Merge pull request #1413 from felixhjh/develop

DLTP-36497 [Bug] 【develop】detection示例检测框偏离

core/general-client/include/general_model.h

@@ -266,10 +266,14 @@ class PredictorClient {
                     const std::vector<std::string>& float_feed_name,
                     const std::vector<std::vector<int>>& float_shape,
                     const std::vector<std::vector<int>>& float_lod_slot_batch,
-                    const std::vector<py::array_t<int64_t>>& int_feed,
-                    const std::vector<std::string>& int_feed_name,
-                    const std::vector<std::vector<int>>& int_shape,
-                    const std::vector<std::vector<int>>& int_lod_slot_batch,
+                    const std::vector<py::array_t<int32_t>> &int32_feed,
+                    const std::vector<std::string> &int32_feed_name,
+                    const std::vector<std::vector<int>> &int32_shape,
+                    const std::vector<std::vector<int>> &int32_lod_slot_batch,
+                    const std::vector<py::array_t<int64_t>> &int64_feed,
+                    const std::vector<std::string> &int64_feed_name,
+                    const std::vector<std::vector<int>> &int64_shape,
+                    const std::vector<std::vector<int>> &int64_lod_slot_batch,
                     const std::vector<std::string>& string_feed,
                     const std::vector<std::string>& string_feed_name,
                     const std::vector<std::vector<int>>& string_shape,

core/general-client/src/general_model.cpp

@@ -168,10 +168,14 @@ int PredictorClient::numpy_predict(
     const std::vector<std::string> &float_feed_name,
     const std::vector<std::vector<int>> &float_shape,
     const std::vector<std::vector<int>> &float_lod_slot_batch,
-    const std::vector<py::array_t<int64_t>> &int_feed,
-    const std::vector<std::string> &int_feed_name,
-    const std::vector<std::vector<int>> &int_shape,
-    const std::vector<std::vector<int>> &int_lod_slot_batch,
+    const std::vector<py::array_t<int32_t>> &int32_feed,
+    const std::vector<std::string> &int32_feed_name,
+    const std::vector<std::vector<int>> &int32_shape,
+    const std::vector<std::vector<int>> &int32_lod_slot_batch,
+    const std::vector<py::array_t<int64_t>> &int64_feed,
+    const std::vector<std::string> &int64_feed_name,
+    const std::vector<std::vector<int>> &int64_shape,
+    const std::vector<std::vector<int>> &int64_lod_slot_batch,
     const std::vector<std::string> &string_feed,
     const std::vector<std::string> &string_feed_name,
     const std::vector<std::vector<int>> &string_shape,
@@ -190,7 +194,8 @@ int PredictorClient::numpy_predict(
   predict_res_batch.set_variant_tag(variant_tag);
   VLOG(2) << "fetch general model predictor done.";
   VLOG(2) << "float feed name size: " << float_feed_name.size();
-  VLOG(2) << "int feed name size: " << int_feed_name.size();
+  VLOG(2) << "int feed name size: " << int32_feed_name.size();
+  VLOG(2) << "int feed name size: " << int64_feed_name.size();
   VLOG(2) << "string feed name size: " << string_feed_name.size();
   VLOG(2) << "max body size : " << brpc::fLU64::FLAGS_max_body_size;
   Request req;
@@ -207,7 +212,11 @@ int PredictorClient::numpy_predict(
     tensor_vec.push_back(req.add_tensor());
   }
 
-  for (auto &name : int_feed_name) {
+  for (auto &name : int32_feed_name) {
+    tensor_vec.push_back(req.add_tensor());
+  }
+
+  for (auto &name : int64_feed_name) {
     tensor_vec.push_back(req.add_tensor());
   }
 
