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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# All contributions by Andy Brock:
# Copyright (c) 2019 Andy Brock
#
# MIT License
""" Layers
This file contains various layers for the BigGAN models.
"""
import os
import numpy as np
import torch
import torch.nn as nn
from torch.nn import init
import torch.optim as optim
import torch.nn.functional as F
from torch.nn import Parameter as P
import sys
sys.path.insert(1, os.path.join(sys.path[0], ".."))
from BigGAN_PyTorch.sync_batchnorm import SynchronizedBatchNorm2d as SyncBN2d
# Projection of x onto y
def proj(x, y):
return torch.mm(y, x.t()) * y / torch.mm(y, y.t())
# Orthogonalize x wrt list of vectors ys
def gram_schmidt(x, ys):
for y in ys:
x = x - proj(x, y)
return x
# Apply num_itrs steps of the power method to estimate top N singular values.
def power_iteration(W, u_, update=True, eps=1e-12):
# Lists holding singular vectors and values
us, vs, svs = [], [], []
for i, u in enumerate(u_):
# Run one step of the power iteration
with torch.no_grad():
v = torch.matmul(u, W)
# Run Gram-Schmidt to subtract components of all other singular vectors
v = F.normalize(gram_schmidt(v, vs), eps=eps)
# Add to the list
vs += [v]
# Update the other singular vector
u = torch.matmul(v, W.t())
# Run Gram-Schmidt to subtract components of all other singular vectors
u = F.normalize(gram_schmidt(u, us), eps=eps)
# Add to the list
us += [u]
if update:
u_[i][:] = u
# Compute this singular value and add it to the list
svs += [torch.squeeze(torch.matmul(torch.matmul(v, W.t()), u.t()))]
# svs += [torch.sum(F.linear(u, W.transpose(0, 1)) * v)]
return svs, us, vs
# Convenience passthrough function
class identity(nn.Module):
def forward(self, input):
return input
# Spectral normalization base class
class SN(object):
def __init__(self, num_svs, num_itrs, num_outputs, transpose=False, eps=1e-12):
# Number of power iterations per step
self.num_itrs = num_itrs
# Number of singular values
self.num_svs = num_svs
# Transposed?
self.transpose = transpose
# Epsilon value for avoiding divide-by-0
self.eps = eps
# Register a singular vector for each sv
for i in range(self.num_svs):
self.register_buffer("u%d" % i, torch.randn(1, num_outputs))
self.register_buffer("sv%d" % i, torch.ones(1))
# Singular vectors (u side)
@property
def u(self):
return [getattr(self, "u%d" % i) for i in range(self.num_svs)]
# Singular values;
# note that these buffers are just for logging and are not used in training.
@property
def sv(self):
return [getattr(self, "sv%d" % i) for i in range(self.num_svs)]
# Compute the spectrally-normalized weight
def W_(self):
W_mat = self.weight.view(self.weight.size(0), -1)
if self.transpose:
W_mat = W_mat.t()
# Apply num_itrs power iterations
for _ in range(self.num_itrs):
svs, us, vs = power_iteration(
W_mat, self.u, update=self.training, eps=self.eps
)
# Update the svs
if self.training:
with torch.no_grad(): # Make sure to do this in a no_grad() context or you'll get memory leaks!
for i, sv in enumerate(svs):
self.sv[i][:] = sv
return self.weight / svs[0]
# 2D Conv layer with spectral norm
class SNConv2d(nn.Conv2d, SN):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
num_svs=1,
num_itrs=1,
eps=1e-12,
):
nn.Conv2d.__init__(
self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
bias,
)
SN.__init__(self, num_svs, num_itrs, out_channels, eps=eps)
def forward(self, x):
return F.conv2d(
x,
self.W_(),
self.bias,
self.stride,
self.padding,
self.dilation,
self.groups,
)
# Linear layer with spectral norm
class SNLinear(nn.Linear, SN):
def __init__(
self, in_features, out_features, bias=True, num_svs=1, num_itrs=1, eps=1e-12
):
nn.Linear.__init__(self, in_features, out_features, bias)
SN.__init__(self, num_svs, num_itrs, out_features, eps=eps)
def forward(self, x):
return F.linear(x, self.W_(), self.bias)
# Embedding layer with spectral norm
# We use num_embeddings as the dim instead of embedding_dim here
# for convenience sake
class SNEmbedding(nn.Embedding, SN):
def __init__(
self,
num_embeddings,
embedding_dim,
padding_idx=None,
max_norm=None,
norm_type=2,
scale_grad_by_freq=False,
sparse=False,
_weight=None,
num_svs=1,
num_itrs=1,
eps=1e-12,
):
nn.Embedding.__init__(
self,
num_embeddings,
embedding_dim,
padding_idx,
max_norm,
norm_type,
scale_grad_by_freq,
sparse,
_weight,
)
SN.__init__(self, num_svs, num_itrs, num_embeddings, eps=eps)
def forward(self, x):
return F.embedding(x, self.W_())
