tests/python/driver/tvmc/conftest.py

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import os
import pytest
import tarfile
import textwrap

import numpy as np

from PIL import Image

import tvm
from tvm import relay
from tvm.driver import tvmc

from tvm.contrib.download import download_testdata

# Support functions


def download_and_untar(model_url, model_sub_path, temp_dir):
    model_tar_name = os.path.basename(model_url)
    model_path = download_testdata(model_url, model_tar_name, module=["tvmc"])

    if model_path.endswith("tgz") or model_path.endswith("gz") or model_path.endswith("tar"):
        tar = tarfile.open(model_path)
        tar.extractall(path=temp_dir)
        tar.close()

    return os.path.join(temp_dir, model_sub_path)


# PyTest fixtures


@pytest.fixture(scope="session")
def tflite_mobilenet_v1_1_quant(tmpdir_factory):
    base_url = "https://storage.googleapis.com/download.tensorflow.org/models"
    model_url = "mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz"
    model_file = download_and_untar(
        "{}/{}".format(base_url, model_url),
        "mobilenet_v1_1.0_224_quant.tflite",
        temp_dir=tmpdir_factory.mktemp("data"),
    )

    return model_file


@pytest.fixture(scope="session")
def pb_mobilenet_v1_1_quant(tmpdir_factory):
    base_url = "https://storage.googleapis.com/download.tensorflow.org/models"
    model_url = "mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz"
    model_file = download_and_untar(
        "{}/{}".format(base_url, model_url),
        "mobilenet_v1_1.0_224_frozen.pb",
        temp_dir=tmpdir_factory.mktemp("data"),
    )

    return model_file


@pytest.fixture(scope="session")
def keras_resnet50(tmpdir_factory):
    try:
        from tensorflow.keras.applications.resnet50 import ResNet50
    except ImportError:
        # not all environments provide TensorFlow, so skip this fixture
        # if that is that case.
        return ""

    model_file_name = "{}/{}".format(tmpdir_factory.mktemp("data"), "resnet50.h5")
    model = ResNet50(include_top=True, weights="imagenet", input_shape=(224, 224, 3), classes=1000)
    model.save(model_file_name)

    return model_file_name


@pytest.fixture(scope="session")
def keras_simple(tmpdir_factory):
    try:
        from tensorflow import keras
    except ImportError:
        # not all environments provide TensorFlow, so skip this fixture
        # if that is that case.
        return ""

    model_file_name = "{}/{}".format(tmpdir_factory.mktemp("data"), "simple_conv.h5")
    model = keras.Sequential(
        [
            keras.layers.InputLayer(input_shape=[32, 32, 3], batch_size=1),
            keras.layers.Conv2D(8, kernel_size=(3, 3)),
            keras.layers.Flatten(),
            keras.layers.Dense(64),
        ]
    )
    model.save(model_file_name)

    return model_file_name


@pytest.fixture(scope="session")
def pytorch_resnet18(tmpdir_factory):
    try:
        import torch
        import torchvision.models as models
    except ImportError:
        # Not all environments provide Pytorch, so skip if that's the case.
        return ""
    model = models.resnet18()
    model_file_name = "{}/{}".format(tmpdir_factory.mktemp("data"), "resnet18.pth")
    # Trace model into torchscript.
    traced_cpu = torch.jit.trace(model, torch.randn(1, 3, 224, 224))
    torch.jit.save(traced_cpu, model_file_name)

    return model_file_name


@pytest.fixture(scope="session")
def pytorch_mobilenetv2_quantized(tmpdir_factory):
    try:
        import torch
        import torchvision.models as models
    except ImportError:
        # Not all environments provide Pytorch, so skip if that's the case.
        return ""
    model = models.quantization.mobilenet_v2(quantize=True)
    model_file_name = "{}/{}".format(tmpdir_factory.mktemp("data"), "mobilenet_v2_quantized.pth")
    # Trace model into torchscript.
    traced_cpu = torch.jit.trace(model, torch.randn(1, 3, 224, 224))
    torch.jit.save(traced_cpu, model_file_name)

    return model_file_name


@pytest.fixture(scope="session")
def onnx_resnet50():
    base_url = "https://github.com/onnx/models/raw/bd206494e8b6a27b25e5cf7199dbcdbfe9d05d1c/vision/classification/resnet/model"
    file_to_download = "resnet50-v2-7.onnx"
    model_file = download_testdata(
        "{}/{}".format(base_url, file_to_download), file_to_download, module=["tvmc"]
    )

    return model_file


@pytest.fixture(scope="session")
def paddle_resnet50(tmpdir_factory):
    base_url = "https://bj.bcebos.com/x2paddle/models"
    model_url = "paddle_resnet50.tar"
    model_file = download_and_untar(
        "{}/{}".format(base_url, model_url),
        "paddle_resnet50/model.pdmodel",
        temp_dir=tmpdir_factory.mktemp("data"),
    )
    return model_file