@@ -247,34 +256,58 @@ int PredictorClient::numpy_predict(
   }
 
   vec_idx = 0;
-  for (auto &name : int_feed_name) {
+  for (auto &name : int32_feed_name) {
     int idx = _feed_name_to_idx[name];
     if (idx >= tensor_vec.size()) {
       LOG(ERROR) << "idx > tensor_vec.size()";
       return -1;
     }
     Tensor *tensor = tensor_vec[idx];
-    int nbytes = int_feed[vec_idx].nbytes();
-    void *rawdata_ptr = (void *)(int_feed[vec_idx].data(0));
-    int total_number = int_feed[vec_idx].size();
+    int nbytes = int32_feed[vec_idx].nbytes();
+    void *rawdata_ptr = (void *)(int32_feed[vec_idx].data(0));
+    int total_number = int32_feed[vec_idx].size();
 
-    for (uint32_t j = 0; j < int_shape[vec_idx].size(); ++j) {
-      tensor->add_shape(int_shape[vec_idx][j]);
+    for (uint32_t j = 0; j < int32_shape[vec_idx].size(); ++j) {
+      tensor->add_shape(int32_shape[vec_idx][j]);
     }
-    for (uint32_t j = 0; j < int_lod_slot_batch[vec_idx].size(); ++j) {
-      tensor->add_lod(int_lod_slot_batch[vec_idx][j]);
+    for (uint32_t j = 0; j < int32_lod_slot_batch[vec_idx].size(); ++j) {
+      tensor->add_lod(int32_lod_slot_batch[vec_idx][j]);
     }
     tensor->set_elem_type(_type[idx]);
     tensor->set_name(_feed_name[idx]);
     tensor->set_alias_name(name);
 
-    if (_type[idx] == P_INT64) {
-      tensor->mutable_int64_data()->Resize(total_number, 0);
-      memcpy(tensor->mutable_int64_data()->mutable_data(), rawdata_ptr, nbytes);
-    } else {
-      tensor->mutable_int_data()->Resize(total_number, 0);
-      memcpy(tensor->mutable_int_data()->mutable_data(), rawdata_ptr, nbytes);
+    tensor->mutable_int_data()->Resize(total_number, 0);
+    memcpy(tensor->mutable_int_data()->mutable_data(), rawdata_ptr, nbytes);
+    vec_idx++;
+  }
+
+
+  // Individual INT_64 feed data of int_input to tensor_content
+  vec_idx = 0;
+  for (auto &name : int64_feed_name) {
+    int idx = _feed_name_to_idx[name];
+    if (idx >= tensor_vec.size()) {
+      LOG(ERROR) << "idx > tensor_vec.size()";
+      return -1;
+    }
+    Tensor *tensor = tensor_vec[idx];
+    int nbytes = int64_feed[vec_idx].nbytes();
+    void *rawdata_ptr = (void *)(int64_feed[vec_idx].data(0));
+    int total_number = int64_feed[vec_idx].size();
+
+    for (uint32_t j = 0; j < int64_shape[vec_idx].size(); ++j) {
+      tensor->add_shape(int64_shape[vec_idx][j]);
     }
+    for (uint32_t j = 0; j < int64_lod_slot_batch[vec_idx].size(); ++j) {
+      tensor->add_lod(int64_lod_slot_batch[vec_idx][j]);
+    }
+    tensor->set_elem_type(_type[idx]);
+    tensor->set_name(_feed_name[idx]);
+    tensor->set_alias_name(name);
+
+    tensor->mutable_int64_data()->Resize(total_number, 0);
+    memcpy(tensor->mutable_int64_data()->mutable_data(), rawdata_ptr, nbytes);
     vec_idx++;
   }
 

