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remove bogus files
This commit is contained in:
Patrick von Platen
859
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859
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from abc import abstractmethod
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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def avg_pool_nd(dims, *args, **kwargs):
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"""
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Create a 1D, 2D, or 3D average pooling module.
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"""
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if dims == 1:
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return nn.AvgPool1d(*args, **kwargs)
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elif dims == 2:
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return nn.AvgPool2d(*args, **kwargs)
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elif dims == 3:
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return nn.AvgPool3d(*args, **kwargs)
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raise ValueError(f"unsupported dimensions: {dims}")
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def conv_nd(dims, *args, **kwargs):
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"""
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Create a 1D, 2D, or 3D convolution module.
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"""
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if dims == 1:
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return nn.Conv1d(*args, **kwargs)
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elif dims == 2:
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return nn.Conv2d(*args, **kwargs)
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elif dims == 3:
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return nn.Conv3d(*args, **kwargs)
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raise ValueError(f"unsupported dimensions: {dims}")
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def conv_transpose_nd(dims, *args, **kwargs):
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"""
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Create a 1D, 2D, or 3D convolution module.
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"""
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if dims == 1:
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return nn.ConvTranspose1d(*args, **kwargs)
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elif dims == 2:
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return nn.ConvTranspose2d(*args, **kwargs)
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elif dims == 3:
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return nn.ConvTranspose3d(*args, **kwargs)
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raise ValueError(f"unsupported dimensions: {dims}")
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def Normalize(in_channels, num_groups=32, eps=1e-6):
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return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=eps, affine=True)
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def nonlinearity(x, swish=1.0):
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# swish
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if swish == 1.0:
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return F.silu(x)
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else:
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return x * F.sigmoid(x * float(swish))
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class TimestepBlock(nn.Module):
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"""
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Any module where forward() takes timestep embeddings as a second argument.
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"""
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@abstractmethod
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def forward(self, x, emb):
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"""
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Apply the module to `x` given `emb` timestep embeddings.
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"""
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class Upsample(nn.Module):
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"""
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An upsampling layer with an optional convolution.
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:param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
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applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
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upsampling occurs in the inner-two dimensions.
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"""
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def __init__(self, channels, use_conv=False, use_conv_transpose=False, dims=2, out_channels=None):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.dims = dims
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self.use_conv_transpose = use_conv_transpose
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if use_conv_transpose:
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self.conv = conv_transpose_nd(dims, channels, self.out_channels, 4, 2, 1)
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elif use_conv:
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self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
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def forward(self, x):
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assert x.shape[1] == self.channels
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if self.use_conv_transpose:
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return self.conv(x)
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if self.dims == 3:
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x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
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else:
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x = F.interpolate(x, scale_factor=2.0, mode="nearest")
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if self.use_conv:
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x = self.conv(x)
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return x
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class Downsample(nn.Module):
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"""
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A downsampling layer with an optional convolution.
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:param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
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applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
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downsampling occurs in the inner-two dimensions.
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"""
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def __init__(self, channels, use_conv=False, dims=2, out_channels=None, padding=1, name="conv"):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.dims = dims
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self.padding = padding
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stride = 2 if dims != 3 else (1, 2, 2)
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self.name = name
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if use_conv:
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conv = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=padding)
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else:
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assert self.channels == self.out_channels
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conv = avg_pool_nd(dims, kernel_size=stride, stride=stride)
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if name == "conv":
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self.conv = conv
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else:
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self.op = conv
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def forward(self, x):
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assert x.shape[1] == self.channels
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if self.use_conv and self.padding == 0 and self.dims == 2:
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pad = (0, 1, 0, 1)
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x = F.pad(x, pad, mode="constant", value=0)
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if self.name == "conv":
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return self.conv(x)
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else:
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return self.op(x)
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# TODO (patil-suraj): needs test
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# class Upsample1d(nn.Module):
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# def __init__(self, dim):
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# super().__init__()
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# self.conv = nn.ConvTranspose1d(dim, dim, 4, 2, 1)
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#
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# def forward(self, x):
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# return self.conv(x)
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# RESNETS
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# unet_score_estimation.py
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class ResnetBlockBigGANppNew(nn.Module):
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def __init__(
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self,
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act,
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in_ch,
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out_ch=None,
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temb_dim=None,
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up=False,
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down=False,
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dropout=0.1,
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fir_kernel=(1, 3, 3, 1),
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skip_rescale=True,
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init_scale=0.0,
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overwrite=True,
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):
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super().__init__()
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out_ch = out_ch if out_ch else in_ch
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self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
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self.up = up
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self.down = down
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self.fir_kernel = fir_kernel
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self.Conv_0 = conv2d(in_ch, out_ch, kernel_size=3, padding=1)
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if temb_dim is not None:
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self.Dense_0 = nn.Linear(temb_dim, out_ch)
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self.Dense_0.weight.data = variance_scaling()(self.Dense_0.weight.shape)
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nn.init.zeros_(self.Dense_0.bias)
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self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
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self.Dropout_0 = nn.Dropout(dropout)
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self.Conv_1 = conv2d(out_ch, out_ch, init_scale=init_scale, kernel_size=3, padding=1)
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if in_ch != out_ch or up or down:
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# 1x1 convolution with DDPM initialization.
