Merge pull request #55 from JDAI-CV/support_onnx_input

Support onnx input
JDAI-CV · Jun 18, 2019 · cf20ec9 · cf20ec9
2 parents 8cab557 + 550c68f
commit cf20ec9
Show file tree

Hide file tree

Showing 6 changed files with 336 additions and 64 deletions.
diff --git a/common/Shaper.cpp b/common/Shaper.cpp
@@ -243,8 +243,9 @@ void Shaper::Eltwise(const std::string &input1_name,
                      const std::string &output_name) {
     auto shape1 = shape_map_.at(input1_name);
     auto shape2 = shape_map_.at(input2_name);
+    // TODO: broadcasting
     auto output_shape =
-        shape1.size() > shape2.size() ? shape1 : shape2;  // broadcasting
+        shape1.size() >= shape2.size() ? shape1 : shape2;
     shape_map_[output_name] = output_shape;
 }
 

diff --git a/generate_code.py b/generate_code.py
@@ -98,14 +98,14 @@ def infer_cfg(cfg, target: Target):
             op['input'] = []
         if 'base_input_num' not in op or op['base_input_num'] == 1:
             op['input'].insert(0,
-                               {'name': 'input', 'nnapi_type': 'tensor', 'cpp_type': 'str', 'needed_by_shaper': True})
+                               {'name': 'input', 'nnapi_type': 'tensor', 'cpp_type': 'str', 'input': True, 'needed_by_shaper': True})
         elif op['base_input_num'] == 2:
-            op['input'] = [{'name': 'input1', 'nnapi_type': 'tensor', 'cpp_type': 'str', 'needed_by_shaper': True},
-                           {'name': 'input2', 'nnapi_type': 'tensor', 'cpp_type': 'str', 'needed_by_shaper': True}] \
+            op['input'] = [{'name': 'input1', 'nnapi_type': 'tensor', 'cpp_type': 'str', 'input': True,  'needed_by_shaper': True},
+                           {'name': 'input2', 'nnapi_type': 'tensor', 'cpp_type': 'str', 'input': True,  'needed_by_shaper': True}] \
                           + op['input']
         elif op['base_input_num'] == 'n':
             op['input'].insert(0,
-                               {'name': 'inputs', 'nnapi_type': 'tensor', 'cpp_type': 'str_list',
+                               {'name': 'inputs', 'nnapi_type': 'tensor', 'cpp_type': 'str_list', 'input': True,
                                 'needed_by_shaper': True})
         elif op['base_input_num'] == 0:
             pass
@@ -145,11 +145,12 @@ def infer_cfg(cfg, target: Target):
                 ipt['name'] = 'bias'
                 ipt['nnapi_type'] = 'tensor'
                 ipt['cpp_type'] = 'optional_str'
-                ipt['learnable'] = True
-            if 'learnable' not in ipt:
-                ipt['learnable'] = False
-            if ipt['learnable'] and 'convert_func' not in ipt:
+                ipt['input'] = True
                 ipt['convert_func'] = 'OnnxToNnapiIdentity'
+            if 'input' not in ipt:
+                ipt['input'] = False
+            if 'convert_func' not in ipt:
+                ipt['convert_func'] = 'OnnxToNnapiAxes0231'
             if 'needed_by_shaper' not in ipt:
                 ipt['needed_by_shaper'] = False
 