core/general-client/src/pybind_general_model.cpp

@@ -117,10 +117,14 @@ PYBIND11_MODULE(serving_client, m) {
               const std::vector<std::string> &float_feed_name,
               const std::vector<std::vector<int>> &float_shape,
               const std::vector<std::vector<int>> &float_lod_slot_batch,
-              const std::vector<py::array_t<int64_t>> &int_feed,
-              const std::vector<std::string> &int_feed_name,
-              const std::vector<std::vector<int>> &int_shape,
-              const std::vector<std::vector<int>> &int_lod_slot_batch,
+              const std::vector<py::array_t<int32_t>> &int32_feed,
+              const std::vector<std::string> &int32_feed_name,
+              const std::vector<std::vector<int>> &int32_shape,
+              const std::vector<std::vector<int>> &int32_lod_slot_batch,
+              const std::vector<py::array_t<int64_t>> &int64_feed,
+              const std::vector<std::string> &int64_feed_name,
+              const std::vector<std::vector<int>> &int64_shape,
+              const std::vector<std::vector<int>> &int64_lod_slot_batch,
               const std::vector<std::string> &string_feed,
               const std::vector<std::string> &string_feed_name,
               const std::vector<std::vector<int>> &string_shape,
@@ -133,10 +137,14 @@ PYBIND11_MODULE(serving_client, m) {
                                        float_feed_name,
                                        float_shape,
                                        float_lod_slot_batch,
-                                       int_feed,
-                                       int_feed_name,
-                                       int_shape,
-                                       int_lod_slot_batch,
+                                       int32_feed,
+                                       int32_feed_name,
+                                       int32_shape,
+                                       int32_lod_slot_batch,
+                                       int64_feed,
+                                       int64_feed_name,
+                                       int64_shape,
+                                       int64_lod_slot_batch,
                                        string_feed,
                                        string_feed_name,
                                        string_shape,

python/examples/cascade_rcnn/get_data.sh

-wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/cascade_rcnn_r50_fpx_1x_serving.tar.gz
-tar xf cascade_rcnn_r50_fpx_1x_serving.tar.gz
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco_serving.tar.gz
+tar xf cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco_serving.tar.gz

python/examples/cascade_rcnn/test_client.py

@@ -12,29 +12,32 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import sys
+import numpy as np
 from paddle_serving_client import Client
 from paddle_serving_app.reader import *
-import numpy as np
-
+import cv2
 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)
+    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")
 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])
+        "im_shape": np.array(list(im.shape[1:])).reshape(-1),
+        "scale_factor": np.array([1.0, 1.0]).reshape(-1),
     },
-    fetch=["multiclass_nms_0.tmp_0"],
+    fetch=["save_infer_model/scale_0.tmp_1"],
     batch=False)
-fetch_map["image"] = '000000570688.jpg'
 print(fetch_map)
+fetch_map["image"] = '000000570688.jpg'
 postprocess(fetch_map)
-print(fetch_map)

python/examples/detection/faster_rcnn_hrnetv2p_w18_1x/README.md

@@ -4,7 +4,7 @@
 
 ### Get The Faster RCNN HRNet Model
 ```
-wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/faster_rcnn_hrnetv2p_w18_1x.tar
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/faster_rcnn_hrnetv2p_w18_1x.tar.gz
 ```
 
 ### Start the service

python/examples/detection/faster_rcnn_hrnetv2p_w18_1x/README_CN.md

@@ -4,7 +4,7 @@
 
 ## 获得Faster RCNN HRNet模型
 ```
-wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/faster_rcnn_hrnetv2p_w18_1x.tar
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/faster_rcnn_hrnetv2p_w18_1x.tar.gz
 ```
 
 

python/examples/detection/faster_rcnn_hrnetv2p_w18_1x/test_client.py

-from paddle_serving_client import Client
-from paddle_serving_app.reader import *
+# 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.
+
 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")
@@ -19,9 +33,11 @@ im = preprocess(sys.argv[1])
 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])
+        "im_shape": np.array(list(im.shape[1:])).reshape(-1),
+        "scale_factor": np.array([1.0, 1.0]).reshape(-1),
     },
-    fetch=["multiclass_nms_0.tmp_0"],
+    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/ppyolo_r50vd_dcn_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((608, 608)), 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")

python/examples/detection/yolov3_darknet53_270e_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((608, 608)), 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")

python/paddle_serving_client/client.py

@@ -335,10 +335,15 @@ class Client(object):
         if len(feed_batch) != 1:
             raise ValueError("len of feed_batch can only be 1.")
 
