import torch
import torchvision
import onnx
import numpy as np

from src.model import SPOTER
from src.identifiers import LANDMARKS

# set parameters of the model
model_name = 'model_A-Z'
num_classes = 26

# load PyTorch model from .pth file
model = SPOTER(num_classes=num_classes, hidden_dim=len(LANDMARKS) *2)
if torch.cuda.is_available():
    state_dict = torch.load('models/' + model_name + '.pth')
else:
    state_dict = torch.load('models/' + model_name + '.pth', map_location=torch.device('cpu'))
model.load_state_dict(state_dict)

# set model to evaluation mode
model.eval()

# create dummy input tensor
dummy_input = torch.randn(10, 108)

# export model to ONNX format
output_file = 'models/' + model_name + '.onnx'
torch.onnx.export(model, dummy_input, output_file, input_names=['input'], output_names=['output'])

torch.onnx.export(model,                                # model being run
                  dummy_input,                          # model input (or a tuple for multiple inputs)
                  'models/' + model_name + '.onnx',     # where to save the model (can be a file or file-like object)
                  export_params=True,                   # store the trained parameter weights inside the model file
                  opset_version=9,                      # the ONNX version to export the model to
                  do_constant_folding=True,             # whether to execute constant folding for optimization
                  input_names = ['X'],                  # the model's input names
                  output_names = ['Y']                  # the model's output names
                  )


# load exported ONNX model for verification
onnx_model = onnx.load(output_file)
onnx.checker.check_model(onnx_model)