@pytest.fixture(scope="session")
def onnx_mnist():
    base_url = "https://github.com/onnx/models/raw/bd206494e8b6a27b25e5cf7199dbcdbfe9d05d1c/vision/classification/mnist/model"
    file_to_download = "mnist-1.onnx"
    model_file = download_testdata(
        "{}/{}".format(base_url, file_to_download), file_to_download, module=["tvmc"]
    )

    return model_file


@pytest.fixture
def tflite_compile_model(tmpdir_factory):
    """Support function that returns a TFLite compiled module"""

    def model_compiler(model_file, **overrides):
        package_path = tmpdir_factory.mktemp("data").join("mock.tar")
        tvmc_model = tvmc.frontends.load_model(model_file)
        args = {"target": "llvm", **overrides}
        return tvmc.compiler.compile_model(tvmc_model, package_path=package_path, **args)

    # Returns a TVMCPackage
    return model_compiler


@pytest.fixture
def relay_compile_model(tmpdir_factory):
    """Support function that returns a TFLite compiled module"""

    def model_compiler(model_file, shape_dict, **overrides):
        package_path = tmpdir_factory.mktemp("data").join("mock.tar")
        tvmc_model = tvmc.frontends.load_model(
            model_file, model_format="relay", shape_dict=shape_dict
        )
        args = {"target": "llvm", **overrides}
        return tvmc.compiler.compile_model(tvmc_model, package_path=package_path, **args)

    # Returns a TVMCPackage
    return model_compiler


@pytest.fixture(scope="session")
def imagenet_cat(tmpdir_factory):
    tmpdir_name = tmpdir_factory.mktemp("data")
    cat_file_name = "imagenet_cat.npz"

    cat_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
    image_path = download_testdata(cat_url, "inputs", module=["tvmc"])
    resized_image = Image.open(image_path).resize((224, 224))
    image_data = np.asarray(resized_image).astype("float32")
    image_data = np.expand_dims(image_data, axis=0)

    cat_file_full_path = os.path.join(tmpdir_name, cat_file_name)
    np.savez(cat_file_full_path, input=image_data)

    return cat_file_full_path


@pytest.fixture(scope="session")
def tflite_mobilenet_v1_0_25_128(tmpdir_factory):
    base_url = "https://storage.googleapis.com/download.tensorflow.org/models"
    model_url = "mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128.tgz"
    model_file = download_and_untar(
        "{}/{}".format(base_url, model_url),
        "mobilenet_v1_0.25_128.tflite",
        temp_dir=tmpdir_factory.mktemp("data"),
    )

    return model_file


@pytest.fixture(scope="session")
def tflite_cnn_s_quantized(tmpdir_factory):
    base_url = "https://github.com/ARM-software/ML-zoo/raw/48a22ee22325d15d2371a6df24eb7d67e21dcc97/models/keyword_spotting/cnn_small/tflite_int8"
    file_to_download = "cnn_s_quantized.tflite"
    model_file = download_testdata(
        "{}/{}".format(base_url, file_to_download), file_to_download, module=["tvmc"]
    )
    return model_file


@pytest.fixture(scope="session")
def relay_text_conv2d(tmpdir_factory):
    file_path = os.path.join(tmpdir_factory.mktemp("model"), "relay.txt")

    RELAY_MODEL = textwrap.dedent(
        """\
        #[version = "0.0.5"]
        def @main(%data : Tensor[(1, 3, 64, 64), uint8], %weight : Tensor[(3, 3, 5, 5), int8]) {
            %1 = nn.conv2d(
                 %data,
                 %weight,
                 padding=[2, 2],
                 channels=3,
                 kernel_size=[5, 5],
                 data_layout="NCHW",
                 kernel_layout="OIHW",
                 out_dtype="int32");
            %2 = cast(nn.max_pool2d(%1, pool_size=[3, 3]), dtype="int8");
            %3 = nn.conv2d(
                 %2,
                 %weight,
                 padding=[2, 2],
                 channels=3,
                 kernel_size=[5, 5],
                 data_layout="NCHW",
                 kernel_layout="OIHW",
                 out_dtype="int32");
            %4 = nn.max_pool2d(%3, pool_size=[3, 3]);
            %4
        }
    """
    )

    with open(file_path, "w") as relay_text:
        relay_text.write(RELAY_MODEL)
    return file_path


@pytest.fixture(scope="session")
def relay_conv2d():
    """
    Simple conv2d Relay implementation.
    """
    dtype = "float32"

    x = relay.var("x", shape=(1, 4, 2, 2), dtype=dtype)
    weight = relay.const(np.random.uniform(size=(2, 4, 2, 2)), dtype=dtype)
    x = relay.nn.conv2d(x, weight)
    func = relay.Function(relay.analysis.free_vars(x), x)
    return tvm.IRModule.from_expr(func)