test_models.py

from common_utils import TestCase, map_nested_tensor_object, freeze_rng_state
from collections import OrderedDict
from itertools import product
import torch
import torch.nn as nn
import numpy as np
from torchvision import models
import unittest
import traceback
import random


def set_rng_seed(seed):
    torch.manual_seed(seed)
    random.seed(seed)
    np.random.seed(seed)


def get_available_classification_models():
    # TODO add a registration mechanism to torchvision.models
    return [k for k, v in models.__dict__.items() if callable(v) and k[0].lower() == k[0] and k[0] != "_"]


def get_available_segmentation_models():
    # TODO add a registration mechanism to torchvision.models
    return [k for k, v in models.segmentation.__dict__.items() if callable(v) and k[0].lower() == k[0] and k[0] != "_"]


def get_available_detection_models():
    # TODO add a registration mechanism to torchvision.models
    return [k for k, v in models.detection.__dict__.items() if callable(v) and k[0].lower() == k[0] and k[0] != "_"]


def get_available_video_models():
    # TODO add a registration mechanism to torchvision.models
    return [k for k, v in models.video.__dict__.items() if callable(v) and k[0].lower() == k[0] and k[0] != "_"]


# models that are in torch hub, as well as r3d_18. we tried testing all models
# but the test was too slow. not included are detection models, because
# they are not yet supported in JIT.
# If 'unwrapper' is provided it will be called with the script model outputs
# before they are compared to the eager model outputs. This is useful if the
# model outputs are different between TorchScript / Eager mode
script_test_models = {
    'deeplabv3_resnet50': {},
    'deeplabv3_resnet101': {},
    'mobilenet_v2': {},
    'resnext50_32x4d': {},
    'fcn_resnet50': {},
    'fcn_resnet101': {},
    'googlenet': {
        'unwrapper': lambda x: x.logits
    },
    'densenet121': {},
    'resnet18': {},
    'alexnet': {},
    'shufflenet_v2_x1_0': {},
    'squeezenet1_0': {},
    'vgg11': {},
    'inception_v3': {
        'unwrapper': lambda x: x.logits
    },
    'r3d_18': {},
    "fasterrcnn_resnet50_fpn": {
        'unwrapper': lambda x: x[1]
    },
    "maskrcnn_resnet50_fpn": {
        'unwrapper': lambda x: x[1]
    },
    "keypointrcnn_resnet50_fpn": {
        'unwrapper': lambda x: x[1]
    },
    "retinanet_resnet50_fpn": {
        'unwrapper': lambda x: x[1]
    }
}


# The following models exhibit flaky numerics under autocast in _test_*_model harnesses.
# This may be caused by the harness environment (e.g. num classes, input initialization
# via torch.rand), and does not prove autocast is unsuitable when training with real data
# (autocast has been used successfully with real data for some of these models).
# TODO:  investigate why autocast numerics are flaky in the harnesses.
#
# For the following models, _test_*_model harnesses skip numerical checks on outputs when
# trying autocast. However, they still try an autocasted forward pass, so they still ensure
# autocast coverage suffices to prevent dtype errors in each model.
autocast_flaky_numerics = (
    "fasterrcnn_resnet50_fpn",
    "inception_v3",
    "keypointrcnn_resnet50_fpn",
    "maskrcnn_resnet50_fpn",
    "resnet101",
    "resnet152",
    "wide_resnet101_2",
    "retinanet_resnet50_fpn",
)


class ModelTester(TestCase):
    def checkModule(self, model, name, args):
        if name not in script_test_models:
            return
        unwrapper = script_test_models[name].get('unwrapper', None)
        return super(ModelTester, self).checkModule(model, args, unwrapper=unwrapper, skip=False)

    def _test_classification_model(self, name, input_shape, dev):
        set_rng_seed(0)
        # passing num_class equal to a number other than 1000 helps in making the test
        # more enforcing in nature
        model = models.__dict__[name](num_classes=50)
        model.eval().to(device=dev)
        # RNG always on CPU, to ensure x in cuda tests is bitwise identical to x in cpu tests
        x = torch.rand(input_shape).to(device=dev)
        out = model(x)
        self.assertExpected(out.cpu(), prec=0.1, strip_suffix="_" + dev)
        self.assertEqual(out.shape[-1], 50)
        self.checkModule(model, name, (x,))

