fix code review alarms

mindspore/ccsrc/backend/kernel_compiler/cpu/gather_d_cpu_kernel.cc

@@ -28,7 +28,7 @@ size_t get_element_num(const std::vector<size_t> &shape) {
 }
 
 template <typename T, typename I>
-void CopyTask(size_t cur, std::vector<size_t> *pos, T *input, I *index, const int &dim, T *output,
+void CopyTask(size_t cur, std::vector<size_t> *pos, T *input, const I *index, const int &dim, T *output,
               const std::vector<size_t> &output_shape, const std::vector<size_t> &out_cargo_size,
               const std::vector<size_t> &input_cargo_size, bool reverse) {
   for (size_t i = 0; i < output_shape[cur]; ++i) {
@@ -65,7 +65,6 @@ template <typename T, typename I>
 void GatherDCPUKernel<T, I>::InitKernel(const CNodePtr &kernel_node) {
   input_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
   index_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 2);
-
   if (input_shape_.size() != index_shape_.size()) {
     MS_LOG(EXCEPTION) << "Invalid shape size, shape size of input: " << input_shape_.size()
                       << ", and index: " << index_shape_.size() << " should be equal";
@@ -81,7 +80,6 @@ bool GatherDCPUKernel<T, I>::Launch(const std::vector<kernel::AddressPtr> &input
   size_t index_size = get_element_num(index_shape_) * sizeof(I);
   size_t dim_size = sizeof(int);
   size_t output_size = get_element_num(output_shape_) * sizeof(T);
-
   if (inputs[0]->size != input_size || inputs[1]->size != dim_size || inputs[2]->size != index_size ||
       outputs[0]->size != output_size) {
     MS_LOG(EXCEPTION) << "invalid input or output data size!";
@@ -92,7 +90,6 @@ bool GatherDCPUKernel<T, I>::Launch(const std::vector<kernel::AddressPtr> &input
   auto index = reinterpret_cast<I *>(inputs[2]->addr);
   auto output = reinterpret_cast<T *>(outputs[0]->addr);
   int32_t input_rank = SizeToInt(input_shape_.size());
-
   if (dim[0] >= input_rank || dim[0] < -input_rank) {
     MS_LOG(EXCEPTION) << "The value of 'dim' should be in [" << -input_rank << ", " << input_rank
                       << "], but got: " << dim[0];

mindspore/ccsrc/backend/kernel_compiler/cpu/gather_d_cpu_kernel.h

@@ -37,7 +37,6 @@ class GatherDCPUKernel : public CPUKernel {
   std::vector<size_t> input_shape_;
   std::vector<size_t> index_shape_;
   std::vector<size_t> output_shape_;
-  int32_t axis_;
 };
 
 MS_REG_CPU_KERNEL_T_S(GatherD,

mindspore/ccsrc/backend/kernel_compiler/cpu/minimum_cpu_kernel.cc

@@ -19,7 +19,6 @@
 
 namespace mindspore {
 namespace kernel {
-
 template <typename T>
 void MinimumCPUKernel<T>::InitKernel(const CNodePtr &kernel_node) {
   CheckParam(kernel_node);
@@ -147,7 +146,7 @@ void MinimumCPUKernel<T>::InitTensorBroadcastShape() {
   }
 }
 
-// Broadcast comparation
+// Broadcast comparison
 template <typename T>
 size_t MinimumCPUKernel<T>::Index(const size_t &index, const size_t &dim) {
   return dim == 1 ? 0 : index;
@@ -216,6 +215,5 @@ void MinimumCPUKernel<T>::BroadcastArithTensors(const T *input_x, const T *input
     output[i] = MinimumFunc(input_x[i], input_y[i]);
   }
 }
-
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/cpu/minimum_grad_cpu_kernel.cc

@@ -115,8 +115,8 @@ void MinimumGradCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs, c
 
   size_t x_tensor_len = GetTensorLen(x_shape_);
   size_t y_tensor_len = GetTensorLen(y_shape_);
-  memset(dx_addr, 0, x_tensor_len * sizeof(T));
-  memset(dy_addr, 0, y_tensor_len * sizeof(T));
+  memset_s(dx_addr, x_tensor_len * sizeof(T), 0x00, x_tensor_len * sizeof(T));
+  memset_s(dy_addr, y_tensor_len * sizeof(T), 0x00, y_tensor_len * sizeof(T));
 
   std::vector<size_t> x_shape(dout_shape.size(), 1);
   std::vector<size_t> y_shape(dout_shape.size(), 1);

model_zoo/research/cv/centernet/src/dataset.py

@@ -276,10 +276,6 @@ class COCOHP(ds.Dataset):
         inp = cv2.warpAffine(img, trans_input, (self.data_opt.input_res[0], self.data_opt.input_res[1]),
                              flags=cv2.INTER_LINEAR)
 
