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# --------------------------------------------------------
# High Resolution Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Rao Fu, RainbowSecret
# --------------------------------------------------------
import os
import copy
import logging
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from .transformer_block import TransformerBlock
from mmcv.cnn import (
build_conv_layer,
build_norm_layer,
build_plugin_layer,
constant_init,
kaiming_init,
)
class BasicBlock(nn.Module):
"""Only replce the second 3x3 Conv with the TransformerBlocker"""
expansion = 1
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type="BN"),
mhsa_flag=False,
num_heads=1,
num_halo_block=1,
num_mlp_ratio=4,
num_sr_ratio=1,
with_rpe=False,
with_ffn=True,
):
super(BasicBlock, self).__init__()
norm_cfg = copy.deepcopy(norm_cfg)
self.in_channels = inplanes
self.out_channels = planes
self.stride = stride
self.with_cp = with_cp
self.downsample = downsample
self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
self.conv1 = build_conv_layer(
conv_cfg,
inplanes,
planes,
3,
stride=stride,
padding=1,
dilation=1,
bias=False,
)
self.add_module(self.norm1_name, norm1)
if not mhsa_flag:
self.conv2 = build_conv_layer(
conv_cfg, planes, planes, 3, padding=1, bias=False
)
self.add_module(self.norm2_name, norm2)
else:
self.conv2 = TransformerBlock(
planes,
num_heads=num_heads,
mlp_ratio=num_mlp_ratio,
sr_ratio=num_sr_ratio,
input_resolution=num_resolution,
with_rpe=with_rpe,
with_ffn=with_ffn,
)
self.relu = nn.ReLU(inplace=True)
@property
def norm1(self):
"""nn.Module: normalization layer after the first convolution layer"""
return getattr(self, self.norm1_name)
@property
def norm2(self):
"""nn.Module: normalization layer after the second convolution layer"""
return getattr(self, self.norm2_name)
def forward(self, x):
"""Forward function."""
def _inner_forward(x):
identity = x
out = self.conv1(x)
out = self.norm1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.norm2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
return out
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