README.md

# tf转onnx

## 环境准备

- tensorflow安装

```bash
pip install tensorflow
```

- tf2onnx 安装（version>= 1.5.5）

```bash
pip install tf2onnx
```

## 模型格式确认

请先确认手里的模型文件的格式，一般情况下：

1. **SavedModel 文件结构**：

   - `saved_model.pb` 或 `saved_model.pbtxt`：这是SavedModel的核心文件，包含了模型的图（graph）和元数据（metadata）。
   - `variables/`：这个文件夹包含两个文件，`variables.data-?????-of-?????` 和 `variables.index`，存储了模型的变量。
   - `assets/`（可选）：这个文件夹存储了任何附加的资源文件。

   如果你的`.pb`文件位于一个包含上述结构的目录中，那么它很可能是一个SavedModel。

2. **Checkpoint 文件结构**：

   - Checkpoint 通常包含三个文件：一个`.index`文件，一个或多个`.data-?????-of-?????`文件，以及一个`checkpoint`文件，这个文件是保存模型变量的。

   如果你的`.pb`文件位于一个包含上述结构的目录中，那么它很可能是一个Checkpoint。

3. **GraphDef 文件结构**：

   - 如果只有一个`.pb`文件，且没有与其相关联的其他文件或目录结构，那么它很可能是GraphDef。

可以使用一下代码对模型文件格式进行检查：

```python
import tensorflow as tf
from google.protobuf import text_format
from tensorflow.core.framework import graph_pb2

def is_saved_model(model_dir):
    try:
        model = tf.saved_model.load(model_dir)
        return True
    except Exception:
        return False

def is_graph_def(pb_file):
    try:
        with tf.io.gfile.GFile(pb_file, "rb") as f:
            graph_def = tf.compat.v1.GraphDef()
            graph_def.ParseFromString(f.read())
        return True
    except Exception:
        return False

def is_checkpoint(model_dir):
    try:
        checkpoint = tf.train.Checkpoint()
        checkpoint.restore(model_dir).expect_partial()
        return True
    except Exception:
        return False

model_path = "/path/to/model"

if is_saved_model(model_path):
    print(f"{model_path} contains a SavedModel.")
elif is_graph_def(model_path):
    print(f"{model_path} contains a GraphDef.")
elif is_checkpoint(model_path):
    print(f"{model_path} contains a Checkpoint.")
else:
    print(f"{model_path} format is unknown.")

```

## 模型输入输出的name和shape确认

使用下面代码对GraphDef格式的模型进行确认

```python
import tensorflow as tf
from tensorflow.python.framework import tensor_util

def load_graph_def(pb_file):
    with tf.io.gfile.GFile(pb_file, "rb") as f:
        graph_def = tf.compat.v1.GraphDef()
        graph_def.ParseFromString(f.read())
    return graph_def

def get_graph_inputs_outputs(graph_def):
    inputs = []
    outputs = []
    
    for node in graph_def.node:
        if node.op == 'Placeholder':
            shape = None
            for attr_value in node.attr.values():
                if attr_value.HasField('shape'):
                    shape = [dim.size for dim in attr_value.shape.dim]
            inputs.append({'name': node.name, 'shape': shape})
        # Assuming outputs are nodes with no outputs themselves, usually not a strict rule
        elif not any(node.name in input for input in [n.input for n in graph_def.node]):
            shape = None
            try:
                tensor_shape = tensor_util.MakeNdarray(node.attr["shape"].shape)
                shape = tensor_shape.shape
            except:
                pass
            outputs.append({'name': node.name, 'shape': shape})
    
    return inputs, outputs

def print_graph_info(inputs, outputs):
    print("Inputs:")
    for input_info in inputs:
        print(f"Name: {input_info['name']}, Shape: {input_info['shape']}")
    print("\nOutputs:")
    for output_info in outputs:
        print(f"Name: {output_info['name']}, Shape: {output_info['shape']}")

# Path to your .pb file
pb_file_path = "resnet50v15_tf.pb"

# Load GraphDef
graph_def = load_graph_def(pb_file_path)

# Get inputs and outputs
inputs, outputs = get_graph_inputs_outputs(graph_def)

# Print inputs and outputs
print_graph_info(inputs, outputs)
```

