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conv.py
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conv.py
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# Copyright 2021 Yan Yan
#
# 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.
import math
import time
import sys
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import init
from torch.nn.parameter import Parameter
from spconv import pytorch as spconv
from spconv import SPCONV_VERSION_NUMBERS
from spconv.core import ConvAlgo
from spconv.debug_utils import spconv_save_debug_data
from spconv.pytorch import functional as Fsp
from spconv.pytorch import ops
from spconv.cppconstants import CPU_ONLY_BUILD
from spconv.pytorch.core import IndiceData, SparseConvTensor, ImplicitGemmIndiceData, expand_nd
from spconv.pytorch.modules import SparseModule
from spconv.constants import SAVED_WEIGHT_LAYOUT, ALL_WEIGHT_IS_KRSC, SPCONV_DEBUG_WEIGHT
from spconv.utils import nullcontext
from torch.nn.init import calculate_gain
from cumm import tensorview as tv
from collections import namedtuple
from torch.nn import functional as F
FILTER_HWIO = False
_MAX_NUM_VOXELS_DURING_TRAINING = "max_num_voxels_during_training"
def _apply_act(x: torch.Tensor, act_type: tv.gemm.Activation, act_alpha: float, act_beta: float):
if act_type == tv.gemm.Activation.None_:
return x
elif act_type == tv.gemm.Activation.ReLU:
return F.relu(x)
elif act_type == tv.gemm.Activation.Sigmoid:
return F.sigmoid(x)
elif act_type == tv.gemm.Activation.LeakyReLU:
return F.leaky_relu(x, act_alpha)
else:
raise NotImplementedError
class SparseConvolutionBase:
def __init__(self,
ndim: int,
in_channels: int,
out_channels: int,
kernel_size: Union[int, List[int], Tuple[int, ...]] = 3,
stride: Union[int, List[int], Tuple[int, ...]] = 1,
padding: Union[int, List[int], Tuple[int, ...]] = 0,
dilation: Union[int, List[int], Tuple[int, ...]] = 1,
groups: int = 1,
bias: bool = True,
subm: bool = False,
output_padding: Union[int, List[int], Tuple[int, ...]] = 0,
transposed: bool = False,
inverse: bool = False,
indice_key: Optional[str] = None,
algo: Optional[ConvAlgo] = None,
fp32_accum: Optional[bool] = None,
record_voxel_count: bool = False,
act_type: tv.gemm.Activation = tv.gemm.Activation.None_,
act_alpha: float = 0,
act_beta: float = 0):
# assert groups == 1, "don't support groups for now"
self.ndim = ndim
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = expand_nd(ndim, kernel_size)
self.stride = expand_nd(ndim, stride)
kv = int(np.prod(self.kernel_size))
kv_stride = int(np.prod(self.stride))
self.dilation = expand_nd(ndim, dilation)
self.padding = expand_nd(ndim, padding)
