import os
import torch
import datetime
import torchvision as vision
# from pytorch_jacinto_ai import vision

# dependencies
# Anaconda Python 3.7 for Linux - download and install from: https://www.anaconda.com/distribution/
# pytorch, torchvision - install using: 
# conda install pytorch torchvision -c pytorch

# some parameters - modify as required
date = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
dataset_name = 'image_folder_classification'
model_names = ['mobilenet_v2', 'resnet18', 'resnet50', 'resnext50_32x4d', 'shufflenet_v2_x1_0']
img_resize = (256,256)
rand_crop = (224,224)

# the saving path - you can choose any path
save_path = './data/checkpoints'
save_path = os.path.join(save_path, dataset_name, date + '_' + dataset_name)
save_path += '_resize{}x{}_traincrop{}x{}'.format(img_resize[1], img_resize[0], rand_crop[1], rand_crop[0])
os.makedirs(save_path, exist_ok=True)

# create a rand input
rand_input = torch.rand(1, 3, rand_crop[0], rand_crop[1])

for model_name in model_names:
    # create the model - replace with your model
    model = vision.models.__dict__[model_name](pretrained=True)
    model.eval()

    # write pytorch model
    model_path=os.path.join(save_path, f'{model_name}_model.pth')
    traced_model = torch.jit.trace(model, rand_input)
    torch.jit.save(traced_model, model_path)

    # write pytorch sate dict
    model_path=os.path.join(save_path, f'{model_name}_state_dict.pth')
    torch.save(model.state_dict(), model_path)

    # write the onnx model
    opset_version=9
    model_path=os.path.join(save_path, f'{model_name}_opset{opset_version}.onnx')
    torch.onnx.export(model, rand_input, model_path, export_params=True, verbose=False, opset_version=opset_version)