# A non-local block as used in SA-GAN
# Note that the implementation as described in the paper is largely incorrect;
# refer to the released code for the actual implementation.
class Attention(nn.Module):
def __init__(self, ch, which_conv=SNConv2d, name="attention"):
super(Attention, self).__init__()
# Channel multiplier
self.ch = ch
self.which_conv = which_conv
self.theta = self.which_conv(
self.ch, self.ch // 8, kernel_size=1, padding=0, bias=False
)
self.phi = self.which_conv(
self.ch, self.ch // 8, kernel_size=1, padding=0, bias=False
)
self.g = self.which_conv(
self.ch, self.ch // 2, kernel_size=1, padding=0, bias=False
)
self.o = self.which_conv(
self.ch // 2, self.ch, kernel_size=1, padding=0, bias=False
)
# Learnable gain parameter
self.gamma = P(torch.tensor(0.0), requires_grad=True)
def forward(self, x, y=None):
# Apply convs
theta = self.theta(x)
phi = F.max_pool2d(self.phi(x), [2, 2])
g = F.max_pool2d(self.g(x), [2, 2])
# Perform reshapes
theta = theta.view(-1, self.ch // 8, x.shape[2] * x.shape[3])
phi = phi.view(-1, self.ch // 8, x.shape[2] * x.shape[3] // 4)
g = g.view(-1, self.ch // 2, x.shape[2] * x.shape[3] // 4)
# Matmul and softmax to get attention maps
beta = F.softmax(torch.bmm(theta.transpose(1, 2), phi), -1)
# Attention map times g path
o = self.o(
torch.bmm(g, beta.transpose(1, 2)).view(
-1, self.ch // 2, x.shape[2], x.shape[3]
)
)
return self.gamma * o + x
# Fused batchnorm op
def fused_bn(x, mean, var, gain=None, bias=None, eps=1e-5):
# Apply scale and shift--if gain and bias are provided, fuse them here
# Prepare scale
scale = torch.rsqrt(var + eps)
# If a gain is provided, use it
if gain is not None:
scale = scale * gain
# Prepare shift
shift = mean * scale
# If bias is provided, use it
if bias is not None:
shift = shift - bias
return x * scale - shift
# return ((x - mean) / ((var + eps) ** 0.5)) * gain + bias # The unfused way.
# Manual BN
# Calculate means and variances using mean-of-squares minus mean-squared
def manual_bn(x, gain=None, bias=None, return_mean_var=False, eps=1e-5):
# Cast x to float32 if necessary
float_x = x.float()
# Calculate expected value of x (m) and expected value of x**2 (m2)
# Mean of x
m = torch.mean(float_x, [0, 2, 3], keepdim=True)
# Mean of x squared
m2 = torch.mean(float_x ** 2, [0, 2, 3], keepdim=True)