@@ -186,20 +187,28 @@ def generate_onnx_converter():
         if op['fused']:
             cogoutl(f"const auto activation = FindActivation(model_proto_, output);")
         for x in op['input']:
-            if x['learnable']:
-                assert x['cpp_type'] in ['str', 'optional_str']
+            if x['input']:
                 if x['cpp_type'] == 'str':
-                    cogoutl(f"""{{
-const auto name = {x['name']};""")
+                    cogoutl(f"""
+                    {{
+                        const auto name = {x['name']};""")
                 elif x['cpp_type'] == 'optional_str':
-                    cogoutl(f"""if ({x['name']}.has_value()) {{
-const auto name = {x['name']}.value();""")
-                cogoutl(f"""const auto &onnx_tensor = onnx_tensors_.at(name);
-const auto new_tensor = {x['convert_func']}(onnx_tensor);
-shaper_.AddShape(name, new_tensor.shape); 
-nnapi_tensors_[name] = new_tensor;
-CreateTensorFb(name, new_tensor);""")
-                cogoutl("}")
+                    cogoutl(f"""
+                    if ({x['name']}.has_value()) {{
+                        const auto name = {x['name']}.value();""")
+                elif x['cpp_type'] == 'str_list':
+                    cogoutl(f"""
+                    for (const auto &name : {x['name']}) {{""")
+                cogoutl(f"""
+                        if (onnx_tensors_.has(name)) {{
+                            const auto &onnx_tensor = onnx_tensors_.at(name);
+                            const auto new_tensor = {x['convert_func']}(onnx_tensor);
+                            shaper_.AddShape(name, new_tensor.shape); 
+                            nnapi_tensors_[name] = new_tensor;
+                            CreateTensorFb(name, new_tensor);
+                        }}
+                    }}
+                """)
             if x['cpp_type'] == 'str_list':
                 cogoutl(f"const auto {x['name']}_fb = FbStrVector({x['name']});")
 

diff --git a/include/dnnlibrary/ModelBuilder.h b/include/dnnlibrary/ModelBuilder.h
@@ -13,9 +13,9 @@
 #include <string>
 #include <vector>
 
-#include <common/data_types.h>
 #include <common/Shaper.h>
 #include <common/StrKeyMap.h>
+#include <common/data_types.h>
 #include <dnnlibrary/Model.h>
 #include <dnnlibrary/NeuralNetworksWrapper.h>
 

diff --git a/include/tools/onnx2daq/OnnxConverter.h b/include/tools/onnx2daq/OnnxConverter.h
@@ -158,16 +158,21 @@ class OnnxConverter {
     // OnnxConverter auto generated methods end
 
     /**
+     * transpose axes to [1, 2, 3, 0]
+     * for onnx dw conv weight to nnapi dw conv weight
      * onnx: [filter_out_channel, filter_in_channel / group, height, width]
      * nnapi: [1, height, width, depth_out]
      */
-    Tensor OnnxToNnapiDwConvWeight(const Tensor &src);
+    Tensor OnnxToNnapiAxes1230(const Tensor &src);
 
     /**
+     * transpose axes to [0, 2, 3, 1]
+     * for nchw (onnx) -> nhwc (nnapi)
+     * or onnx conv weight to nnapi conv (not dw conv) weight:
      * onnx: [filter_out_channel, filter_in_channel, height, width]
      * nnapi: [depth_out, height, width, depth_in]
      */
-    Tensor OnnxToNnapiVanillaConvWeight(const Tensor &src);
+    Tensor OnnxToNnapiAxes0231(const Tensor &src);
 
     /**
      * Just return the same tensor

diff --git a/ops.yml b/ops.yml
@@ -7,9 +7,8 @@
         -
             name: weight
             nnapi_type: tensor
-            # "learnable" stands for the tensor is read from serialized model file
-            learnable: true
-            convert_func: OnnxToNnapiVanillaConvWeight
+            # "input" stands for onnx input instead of attribute (TODO: a new name)
+            input: true
             cpp_type: str
             needed_by_shaper: true
         -
@@ -202,9 +201,10 @@
         -
             name: weight
             nnapi_type: tensor
-            learnable: true
+            input: true
             cpp_type: str
             needed_by_shaper: true
+            convert_func: OnnxToNnapiIdentity
         -
             predefined: optional_bias
     nnapi: FULLY_CONNECTED
@@ -238,8 +238,8 @@
         -
             name: weight
             nnapi_type: tensor
-            learnable: true
-            convert_func: OnnxToNnapiDwConvWeight
+            input: true
+            convert_func: OnnxToNnapiAxes1230
             cpp_type: str
             needed_by_shaper: true
         -