        if dev == "cuda":
            with torch.cuda.amp.autocast():
                out = model(x)
                # See autocast_flaky_numerics comment at top of file.
                if name not in autocast_flaky_numerics:
                    self.assertExpected(out.cpu(), prec=0.1, strip_suffix="_" + dev)
                self.assertEqual(out.shape[-1], 50)

    def _test_segmentation_model(self, name, dev):
        # passing num_class equal to a number other than 1000 helps in making the test
        # more enforcing in nature
        model = models.segmentation.__dict__[name](num_classes=50, pretrained_backbone=False)
        model.eval().to(device=dev)
        input_shape = (1, 3, 300, 300)
        # RNG always on CPU, to ensure x in cuda tests is bitwise identical to x in cpu tests
        x = torch.rand(input_shape).to(device=dev)
        out = model(x)
        self.assertEqual(tuple(out["out"].shape), (1, 50, 300, 300))
        self.checkModule(model, name, (x,))

        if dev == "cuda":
            with torch.cuda.amp.autocast():
                out = model(x)
                self.assertEqual(tuple(out["out"].shape), (1, 50, 300, 300))

    def _test_detection_model(self, name, dev):
        set_rng_seed(0)
        kwargs = {}
        if "retinanet" in name:
            kwargs["score_thresh"] = 0.013
        model = models.detection.__dict__[name](num_classes=50, pretrained_backbone=False, **kwargs)
        model.eval().to(device=dev)
        input_shape = (3, 300, 300)
        # RNG always on CPU, to ensure x in cuda tests is bitwise identical to x in cpu tests
        x = torch.rand(input_shape).to(device=dev)
        model_input = [x]
        out = model(model_input)
        self.assertIs(model_input[0], x)

        def check_out(out):
            self.assertEqual(len(out), 1)

            def subsample_tensor(tensor):
                num_elems = tensor.numel()
                num_samples = 20
                if num_elems <= num_samples:
                    return tensor

                flat_tensor = tensor.flatten()
                ith_index = num_elems // num_samples
                return flat_tensor[ith_index - 1::ith_index]

            def compute_mean_std(tensor):
                # can't compute mean of integral tensor
                tensor = tensor.to(torch.double)
                mean = torch.mean(tensor)
                std = torch.std(tensor)
                return {"mean": mean, "std": std}

            if name == "maskrcnn_resnet50_fpn":
                # maskrcnn_resnet_50_fpn numerically unstable across platforms, so for now
                # compare results with mean and std
                test_value = map_nested_tensor_object(out, tensor_map_fn=compute_mean_std)
                # mean values are small, use large prec
                self.assertExpected(test_value, prec=.01, strip_suffix="_" + dev)
            else:
                self.assertExpected(map_nested_tensor_object(out, tensor_map_fn=subsample_tensor),
                                    prec=0.01,
                                    strip_suffix="_" + dev)

        check_out(out)

        scripted_model = torch.jit.script(model)
        scripted_model.eval()
        scripted_out = scripted_model(model_input)[1]
        self.assertEqual(scripted_out[0]["boxes"], out[0]["boxes"])
        self.assertEqual(scripted_out[0]["scores"], out[0]["scores"])
        # labels currently float in script: need to investigate (though same result)
        self.assertEqual(scripted_out[0]["labels"].to(dtype=torch.long), out[0]["labels"])
        self.assertTrue("boxes" in out[0])
        self.assertTrue("scores" in out[0])
        self.assertTrue("labels" in out[0])
        # don't check script because we are compiling it here:
        # TODO: refactor tests
        # self.check_script(model, name)
        self.checkModule(model, name, ([x],))

        if dev == "cuda":
            with torch.cuda.amp.autocast():
                out = model(model_input)
                # See autocast_flaky_numerics comment at top of file.
                if name not in autocast_flaky_numerics:
                    check_out(out)

    def _test_detection_model_validation(self, name):
        set_rng_seed(0)
        model = models.detection.__dict__[name](num_classes=50, pretrained_backbone=False)
        input_shape = (3, 300, 300)
        x = [torch.rand(input_shape)]