-        # caution: image normalization and transpose to nchw will both be done on device
-        # inp = (inp.astype(np.float32) / 255. - self.data_opt.mean) / self.data_opt.std
-        # inp = inp.transpose(2, 0, 1)
-
         assert self.data_opt.output_res[0] == self.data_opt.output_res[1]
         output_res = self.data_opt.output_res[0]
         num_joints = self.data_opt.num_joints

model_zoo/research/cv/centernet/src/dcn_v2.py

@@ -252,7 +252,6 @@ class DeformConv2d(nn.Cell):
                     self.expand_dims(g_rt, 1) * x_q_rt)
 
         if self.modulation:
-            # modulation (b, 1, h, w, N)
             m = self.sigmoid(self.m_conv(x))
             m = self.transpose(m, self.perm_list)
             m = self.expand_dims(m, 1)

model_zoo/research/cv/centernet/src/post_process.py

@@ -56,7 +56,6 @@ def merge_outputs(detections, soft_nms=True):
 def convert_eval_format(detections, img_id):
     """convert detection to annotation json format"""
     # detections. scores: (b, K); bboxes: (b, K, 4); kps: (b, K, J * 2); clses: (b, K)
-    # only batch_size = 1 is supported
     detections = np.array(detections).reshape((-1, 39))
     pred_anno = {"images": [], "annotations": []}
     num_objs, _ = detections.shape

model_zoo/research/cv/centernet/src/visual.py

@@ -124,13 +124,26 @@ def visual_image(img, annos, save_path, ratio=None, height=None, width=None, nam
     h, w = img.shape[0], img.shape[1]
     num_objects = len(annos)
     num = 0
+
+    def define_color(pair):
+        """define line color"""
+        left_part = [0, 1, 3, 5, 7, 9, 11, 13, 15]
+        right_part = [0, 2, 4, 6, 8, 10, 12, 14, 16]
+        if pair[0] in left_part and pair[1] in left_part:
+            color = (255, 0, 0)
+        elif pair[0] in right_part and pair[1] in right_part:
+            color = (0, 0, 255)
+        else:
+            color = (139, 0, 255)
+        return color
+
+    def visible(a, w, h):
+        return a[0] >= 0 and a[0] < w and a[1] >= 0 and a[1] < h
+
     for i in range(num_objects):
         ann = annos[i]
         bbox = coco_box_to_bbox(ann['bbox'])
-        if "score" in ann:
-            score = ann["score"]
-            if score < score_threshold and num != 0:
-                continue
+        if "score" in ann and (ann["score"] >= score_threshold or num == 0):
             num += 1
             txt = ("p" + "{:.2f}".format(ann["score"]))
             cv2.putText(img, txt, (bbox[0], bbox[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
@@ -143,33 +156,19 @@ def visual_image(img, annos, save_path, ratio=None, height=None, width=None, nam
         keypoints = ann["keypoints"]
         keypoints = np.array(keypoints, dtype=np.int32).reshape(_NUM_JOINTS, 3).tolist()
 
-        left_part = [0, 1, 3, 5, 7, 9, 11, 13, 15]
-        right_part = [0, 2, 4, 6, 8, 10, 12, 14, 16]
         for pair in data_cfg.edges:
             partA = pair[0]
             partB = pair[1]
-            if partA in left_part and partB in left_part:
-                color = (255, 0, 0)
-            elif partA in right_part and partB in right_part:
-                color = (0, 0, 255)
-            else:
-                color = (139, 0, 255)
+            color = define_color(pair)
             p_a = tuple(keypoints[partA][:2])
             p_b = tuple(keypoints[partB][:2])
             mask_a = keypoints[partA][2]
             mask_b = keypoints[partB][2]
-            if (p_a[0] >= 0 and p_a[0] < w and p_a[1] >= 0 and p_a[1] < h and
-                    p_b[0] >= 0 and p_b[0] < w and p_b[1] >= 0 and p_b[1] < h and
-                    mask_a * mask_b > 0):
+            if (visible(p_a, w, h) and visible(p_b, w, h) and mask_a * mask_b > 0):
                 cv2.line(img, p_a, p_b, color, 2)
                 cv2.circle(img, p_a, 3, color, thickness=-1, lineType=cv2.FILLED)
                 cv2.circle(img, p_b, 3, color, thickness=-1, lineType=cv2.FILLED)
-    if annos and "image_id" in annos[0]:
-        img_id = annos[0]["image_id"]
-    else:
-        img_id = random.randint(0, 9999999)
-    if name is None:
-        image_name = "cv_image_" + str(img_id) + ".png"
-    else:
-        image_name = "cv_image_" + str(img_id) + name + ".png"
+
+    img_id = annos[0]["image_id"] if annos and "image_id" in annos[0] else random.randint(0, 9999999)
+    image_name = "cv_image_" + str(img_id) + ".png" if name is None else "cv_image_" + str(img_id) + name + ".png"
     cv2.imwrite("{}/{}".format(save_path, image_name), img)