Updated README.md

.gitignore

@@ -127,3 +127,6 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+# MS Code
+.vscode/

README.md

@@ -1,6 +1,5 @@
 # onnx-pytorch
 
-
 ![Build Status](https://github.com/fumihwh/onnx-pytorch/actions/workflows/main.yml/badge.svg?branch=main)
 
 
@@ -9,14 +8,17 @@ Currently support `onnx==1.9.0` and `torch==1.8.1`.
 
 ## Installation
 
-- From PyPI
-```
+### From PyPI
+
+```bash
 pip install onnx-pytorch
 ```
 
-- From source
-```
+### From source
+
+```bash
 git clone https://github.com/fumihwh/onnx-pytorch.git
+cd onnx-pytorch
 pip install -r requirements.txt
 pip install -e .
 ```
@@ -25,7 +27,8 @@ pip install -e .
 ## Usage
 
 ### By Command Line
-```
+
+```bash
 python -m onnx_pytorch.code_gen -h
 
 usage: code_gen.py [-h] [--onnx_model_path ONNX_MODEL_PATH] [--output_dir OUTPUT_DIR] [--overwrite OVERWRITE] [--tensor_inplace TENSOR_INPLACE] [--continue_on_error CONTINUE_ON_ERROR] [--simplify_names SIMPLIFY_NAMES]
@@ -47,26 +50,32 @@ optional arguments:
 ```
 
 ### By Python
-```
+
+```python
 from onnx_pytorch import code_gen
 code_gen.gen("/path/to/onnx_model", "/path/to/output_dir")
 ```
 
 A `model.py` file and `variables` folder will be created under `output_dir`.
 
 ## Tutorial
+
 - Download resnet18 onnx model
-
-```wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet18-v2-7.onnx```
 
-- Use onnx-pytorch to generate pytorch code and variables.
+```bash
+wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet18-v2-7.onnx
 ```
+
+- Use onnx-pytorch to generate pytorch code and variables.
+
+```python
 from onnx_pytorch import code_gen
 code_gen.gen("resnet18-v2-7.onnx", "./")
 ```
 
 - Test result
-```
+
+```python
 import numpy as np
 import onnx
 import onnxruntime
@@ -94,4 +103,4 @@ print(
                 ort_outputs[0],
                 atol=1e-5,
                 rtol=1e-5))
-```
+```

onnx_pytorch/code_gen.py

@@ -247,13 +247,25 @@ def run(self):
               f"OpCodeGenerator is unimplemented for {n.op_type}.")
       else:
         try:
+          print(f"op_code_gen: {op_code_gen}")
+          print(f"n.op_type: {n.op_type}")
           if hasattr(op_code_gen,
                      "gen_method") and n.op_type not in self.method_parts:
+            print("Point 1")
             self.method_parts[n.op_type] = op_code_gen.gen_method()
+            print("Point 2")
+          print("Point 2A")
+          """
+          gened = op_code_gen.gen(n, value_infos, initializers, self.rename_helper,
+                              self.tensor_inplace)
+          """
           gened = op_code_gen.gen(n, value_infos, initializers)
+          print("Point 3")
           self.add_init_part(gened["init"])
+          print("Point 4")
           self.add_forward_part(gened["forward"])
         except BaseException as e:
+          print(f"e: {e}")
           if self.continue_on_error:
             logging.warning(e)
             self.add_forward_part(n.__repr__())
@@ -316,7 +328,7 @@ def get_model_code_generator(
     assert os.path.isfile(onnx_model), f"{onnx_model} is not a file."
     assert os.path.exists(
         output_dir
-    ) and overwrite is not True, f"{output_dir} is not empty and overwrite is not True."
+    ) and overwrite, f"{output_dir} is not empty and overwrite is not True."
     assert os.path.isdir(output_dir), f"{output_dir} is not directory."
     kwargs["onnx_model"] = onnx.load(onnx_model)
   if overwrite:

onnx_pytorch/op_code_generators/Pow.py

@@ -0,0 +1,19 @@
+import onnx
+import torch
+
+from onnx_pytorch.op_code_generators import OpCodeGenerator
+
+
+class PowOpCodeGenerator(OpCodeGenerator):
+
+  def __init__(self,
+               onnx_ver=onnx.defs.onnx_opset_version(),
+               torch_ver=torch.__version__):
+    super(PowOpCodeGenerator, self).__init__(onnx_ver, torch_ver)
+
+  def gen(self, node, value_infos, initializers, rename_helper, tensor_inplace):
+    inputs_str, outputs_str = self.gen_input_output_string(
+        node, initializers, rename_helper, tensor_inplace)
+    init_str, forward_str = [], []
+    forward_str.append(f"{outputs_str[0]} = torch.pow({', '.join(inputs_str)})")
+    return {"init": init_str, "forward": forward_str}

onnx_pytorch/op_code_generators/Range.py

@@ -0,0 +1,19 @@
+import onnx
+import torch
+
+from onnx_pytorch.op_code_generators import OpCodeGenerator
+
+
+class RangeOpCodeGenerator(OpCodeGenerator):
+
+  def __init__(self,
+               onnx_ver=onnx.defs.onnx_opset_version(),
+               torch_ver=torch.__version__):
+    super(RangeOpCodeGenerator, self).__init__(onnx_ver, torch_ver)
+
+  def gen(self, node, value_infos, initializers, rename_helper, tensor_inplace):
+    inputs_str, outputs_str = self.gen_input_output_string(
+        node, initializers, rename_helper, tensor_inplace)
+    init_str, forward_str = [], []
+    forward_str.append(f"{outputs_str[0]} = torch.arange({', '.join(inputs_str)})")
+    return {"init": init_str, "forward": forward_str}

onnx_pytorch/op_code_generators/Tanh.py

@@ -0,0 +1,19 @@
+import onnx
+import torch
+
+from onnx_pytorch.op_code_generators import OpCodeGenerator
+
+
+class TanhOpCodeGenerator(OpCodeGenerator):
+
+  def __init__(self,
+               onnx_ver=onnx.defs.onnx_opset_version(),
+               torch_ver=torch.__version__):
+    super(TanhOpCodeGenerator, self).__init__(onnx_ver, torch_ver)
+
+  def gen(self, node, value_infos, initializers):
+    inputs_str, outputs_str = self.gen_input_output_string(
+        node, initializers, self.rename_helper, self.tensor_inplace)
+    init_str, forward_str = [], []
+    forward_str.append(f"{outputs_str[0]} = torch.tanh({inputs_str[0]})")
+    return {"init": init_str, "forward": forward_str}

requirements.txt

@@ -1,5 +1,5 @@
 mpmath==1.2.1
-numpy==1.20.2
+numpy==1.19.5
 onnx==1.9.0
 onnxruntime==1.7.0
 packaging==20.9