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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Miscellaneous utility functions
"""
import torch
from torch import nn
from torch.nn import functional as F
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.layers import Conv2d, DYReLU
from maskrcnn_benchmark.modeling.poolers import Pooler
def get_group_gn(dim, dim_per_gp, num_groups):
"""get number of groups used by GroupNorm, based on number of channels."""
assert dim_per_gp == -1 or num_groups == -1, \
"GroupNorm: can only specify G or C/G."
if dim_per_gp > 0:
assert dim % dim_per_gp == 0, \
"dim: {}, dim_per_gp: {}".format(dim, dim_per_gp)
group_gn = dim // dim_per_gp
else:
assert dim % num_groups == 0, \
"dim: {}, num_groups: {}".format(dim, num_groups)
group_gn = num_groups
return group_gn
def group_norm(out_channels, affine=True, divisor=1):
out_channels = out_channels // divisor
dim_per_gp = cfg.MODEL.GROUP_NORM.DIM_PER_GP // divisor
num_groups = cfg.MODEL.GROUP_NORM.NUM_GROUPS // divisor
eps = cfg.MODEL.GROUP_NORM.EPSILON # default: 1e-5
return torch.nn.GroupNorm(
get_group_gn(out_channels, dim_per_gp, num_groups),
out_channels,
eps,
affine
)
def make_conv3x3(
in_channels,
out_channels,
dilation=1,
stride=1,
use_gn=False,
use_relu=False,
kaiming_init=True
):
conv = Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=stride,
padding=dilation,
dilation=dilation,
bias=False if use_gn else True
)
if kaiming_init:
nn.init.kaiming_normal_(
conv.weight, mode="fan_out", nonlinearity="relu"
)
else:
torch.nn.init.normal_(conv.weight, std=0.01)
if not use_gn:
nn.init.constant_(conv.bias, 0)
module = [conv,]
if use_gn:
module.append(group_norm(out_channels))
if use_relu:
module.append(nn.ReLU(inplace=True))
if len(module) > 1:
return nn.Sequential(*module)
return conv
def make_fc(dim_in, hidden_dim, use_gn=False):
'''
Caffe2 implementation uses XavierFill, which in fact
corresponds to kaiming_uniform_ in PyTorch
'''
if use_gn:
fc = nn.Linear(dim_in, hidden_dim, bias=False)
nn.init.kaiming_uniform_(fc.weight, a=1)
return nn.Sequential(fc, group_norm(hidden_dim))
fc = nn.Linear(dim_in, hidden_dim)
nn.init.kaiming_uniform_(fc.weight, a=1)
nn.init.constant_(fc.bias, 0)
return fc
def conv_with_kaiming_uniform(use_gn=False, use_relu=False, use_dyrelu=False):
def make_conv(
in_channels, out_channels, kernel_size, stride=1, dilation=1
):
conv = Conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=dilation * (kernel_size - 1) // 2,
dilation=dilation,
bias=False if use_gn else True
)
# Caffe2 implementation uses XavierFill, which in fact
# corresponds to kaiming_uniform_ in PyTorch
nn.init.kaiming_uniform_(conv.weight, a=1)
if not use_gn:
nn.init.constant_(conv.bias, 0)
module = [conv,]
if use_gn:
module.append(group_norm(out_channels))
if use_relu:
module.append(nn.ReLU(inplace=True))
if use_dyrelu:
module.append(DYReLU(out_channels, out_channels, use_spatial=True))
if len(module) > 1:
return nn.Sequential(*module)
return conv
return make_conv
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