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# Owner(s): ["module: onnx"]
import unittest
import torch
from model_defs.dcgan import _netD, _netG, bsz, imgsz, nz, weights_init
from model_defs.emb_seq import EmbeddingNetwork1, EmbeddingNetwork2
from model_defs.mnist import MNIST
from model_defs.op_test import ConcatNet, DummyNet, FakeQuantNet, PermuteNet, PReluNet
from model_defs.squeezenet import SqueezeNet
from model_defs.srresnet import SRResNet
from model_defs.super_resolution import SuperResolutionNet
from pytorch_test_common import skipIfUnsupportedMinOpsetVersion, skipScriptTest
from torch import quantization
from torch.autograd import Variable
from torch.onnx import OperatorExportTypes
from torch.testing._internal import common_utils
from torch.testing._internal.common_utils import skipIfNoLapack
from torchvision.models import shufflenet_v2_x1_0
from torchvision.models.alexnet import alexnet
from torchvision.models.densenet import densenet121
from torchvision.models.googlenet import googlenet
from torchvision.models.inception import inception_v3
from torchvision.models.mnasnet import mnasnet1_0
from torchvision.models.mobilenet import mobilenet_v2
from torchvision.models.resnet import resnet50
from torchvision.models.segmentation import deeplabv3_resnet101, fcn_resnet101
from torchvision.models.vgg import vgg16, vgg16_bn, vgg19, vgg19_bn
from torchvision.models.video import mc3_18, r2plus1d_18, r3d_18
from verify import verify
if torch.cuda.is_available():
def toC(x):
return x.cuda()
else:
def toC(x):
return x
BATCH_SIZE = 2
class TestModels(common_utils.TestCase):
opset_version = 9 # Caffe2 doesn't support the default.
keep_initializers_as_inputs = False
def exportTest(self, model, inputs, rtol=1e-2, atol=1e-7, **kwargs):
import caffe2.python.onnx.backend as backend
with torch.onnx.select_model_mode_for_export(
model, torch.onnx.TrainingMode.EVAL
):
graph = torch.onnx.utils._trace(model, inputs, OperatorExportTypes.ONNX)
torch._C._jit_pass_lint(graph)
verify(
model,
inputs,
backend,
rtol=rtol,
atol=atol,
opset_version=self.opset_version,
)
def test_ops(self):
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(DummyNet()), toC(x))
def test_prelu(self):
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(PReluNet(), x)
@skipScriptTest()
def test_concat(self):
input_a = Variable(torch.randn(BATCH_SIZE, 3))
input_b = Variable(torch.randn(BATCH_SIZE, 3))
inputs = ((toC(input_a), toC(input_b)),)
self.exportTest(toC(ConcatNet()), inputs)
def test_permute(self):
x = Variable(torch.randn(BATCH_SIZE, 3, 10, 12))
self.exportTest(PermuteNet(), x)
@skipScriptTest()
def test_embedding_sequential_1(self):
x = Variable(torch.randint(0, 10, (BATCH_SIZE, 3)))
self.exportTest(EmbeddingNetwork1(), x)
@skipScriptTest()
def test_embedding_sequential_2(self):
x = Variable(torch.randint(0, 10, (BATCH_SIZE, 3)))
self.exportTest(EmbeddingNetwork2(), x)
@unittest.skip("This model takes too much memory")
def test_srresnet(self):
x = Variable(torch.randn(1, 3, 224, 224).fill_(1.0))
self.exportTest(
toC(SRResNet(rescale_factor=4, n_filters=64, n_blocks=8)), toC(x)
)
@skipIfNoLapack
def test_super_resolution(self):
x = Variable(torch.randn(BATCH_SIZE, 1, 224, 224).fill_(1.0))
self.exportTest(toC(SuperResolutionNet(upscale_factor=3)), toC(x), atol=1e-6)
def test_alexnet(self):
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(alexnet()), toC(x))
def test_mnist(self):
x = Variable(torch.randn(BATCH_SIZE, 1, 28, 28).fill_(1.0))
self.exportTest(toC(MNIST()), toC(x))
@unittest.skip("This model takes too much memory")
def test_vgg16(self):
# VGG 16-layer model (configuration "D")
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(vgg16()), toC(x))
@unittest.skip("This model takes too much memory")
def test_vgg16_bn(self):
# VGG 16-layer model (configuration "D") with batch normalization
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(vgg16_bn()), toC(x))
@unittest.skip("This model takes too much memory")
def test_vgg19(self):
# VGG 19-layer model (configuration "E")
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(vgg19()), toC(x))
@unittest.skip("This model takes too much memory")
def test_vgg19_bn(self):
# VGG 19-layer model (configuration "E") with batch normalization
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(vgg19_bn()), toC(x))
def test_resnet(self):
# ResNet50 model
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(resnet50()), toC(x), atol=1e-6)
# This test is numerically unstable. Sporadic single element mismatch occurs occasionally.
def test_inception(self):
x = Variable(torch.randn(BATCH_SIZE, 3, 299, 299))
self.exportTest(toC(inception_v3()), toC(x), acceptable_error_percentage=0.01)
def test_squeezenet(self):
# SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and
# <0.5MB model size
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
sqnet_v1_0 = SqueezeNet(version=1.1)
self.exportTest(toC(sqnet_v1_0), toC(x))
