/
resnet.py
868 lines (697 loc) 路 31.3 KB
/
resnet.py
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from functools import partial
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Upsample2D(nn.Module):
"""
An upsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
upsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_conv_transpose = use_conv_transpose
self.name = name
conv = None
if use_conv_transpose:
conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1)
elif use_conv:
conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1)
# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if name == "conv":
self.conv = conv
else:
self.Conv2d_0 = conv
def forward(self, x):
assert x.shape[1] == self.channels
if self.use_conv_transpose:
return self.conv(x)
x = F.interpolate(x, scale_factor=2.0, mode="nearest")
# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if self.use_conv:
if self.name == "conv":
x = self.conv(x)
else:
x = self.Conv2d_0(x)
return x
class Downsample2D(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.padding = padding
stride = 2
self.name = name
if use_conv:
conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
else:
assert self.channels == self.out_channels
conv = nn.AvgPool2d(kernel_size=stride, stride=stride)
# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if name == "conv":
self.Conv2d_0 = conv
self.conv = conv
elif name == "Conv2d_0":
self.conv = conv
else:
self.conv = conv
def forward(self, x):
assert x.shape[1] == self.channels
if self.use_conv and self.padding == 0:
pad = (0, 1, 0, 1)
x = F.pad(x, pad, mode="constant", value=0)
assert x.shape[1] == self.channels
x = self.conv(x)
return x
class FirUpsample2D(nn.Module):
def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
super().__init__()
out_channels = out_channels if out_channels else channels
if use_conv:
self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
self.use_conv = use_conv
self.fir_kernel = fir_kernel
self.out_channels = out_channels
def _upsample_2d(self, x, w=None, k=None, factor=2, gain=1):
"""Fused `upsample_2d()` followed by `Conv2d()`.
Args:
Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary:
order.
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
w: Weight tensor of the shape `[filterH, filterW, inChannels,
outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
k: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same datatype as
`x`.
"""
assert isinstance(factor, int) and factor >= 1
# Setup filter kernel.
if k is None:
k = [1] * factor
# setup kernel
k = np.asarray(k, dtype=np.float32)
if k.ndim == 1:
k = np.outer(k, k)
k /= np.sum(k)
k = k * (gain * (factor**2))
if self.use_conv:
convH = w.shape[2]
convW = w.shape[3]
inC = w.shape[1]
p = (k.shape[0] - factor) - (convW - 1)
stride = (factor, factor)
# Determine data dimensions.
stride = [1, 1, factor, factor]
output_shape = ((x.shape[2] - 1) * factor + convH, (x.shape[3] - 1) * factor + convW)
output_padding = (
output_shape[0] - (x.shape[2] - 1) * stride[0] - convH,
output_shape[1] - (x.shape[3] - 1) * stride[1] - convW,
)
assert output_padding[0] >= 0 and output_padding[1] >= 0
inC = w.shape[1]
num_groups = x.shape[1] // inC
# Transpose weights.
w = torch.reshape(w, (num_groups, -1, inC, convH, convW))
w = w[..., ::-1, ::-1].permute(0, 2, 1, 3, 4)
w = torch.reshape(w, (num_groups * inC, -1, convH, convW))
x = F.conv_transpose2d(x, w, stride=stride, output_padding=output_padding, padding=0)
x = upfirdn2d_native(x, torch.tensor(k, device=x.device), pad=((p + 1) // 2 + factor - 1, p // 2 + 1))
else:
p = k.shape[0] - factor
x = upfirdn2d_native(
x, torch.tensor(k, device=x.device), up=factor, pad=((p + 1) // 2 + factor - 1, p // 2)
)
return x
def forward(self, x):
if self.use_conv:
h = self._upsample_2d(x, self.Conv2d_0.weight, k=self.fir_kernel)
h = h + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
else:
h = self._upsample_2d(x, k=self.fir_kernel, factor=2)
return h
class FirDownsample2D(nn.Module):
def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
super().__init__()
out_channels = out_channels if out_channels else channels
if use_conv:
self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
self.fir_kernel = fir_kernel
self.use_conv = use_conv
self.out_channels = out_channels
def _downsample_2d(self, x, w=None, k=None, factor=2, gain=1):
"""Fused `Conv2d()` followed by `downsample_2d()`.
Args:
Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary:
order.
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. w: Weight tensor of the shape `[filterH,
filterW, inChannels, outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] //
numGroups`. k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] *
factor`, which corresponds to average pooling. factor: Integer downsampling factor (default: 2). gain:
Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and same
datatype as `x`.