self.conv1x1 = kv == 1
# TODO we should deprecate support for ksize == 1 but stride != 1.
if not subm:
self.conv1x1 &= kv_stride == 1
if self.conv1x1:
assert self.padding == [
0
] * ndim, "padding must be zero for 1x1 conv (k=1,s=1)"
self.transposed = transposed
self.inverse = inverse
self.output_padding = expand_nd(ndim, output_padding)
self.groups = groups
self.subm = subm
self.indice_key = indice_key
self.record_voxel_count = record_voxel_count
if algo is None:
if kv <= 128 and not CPU_ONLY_BUILD:
if kv < 8:
algo = ConvAlgo.MaskImplicitGemm
else:
algo = ConvAlgo.MaskImplicitGemm
else:
algo = ConvAlgo.Native
# if kv > 32:
# assert algo == ConvAlgo.Native, "implicit gemm don't support kv >= 32 for now"
if groups>1:
# assert algo == ConvAlgo.Native, "channel wise groups don't support gemm for now"
assert groups == in_channels, "channel wise groups only support groups = in_channels for now"
assert out_channels == in_channels, "channel wise groups only support out_channels = in_channels for now"
self.group_mask = torch.eye(in_channels).unsqueeze(0).expand(kv, -1, -1).float().cuda()
if CPU_ONLY_BUILD:
assert algo == ConvAlgo.Native, "cpu only build only support native algorithm"
self.algo = algo
self.fp32_accum = fp32_accum
# self.algo = ConvAlgo.Native
if self.algo == ConvAlgo.Native and not ALL_WEIGHT_IS_KRSC:
if groups > 1:
self.group_mask = self.group_mask.view(*self.kernel_size, in_channels, out_channels)
if FILTER_HWIO:
# RSCK
weight_shape = [*self.kernel_size, in_channels//groups, out_channels]
else:
# RSKC
weight_shape = [*self.kernel_size, out_channels, in_channels//groups]
else:
# KRSC
weight_shape = [out_channels, *self.kernel_size, in_channels//groups]
if groups > 1:
self.group_mask = self.group_mask.permute(1,0,2).view(out_channels, *self.kernel_size, in_channels)
self.weight_shape = weight_shape
self.act_type = act_type
self.act_alpha = act_alpha
self.act_beta = act_beta
self.scale = 1.0
self.zero_point = 0
if self.conv1x1:
assert act_type == tv.gemm.Activation.None_, "conv1x1 don't support fused act"
def is_inverseable(self):
return self.indice_key is not None and not self.subm
def _conv_forward(self, training: bool, input: SparseConvTensor, weight: torch.Tensor, bias: Optional[torch.Tensor], add_input: Optional[SparseConvTensor] = None,
channel_scale: Optional[torch.Tensor] = None, output_scale: Optional[float] = None, name: Optional[str] = None,
sparse_unique_name: str = "",
act_type: tv.gemm.Activation = tv.gemm.Activation.None_,
act_alpha: float = 0,
act_beta: float = 0):
# assert isinstance(input, SparseConvTensor)
is_int8 = input.is_quantized and weight.is_quantized
if is_int8:
assert output_scale is not None and channel_scale is not None, "int8 must be called in static quantized module"
assert bias is not None, "currently you must specify a bias"
assert input.features.shape[
1] == self.in_channels, "channel size mismatch"
features = input.features
device = features.device
indices = input.indices
spatial_shape = input.spatial_shape
batch_size = input.batch_size
bias_for_training = bias if training else None
bias_for_infer = bias if not training else None
output_add_scale = 0.0
if is_int8:
if add_input is not None:
output_add_scale = add_input.q_scale()
if training:
msg = "act don't support backward, only used in inference"
assert self.act_type == tv.gemm.Activation.None_, msg
if not self.subm:
if self.transposed:
out_spatial_shape = ops.get_deconv_output_size(
spatial_shape, self.kernel_size, self.stride, self.padding,
self.dilation, self.output_padding)
else:
out_spatial_shape = ops.get_conv_output_size(
spatial_shape, self.kernel_size, self.stride, self.padding,
self.dilation)
else:
out_spatial_shape = spatial_shape
# print(self._sparse_unique_name, spatial_shape, out_spatial_shape)
# input.update_grid(out_spatial_shape)
# t = time.time()
if self.groups > 1 and self.groups == self.in_channels:
# print(weight.shape, self.group_mask.shape)
weight = weight * self.group_mask
out_tensor = input.shadow_copy()
if input.benchmark:
if name is None:
raise ValueError(
"you need to assign name to spmodules before benchmark (spconv.utils.bench.assign_name_to_spmod)"
)
if name not in input.benchmark_record:
input.benchmark_record[name] = {
"type": "SparseConvolution",
"indice_gen_time": [],
"time": [],
"num_points": [],
"num_out_points": [],
"params": {
"kernel_size": self.kernel_size,
"stride": self.stride,
"padding": self.padding,
"dilation": self.dilation,
"output_padding": self.output_padding,
"subm": self.subm,
"transposed": self.transposed,
"input_channels": self.in_channels,
"out_channels": self.out_channels,
}
}
if self.conv1x1 and not is_int8:
# in int8 test mode, we don't implement conv1x1 via mm.
if FILTER_HWIO:
features = torch.mm(
input.features,
weight.view(self.out_channels, self.in_channels).T)
else:
features = torch.mm(
input.features,
weight.view(self.in_channels, self.out_channels))
if bias is not None:
features += bias
out_tensor = out_tensor.replace_feature(features)
# padding may change spatial shape of conv 1x1.
out_tensor.spatial_shape = out_spatial_shape
return out_tensor
indice_dict = input.indice_dict.copy()
# only support contiguous tensor for now
if not features.is_contiguous():
features = features.contiguous()
algo = self.algo
if self.indice_key is not None:
datas = input.find_indice_pair(self.indice_key)
if datas is not None:
msg = "due to limitation of pytorch, you must provide same algo to layers share same indice key."
assert algo == datas.algo, msg
# algo = datas.algo
profile_ctx = nullcontext()
if input._timer is not None and sparse_unique_name:
profile_ctx = input._timer.namespace(sparse_unique_name)
with profile_ctx:
if algo == ConvAlgo.Native:
datas = input.find_indice_pair(self.indice_key)
if datas is not None:
assert isinstance(datas, IndiceData)
if self.inverse:
assert datas is not None and self.indice_key is not None
assert datas.is_subm is False, "inverse conv can only be used with standard conv and pool ops."
outids = datas.indices
indice_pairs = datas.indice_pairs
indice_pair_num = datas.indice_pair_num
out_spatial_shape = datas.spatial_shape
self._check_inverse_reuse_valid(input, spatial_shape,
datas)
else:
if self.indice_key is not None and datas is not None:
outids = datas.out_indices
indice_pairs = datas.indice_pairs
indice_pair_num = datas.indice_pair_num
assert self.subm, "only support reuse subm indices"
self._check_subm_reuse_valid(input, spatial_shape,
datas)
else:
if input.benchmark:
torch.cuda.synchronize()
t = time.time()
try:
outids, indice_pairs, indice_pair_num = ops.get_indice_pairs(
indices, batch_size, spatial_shape, algo,
self.kernel_size, self.stride, self.padding,
self.dilation, self.output_padding, self.subm,
self.transposed)
except Exception as e:
msg = "[Exception|native_pair]"
msg += f"indices={indices.shape},bs={batch_size},ss={spatial_shape},"
msg += f"algo={algo},ksize={self.kernel_size},stride={self.stride},"
msg += f"padding={self.padding},dilation={self.dilation},subm={self.subm},"
msg += f"transpose={self.transposed}"
print(msg, file=sys.stderr)
spconv_save_debug_data(indices)
raise e
if input.benchmark:
torch.cuda.synchronize()
interval = time.time() - t
out_tensor.benchmark_record[
name]["indice_gen_time"].append(interval)
indice_data = IndiceData(outids,
indices,
indice_pairs,
indice_pair_num,
spatial_shape,
out_spatial_shape,
is_subm=self.subm,
algo=algo,
ksize=self.kernel_size,
stride=self.stride,
padding=self.padding,
dilation=self.dilation)
if self.indice_key is not None:
msg = f"your indice key {self.indice_key} already exists in this sparse tensor."
