCNNPostprocess("label_list.txt", "output")
+postprocess = RCNNPostprocess("label_list.txt", "output", [608, 608])
 client = Client()
 
 client.load_client_config("serving_client/serving_client_conf.prototxt")
@@ -33,9 +33,10 @@ im = preprocess(sys.argv[1])
 fetch_map = client.predict(
     feed={
         "image": im,
-        "im_shape": np.array([512, 512]),
         "scale_factor": np.array([1.0, 1.0]).reshape(-1),
     },
     fetch=["save_infer_model/scale_0.tmp_1"],
     batch=False)
 print(fetch_map)
+fetch_map["image"] = sys.argv[1]
+postprocess(fetch_map)