# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Tuple
from ..functional import conv1d, conv2d, conv_transpose2d
from ..module import Module
from ..parameter import Parameter
[docs]
class Conv2d(Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Tuple[int, int],
stride: Tuple[int, int] = (1, 1),
padding: Tuple[int, int] = (0, 0),
dilation: Tuple[int, int] = (1, 1),
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros', # TODO: refine this type
dtype=None) -> None:
super().__init__()
if groups <= 0:
raise ValueError('groups must be a positive integer')
if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups')
if out_channels % groups != 0:
raise ValueError('out_channels must be divisible by groups')
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.groups = groups
self.padding_mode = padding_mode
self.weight = Parameter(shape=(out_channels, in_channels // groups,
*kernel_size),
dtype=dtype)
if bias:
self.bias = Parameter(shape=(out_channels, ), dtype=dtype)
else:
self.register_parameter('bias', None)
[docs]
def forward(self, input):
return conv2d(input, self.weight.value,
None if self.bias is None else self.bias.value,
self.stride, self.padding, self.dilation, self.groups)
[docs]
class ConvTranspose2d(Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Tuple[int, int],
stride: Tuple[int, int] = (1, 1),
padding: Tuple[int, int] = (0, 0),
output_padding: Tuple[int, int] = (0, 0),
dilation: Tuple[int, int] = (1, 1),
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros', # TODO: refine this type
dtype=None) -> None:
super().__init__()
if groups <= 0:
raise ValueError('groups must be a positive integer')
if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups')
if out_channels % groups != 0:
raise ValueError('out_channels must be divisible by groups')
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.output_padding = output_padding
self.dilation = dilation
self.groups = groups
self.padding_mode = padding_mode
self.weight = Parameter(shape=(in_channels, out_channels // groups,
*kernel_size),
dtype=dtype)
if bias:
self.bias = Parameter(shape=(out_channels, ), dtype=dtype)
else:
self.register_parameter('bias', None)
def _output_padding(self,
input,
output_size,
stride,
padding,
kernel_size,
num_spatial_dims: int,
dilation=None):
if output_size is None:
ret = self.output_padding
else:
has_batch_dim = input.dim() == num_spatial_dims + 2
num_non_spatial_dims = 2 if has_batch_dim else 1
if len(output_size) == num_non_spatial_dims + num_spatial_dims:
output_size = output_size[num_non_spatial_dims:]
if len(output_size) != num_spatial_dims:
raise ValueError(
"ConvTranspose{}D: for {}D input, output_size must have {} or {} elements (got {})"
.format(num_spatial_dims, input.dim(), num_spatial_dims,
num_non_spatial_dims + num_spatial_dims,
len(output_size)))
min_sizes = []
max_sizes = []
for d in range(num_spatial_dims):
dim_size = (
(input.size(d + num_non_spatial_dims) - 1) * stride[d] -
2 * padding[d] +
(dilation[d] if dilation is not None else 1) *
(kernel_size[d] - 1) + 1)
min_sizes.append(dim_size)
max_sizes.append(min_sizes[d] + stride[d] - 1)
for i in range(len(output_size)):
size = output_size[i]
min_size = min_sizes[i]
max_size = max_sizes[i]
if size < min_size or size > max_size:
raise ValueError((
"requested an output size of {}, but valid sizes range "
"from {} to {} (for an input of {})").format(
output_size, min_sizes, max_sizes,
input.size()[2:]))
res = []
for d in range(num_spatial_dims):
res.append(output_size[d] - min_sizes[d])
ret = res
return ret
[docs]
def forward(self, input, output_size=None):
num_spatial_dims = 2
output_padding = self._output_padding(input, output_size, self.stride,
self.padding, self.kernel_size,
num_spatial_dims, self.dilation)
return conv_transpose2d(input, self.weight.value,
None if self.bias is None else self.bias.value,
self.stride, self.padding, output_padding,
self.dilation, self.groups)
[docs]
class Conv1d(Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
padding: int = 0,
dilation: int = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros', # TODO: refine this type
dtype=None) -> None:
super().__init__()
if groups <= 0:
raise ValueError('groups must be a positive integer')
if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups')
if out_channels % groups != 0:
raise ValueError('out_channels must be divisible by groups')
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.groups = groups
self.padding_mode = padding_mode
self.weight = Parameter(shape=(out_channels, in_channels // groups,
kernel_size, 1),
dtype=dtype)
if bias:
self.bias = Parameter(shape=(out_channels, ), dtype=dtype)
else:
self.register_parameter('bias', None)
[docs]
def forward(self, input):
return conv1d(input, self.weight.value,
None if self.bias is None else self.bias.value,
self.stride, self.padding, self.dilation, self.groups)