assert self.indice_key not in indice_dict, msg
indice_dict[self.indice_key] = indice_data
if input.benchmark:
torch.cuda.synchronize()
t = time.time()
indice_pairs_calc = indice_pairs
if indice_pairs.device != features.device:
indice_pairs_calc = indice_pairs.to(features.device)
if self.subm:
out_features = Fsp.indice_subm_conv(
features,
weight,
indice_pairs_calc,
indice_pair_num,
outids.shape[0],
algo,
input._timer,
bias_for_infer,
act_alpha,
act_beta,
act_type)
else:
if self.inverse:
out_features = Fsp.indice_inverse_conv(
features,
weight,
indice_pairs_calc,
indice_pair_num,
outids.shape[0],
algo,
input._timer,
bias_for_infer,
act_alpha,
act_beta,
act_type)
else:
out_features = Fsp.indice_conv(
features,
weight,
indice_pairs_calc,
indice_pair_num,
outids.shape[0],
algo,
input._timer,
bias_for_infer,
act_type,
act_beta,
act_type)
else:
datas = input.find_indice_pair(self.indice_key)
if datas is not None:
assert isinstance(datas, ImplicitGemmIndiceData)
if self.inverse:
assert datas is not None and self.indice_key is not None
assert datas.is_subm is False, "inverse conv can only be used with standard conv and pool ops."
outids = datas.indices
pair_fwd = datas.pair_bwd
pair_bwd = datas.pair_fwd
pair_mask_fwd_splits = datas.pair_mask_bwd_splits
pair_mask_bwd_splits = datas.pair_mask_fwd_splits
mask_argsort_fwd_splits = datas.mask_argsort_bwd_splits
mask_argsort_bwd_splits = datas.mask_argsort_fwd_splits
masks = datas.masks
out_spatial_shape = datas.spatial_shape
# assert datas.ksize == self.kernel_size, "inverse conv must have same kernel size as its couple conv"
self._check_inverse_reuse_valid(input, spatial_shape,
datas)
else:
if self.indice_key is not None and datas is not None:
outids = datas.out_indices
pair_fwd = datas.pair_fwd
pair_bwd = datas.pair_bwd
pair_mask_fwd_splits = datas.pair_mask_fwd_splits
pair_mask_bwd_splits = datas.pair_mask_bwd_splits
mask_argsort_fwd_splits = datas.mask_argsort_fwd_splits
mask_argsort_bwd_splits = datas.mask_argsort_bwd_splits
masks = datas.masks
assert self.subm, "only support reuse subm indices"
self._check_subm_reuse_valid(input, spatial_shape,
datas)
else:
if input.benchmark:
torch.cuda.synchronize()
t = time.time()
with input._timer.namespace("gen_pairs"):
# we need to gen bwd indices for regular conv
# because it may be inversed.
try:
res = ops.get_indice_pairs_implicit_gemm(
indices,
batch_size,
spatial_shape,
algo,
ksize=self.kernel_size,
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
out_padding=self.output_padding,
subm=self.subm,
transpose=self.transposed,
is_train=(not self.subm) or training,
alloc=input.thrust_allocator,
timer=input._timer)
except Exception as e:
msg = "[Exception|implicit_gemm_pair]"
msg += f"indices={indices.shape},bs={batch_size},ss={spatial_shape},"
msg += f"algo={algo},ksize={self.kernel_size},stride={self.stride},"
msg += f"padding={self.padding},dilation={self.dilation},subm={self.subm},"
msg += f"transpose={self.transposed}"
print(msg, file=sys.stderr)
spconv_save_debug_data(indices)
raise e
if input.benchmark:
torch.cuda.synchronize()
interval = time.time() - t
out_tensor.benchmark_record[
name]["indice_gen_time"].append(interval)
outids = res[0]
num_inds_per_loc = res[1]
pair_fwd = res[2]
pair_bwd = res[3]
pair_mask_fwd_splits = res[4]
pair_mask_bwd_splits = res[5]
mask_argsort_fwd_splits = res[6]
mask_argsort_bwd_splits = res[7]
masks = res[8]
if self.indice_key is not None:
indice_data = ImplicitGemmIndiceData(
outids,
indices,
pair_fwd,
pair_bwd,
pair_mask_fwd_splits=pair_mask_fwd_splits,
pair_mask_bwd_splits=pair_mask_bwd_splits,
mask_argsort_fwd_splits=mask_argsort_fwd_splits,
mask_argsort_bwd_splits=mask_argsort_bwd_splits,
masks=masks,
is_subm=self.subm,
spatial_shape=spatial_shape,
out_spatial_shape=out_spatial_shape,
algo=algo,
ksize=self.kernel_size,
stride=self.stride,
padding=self.padding,
dilation=self.dilation)
msg = f"your indice key {self.indice_key} already exists in this sparse tensor."
