# Copyright 2020 Google LLC
#
# 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.
# Lint as: python3
"""Bottleneck ResNet v2 with GroupNorm and Weight Standardization."""
import math
from os.path import join as pjoin
from collections import OrderedDict # pylint: disable=g-importing-member
import torch
import torch.nn as nn
import torch.nn.functional as F
def np2th(weights, conv=False):
"""Possibly convert HWIO to OIHW."""
if conv:
weights = weights.transpose([3, 2, 0, 1])
return torch.from_numpy(weights)
class StdConv2d(nn.Conv2d):
def forward(self, x):
w = self.weight
v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
w = (w - m) / torch.sqrt(v + 1e-5)
return F.conv2d(x, w, self.bias, self.stride, self.padding,
self.dilation, self.groups)
def conv3x3(cin, cout, stride=1, groups=1, bias=False):
return StdConv2d(cin, cout, kernel_size=3, stride=stride,
padding=1, bias=bias, groups=groups)
def conv1x1(cin, cout, stride=1, bias=False):
return StdConv2d(cin, cout, kernel_size=1, stride=stride,
padding=0, bias=bias)
class PreActBottleneck(nn.Module):
"""Pre-activation (v2) bottleneck block.
"""
def __init__(self, cin, cout=None, cmid=None, stride=1):
super().__init__()
cout = cout or cin
cmid = cmid or cout//4
self.gn1 = nn.GroupNorm(32, cmid, eps=1e-6)
self.conv1 = conv1x1(cin, cmid, bias=False)
self.gn2 = nn.GroupNorm(32, cmid, eps=1e-6)
self.conv2 = conv3x3(cmid, cmid, stride, bias=False) # Original code has it on conv1!!
self.gn3 = nn.GroupNorm(32, cout, eps=1e-6)
self.conv3 = conv1x1(cmid, cout, bias=False)
self.relu = nn.ReLU(inplace=True)
if (stride != 1 or cin != cout):
# Projection also with pre-activation according to paper.
self.downsample = conv1x1(cin, cout, stride, bias=False)
self.gn_proj = nn.GroupNorm(cout, cout)
def forward(self, x):
# Residual branch
residual = x
if hasattr(self, 'downsample'):
residual = self.downsample(x)
residual = self.gn_proj(residual)
# Unit's branch
y = self.relu(self.gn1(self.conv1(x)))
y = self.relu(self.gn2(self.conv2(y)))
y = self.gn3(self.conv3(y))
y = self.relu(residual + y)
return y
def load_from(self, weights, n_block, n_unit):
conv1_weight = np2th(weights[pjoin(n_block, n_unit, "conv1/kernel")], conv=True)
conv2_weight = np2th(weights[pjoin(n_block, n_unit, "conv2/kernel")], conv=True)
conv3_weight = np2th(weights[pjoin(n_block, n_unit, "conv3/kernel")], conv=True)
gn1_weight = np2th(weights[pjoin(n_block, n_unit, "gn1/scale")])
gn1_bias = np2th(weights[pjoin(n_block, n_unit, "gn1/bias")])
gn2_weight = np2th(weights[pjoin(n_block, n_unit, "gn2/scale")])
gn2_bias = np2th(weights[pjoin(n_block, n_unit, "gn2/bias")])
gn3_weight = np2th(weights[pjoin(n_block, n_unit, "gn3/scale")])
gn3_bias = np2th(weights[pjoin(n_block, n_unit, "gn3/bias")])
self.conv1.weight.copy_(conv1_weight)
self.conv2.weight.copy_(conv2_weight)
self.conv3.weight.copy_(conv3_weight)
self.gn1.weight.copy_(gn1_weight.view(-1))
self.gn1.bias.copy_(gn1_bias.view(-1))
self.gn2.weight.copy_(gn2_weight.view(-1))
self.gn2.bias.copy_(gn2_bias.view(-1))
self.gn3.weight.copy_(gn3_weight.view(-1))
self.gn3.bias.copy_(gn3_bias.view(-1))
if hasattr(self, 'downsample'):
proj_conv_weight = np2th(weights[pjoin(n_block, n_unit, "conv_proj/kernel")], conv=True)
proj_gn_weight = np2th(weights[pjoin(n_block, n_unit, "gn_proj/scale")])
proj_gn_bias = np2th(weights[pjoin(n_block, n_unit, "gn_proj/bias")])
self.downsample.weight.copy_(proj_conv_weight)
self.gn_proj.weight.copy_(proj_gn_weight.view(-1))
self.gn_proj.bias.copy_(proj_gn_bias.view(-1))
class ResNetV2(nn.Module):
"""Implementation of Pre-activation (v2) ResNet mode."""
def __init__(self, block_units, width_factor):
super().__init__()
width = int(64 * width_factor)
self.width = width
# The following will be unreadable if we split lines.
# pylint: disable=line-too-long
self.root = nn.Sequential(OrderedDict([
('conv', StdConv2d(3, width, kernel_size=7, stride=2, bias=False, padding=3)),
('gn', nn.GroupNorm(32, width, eps=1e-6)),
('relu', nn.ReLU(inplace=True)),
('pool', nn.MaxPool2d(kernel_size=3, stride=2, padding=0))
]))
self.body = nn.Sequential(OrderedDict([
('block1', nn.Sequential(OrderedDict(
[('unit1', PreActBottleneck(cin=width, cout=width*4, cmid=width))] +
[(f'unit{i:d}', PreActBottleneck(cin=width*4, cout=width*4, cmid=width)) for i in range(2, block_units[0] + 1)],
))),
('block2', nn.Sequential(OrderedDict(
[('unit1', PreActBottleneck(cin=width*4, cout=width*8, cmid=width*2, stride=2))] +
[(f'unit{i:d}', PreActBottleneck(cin=width*8, cout=width*8, cmid=width*2)) for i in range(2, block_units[1] + 1)],
))),
('block3', nn.Sequential(OrderedDict(
[('unit1', PreActBottleneck(cin=width*8, cout=width*16, cmid=width*4, stride=2))] +
[(f'unit{i:d}', PreActBottleneck(cin=width*16, cout=width*16, cmid=width*4)) for i in range(2, block_units[2] + 1)],
))),
]))
def forward(self, x):
x = self.root(x)
x = self.body(x)
return x