"""
assert isinstance(factor, int) and factor >= 1
if k is None:
k = [1] * factor
# setup kernel
k = np.asarray(k, dtype=np.float32)
if k.ndim == 1:
k = np.outer(k, k)
k /= np.sum(k)
k = k * gain
if self.use_conv:
_, _, convH, convW = w.shape
p = (k.shape[0] - factor) + (convW - 1)
s = [factor, factor]
x = upfirdn2d_native(x, torch.tensor(k, device=x.device), pad=((p + 1) // 2, p // 2))
x = F.conv2d(x, w, stride=s, padding=0)
else:
p = k.shape[0] - factor
x = upfirdn2d_native(x, torch.tensor(k, device=x.device), down=factor, pad=((p + 1) // 2, p // 2))
return x
def forward(self, x):
if self.use_conv:
x = self._downsample_2d(x, w=self.Conv2d_0.weight, k=self.fir_kernel)
x = x + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
else:
x = self._downsample_2d(x, k=self.fir_kernel, factor=2)
return x
class ResnetBlock(nn.Module):
def __init__(
self,
*,
in_channels,
out_channels=None,
conv_shortcut=False,
dropout=0.0,
temb_channels=512,
groups=32,
groups_out=None,
pre_norm=True,
eps=1e-6,
non_linearity="swish",
time_embedding_norm="default",
kernel=None,
output_scale_factor=1.0,
use_nin_shortcut=None,
up=False,
down=False,
):
super().__init__()
self.pre_norm = pre_norm
self.pre_norm = True
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.time_embedding_norm = time_embedding_norm
self.up = up
self.down = down
self.output_scale_factor = output_scale_factor
if groups_out is None:
groups_out = groups
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
if non_linearity == "swish":
self.nonlinearity = lambda x: F.silu(x)
elif non_linearity == "mish":
self.nonlinearity = Mish()
elif non_linearity == "silu":
self.nonlinearity = nn.SiLU()
self.upsample = self.downsample = None
if self.up:
if kernel == "fir":
fir_kernel = (1, 3, 3, 1)
self.upsample = lambda x: upsample_2d(x, k=fir_kernel)
elif kernel == "sde_vp":
self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
else:
self.upsample = Upsample2D(in_channels, use_conv=False)
elif self.down:
if kernel == "fir":
fir_kernel = (1, 3, 3, 1)
self.downsample = lambda x: downsample_2d(x, k=fir_kernel)
elif kernel == "sde_vp":
self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
else:
self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
self.use_nin_shortcut = self.in_channels != self.out_channels if use_nin_shortcut is None else use_nin_shortcut
self.conv_shortcut = None
if self.use_nin_shortcut:
self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
def forward(self, x, temb, hey=False):
h = x
h = self.norm1(h)
h = self.nonlinearity(h)
if self.upsample is not None:
x = self.upsample(x)
h = self.upsample(h)
elif self.downsample is not None:
x = self.downsample(x)
h = self.downsample(h)
h = self.conv1(h)
if temb is not None:
temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
h = h + temb
h = self.norm2(h)
h = self.nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if self.conv_shortcut is not None:
x = self.conv_shortcut(x)
out = (x + h) / self.output_scale_factor
return out
def set_weight(self, resnet):
self.norm1.weight.data = resnet.norm1.weight.data
self.norm1.bias.data = resnet.norm1.bias.data
self.conv1.weight.data = resnet.conv1.weight.data
self.conv1.bias.data = resnet.conv1.bias.data
self.time_emb_proj.weight.data = resnet.temb_proj.weight.data
self.time_emb_proj.bias.data = resnet.temb_proj.bias.data
self.norm2.weight.data = resnet.norm2.weight.data
self.norm2.bias.data = resnet.norm2.bias.data
self.conv2.weight.data = resnet.conv2.weight.data
self.conv2.bias.data = resnet.conv2.bias.data
if self.use_nin_shortcut:
self.conv_shortcut.weight.data = resnet.nin_shortcut.weight.data
self.conv_shortcut.bias.data = resnet.nin_shortcut.bias.data
# THE FOLLOWING SHOULD BE DELETED ONCE ALL CHECKPOITNS ARE CONVERTED
# unet.py, unet_grad_tts.py, unet_ldm.py, unet_glide.py, unet_score_vde.py
# => All 2D-Resnets are included here now!
class ResnetBlock2D(nn.Module):
def __init__(
self,
*,
in_channels,
out_channels=None,
conv_shortcut=False,
dropout=0.0,
temb_channels=512,
groups=32,
groups_out=None,
pre_norm=True,
eps=1e-6,
non_linearity="swish",
time_embedding_norm="default",
kernel=None,
output_scale_factor=1.0,
use_nin_shortcut=None,
up=False,
down=False,
overwrite_for_grad_tts=False,
overwrite_for_ldm=False,
overwrite_for_glide=False,
overwrite_for_score_vde=False,
):
super().__init__()
self.pre_norm = pre_norm
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.time_embedding_norm = time_embedding_norm
self.up = up
self.down = down
self.output_scale_factor = output_scale_factor
if groups_out is None:
groups_out = groups
if self.pre_norm:
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
else:
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
if time_embedding_norm == "default" and temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
elif time_embedding_norm == "scale_shift" and temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
if non_linearity == "swish":
self.nonlinearity = lambda x: F.silu(x)
elif non_linearity == "mish":
self.nonlinearity = Mish()
elif non_linearity == "silu":
self.nonlinearity = nn.SiLU()
self.upsample = self.downsample = None
if self.up:
if kernel == "fir":
fir_kernel = (1, 3, 3, 1)
self.upsample = lambda x: upsample_2d(x, k=fir_kernel)
elif kernel == "sde_vp":
self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
else:
self.upsample = Upsample2D(in_channels, use_conv=False)
elif self.down:
if kernel == "fir":
fir_kernel = (1, 3, 3, 1)
self.downsample = lambda x: downsample_2d(x, k=fir_kernel)
elif kernel == "sde_vp":
self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
else:
self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
self.use_nin_shortcut = self.in_channels != self.out_channels if use_nin_shortcut is None else use_nin_shortcut
self.nin_shortcut = None
if self.use_nin_shortcut:
self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
# TODO(SURAJ, PATRICK): ALL OF THE FOLLOWING OF THE INIT METHOD CAN BE DELETED ONCE WEIGHTS ARE CONVERTED
self.is_overwritten = False
self.overwrite_for_glide = overwrite_for_glide
self.overwrite_for_grad_tts = overwrite_for_grad_tts
self.overwrite_for_ldm = overwrite_for_ldm or overwrite_for_glide
self.overwrite_for_score_vde = overwrite_for_score_vde
if self.overwrite_for_grad_tts:
dim = in_channels
dim_out = out_channels
time_emb_dim = temb_channels
self.mlp = torch.nn.Sequential(Mish(), torch.nn.Linear(time_emb_dim, dim_out))
self.pre_norm = pre_norm
self.block1 = Block(dim, dim_out, groups=groups)
self.block2 = Block(dim_out, dim_out, groups=groups)
if dim != dim_out:
self.res_conv = torch.nn.Conv2d(dim, dim_out, 1)
else:
self.res_conv = torch.nn.Identity()
elif self.overwrite_for_ldm:
channels = in_channels
emb_channels = temb_channels
use_scale_shift_norm = False
non_linearity = "silu"
self.in_layers = nn.Sequential(
normalization(channels, swish=1.0),
nn.Identity(),
nn.Conv2d(channels, self.out_channels, 3, padding=1),
)
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if self.time_embedding_norm == "scale_shift" else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels, swish=0.0 if use_scale_shift_norm else 1.0),
nn.SiLU() if use_scale_shift_norm else nn.Identity(),
nn.Dropout(p=dropout),
zero_module(nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1)),
)
if self.out_channels == in_channels:
self.skip_connection = nn.Identity()
else:
self.skip_connection = nn.Conv2d(channels, self.out_channels, 1)
self.set_weights_ldm()
elif self.overwrite_for_score_vde:
in_ch = in_channels
out_ch = out_channels
eps = 1e-6
num_groups = min(in_ch // 4, 32)
num_groups_out = min(out_ch // 4, 32)
temb_dim = temb_channels
self.GroupNorm_0 = nn.GroupNorm(num_groups=num_groups, num_channels=in_ch, eps=eps)
self.up = up
self.down = down
self.Conv_0 = nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1)
if temb_dim is not None:
self.Dense_0 = nn.Linear(temb_dim, out_ch)
nn.init.zeros_(self.Dense_0.bias)
self.GroupNorm_1 = nn.GroupNorm(num_groups=num_groups_out, num_channels=out_ch, eps=eps)
self.Dropout_0 = nn.Dropout(dropout)
self.Conv_1 = nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)
if in_ch != out_ch or up or down:
# 1x1 convolution with DDPM initialization.
self.Conv_2 = nn.Conv2d(in_ch, out_ch, kernel_size=1, padding=0)
self.in_ch = in_ch
self.out_ch = out_ch
self.set_weights_score_vde()
def set_weights_grad_tts(self):
self.conv1.weight.data = self.block1.block[0].weight.data
self.conv1.bias.data = self.block1.block[0].bias.data
self.norm1.weight.data = self.block1.block[1].weight.data
self.norm1.bias.data = self.block1.block[1].bias.data
self.conv2.weight.data = self.block2.block[0].weight.data
self.conv2.bias.data = self.block2.block[0].bias.data
self.norm2.weight.data = self.block2.block[1].weight.data
self.norm2.bias.data = self.block2.block[1].bias.data
self.temb_proj.weight.data = self.mlp[1].weight.data
self.temb_proj.bias.data = self.mlp[1].bias.data
if self.in_channels != self.out_channels:
self.nin_shortcut.weight.data = self.res_conv.weight.data
self.nin_shortcut.bias.data = self.res_conv.bias.data
def set_weights_ldm(self):
self.norm1.weight.data = self.in_layers[0].weight.data
self.norm1.bias.data = self.in_layers[0].bias.data
self.conv1.weight.data = self.in_layers[-1].weight.data
self.conv1.bias.data = self.in_layers[-1].bias.data
self.temb_proj.weight.data = self.emb_layers[-1].weight.data
self.temb_proj.bias.data = self.emb_layers[-1].bias.data
self.norm2.weight.data = self.out_layers[0].weight.data
self.norm2.bias.data = self.out_layers[0].bias.data
self.conv2.weight.data = self.out_layers[-1].weight.data
self.conv2.bias.data = self.out_layers[-1].bias.data
if self.in_channels != self.out_channels:
self.nin_shortcut.weight.data = self.skip_connection.weight.data
self.nin_shortcut.bias.data = self.skip_connection.bias.data
def set_weights_score_vde(self):
self.conv1.weight.data = self.Conv_0.weight.data
self.conv1.bias.data = self.Conv_0.bias.data
self.norm1.weight.data = self.GroupNorm_0.weight.data
self.norm1.bias.data = self.GroupNorm_0.bias.data
self.conv2.weight.data = self.Conv_1.weight.data
self.conv2.bias.data = self.Conv_1.bias.data
self.norm2.weight.data = self.GroupNorm_1.weight.data
self.norm2.bias.data = self.GroupNorm_1.bias.data
self.temb_proj.weight.data = self.Dense_0.weight.data
self.temb_proj.bias.data = self.Dense_0.bias.data
if self.in_channels != self.out_channels or self.up or self.down:
self.nin_shortcut.weight.data = self.Conv_2.weight.data
self.nin_shortcut.bias.data = self.Conv_2.bias.data
def forward(self, x, temb, hey=False, mask=1.0):
# TODO(Patrick) eventually this class should be split into multiple classes
# too many if else statements
if self.overwrite_for_grad_tts and not self.is_overwritten:
self.set_weights_grad_tts()
self.is_overwritten = True
# elif self.overwrite_for_score_vde and not self.is_overwritten:
# self.set_weights_score_vde()
# self.is_overwritten = True
# h2 tensor(110029.2109)
# h3 tensor(49596.9492)
h = x
h = h * mask
if self.pre_norm:
h = self.norm1(h)
h = self.nonlinearity(h)
if self.upsample is not None:
x = self.upsample(x)
h = self.upsample(h)
elif self.downsample is not None:
x = self.downsample(x)
h = self.downsample(h)
h = self.conv1(h)
if not self.pre_norm:
h = self.norm1(h)
h = self.nonlinearity(h)
h = h * mask
if temb is not None:
temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
else:
temb = 0
if self.time_embedding_norm == "scale_shift":
scale, shift = torch.chunk(temb, 2, dim=1)
h = self.norm2(h)
h = h + h * scale + shift
h = self.nonlinearity(h)
elif self.time_embedding_norm == "default":
h = h + temb
h = h * mask
if self.pre_norm:
h = self.norm2(h)
h = self.nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if not self.pre_norm:
h = self.norm2(h)
h = self.nonlinearity(h)
h = h * mask
x = x * mask
if self.nin_shortcut is not None:
x = self.nin_shortcut(x)
out = (x + h) / self.output_scale_factor
return out
# TODO(Patrick) - just there to convert the weights; can delete afterward
class Block(torch.nn.Module):
def __init__(self, dim, dim_out, groups=8):
super(Block, self).__init__()
self.block = torch.nn.Sequential(
torch.nn.Conv2d(dim, dim_out, 3, padding=1), torch.nn.GroupNorm(groups, dim_out), Mish()
)
# HELPER Modules
def normalization(channels, swish=0.0):
"""
Make a standard normalization layer, with an optional swish activation.