assert self.indice_key not in indice_dict, msg
indice_dict[self.indice_key] = indice_data
if input.benchmark:
torch.cuda.synchronize()
t = time.time()
num_activate_out = outids.shape[0]
weight_cur = weight
bias_cur = bias_for_infer
# if self.enable_int8_test_mode:
# assert features.dtype == torch.int8, "in int8 test mode, feature must be int8"
# weight_cur = self._int8_weight
# bias_cur = self._int8_bias
if training:
out_features = Fsp.implicit_gemm(
features, weight_cur, pair_fwd, pair_bwd,
pair_mask_fwd_splits, pair_mask_bwd_splits,
mask_argsort_fwd_splits, mask_argsort_bwd_splits,
num_activate_out, masks, training, self.subm,
input._timer, self.fp32_accum,
bias_cur,
act_alpha,
act_beta,
act_type)
else:
output_dtype = None
if output_scale is None:
output_dtype = weight.dtype
out_features, _, _ = ops.implicit_gemm(
features, weight_cur, pair_fwd, pair_mask_fwd_splits,
mask_argsort_fwd_splits,
num_activate_out, masks, training, self.subm,
input._timer, self.fp32_accum,
bias_cur,
act_alpha,
act_beta,
act_type,
# TODO do we really need output scale to scale bias in kernel?
1.0 if output_scale is None else output_scale, # output_scale
channel_scale, # scale
output_add=add_input.features if add_input is not None else None,
output_add_scale=output_add_scale,
output_dtype=output_dtype)
if bias_for_training is not None:
out_features += bias_for_training
if input.benchmark:
torch.cuda.synchronize()
interval = time.time() - t
out_tensor.benchmark_record[name]["time"].append(interval)
out_tensor.benchmark_record[name]["num_points"].append(
features.shape[0])
out_tensor.benchmark_record[name]["num_out_points"].append(
out_features.shape[0])
if not self.subm and not self.inverse and self.record_voxel_count:
if hasattr(self, _MAX_NUM_VOXELS_DURING_TRAINING):
ops.maximum_value_int_(
getattr(self, _MAX_NUM_VOXELS_DURING_TRAINING),
outids.shape[0])
out_tensor = out_tensor.replace_feature(out_features)
out_tensor.indices = outids
out_tensor.indice_dict = indice_dict
out_tensor.spatial_shape = out_spatial_shape
if add_input is not None and not is_int8:
# in int8, we apply add + act in kernel.
out_tensor = out_tensor.replace_feature(_apply_act(out_tensor.features + add_input.features, self.act_type, self.act_alpha, self.act_beta))
return out_tensor
def _check_subm_reuse_valid(self, inp: SparseConvTensor,
spatial_shape: List[int],
datas: Union[ImplicitGemmIndiceData,
IndiceData]):
assert datas.is_subm, "only support reuse subm indices"
if self.kernel_size != datas.ksize:
raise ValueError(
f"subm with same indice_key must have same kernel"
f" size, expect {datas.ksize}, this layer {self.kernel_size}")
if self.dilation != datas.dilation:
raise ValueError(
f"subm with same indice_key must have same dilation"
f", expect {datas.dilation}, this layer {self.dilation}")
if inp.spatial_shape != datas.spatial_shape:
raise ValueError(
f"subm with same indice_key must have same spatial structure"
f", expect {datas.spatial_shape}, input {spatial_shape}")
if inp.indices.shape[0] != datas.indices.shape[0]:
raise ValueError(
f"subm with same indice_key must have same num of indices"
f", expect {datas.indices.shape[0]}, input {inp.indices.shape[0]}"
)
def _check_inverse_reuse_valid(self, inp: SparseConvTensor,
spatial_shape: List[int],
datas: Union[ImplicitGemmIndiceData,
IndiceData]):
if self.kernel_size != datas.ksize:
raise ValueError(
f"Inverse with same indice_key must have same kernel"
f" size, expect {datas.ksize}, this layer {self.kernel_size}, "
"please check Inverse Convolution in docs/USAGE.md.")
