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test_deconvolution_aten.py
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import torch
from parameterized import param, parameterized
from torch.testing._internal.common_utils import run_tests
from torch_tensorrt import Input
from .harness import DispatchTestCase
class TestDeconvolutionConverter(DispatchTestCase):
@parameterized.expand(
[
("default", 1),
param("no_bias", 1, bias=False),
("tuple_parameters", 1, (1), (1)),
param("non_zero_padding", 1, padding=1),
param("dilation", 1, dilation=2),
param("groups", 1, groups=3),
param("output_padding_1", 3, stride=2, padding=1, output_padding=1),
param("output_padding_2", 3, stride=2, padding=2, output_padding=1),
param("output_padding_3", 3, stride=2, padding=3, output_padding=1),
param("output_padding_4", 3, stride=3, padding=2, output_padding=1),
param("output_padding_5", 3, stride=3, padding=3, output_padding=1),
param("output_padding_6", 3, stride=3, padding=3, output_padding=2),
param(
"combined_params",
3,
stride=3,
padding=3,
dilation=2,
groups=3,
output_padding=2,
),
]
)
def test_deconv1d(
self,
_,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
output_padding=0,
):
class TestModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.deconv = torch.nn.ConvTranspose1d(
in_channels=3,
out_channels=3,
kernel_size=kernel_size,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
bias=bias,
dilation=dilation,
)
def forward(self, x):
return self.deconv(x)
inputs = [torch.randn(1, 3, 32)]
self.run_test(
TestModule(),
inputs,
use_dynamo_tracer=True,
enable_passes=True,
)
def test_deconv1d_with_dynamic_shape(
self,
kernel_size=1,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
):
class TestModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.deconv = torch.nn.ConvTranspose1d(
in_channels=3,
out_channels=3,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
)
def forward(self, x):
return self.deconv(x)
input_specs = [
Input(
shape=(-1, 3, 3),
dtype=torch.float32,
shape_ranges=[((1, 3, 3), (3, 3, 3), (5, 3, 3))],
),
]
self.run_test_with_dynamic_shape(
TestModule(),
input_specs,
use_dynamo_tracer=True,
enable_passes=True,
)
@parameterized.expand(
[
("default", 1),
param("no_bias", 1, bias=False),
("tuple_parameters", 1, (1, 1), (1, 1)),
param("non_zero_padding", 1, padding=1),
param("dilation", 1, dilation=2),
param("groups", 1, groups=3),
param("output_padding_1", 3, stride=2, padding=1, output_padding=1),
param("output_padding_2", 3, stride=2, padding=1, output_padding=1),
param("output_padding_3", 3, stride=2, padding=2, output_padding=1),
param("output_padding_4", 3, stride=2, padding=3, output_padding=1),
param("output_padding_5", 3, stride=3, padding=2, output_padding=1),
param("output_padding_6", 3, stride=3, padding=3, output_padding=1),
param("output_padding_7", 3, stride=3, padding=3, output_padding=2),
param(
"combined_params",
3,
stride=3,
padding=3,
dilation=2,
groups=3,
output_padding=2,
),
]
)
def test_deconv2d(
self,
_,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
output_padding=0,
):
class TestModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.deconv = torch.nn.ConvTranspose2d(
in_channels=3,
out_channels=3,
kernel_size=kernel_size,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
bias=bias,
dilation=dilation,
)
def forward(self, x):
return self.deconv(x)
inputs = [torch.randn(1, 3, 32, 32)]
self.run_test(
TestModule(),
inputs,
use_dynamo_tracer=True,
enable_passes=True,
)
# Testing with (-1, -1, -1, -1) results into Error:
# AssertionError: Channel dim can't be dynamic for deconvolution.
def test_deconv2d_with_dynamic_shape(self):
class TestModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.deconv = torch.nn.ConvTranspose2d(3, 3, 1)
def forward(self, x):
return self.deconv(x)
input_specs = [
Input(
shape=(-1, 3, -1, -1),
dtype=torch.float32,
shape_ranges=[((1, 3, 1, 1), (2, 3, 4, 4), (32, 3, 128, 128))],
),
]
self.run_test_with_dynamic_shape(
TestModule(),
input_specs,
use_dynamo_tracer=True,
enable_passes=True,
)
@parameterized.expand(
[
("default", 1),
param("no_bias", 1, bias=False),
("tuple_parameters", 1, (1, 1, 1), (1, 1, 1)),
param("non_zero_padding", 1, padding=1),
param("dilation", 1, dilation=2),
param("groups", 1, groups=3),
param("output_padding_1", 3, stride=2, padding=1, output_padding=1),
param("output_padding_2", 3, stride=2, padding=2, output_padding=1),
param("output_padding_3", 3, stride=3, padding=3, output_padding=1),
param("output_padding_4", 3, stride=3, padding=3, output_padding=2),
param(
"combined_params",
3,
stride=3,
padding=3,
dilation=2,
groups=3,
output_padding=2,
),
]
)
def test_deconv3d(
self,
_,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
output_padding=0,
):
class TestModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.deconv = torch.nn.ConvTranspose3d(
in_channels=3,
out_channels=3,
kernel_size=kernel_size,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
bias=bias,
dilation=dilation,
)
def forward(self, x):
return self.deconv(x)
inputs = [torch.randn(1, 3, 32, 32, 32)]
self.run_test(
TestModule(),
inputs,
use_dynamo_tracer=True,
enable_passes=True,
)
# Testing with (-1, -1, -1, -1, -1) results into Error:
# AssertionError: Channel dim can't be dynamic for deconvolution.
def test_deconv3d_with_dynamic_shape(self):
class TestModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.deconv = torch.nn.ConvTranspose3d(3, 3, 1)
def forward(self, x):
return self.deconv(x)
input_specs = [
Input(
shape=(-1, 3, -1, -1, -1),
dtype=torch.float32,
shape_ranges=[((1, 3, 1, 1, 1), (2, 3, 4, 4, 4), (8, 3, 32, 32, 32))],
),
]
self.run_test_with_dynamic_shape(
TestModule(),
input_specs,
use_dynamo_tracer=True,
enable_passes=True,
)
if __name__ == "__main__":
run_tests()