:param channels: number of input channels. :return: an nn.Module for normalization.
"""
return GroupNorm32(num_channels=channels, num_groups=32, swish=swish)
class GroupNorm32(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-5):
super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps)
self.swish = swish
def forward(self, x):
y = super().forward(x.float()).to(x.dtype)
if self.swish == 1.0:
y = F.silu(y)
elif self.swish:
y = y * F.sigmoid(y * float(self.swish))
return y
def linear(*args, **kwargs):
"""
Create a linear module.
"""
return nn.Linear(*args, **kwargs)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
class Mish(torch.nn.Module):
def forward(self, x):
return x * torch.tanh(torch.nn.functional.softplus(x))
class Conv1dBlock(nn.Module):
"""
Conv1d --> GroupNorm --> Mish
"""
def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8):
super().__init__()
self.block = nn.Sequential(
nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2),
RearrangeDim(),
# Rearrange("batch channels horizon -> batch channels 1 horizon"),
nn.GroupNorm(n_groups, out_channels),
RearrangeDim(),
# Rearrange("batch channels 1 horizon -> batch channels horizon"),
nn.Mish(),
)
def forward(self, x):
return self.block(x)
class RearrangeDim(nn.Module):
def __init__(self):
super().__init__()
def forward(self, tensor):
if len(tensor.shape) == 2:
return tensor[:, :, None]
if len(tensor.shape) == 3:
return tensor[:, :, None, :]
elif len(tensor.shape) == 4:
return tensor[:, :, 0, :]
else:
raise ValueError(f"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.")
def upsample_2d(x, k=None, factor=2, gain=1):
r"""Upsample2D a batch of 2D images with the given filter.
Args:
Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified
`gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is a:
multiple of the upsampling factor.
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
k: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H * factor, W * factor]`
"""
assert isinstance(factor, int) and factor >= 1
if k is None:
k = [1] * factor
k = np.asarray(k, dtype=np.float32)
if k.ndim == 1:
k = np.outer(k, k)
k /= np.sum(k)
k = k * (gain * (factor**2))
p = k.shape[0] - factor
return upfirdn2d_native(x, torch.tensor(k, device=x.device), up=factor, pad=((p + 1) // 2 + factor - 1, p // 2))
def downsample_2d(x, k=None, factor=2, gain=1):
r"""Downsample2D a batch of 2D images with the given filter.
Args:
Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the
given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the
specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its
shape is a multiple of the downsampling factor.
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
k: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to average pooling.
factor: Integer downsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H // factor, W // factor]`
"""
assert isinstance(factor, int) and factor >= 1
if k is None:
k = [1] * factor
k = np.asarray(k, dtype=np.float32)
if k.ndim == 1:
k = np.outer(k, k)
k /= np.sum(k)
k = k * gain
p = k.shape[0] - factor
return upfirdn2d_native(x, torch.tensor(k, device=x.device), down=factor, pad=((p + 1) // 2, p // 2))
def upfirdn2d_native(input, kernel, up=1, down=1, pad=(0, 0)):
up_x = up_y = up
down_x = down_y = down
pad_x0 = pad_y0 = pad[0]
pad_x1 = pad_y1 = pad[1]
_, channel, in_h, in_w = input.shape
input = input.reshape(-1, in_h, in_w, 1)
_, in_h, in_w, minor = input.shape
kernel_h, kernel_w = kernel.shape
out = input.view(-1, in_h, 1, in_w, 1, minor)
out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
out = out.view(-1, in_h * up_y, in_w * up_x, minor)
out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
out = out[
:,
max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
:,
]
out = out.permute(0, 3, 1, 2)
out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
out = F.conv2d(out, w)
out = out.reshape(
-1,
minor,
in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
)
out = out.permute(0, 2, 3, 1)
out = out[:, ::down_y, ::down_x, :]
out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
return out.view(-1, channel, out_h, out_w)