if inp.spatial_shape != datas.out_spatial_shape:
raise ValueError(
f"Inverse with same indice_key must have same spatial structure (spatial shape)"
f", expect {datas.spatial_shape}, input {spatial_shape}, "
"please check Inverse Convolution in docs/USAGE.md.")
if inp.indices.shape[0] != datas.out_indices.shape[0]:
raise ValueError(
f"Inverse with same indice_key must have same num of indices"
f", expect {datas.indices.shape[0]}, input {inp.indices.shape[0]}, "
"please check Inverse Convolution in ."
)
class SparseConvolution(SparseConvolutionBase, SparseModule):
__constants__ = [
'stride', 'padding', 'dilation', 'groups', 'bias', 'subm', 'inverse',
'transposed', 'output_padding'
]
def __init__(self,
ndim: int,
in_channels: int,
out_channels: int,
kernel_size: Union[int, List[int], Tuple[int, ...]] = 3,
stride: Union[int, List[int], Tuple[int, ...]] = 1,
padding: Union[int, List[int], Tuple[int, ...]] = 0,
dilation: Union[int, List[int], Tuple[int, ...]] = 1,
groups: int = 1,
bias: bool = True,
subm: bool = False,
output_padding: Union[int, List[int], Tuple[int, ...]] = 0,
transposed: bool = False,
inverse: bool = False,
indice_key: Optional[str] = None,
algo: Optional[ConvAlgo] = None,
fp32_accum: Optional[bool] = None,
record_voxel_count: bool = False,
act_type: tv.gemm.Activation = tv.gemm.Activation.None_,
act_alpha: float = 0,
act_beta: float = 0,
name=None,
device=None,
dtype=None):
factory_kwargs = {"device": device, "dtype": dtype}
SparseConvolutionBase.__init__(self, ndim, in_channels, out_channels, kernel_size, stride, padding, dilation, groups,
bias=False,
subm=subm,
output_padding=output_padding,
transposed=transposed,
inverse=inverse,
indice_key=indice_key,
algo=algo,
fp32_accum=fp32_accum,
record_voxel_count=record_voxel_count,
act_type=act_type,
act_alpha=act_alpha,
act_beta=act_beta)
SparseModule.__init__(self, name=name)
if record_voxel_count and not self.subm and not self.inverse:
# we record maximum voxel num in both inference and training if
# record_voxel_count flag setting.
self.register_buffer(_MAX_NUM_VOXELS_DURING_TRAINING,
torch.zeros(1, dtype=torch.int32, device=device))
self.weight = Parameter(torch.zeros(*self.weight_shape, **factory_kwargs))
if bias:
self.bias = Parameter(torch.zeros(out_channels, **factory_kwargs))
else:
self.register_parameter('bias', None)
self.reset_parameters()
if hasattr(self, "_register_load_state_dict_pre_hook"):
self._register_load_state_dict_pre_hook(
self._load_weight_different_layout)
def get_max_num_voxels(self) -> Optional[torch.Tensor]:
if hasattr(self, _MAX_NUM_VOXELS_DURING_TRAINING):
return getattr(self, _MAX_NUM_VOXELS_DURING_TRAINING)
return None
# def set_int8_test(self, enable: bool, input_scale: float, output_scale: Optional[float] = None, weight_scale: Optional[torch.Tensor] = None):
# self._int8_input_scale = input_scale
# self._int8_output_scale = output_scale
# if weight_scale is not None:
# self._int8_weight_scale = weight_scale
# self.enable_int8_test_mode = enable
def _load_weight_different_layout(self, state_dict, prefix, local_metadata,
strict, missing_keys, unexpected_keys,
error_msgs):
name = prefix + _MAX_NUM_VOXELS_DURING_TRAINING
if self.record_voxel_count and not self.subm and not self.inverse and name not in state_dict:
state_dict[name] = torch.zeros(
1, dtype=torch.int32)
if not SAVED_WEIGHT_LAYOUT:
return
key = prefix + "weight"
assert key in state_dict
ndim = self.ndim
if SAVED_WEIGHT_LAYOUT == "RSKC":
state_dict[key] = state_dict[key].permute(ndim, *range(ndim),
ndim + 1).contiguous()
elif SAVED_WEIGHT_LAYOUT == "RSCK":
state_dict[key] = state_dict[key].permute(ndim + 1, *range(ndim),
ndim).contiguous()
if ALL_WEIGHT_IS_KRSC or self.algo != ConvAlgo.Native:
# in spconv 2.2, we only support KRSC layout.
if SAVED_WEIGHT_LAYOUT == "RSKC":
state_dict[key] = state_dict[key].permute(
ndim, *range(ndim), ndim + 1).contiguous()
elif SAVED_WEIGHT_LAYOUT == "RSCK":
state_dict[key] = state_dict[key].permute(
ndim + 1, *range(ndim), ndim).contiguous()
else:
if self.algo == ConvAlgo.Native:
# to RSCK
if SAVED_WEIGHT_LAYOUT == "RSKC":
state_dict[key] = state_dict[key].permute(
*range(ndim), ndim + 1, ndim).contiguous()
elif SAVED_WEIGHT_LAYOUT == "KRSC":
state_dict[key] = state_dict[key].permute(
*range(1, ndim + 1), 0, ndim + 1).contiguous()
def extra_repr(self):
s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}'
', stride={stride}')
if self.padding != (0, ) * len(self.padding):
s += ', padding={padding}'
if self.dilation != (1, ) * len(self.dilation):
s += ', dilation={dilation}'
if self.output_padding != (0, ) * len(self.output_padding):
s += ', output_padding={output_padding}'
if self.groups != 1:
s += ', groups={groups}'
if self.bias is None:
s += ', bias=False'
if self.algo is not None:
s += f', algo={self.algo}'
if self.act_type != tv.gemm.Activation.None_:
s += f', act={self.act_type}'
return s.format(**self.__dict__)
def _calculate_fan_in_and_fan_out(self):
receptive_field_size = 1
# math.prod is not always available, accumulate the product manually
# we could use functools.reduce but that is not supported by TorchScript
for s in self.kernel_size:
receptive_field_size *= s
fan_in = self.in_channels * receptive_field_size
fan_out = self.out_channels * receptive_field_size
return fan_in, fan_out
def _calculate_correct_fan(self, mode):
mode = mode.lower()
valid_modes = ['fan_in', 'fan_out']
if mode not in valid_modes:
raise ValueError(
"Mode {} not supported, please use one of {}".format(
mode, valid_modes))
fan_in, fan_out = self._calculate_fan_in_and_fan_out()
return fan_in if mode == 'fan_in' else fan_out
def _custom_kaiming_uniform_(self,
tensor,
a=0,
mode='fan_in',
nonlinearity='leaky_relu'):
r"""same as torch.init.kaiming_uniform_, with KRSC layout support
"""
fan = self._calculate_correct_fan(mode)
gain = calculate_gain(nonlinearity, a)
std = gain / math.sqrt(fan)
bound = math.sqrt(
3.0) * std # Calculate uniform bounds from standard deviation