## 模型转换

使用tf2onnx工具进行模型转换，详细工具说明可以查看tf2onnx工具官网，tf2onnx项目地址：https://github.com/onnx/tensorflow-onnx 建议大家阅读 tf2onnx 的 README.md 文件，里面有详细的对该工具各个参数的说明。

```
options:
  -h, --help            show this help message and exit
  --input INPUT         input from graphdef
  --graphdef GRAPHDEF   input from graphdef
  --saved-model SAVED_MODEL
                        input from saved model
  --tag TAG             tag to use for saved_model
  --signature_def SIGNATURE_DEF
                        signature_def from saved_model to use
  --concrete_function CONCRETE_FUNCTION
                        For TF2.x saved_model, index of func signature in __call__ (--signature_def is ignored)
  --checkpoint CHECKPOINT
                        input from checkpoint
  --keras KERAS         input from keras model
  --tflite TFLITE       input from tflite model
  --tfjs TFJS           input from tfjs model
  --large_model         use the large model format (for models > 2GB)
  --output OUTPUT       output model file
  --inputs INPUTS       model input_names (optional for saved_model, keras, and tflite)
  --outputs OUTPUTS     model output_names (optional for saved_model, keras, and tflite)
  --ignore_default IGNORE_DEFAULT
                        comma-separated list of names of PlaceholderWithDefault ops to change into Placeholder ops
  --use_default USE_DEFAULT
                        comma-separated list of names of PlaceholderWithDefault ops to change into Identity ops using
                        their default value
  --rename-inputs RENAME_INPUTS
                        input names to use in final model (optional)
  --rename-outputs RENAME_OUTPUTS
                        output names to use in final model (optional)
  --use-graph-names     (saved model only) skip renaming io using signature names
  --opset OPSET         opset version to use for onnx domain
  --dequantize          remove quantization from model. Only supported for tflite currently.
  --custom-ops CUSTOM_OPS
                        comma-separated map of custom ops to domains in format OpName:domain. Domain
                        'ai.onnx.converters.tensorflow' is used by default.
  --extra_opset EXTRA_OPSET
                        extra opset with format like domain:version, e.g. com.microsoft:1
  --load_op_libraries LOAD_OP_LIBRARIES
                        comma-separated list of tf op library paths to register before loading model
  --target {rs4,rs5,rs6,caffe2,tensorrt,nhwc}
                        target platform
  --continue_on_error   continue_on_error
  --verbose, -v         verbose output, option is additive
  --debug               debug mode
  --output_frozen_graph OUTPUT_FROZEN_GRAPH
                        output frozen tf graph to file
  --inputs-as-nchw INPUTS_AS_NCHW
                        transpose inputs as from nhwc to nchw
  --outputs-as-nchw OUTPUTS_AS_NCHW
                        transpose outputs as from nhwc to nchw

Usage Examples:

python -m tf2onnx.convert --saved-model saved_model_dir --output model.onnx
python -m tf2onnx.convert --input frozen_graph.pb  --inputs X:0 --outputs output:0 --output model.onnx
python -m tf2onnx.convert --checkpoint checkpoint.meta  --inputs X:0 --outputs output:0 --output model.onn
```

下面是将GraphDef格式的模型转换onnx的示例， resnet50v15_tf.pb模型[下载地址](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/modelzoo/Official/cv/Resnet50v1.5_for_ACL/resnet50v15_tf.pb)

```bash
python -m tf2onnx.convert --graphdef  resnet50v15_tf.pb --output model_nchw.onnx --inputs input_tensor:0 --outputs global_step:0,ArgMax:0,softmax_tensor:0 --inputs-as-nchw input_tensor:0
```

# tflite转onnx

同样使用tf2onnx工具，例如将ResNet50.tflite模型转为onnx模型，模型[下载地址](https://hf-mirror.com/qualcomm/ResNet50/resolve/main/ResNet50.tflite?download=true)：

```bash
python -m tf2onnx.convert --opset 16 --tflite ResNet50.tflite --output model.onnx
```