        # validate that targets are present in training
        self.assertRaises(ValueError, model, x)

        # validate type
        targets = [{'boxes': 0.}]
        self.assertRaises(ValueError, model, x, targets=targets)

        # validate boxes shape
        for boxes in (torch.rand((4,)), torch.rand((1, 5))):
            targets = [{'boxes': boxes}]
            self.assertRaises(ValueError, model, x, targets=targets)

        # validate that no degenerate boxes are present
        boxes = torch.tensor([[1, 3, 1, 4], [2, 4, 3, 4]])
        targets = [{'boxes': boxes}]
        self.assertRaises(ValueError, model, x, targets=targets)

    def _test_video_model(self, name, dev):
        # the default input shape is
        # bs * num_channels * clip_len * h *w
        input_shape = (1, 3, 4, 112, 112)
        # test both basicblock and Bottleneck
        model = models.video.__dict__[name](num_classes=50)
        model.eval().to(device=dev)
        # RNG always on CPU, to ensure x in cuda tests is bitwise identical to x in cpu tests
        x = torch.rand(input_shape).to(device=dev)
        out = model(x)
        self.checkModule(model, name, (x,))
        self.assertEqual(out.shape[-1], 50)

        if dev == "cuda":
            with torch.cuda.amp.autocast():
                out = model(x)
                self.assertEqual(out.shape[-1], 50)

    def _make_sliced_model(self, model, stop_layer):
        layers = OrderedDict()
        for name, layer in model.named_children():
            layers[name] = layer
            if name == stop_layer:
                break
        new_model = torch.nn.Sequential(layers)
        return new_model

    def test_memory_efficient_densenet(self):
        input_shape = (1, 3, 300, 300)
        x = torch.rand(input_shape)

        for name in ['densenet121', 'densenet169', 'densenet201', 'densenet161']:
            model1 = models.__dict__[name](num_classes=50, memory_efficient=True)
            params = model1.state_dict()
            num_params = sum([x.numel() for x in model1.parameters()])
            model1.eval()
            out1 = model1(x)
            out1.sum().backward()
            num_grad = sum([x.grad.numel() for x in model1.parameters() if x.grad is not None])

            model2 = models.__dict__[name](num_classes=50, memory_efficient=False)
            model2.load_state_dict(params)
            model2.eval()
            out2 = model2(x)

            max_diff = (out1 - out2).abs().max()

            self.assertTrue(num_params == num_grad)
            self.assertTrue(max_diff < 1e-5)

    def test_resnet_dilation(self):
        # TODO improve tests to also check that each layer has the right dimensionality
        for i in product([False, True], [False, True], [False, True]):
            model = models.__dict__["resnet50"](replace_stride_with_dilation=i)
            model = self._make_sliced_model(model, stop_layer="layer4")
            model.eval()
            x = torch.rand(1, 3, 224, 224)
            out = model(x)
            f = 2 ** sum(i)
            self.assertEqual(out.shape, (1, 2048, 7 * f, 7 * f))

    def test_mobilenetv2_residual_setting(self):
        model = models.__dict__["mobilenet_v2"](inverted_residual_setting=[[1, 16, 1, 1], [6, 24, 2, 2]])
        model.eval()
        x = torch.rand(1, 3, 224, 224)
        out = model(x)
        self.assertEqual(out.shape[-1], 1000)

    def test_mobilenetv2_norm_layer(self):
        model = models.__dict__["mobilenet_v2"]()
        self.assertTrue(any(isinstance(x, nn.BatchNorm2d) for x in model.modules()))

        def get_gn(num_channels):
            return nn.GroupNorm(32, num_channels)

        model = models.__dict__["mobilenet_v2"](norm_layer=get_gn)
        self.assertFalse(any(isinstance(x, nn.BatchNorm2d) for x in model.modules()))
        self.assertTrue(any(isinstance(x, nn.GroupNorm) for x in model.modules()))

