xautodl/tests/test_super_vit.py

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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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# pytest ./tests/test_super_vit.py -s               #
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import sys
import unittest

import torch
from xautodl.xmodels import transformers
from xautodl.utils.flop_benchmark import count_parameters


class TestSuperViT(unittest.TestCase):
    """Test the super re-arrange layer."""

    def test_super_vit(self):
        model = transformers.get_transformer("vit-base-16")
        tensor = torch.rand((16, 3, 224, 224))
        print("The tensor shape: {:}".format(tensor.shape))
        # print(model)
        outs = model(tensor)
        print("The output tensor shape: {:}".format(outs.shape))

    def test_imagenet(self):
        name2config = transformers.name2config
        print("There are {:} models in total.".format(len(name2config)))
        for name, config in name2config.items():
            if "cifar" in name:
                tensor = torch.rand((16, 3, 32, 32))
            else:
                tensor = torch.rand((16, 3, 224, 224))
            model = transformers.get_transformer(config)
            outs = model(tensor)
            size = count_parameters(model, "mb", True)
            print(
                "{:10s} : size={:.2f}MB, out-shape: {:}".format(
                    name, size, tuple(outs.shape)
                )
            )