# base
python converter.py --weights "./your_model.onnx"

# give save path
python converter.py --weights "./your_model.onnx" --outpath "./save_path"

# save tflite model
python converter.py --weights "./your_model.onnx" --outpath "./save_path" --formats "tflite"

# save keras and tflite model
python converter.py --weights "./your_model.onnx" --outpath "./save_path" --formats "tflite" "keras"

# cutoff model, redefine inputs and outputs, support middle layers
python converter.py --weights "./your_model.onnx" --outpath "./save_path" --formats "tflite" --input-node-names "layer_inputname" --output-node-names "layer_outname1" "layer_outname2"

# quantify model weight, only weight
python converter.py --weights "./your_model.onnx" --formats "tflite" --weigthquant

# quantify model weight, include input and output
## recommend
python converter.py --weights "./your_model.onnx" --formats "tflite" --int8 --imgroot "./dataset_path" --int8mean 0 0 0 --int8std 255 255 255
## generate random data, instead of read from image file
python converter.py --weights "./your_model.onnx" --formats "tflite" --int8

import torch
import torchvision
_input = torch.randn(1, 3, 224, 224)
model = torchvision.models.mobilenet_v2(True)
# use default settings is ok
torch.onnx.export(model, _input, './mobilenetV2.onnx', opset_version=11)# or opset_version=13

from converter import onnx_converter
onnx_converter(
    onnx_model_path = "./mobilenetV2.onnx",
    need_simplify = True,
    output_path = "./",
    target_formats = ['tflite'], # or ['keras'], ['keras', 'tflite']
    weight_quant = False,
    int8_model = False,
    int8_mean = None,
    int8_std = None,
    image_root = None
)

import torch
import torch.nn as nn
import torch.nn.functional as F

class MyModel(nn.Module):
    def __init__(self):
        self.conv = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
        )
    
    def forward(self, x):
        return self.conv(x)

model = MyModel()
model.load_state_dict(torch.load("model_checkpoint.pth", map_location="cpu"))

_input = torch.randn(1, 3, 224, 224)
torch.onnx.export(model, _input, './mymodel.onnx', opset_version=11)# or opset_version=13

from converter import onnx_converter
onnx_converter(
    onnx_model_path = "./mymodel.onnx",
    need_simplify = True,
    output_path = "./",
    target_formats = ['tflite'], #or ['keras'], ['keras', 'tflite']
    weight_quant = False,
    int8_model = True, # do quantification
    int8_mean = [123.675, 116.28, 103.53], # give mean of image preprocessing 
    int8_std = [58.395, 57.12, 57.375], # give std of image preprocessing 
    image_root = "./dataset/train" # give image folder of train
)

# all operators regist through OPERATOR register.
# regist operator's name is onnx operator name. 
@OPERATOR.register_operator("HardSigmoid")
class TFHardSigmoid():
    def __init__(self, tensor_grap, node_weights, node_inputs, node_attribute, *args, **kwargs) -> None:
        '''
        :param tensor_grap: dict, key is node name, value is tensorflow-keras node output tensor.
        :param node_weights: dict, key is node name, value is static data, such as weight/bias/constant, weight should be transfom by dimension_utils.tensor_NCD_to_NDC_format at most time.
        :param node_inputs: List[str], stored node input names, indicates which nodes the input comes from, tensor_grap and node_weights are possible.
        :param node_attribute: dict, key is attribute name, such as 'axis' or 'perm'. value type is indeterminate, such as List[int] or int or float. notice that type of 'axis' value should be adjusted form NCHW to NHWC by dimension_utils.channel_to_last_dimension or dimension_utils.shape_NCD_to_NDC_format.
        '''
        super().__init__()
        self.alpha = node_attribute.get("alpha", 0.2)
        self.beta = node_attribute.get("beta", 0.5)

    def __call__(self, inputs):
        return tf.clip_by_value(self.alpha*inputs+self.beta, 0, 1)