test_models_quantized_onnxruntime.py

# Owner(s): ["module: onnx"]

import os
import unittest

import onnx_test_common
import parameterized
import PIL
import torchvision

import torch
from torch import nn
from torch.testing._internal import common_utils


def _get_test_image_tensor():
    data_dir = os.path.join(os.path.dirname(__file__), "assets")
    img_path = os.path.join(data_dir, "grace_hopper_517x606.jpg")
    input_image = PIL.Image.open(img_path)
    # Based on example from https://pytorch.org/hub/pytorch_vision_resnet/
    preprocess = torchvision.transforms.Compose(
        [
            torchvision.transforms.Resize(256),
            torchvision.transforms.CenterCrop(224),
            torchvision.transforms.ToTensor(),
            torchvision.transforms.Normalize(
                mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
            ),
        ]
    )
    return preprocess(input_image).unsqueeze(0)


# Due to precision error from quantization, check only that the top prediction matches.
class _TopPredictor(nn.Module):
    def __init__(self, base_model):
        super().__init__()
        self.base_model = base_model

    def forward(self, x):
        x = self.base_model(x)
        _, topk_id = torch.topk(x[0], 1)
        return topk_id


# TODO: All torchvision quantized model test can be written as single parameterized test case,
# after per-parameter test decoration is supported via #79979, or after they are all enabled,
# whichever is first.
@parameterized.parameterized_class(
    ("is_script",),
    [(True,), (False,)],
    class_name_func=onnx_test_common.parameterize_class_name,
)
class TestQuantizedModelsONNXRuntime(onnx_test_common._TestONNXRuntime):
    def run_test(self, model, inputs, *args, **kwargs):
        model = _TopPredictor(model)
        return super().run_test(model, inputs, *args, **kwargs)

    def test_mobilenet_v3(self):
        model = torchvision.models.quantization.mobilenet_v3_large(
            pretrained=True, quantize=True
        )
        self.run_test(model, _get_test_image_tensor())

    @unittest.skip("quantized::cat not supported")
    def test_inception_v3(self):
        model = torchvision.models.quantization.inception_v3(
            pretrained=True, quantize=True
        )
        self.run_test(model, _get_test_image_tensor())

    @unittest.skip("quantized::cat not supported")
    def test_googlenet(self):
        model = torchvision.models.quantization.googlenet(
            pretrained=True, quantize=True
        )
        self.run_test(model, _get_test_image_tensor())

    @unittest.skip("quantized::cat not supported")
    def test_shufflenet_v2_x0_5(self):
        model = torchvision.models.quantization.shufflenet_v2_x0_5(
            pretrained=True, quantize=True
        )
        self.run_test(model, _get_test_image_tensor())

    def test_resnet18(self):
        model = torchvision.models.quantization.resnet18(pretrained=True, quantize=True)
        self.run_test(model, _get_test_image_tensor())

    def test_resnet50(self):
        model = torchvision.models.quantization.resnet50(pretrained=True, quantize=True)
        self.run_test(model, _get_test_image_tensor())

    def test_resnext101_32x8d(self):
        model = torchvision.models.quantization.resnext101_32x8d(
            pretrained=True, quantize=True
        )
        self.run_test(model, _get_test_image_tensor())


if __name__ == "__main__":
    common_utils.run_tests()