refine code

deepmd/entrypoints/freeze.py

@@ -367,7 +367,7 @@ def freeze_graph(
     )
     for name, param in st_model.named_buffers():
         print(
-            f"[{name}, {param.shape}] generated name in static_model is: {param.name}"
+            f"[{name}, {param.dtype}, {param.shape}] generated name in static_model is: {param.name}"
         )
     # 跳过对program的裁剪，从而保留rcut、ntypes等不参与前向的参数，从而在C++端可以获取这些参数
     skip_prune_program = True

deepmd/infer/deep_pot.py

@@ -155,7 +155,9 @@ def __init__(
         self.rcut = float(self.model.descrpt.buffer_rcut)
         self.dfparam = 0
         self.daparam = 0
-        self.t_tmap = "".join([chr(idx) for idx in self.model.buffer_tmap.tolist()])
+        # self.t_tmap = "".join([chr(idx) for idx in self.model.buffer_tmap.tolist()])
+        self.t_tmap = [chr(idx) for idx in self.model.buffer_tmap.tolist()]
+        self.t_tmap = [c for c in self.t_tmap if c != " "]
 
         # setup modifier
         try:

source/api_cc/src/DeepPot.cc

@@ -719,7 +719,7 @@ void DeepPot::init(const std::string& model,
   DPGetDeviceCount(gpu_num);  // check current device environment
 #endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
   if (use_paddle_inference) {
-    /*
+    /* water se_e2_a
     tensorflow::DT_DOUBLE = 2
     tensorflow::DT_FLOAT = 1
     paddle_infer::DataType::FLOAT64 = 7
@@ -749,6 +749,26 @@ void DeepPot::init(const std::string& model,
     [fitting.buffer_dfparam, []] generated name in static_model is: generated_tensor_9
     [fitting.buffer_daparam, []] generated name in static_model is: generated_tensor_10
     **/
+    /* spin se_e2_a
+    [buffer_tmap, [4]] generated name in static_model is: generated_tensor_14
+    [buffer_model_type, [4]] generated name in static_model is: generated_tensor_15
+    [buffer_model_version, [1]] generated name in static_model is: generated_tensor_16
+    [descrpt.buffer_rcut, []] generated name in static_model is: generated_tensor_3
+    [descrpt.buffer_ntypes, []] generated name in static_model is: generated_tensor_4
+    [descrpt.avg_zero, [3, 720]] generated name in static_model is: eager_tmp_0
+    [descrpt.std_ones, [3, 720]] generated name in static_model is: eager_tmp_1
+    [descrpt.t_rcut, []] generated name in static_model is: generated_tensor_5
+    [descrpt.buffer_sel, [3]] generated name in static_model is: generated_tensor_6
+    [descrpt.buffer_ndescrpt, []] generated name in static_model is: generated_tensor_7
+    [descrpt.buffer_original_sel, [3]] generated name in static_model is: generated_tensor_8
+    [descrpt.t_avg, [3, 720]] generated name in static_model is: generated_tensor_9
+    [descrpt.t_std, [3, 720]] generated name in static_model is: generated_tensor_10
+    [descrpt.spin.buffer_ntypes_spin, [1]] generated name in static_model is: generated_tensor_0
+    [descrpt.spin.buffer_virtual_len, [1, 1]] generated name in static_model is: generated_tensor_1
+    [descrpt.spin.buffer_spin_norm, [1, 1]] generated name in static_model is: generated_tensor_2
+    [fitting.buffer_dfparam, []] generated name in static_model is: generated_tensor_11
+    [fitting.buffer_daparam, []] generated name in static_model is: generated_tensor_12                                                                                  [fitting.t_bias_atom_e, [2]] generated name in static_model is: generated_tensor_13
+   */
     model_version = paddle_get_scalar<std::string>("generated_tensor_14");
     dtype = predictor_get_dtype(predictor, "generated_tensor_0");
     if (dtype == paddle_infer::DataType::FLOAT64) {
@@ -757,7 +777,7 @@ void DeepPot::init(const std::string& model,
       rcut = paddle_get_scalar<float>("generated_tensor_0");
     }
     ntypes = paddle_get_scalar<int32_t>("generated_tensor_2");
-    // ntypes_spin = paddle_get_scalar<int64_t>("buffer_ntypes_spin");
+    // ntypes_spin = paddle_get_scalar<int32_t>("buffer_ntypes_spin");
     ntypes_spin = 0;
     dfparam = paddle_get_scalar<int64_t>("generated_tensor_9");
     daparam = paddle_get_scalar<int64_t>("generated_tensor_10");