-        int_slot = []
-        int_feed_names = []
-        int_shape = []
-        int_lod_slot_batch = []
+        int32_slot = []
+        int32_feed_names = []
+        int32_shape = []
+        int32_lod_slot_batch = []
+
+        int64_slot = []
+        int64_feed_names = []
+        int64_shape = []
+        int64_lod_slot_batch = []
 
         float_slot = []
         float_feed_names = []
@@ -364,27 +369,39 @@ class Client(object):
 
             self.shape_check(feed_dict, key)
             if self.feed_types_[key] in int_type:
-                int_feed_names.append(key)
                 shape_lst = []
                 if batch == False:
                     feed_dict[key] = np.expand_dims(feed_dict[key], 0).repeat(
                         1, axis=0)
-                if isinstance(feed_dict[key], np.ndarray):
-                    shape_lst.extend(list(feed_dict[key].shape))
-                    int_shape.append(shape_lst)
-                else:
-                    int_shape.append(self.feed_shapes_[key])
-                if "{}.lod".format(key) in feed_dict:
-                    int_lod_slot_batch.append(feed_dict["{}.lod".format(key)])
+                # verify different input int_type
+                if(self.feed_types_[key] == int64_type):
+                    int64_feed_names.append(key)
+                    if isinstance(feed_dict[key], np.ndarray):
+                        shape_lst.extend(list(feed_dict[key].shape))
+                        int64_shape.append(shape_lst)
+                        self.has_numpy_input = True
+                    else:
+                        int64_shape.append(self.feed_shapes_[key])
+                        self.all_numpy_input = False
+                    if "{}.lod".format(key) in feed_dict:
+                        int64_lod_slot_batch.append(feed_dict["{}.lod".format(key)])
+                    else:
+                        int64_lod_slot_batch.append([])
+                    int64_slot.append(np.ascontiguousarray(feed_dict[key]))
                 else:
-                    int_lod_slot_batch.append([])
-
-                if isinstance(feed_dict[key], np.ndarray):
-                    int_slot.append(np.ascontiguousarray(feed_dict[key]))
-                    self.has_numpy_input = True
-                else:
-                    int_slot.append(np.ascontiguousarray(feed_dict[key]))
-                    self.all_numpy_input = False
+                    int32_feed_names.append(key)
+                    if isinstance(feed_dict[key], np.ndarray):
+                        shape_lst.extend(list(feed_dict[key].shape))
+                        int32_shape.append(shape_lst)
+                        self.has_numpy_input = True
+                    else:
+                        int32_shape.append(self.feed_shapes_[key])
+                        self.all_numpy_input = False
+                    if "{}.lod".format(key) in feed_dict:
+                        int32_lod_slot_batch.append(feed_dict["{}.lod".format(key)])
+                    else:
+                        int32_lod_slot_batch.append([])
+                    int32_slot.append(np.ascontiguousarray(feed_dict[key]))
 
             elif self.feed_types_[key] in float_type:
                 float_feed_names.append(key)
@@ -430,7 +447,8 @@ class Client(object):
         if self.all_numpy_input:
             res = self.client_handle_.numpy_predict(
                 float_slot, float_feed_names, float_shape, float_lod_slot_batch,
-                int_slot, int_feed_names, int_shape, int_lod_slot_batch,
+                int32_slot, int32_feed_names, int32_shape, int32_lod_slot_batch,
+                int64_slot, int64_feed_names, int64_shape, int64_lod_slot_batch,
                 string_slot, string_feed_names, string_shape,
                 string_lod_slot_batch, fetch_names, result_batch_handle,
                 self.pid, log_id)