# Calculate variance as mean of squared minus mean squared.
var = m2 - m ** 2
# Cast back to float 16 if necessary
var = var.type(x.type())
m = m.type(x.type())
# Return mean and variance for updating stored mean/var if requested
if return_mean_var:
return fused_bn(x, m, var, gain, bias, eps), m.squeeze(), var.squeeze()
else:
return fused_bn(x, m, var, gain, bias, eps)
# My batchnorm, supports standing stats
class myBN(nn.Module):
def __init__(self, num_channels, eps=1e-5, momentum=0.1):
super(myBN, self).__init__()
# momentum for updating running stats
self.momentum = momentum
# epsilon to avoid dividing by 0
self.eps = eps
# Momentum
self.momentum = momentum
# Register buffers
self.register_buffer("stored_mean", torch.zeros(num_channels))
self.register_buffer("stored_var", torch.ones(num_channels))
self.register_buffer("accumulation_counter", torch.zeros(1))
# Accumulate running means and vars
self.accumulate_standing = False
# reset standing stats
def reset_stats(self):
self.stored_mean[:] = 0
self.stored_var[:] = 0
self.accumulation_counter[:] = 0
def forward(self, x, gain, bias):
if self.training:
out, mean, var = manual_bn(
x, gain, bias, return_mean_var=True, eps=self.eps
)
# If accumulating standing stats, increment them
if self.accumulate_standing:
self.stored_mean[:] = self.stored_mean + mean.data
self.stored_var[:] = self.stored_var + var.data
self.accumulation_counter += 1.0
# If not accumulating standing stats, take running averages
else:
self.stored_mean[:] = (
self.stored_mean * (1 - self.momentum) + mean * self.momentum
)
self.stored_var[:] = (
self.stored_var * (1 - self.momentum) + var * self.momentum
)
return out
# If not in training mode, use the stored statistics
else:
mean = self.stored_mean.view(1, -1, 1, 1)
var = self.stored_var.view(1, -1, 1, 1)
# If using standing stats, divide them by the accumulation counter
if self.accumulate_standing:
mean = mean / self.accumulation_counter
var = var / self.accumulation_counter
return fused_bn(x, mean, var, gain, bias, self.eps)
# Simple function to handle groupnorm norm stylization
def groupnorm(x, norm_style):
# If number of channels specified in norm_style:
if "ch" in norm_style:
ch = int(norm_style.split("_")[-1])
groups = max(int(x.shape[1]) // ch, 1)
# If number of groups specified in norm style
elif "grp" in norm_style:
groups = int(norm_style.split("_")[-1])
# If neither, default to groups = 16
else:
groups = 16
return F.group_norm(x, groups)
# Class-conditional bn
# output size is the number of channels, input size is for the linear layers
# Andy's Note: this class feels messy but I'm not really sure how to clean it up
# Suggestions welcome! (By which I mean, refactor this and make a pull request
# if you want to make this more readable/usable).
class ccbn(nn.Module):
def __init__(
self,
output_size,
input_size,
which_linear,
eps=1e-5,
momentum=0.1,
cross_replica=False,
mybn=False,
norm_style="bn",
):
super(ccbn, self).__init__()
self.output_size, self.input_size = output_size, input_size
# Prepare gain and bias layers
self.gain = which_linear(input_size, output_size)
self.bias = which_linear(input_size, output_size)
# epsilon to avoid dividing by 0
self.eps = eps
# Momentum
self.momentum = momentum
# Use cross-replica batchnorm?
self.cross_replica = cross_replica
# Use my batchnorm?
self.mybn = mybn
# Norm style?
self.norm_style = norm_style
if self.cross_replica:
# self.bn = SyncBN2d(output_size, eps=self.eps, momentum=self.momentum, affine=False)
self.bn = nn.BatchNorm2d(
output_size, eps=self.eps, momentum=self.momentum, affine=False
)
elif self.mybn:
self.bn = myBN(output_size, self.eps, self.momentum)
elif self.norm_style in ["bn", "in"]:
self.register_buffer("stored_mean", torch.zeros(output_size))
self.register_buffer("stored_var", torch.ones(output_size))
def forward(self, x, y):
# Calculate class-conditional gains and biases
gain = (1 + self.gain(y)).view(y.size(0), -1, 1, 1)
bias = self.bias(y).view(y.size(0), -1, 1, 1)
# If using my batchnorm
if self.cross_replica:
out = self.bn(x)
out = out * gain + bias
return out
elif self.mybn:
return self.bn(x, gain=gain, bias=bias)
else:
if self.norm_style == "bn":
out = F.batch_norm(
x,
self.stored_mean,
self.stored_var,
None,
None,
self.training,
0.1,
self.eps,
)
elif self.norm_style == "in":
out = F.instance_norm(
x,
self.stored_mean,
self.stored_var,
None,
None,
self.training,
0.1,
self.eps,
)
elif self.norm_style == "gn":
out = groupnorm(x, self.normstyle)
elif self.norm_style == "nonorm":
out = x
return out * gain + bias
def extra_repr(self):
s = "out: {output_size}, in: {input_size},"
s += " cross_replica={cross_replica}"
return s.format(**self.__dict__)
# Normal, non-class-conditional BN
class bn(nn.Module):
def __init__(
self,
output_size,
eps=1e-5,
momentum=0.1,
cross_replica=False,
mybn=False,
**kwargs
):
super(bn, self).__init__()