with torch.no_grad():
return tensor.uniform_(-bound, bound)
def reset_parameters(self):
if SPCONV_DEBUG_WEIGHT:
self._custom_kaiming_uniform_(self.weight, a=math.sqrt(0.005))
else:
self._custom_kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = self._calculate_fan_in_and_fan_out()
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
def forward(self, input: SparseConvTensor, add_input: Optional[SparseConvTensor] = None):
return self._conv_forward(self.training, input, self.weight, self.bias, add_input,
name=self.name, sparse_unique_name=self._sparse_unique_name, act_type=self.act_type,
act_alpha=self.act_alpha, act_beta=self.act_beta)
class SparseConv1d(SparseConvolution):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
indice_key=None,
algo: Optional[ConvAlgo] = None,
fp32_accum: Optional[bool] = None,
record_voxel_count: bool = False,
name=None):
super(SparseConv1d,
self).__init__(1,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
bias,
indice_key=indice_key,
algo=algo,
fp32_accum=fp32_accum,
record_voxel_count=record_voxel_count,
name=name)
class SparseConv2d(SparseConvolution):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
indice_key=None,
algo: Optional[ConvAlgo] = None,
fp32_accum: Optional[bool] = None,
record_voxel_count: bool = False,
name=None):
super(SparseConv2d,
self).__init__(2,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
bias,
indice_key=indice_key,
algo=algo,
fp32_accum=fp32_accum,
record_voxel_count=record_voxel_count,
name=name)
class SparseConv3d(SparseConvolution):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
indice_key=None,
algo: Optional[ConvAlgo] = None,
fp32_accum: Optional[bool] = None,
record_voxel_count: bool = False,
name=None):
super(SparseConv3d,
self).__init__(3,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
bias,
indice_key=indice_key,
algo=algo,
fp32_accum=fp32_accum,
record_voxel_count=record_voxel_count,
name=name)
class SparseConv4d(SparseConvolution):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
indice_key=None,
algo: Optional[ConvAlgo] = None,
fp32_accum: Optional[bool] = None,
record_voxel_count: bool = False,
name=None):
super(SparseConv4d,
self).__init__(4,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
bias,
indice_key=indice_key,
algo=algo,
fp32_accum=fp32_accum,
record_voxel_count=record_voxel_count,
name=name)
class SparseConvTranspose1d(SparseConvolution):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
indice_key=None,
algo: Optional[ConvAlgo] = None,
fp32_accum: Optional[bool] = None,
record_voxel_count: bool = False,
name=None):
super(SparseConvTranspose1d,
self).__init__(1,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
bias,
transposed=True,
indice_key=indice_key,
algo=algo,
fp32_accum=fp32_accum,
record_voxel_count=record_voxel_count,
name=name)
class SparseConvTranspose2d(SparseConvolution):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
indice_key=None,
algo: Optional[ConvAlgo] = None,
fp32_accum: Optional[bool] = None,
record_voxel_count: bool = False,
name=None):
super(SparseConvTranspose2d,
self).__init__(2,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
bias,
transposed=True,
indice_key=indice_key,
algo=algo,
fp32_accum=fp32_accum,
record_voxel_count=record_voxel_count,
name=name)
class SparseConvTranspose3d(SparseConvolution):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
indice_key=None,
algo: Optional[ConvAlgo] = None,
fp32_accum: Optional[bool] = None,
record_voxel_count: bool = False,
name=None):
super(SparseConvTranspose3d,
self).__init__(3,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
bias,
transposed=True,
indice_key=indice_key,
algo=algo,
fp32_accum=fp32_accum,
record_voxel_count=record_voxel_count,
name=name)
class SparseConvTranspose4d(SparseConvolution):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
indice_key=None,
algo: Optional[ConvAlgo] = None,
fp32_accum: Optional[bool] = None,
record_voxel_count: bool = False,