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More NSG improvements (v3)

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Move to a fully fixup residual network for the switch (no
batch norms). Fix a bunch of other small bugs. Add in a
temporary latent feed-forward from the bottom of the
switch. Fix several initialization issues.
This commit is contained in:
James Betker 2020-06-29 20:26:51 -06:00
parent 4b82d0815d
commit 773753073f
3 changed files with 96 additions and 45 deletions
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119
codes/models/archs/NestedSwitchGenerator.py
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@ -5,7 +5,60 @@ from switched_conv import BareConvSwitch, compute_attention_specificity
from switched_conv_util import save_attention_to_image
from functools import partial
import torch.nn.functional as F
from torchvision.models.resnet import BasicBlock, Bottleneck
import numpy as np
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
# Taken from Fixup resnet implementation https://github.com/hongyi-zhang/Fixup/blob/master/imagenet/models/fixup_resnet_imagenet.py
class FixupBottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(FixupBottleneck, self).__init__()
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.bias1a = nn.Parameter(torch.zeros(1))
self.conv1 = conv1x1(inplanes, planes)
self.bias1b = nn.Parameter(torch.zeros(1))
self.bias2a = nn.Parameter(torch.zeros(1))
self.conv2 = conv3x3(planes, planes, stride)
self.bias2b = nn.Parameter(torch.zeros(1))
self.bias3a = nn.Parameter(torch.zeros(1))
self.conv3 = conv1x1(planes, planes * self.expansion)
self.scale = nn.Parameter(torch.ones(1))
self.bias3b = nn.Parameter(torch.zeros(1))
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x + self.bias1a)
out = self.relu(out + self.bias1b)
out = self.conv2(out + self.bias2a)
out = self.relu(out + self.bias2b)
out = self.conv3(out + self.bias3a)
out = out * self.scale + self.bias3b
if self.downsample is not None:
identity = self.downsample(x + self.bias1a)
out += identity
out = self.relu(out)
return out
class Switch(nn.Module):
@ -21,6 +74,10 @@ class Switch(nn.Module):
self.scale = nn.Parameter(torch.ones(1))
self.bias = nn.Parameter(torch.zeros(1))
if not self.pass_chain_forward:
self.c_constric = MultiConvBlock(32, 32, 16, 3, 3)
self.c_conjoin = ConvBnLelu(32, 16, kernel_size=1, bn=False)
# x is the input fed to the transform blocks.
# m is the output of the multiplexer which will be used to select from those transform blocks.
# chain is a chain of shared processing outputs used by the individual transforms.
@ -30,11 +87,21 @@ class Switch(nn.Module):
xformed = [o[0] for o in pcf]
atts = [o[1] for o in pcf]
else:
# These adjustments were determined statistically from numeric_stability.py and should start this context
# out in a normal distribution.
context = (chain[-1] - 6) / 9.4
context = F.pixel_shuffle(context, 4)
context = self.c_constric(context)
context = F.interpolate(context, size=x.shape[2:], mode='nearest')
context = torch.cat([x, context], dim=1)
context = self.c_conjoin(context)
if self.add_noise:
rand_feature = torch.randn_like(x)
xformed = [t.forward(x, rand_feature) for t in self.transforms]
xformed = [t.forward(context, rand_feature) for t in self.transforms]
else:
xformed = [t.forward(x) for t in self.transforms]
xformed = [t.forward(context) for t in self.transforms]
# Interpolate the multiplexer across the entire shape of the image.
m = F.interpolate(m, size=x.shape[2:], mode='nearest')
@ -65,13 +132,13 @@ class Processor(nn.Module):
# Downsample block used for bottleneck.
downsample = nn.Sequential(
nn.Conv2d(base_filters, self.output_filter_count, kernel_size=1, stride=2),
nn.Conv2d(base_filters, self.output_filter_count, kernel_size=1, stride=2, bias=False),
nn.BatchNorm2d(self.output_filter_count),
)
# Bottleneck block outputs the requested filter sizex4, but we only want x2.
self.initial = Bottleneck(base_filters, base_filters // 2, stride=2 if reduce else 1, downsample=downsample)
self.initial = FixupBottleneck(base_filters, base_filters // 2, stride=2 if reduce else 1, downsample=downsample)
self.res_blocks = nn.ModuleList([BasicBlock(self.output_filter_count, self.output_filter_count) for _ in range(processing_depth)])
self.res_blocks = nn.ModuleList([FixupBottleneck(self.output_filter_count, self.output_filter_count // 4) for _ in range(processing_depth)])
def forward(self, x):
x = self.initial(x)
@ -90,15 +157,7 @@ class Constrictor(nn.Module):
gap_div_4 = int(gap / 4)
self.cbl1 = ConvBnLelu(filters, filters - (gap_div_4 * 2), kernel_size=1, bn=True)
self.cbl2 = ConvBnLelu(filters - (gap_div_4 * 2), filters - (gap_div_4 * 3), kernel_size=1, bn=True)
self.cbl3 = nn.Conv2d(filters - (gap_div_4 * 3), output_filters, kernel_size=1)
# Init params.
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
self.cbl3 = ConvBnLelu(filters - (gap_div_4 * 3), output_filters, kernel_size=1, lelu=False, bn=False)
def forward(self, x):
x = self.cbl1(x)
@ -150,23 +209,19 @@ class NestedSwitchComputer(nn.Module):
self.switch = RecursiveSwitchedTransform(transform_filters, filters, nesting_depth-1, transforms_at_leaf, trans_kernel_size, trans_num_layers-1, trans_scale_init, initial_temp=initial_temp, add_scalable_noise_to_transforms=add_scalable_noise_to_transforms)
self.anneal = ConvBnLelu(transform_filters, transform_filters, kernel_size=1, bn=False)
# Init the parameters in the trunk.
# Init the parameters in the trunk. Uses the fixup algorithm for residual conv initialization.
self.num_layers = nesting_depth + nesting_depth * num_switch_processing_layers
for m in self.processing_trunk.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if isinstance(m, FixupBottleneck):
nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.25))
nn.init.normal_(m.conv2.weight, mean=0, std=np.sqrt(2 / (m.conv2.weight.shape[0] * np.prod(m.conv2.weight.shape[2:]))) * self.num_layers ** (-0.25))
nn.init.constant_(m.conv3.weight, 0)
if m.downsample is not None:
nn.init.normal_(m.downsample[0].weight, mean=0, std=np.sqrt(2 / (m.downsample[0].weight.shape[0] * np.prod(m.downsample[0].weight.shape[2:]))))
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
nn.init.kaiming_normal_(self.anneal.conv.weight, mode='fan_out', nonlinearity='leaky_relu')
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
for m in self.processing_trunk.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
nn.init.kaiming_normal_(self.multiplexer_init_conv.weight, nonlinearity="relu")
def forward(self, x):
trunk = []
@ -175,6 +230,7 @@ class NestedSwitchComputer(nn.Module):
trunk_input = m.forward(trunk_input)
trunk.append(trunk_input)
self.trunk = (trunk[-1] - 6) / 9.4
x, att = self.switch.forward(x, trunk)
return self.anneal(x), att
@ -187,8 +243,8 @@ class NestedSwitchedGenerator(nn.Module):
trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
heightened_final_step=50000, upsample_factor=1, add_scalable_noise_to_transforms=False):
super(NestedSwitchedGenerator, self).__init__()
self.initial_conv = ConvBnLelu(3, transformation_filters, kernel_size=7, bn=False)
self.final_conv = ConvBnLelu(transformation_filters, 3, kernel_size=1, bn=False)
self.initial_conv = ConvBnLelu(3, transformation_filters, kernel_size=7, lelu=False, bn=False)
self.final_conv = ConvBnLelu(transformation_filters, 3, kernel_size=1, lelu=False, bn=False)
switches = []
for sw_reduce, sw_proc, trans_count, kernel, layers in zip(switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers):
@ -196,8 +252,6 @@ class NestedSwitchedGenerator(nn.Module):
nesting_depth=sw_reduce, transforms_at_leaf=trans_count, trans_kernel_size=kernel, trans_num_layers=layers,
trans_scale_init=.2/len(switch_reductions), initial_temp=initial_temp, add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
self.switches = nn.ModuleList(switches)
nn.init.kaiming_normal_(self.initial_conv.conv.weight, mode='fan_out', nonlinearity='leaky_relu')
nn.init.kaiming_normal_(self.final_conv.conv.weight, mode='fan_in', nonlinearity='leaky_relu')
self.transformation_counts = trans_counts
self.init_temperature = initial_temp
@ -208,7 +262,6 @@ class NestedSwitchedGenerator(nn.Module):
self.upsample_factor = upsample_factor
def forward(self, x):
k = x
# This network is entirely a "repair" network and operates on full-resolution images. Upsample first if that
# is called for, then repair.
if self.upsample_factor > 1:

5
codes/models/archs/ResGen_arch.py
View File

@ -106,10 +106,7 @@ class FixupResNet(nn.Module):
nn.init.constant_(m.conv2.weight, 0)
if m.downsample is not None:
nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
'''
elif isinstance(m, nn.Linear):
nn.init.constant_(m.weight, 0)
nn.init.constant_(m.bias, 0)'''
def _make_layer(self, block, planes, blocks, stride=1, conv_type=conv3x3):
defilter = None

17
codes/models/archs/SwitchedResidualGenerator_arch.py
View File

@ -8,6 +8,7 @@ from models.archs.arch_util import initialize_weights
from switched_conv_util import save_attention_to_image
''' Convenience class with Conv->BN->LeakyRelu. Includes Kaiming weight initialization. '''
class ConvBnLelu(nn.Module):
def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, lelu=True, bn=True):
super(ConvBnLelu, self).__init__()
@ -23,6 +24,14 @@ class ConvBnLelu(nn.Module):
else:
self.lelu = None
# Init params.
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, a=.1, mode='fan_out', nonlinearity='leaky_relu' if self.lelu else 'linear')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.conv(x)
if self.bn:
@ -44,14 +53,6 @@ class MultiConvBlock(nn.Module):
self.scale = nn.Parameter(torch.full((1,), fill_value=scale_init))
self.bias = nn.Parameter(torch.zeros(1))
# Init params.
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='leaky_relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x, noise=None):
if noise is not None:
noise = noise * self.noise_scale