self.output_size = output_size
# epsilon to avoid dividing by 0
self.eps = eps
# Momentum
self.momentum = momentum
# Use cross-replica batchnorm?
self.cross_replica = cross_replica
# Use my batchnorm?
self.mybn = mybn
if self.cross_replica:
# self.bn = SyncBN2d(output_size, eps=self.eps, momentum=self.momentum, affine=False)
self.bn = nn.BatchNorm2d(
output_size, eps=self.eps, momentum=self.momentum, affine=True
)
elif mybn:
# Prepare gain and bias layers
self.bn = myBN(output_size, self.eps, self.momentum)
# Register buffers if neither of the above
else:
self.register_buffer("stored_mean", torch.zeros(output_size))
self.register_buffer("stored_var", torch.ones(output_size))
if not self.cross_replica:
self.gain = P(torch.ones(output_size), requires_grad=True)
self.bias = P(torch.zeros(output_size), requires_grad=True)
def forward(self, x, y=None):
if self.cross_replica:
out = self.bn(x)
return out
elif self.mybn:
gain = self.gain.view(1, -1, 1, 1)
bias = self.bias.view(1, -1, 1, 1)
return self.bn(x, gain=gain, bias=bias)
else:
return F.batch_norm(
x,
self.stored_mean,
self.stored_var,
self.gain,
self.bias,
self.training,
self.momentum,
self.eps,
)
# Generator blocks
# Note that this class assumes the kernel size and padding (and any other
# settings) have been selected in the main generator module and passed in
# through the which_conv arg. Similar rules apply with which_bn (the input
# size [which is actually the number of channels of the conditional info] must
# be preselected)
class GBlock(nn.Module):
def __init__(
self,
in_channels,
out_channels,
which_conv=nn.Conv2d,
which_bn=bn,
activation=None,
upsample=None,
):
super(GBlock, self).__init__()
self.in_channels, self.out_channels = in_channels, out_channels
self.which_conv, self.which_bn = which_conv, which_bn
self.activation = activation
self.upsample = upsample
# Conv layers
self.conv1 = self.which_conv(self.in_channels, self.out_channels)
self.conv2 = self.which_conv(self.out_channels, self.out_channels)
self.learnable_sc = in_channels != out_channels or upsample
if self.learnable_sc:
self.conv_sc = self.which_conv(
in_channels, out_channels, kernel_size=1, padding=0
)
# Batchnorm layers
self.bn1 = self.which_bn(in_channels)
self.bn2 = self.which_bn(out_channels)
# upsample layers
self.upsample = upsample
def forward(self, x, y):
h = self.activation(self.bn1(x, y))
if self.upsample:
h = self.upsample(h)
x = self.upsample(x)
h = self.conv1(h)
h = self.activation(self.bn2(h, y))
h = self.conv2(h)
if self.learnable_sc:
x = self.conv_sc(x)
return h + x
# Residual block for the discriminator
class DBlock(nn.Module):
def __init__(
self,
in_channels,
out_channels,
which_conv=SNConv2d,
wide=True,
preactivation=False,
activation=None,
downsample=None,
):
super(DBlock, self).__init__()
self.in_channels, self.out_channels = in_channels, out_channels
# If using wide D (as in SA-GAN and BigGAN), change the channel pattern
self.hidden_channels = self.out_channels if wide else self.in_channels
self.which_conv = which_conv
self.preactivation = preactivation
self.activation = activation
self.downsample = downsample
# Conv layers
self.conv1 = self.which_conv(self.in_channels, self.hidden_channels)
self.conv2 = self.which_conv(self.hidden_channels, self.out_channels)
self.learnable_sc = (
True if (in_channels != out_channels) or downsample else False
)
if self.learnable_sc:
self.conv_sc = self.which_conv(
in_channels, out_channels, kernel_size=1, padding=0
)
def shortcut(self, x):
if self.preactivation:
if self.learnable_sc:
x = self.conv_sc(x)
if self.downsample:
x = self.downsample(x)
else:
if self.downsample:
x = self.downsample(x)
if self.learnable_sc:
x = self.conv_sc(x)
return x
def forward(self, x):
if self.preactivation:
# h = self.activation(x) # NOT TODAY SATAN
# Andy's note: This line *must* be an out-of-place ReLU or it
# will negatively affect the shortcut connection.
h = F.relu(x)
else:
h = x
h = self.conv1(h)
h = self.conv2(self.activation(h))
if self.downsample:
h = self.downsample(h)
return h + self.shortcut(x)
# dogball
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