# SqueezeNet 1.1 has 2.4x less computation and slightly fewer params
# than SqueezeNet 1.0, without sacrificing accuracy.
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
sqnet_v1_1 = SqueezeNet(version=1.1)
self.exportTest(toC(sqnet_v1_1), toC(x))
def test_densenet(self):
# Densenet-121 model
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(densenet121()), toC(x), rtol=1e-2, atol=1e-5)
@skipScriptTest()
def test_dcgan_netD(self):
netD = _netD(1)
netD.apply(weights_init)
input = Variable(torch.empty(bsz, 3, imgsz, imgsz).normal_(0, 1))
self.exportTest(toC(netD), toC(input))
@skipScriptTest()
def test_dcgan_netG(self):
netG = _netG(1)
netG.apply(weights_init)
input = Variable(torch.empty(bsz, nz, 1, 1).normal_(0, 1))
self.exportTest(toC(netG), toC(input))
@skipIfUnsupportedMinOpsetVersion(10)
def test_fake_quant(self):
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(FakeQuantNet()), toC(x))
@skipIfUnsupportedMinOpsetVersion(10)
def test_qat_resnet_pertensor(self):
# Quantize ResNet50 model
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
qat_resnet50 = resnet50()
# Use per tensor for weight. Per channel support will come with opset 13
qat_resnet50.qconfig = quantization.QConfig(
activation=quantization.default_fake_quant,
weight=quantization.default_fake_quant,
)
quantization.prepare_qat(qat_resnet50, inplace=True)
qat_resnet50.apply(torch.ao.quantization.enable_observer)
qat_resnet50.apply(torch.ao.quantization.enable_fake_quant)
_ = qat_resnet50(x)
for module in qat_resnet50.modules():
if isinstance(module, quantization.FakeQuantize):
module.calculate_qparams()
qat_resnet50.apply(torch.ao.quantization.disable_observer)
self.exportTest(toC(qat_resnet50), toC(x))
@skipIfUnsupportedMinOpsetVersion(13)
def test_qat_resnet_per_channel(self):
# Quantize ResNet50 model
x = torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0)
qat_resnet50 = resnet50()
qat_resnet50.qconfig = quantization.QConfig(
activation=quantization.default_fake_quant,
weight=quantization.default_per_channel_weight_fake_quant,
)
quantization.prepare_qat(qat_resnet50, inplace=True)
qat_resnet50.apply(torch.ao.quantization.enable_observer)
qat_resnet50.apply(torch.ao.quantization.enable_fake_quant)
_ = qat_resnet50(x)
for module in qat_resnet50.modules():
if isinstance(module, quantization.FakeQuantize):
module.calculate_qparams()
qat_resnet50.apply(torch.ao.quantization.disable_observer)
self.exportTest(toC(qat_resnet50), toC(x))
@skipScriptTest(min_opset_version=15) # None type in outputs
def test_googlenet(self):
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(googlenet()), toC(x), rtol=1e-3, atol=1e-5)
def test_mnasnet(self):
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(mnasnet1_0()), toC(x), rtol=1e-3, atol=1e-5)
def test_mobilenet(self):
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(mobilenet_v2()), toC(x), rtol=1e-3, atol=1e-5)
@skipScriptTest() # prim_data
def test_shufflenet(self):
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(shufflenet_v2_x1_0()), toC(x), rtol=1e-3, atol=1e-5)
@skipIfUnsupportedMinOpsetVersion(11)
def test_fcn(self):
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(
toC(fcn_resnet101(pretrained=False, pretrained_backbone=False)),
toC(x),
rtol=1e-3,
atol=1e-5,
)
@skipIfUnsupportedMinOpsetVersion(11)
def test_deeplab(self):
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(
toC(deeplabv3_resnet101(pretrained=False, pretrained_backbone=False)),
toC(x),
rtol=1e-3,
atol=1e-5,
)
def test_r3d_18_video(self):
x = Variable(torch.randn(1, 3, 4, 112, 112).fill_(1.0))
self.exportTest(toC(r3d_18()), toC(x), rtol=1e-3, atol=1e-5)
def test_mc3_18_video(self):
x = Variable(torch.randn(1, 3, 4, 112, 112).fill_(1.0))
self.exportTest(toC(mc3_18()), toC(x), rtol=1e-3, atol=1e-5)
def test_r2plus1d_18_video(self):
x = Variable(torch.randn(1, 3, 4, 112, 112).fill_(1.0))
self.exportTest(toC(r2plus1d_18()), toC(x), rtol=1e-3, atol=1e-5)
if __name__ == "__main__":
common_utils.run_tests()
|