    def test_fasterrcnn_double(self):
        model = models.detection.fasterrcnn_resnet50_fpn(num_classes=50, pretrained_backbone=False)
        model.double()
        model.eval()
        input_shape = (3, 300, 300)
        x = torch.rand(input_shape, dtype=torch.float64)
        model_input = [x]
        out = model(model_input)
        self.assertIs(model_input[0], x)
        self.assertEqual(len(out), 1)
        self.assertTrue("boxes" in out[0])
        self.assertTrue("scores" in out[0])
        self.assertTrue("labels" in out[0])

    def test_googlenet_eval(self):
        m = torch.jit.script(models.googlenet(pretrained=True).eval())
        self.checkModule(m, "googlenet", torch.rand(1, 3, 224, 224))

    @unittest.skipIf(not torch.cuda.is_available(), 'needs GPU')
    def test_fasterrcnn_switch_devices(self):
        def checkOut(out):
            self.assertEqual(len(out), 1)
            self.assertTrue("boxes" in out[0])
            self.assertTrue("scores" in out[0])
            self.assertTrue("labels" in out[0])

        model = models.detection.fasterrcnn_resnet50_fpn(num_classes=50, pretrained_backbone=False)
        model.cuda()
        model.eval()
        input_shape = (3, 300, 300)
        x = torch.rand(input_shape, device='cuda')
        model_input = [x]
        out = model(model_input)
        self.assertIs(model_input[0], x)

        checkOut(out)

        with torch.cuda.amp.autocast():
            out = model(model_input)

        checkOut(out)

        # now switch to cpu and make sure it works
        model.cpu()
        x = x.cpu()
        out_cpu = model([x])

        checkOut(out_cpu)

    def test_generalizedrcnn_transform_repr(self):

        min_size, max_size = 224, 299
        image_mean = [0.485, 0.456, 0.406]
        image_std = [0.229, 0.224, 0.225]

        t = models.detection.transform.GeneralizedRCNNTransform(min_size=min_size,
                                                                max_size=max_size,
                                                                image_mean=image_mean,
                                                                image_std=image_std)

        # Check integrity of object __repr__ attribute
        expected_string = 'GeneralizedRCNNTransform('
        _indent = '\n    '
        expected_string += '{0}Normalize(mean={1}, std={2})'.format(_indent, image_mean, image_std)
        expected_string += '{0}Resize(min_size=({1},), max_size={2}, '.format(_indent, min_size, max_size)
        expected_string += "mode='bilinear')\n)"
        self.assertEqual(t.__repr__(), expected_string)


_devs = ["cpu", "cuda"] if torch.cuda.is_available() else ["cpu"]


for model_name in get_available_classification_models():
    for dev in _devs:
        # for-loop bodies don't define scopes, so we have to save the variables
        # we want to close over in some way
        def do_test(self, model_name=model_name, dev=dev):
            input_shape = (1, 3, 224, 224)
            if model_name in ['inception_v3']:
                input_shape = (1, 3, 299, 299)
            self._test_classification_model(model_name, input_shape, dev)

        setattr(ModelTester, "test_" + model_name + "_" + dev, do_test)


for model_name in get_available_segmentation_models():
    for dev in _devs:
        # for-loop bodies don't define scopes, so we have to save the variables
        # we want to close over in some way
        def do_test(self, model_name=model_name, dev=dev):
            self._test_segmentation_model(model_name, dev)

        setattr(ModelTester, "test_" + model_name + "_" + dev, do_test)


for model_name in get_available_detection_models():
    for dev in _devs:
        # for-loop bodies don't define scopes, so we have to save the variables
        # we want to close over in some way
        def do_test(self, model_name=model_name, dev=dev):
            self._test_detection_model(model_name, dev)

        setattr(ModelTester, "test_" + model_name + "_" + dev, do_test)

    def do_validation_test(self, model_name=model_name):
        self._test_detection_model_validation(model_name)

    setattr(ModelTester, "test_" + model_name + "_validation", do_validation_test)


for model_name in get_available_video_models():
    for dev in _devs:
        def do_test(self, model_name=model_name, dev=dev):
            self._test_video_model(model_name, dev)

        setattr(ModelTester, "test_" + model_name + "_" + dev, do_test)

if __name__ == '__main__':
    unittest.main()