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self.Conv_2 = conv2d(in_ch, out_ch, kernel_size=1, padding=0)
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self.skip_rescale = skip_rescale
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self.act = act
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self.in_ch = in_ch
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self.out_ch = out_ch
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if self.overwrite:
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in_channels = in_ch
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out_channels = out_ch
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groups = min(in_ch // 4, 32)
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eps = 1e-6
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self.pre_norm = True
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temb_channels = temb_dim
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non_linearity = "silu"
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time_embedding_norm = "default"
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if self.pre_norm:
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self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
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else:
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self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
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self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if time_embedding_norm == "default":
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self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
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elif time_embedding_norm == "scale_shift":
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self.temb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
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self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
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self.dropout = torch.nn.Dropout(dropout)
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self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if non_linearity == "swish":
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self.nonlinearity = nonlinearity
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elif non_linearity == "mish":
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self.nonlinearity = Mish()
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elif non_linearity == "silu":
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self.nonlinearity = nn.SiLU()
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if up:
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self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
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self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
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elif down:
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self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
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self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
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if self.in_channels != self.out_channels:
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self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
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def forward(self, x, temb=None):
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h = self.act(self.GroupNorm_0(x))
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if self.up:
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h = upsample_2d(h, self.fir_kernel, factor=2)
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x = upsample_2d(x, self.fir_kernel, factor=2)
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elif self.down:
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h = downsample_2d(h, self.fir_kernel, factor=2)
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x = downsample_2d(x, self.fir_kernel, factor=2)
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h = self.Conv_0(h)
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# Add bias to each feature map conditioned on the time embedding
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if temb is not None:
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h += self.Dense_0(self.act(temb))[:, :, None, None]
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h = self.act(self.GroupNorm_1(h))
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h = self.Dropout_0(h)
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h = self.Conv_1(h)
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if self.in_ch != self.out_ch or self.up or self.down:
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x = self.Conv_2(x)
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if not self.skip_rescale:
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return x + h
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else:
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return (x + h) / np.sqrt(2.0)
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def forward_2(self, x, temb, mask=1.0):
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# TODO(Patrick) eventually this class should be split into multiple classes
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# too many if else statements
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if self.overwrite_for_grad_tts and not self.is_overwritten:
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self.set_weights_grad_tts()
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self.is_overwritten = True
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elif self.overwrite_for_ldm and not self.is_overwritten:
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self.set_weights_ldm()
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self.is_overwritten = True
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h = x
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h = h * mask
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if self.pre_norm:
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h = self.norm1(h)
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h = self.nonlinearity(h)
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if self.up or self.down:
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x = self.x_upd(x)
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h = self.h_upd(h)
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h = self.conv1(h)
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if not self.pre_norm:
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h = self.norm1(h)
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h = self.nonlinearity(h)
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h = h * mask
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temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
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if self.time_embedding_norm == "scale_shift":
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scale, shift = torch.chunk(temb, 2, dim=1)
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h = self.norm2(h)
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h = h + h * scale + shift
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h = self.nonlinearity(h)
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elif self.time_embedding_norm == "default":
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h = h + temb
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h = h * mask
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if self.pre_norm:
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h = self.norm2(h)
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h = self.nonlinearity(h)
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h = self.dropout(h)
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h = self.conv2(h)
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if not self.pre_norm:
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h = self.norm2(h)
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h = self.nonlinearity(h)
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h = h * mask
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x = x * mask
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if self.in_channels != self.out_channels:
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x = self.nin_shortcut(x)
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return x + h
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# unet.py, unet_grad_tts.py, unet_ldm.py, unet_glide.py
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class ResnetBlock(nn.Module):
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def __init__(
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self,
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*,
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in_channels,
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out_channels=None,
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conv_shortcut=False,
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dropout=0.0,
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temb_channels=512,
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groups=32,
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pre_norm=True,
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eps=1e-6,
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non_linearity="swish",
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time_embedding_norm="default",
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up=False,
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down=False,
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overwrite_for_grad_tts=False,
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overwrite_for_ldm=False,
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overwrite_for_glide=False,
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):
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super().__init__()
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self.pre_norm = pre_norm
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self.in_channels = in_channels
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out_channels = in_channels if out_channels is None else out_channels
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self.out_channels = out_channels
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self.use_conv_shortcut = conv_shortcut
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self.time_embedding_norm = time_embedding_norm
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self.up = up
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self.down = down
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if self.pre_norm:
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self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
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else:
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self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
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self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if time_embedding_norm == "default":
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self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
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elif time_embedding_norm == "scale_shift":
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self.temb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
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self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
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self.dropout = torch.nn.Dropout(dropout)
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self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if non_linearity == "swish":
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self.nonlinearity = nonlinearity
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elif non_linearity == "mish":
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self.nonlinearity = Mish()
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elif non_linearity == "silu":
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self.nonlinearity = nn.SiLU()
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if up:
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self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
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self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
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elif down:
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self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
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self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
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if self.in_channels != self.out_channels:
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self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
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# TODO(SURAJ, PATRICK): ALL OF THE FOLLOWING OF THE INIT METHOD CAN BE DELETED ONCE WEIGHTS ARE CONVERTED
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self.is_overwritten = False
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self.overwrite_for_glide = overwrite_for_glide
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self.overwrite_for_grad_tts = overwrite_for_grad_tts
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self.overwrite_for_ldm = overwrite_for_ldm or overwrite_for_glide
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if self.overwrite_for_grad_tts:
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dim = in_channels
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dim_out = out_channels
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time_emb_dim = temb_channels
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self.mlp = torch.nn.Sequential(Mish(), torch.nn.Linear(time_emb_dim, dim_out))
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self.pre_norm = pre_norm
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self.block1 = Block(dim, dim_out, groups=groups)
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self.block2 = Block(dim_out, dim_out, groups=groups)
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if dim != dim_out:
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self.res_conv = torch.nn.Conv2d(dim, dim_out, 1)
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else:
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self.res_conv = torch.nn.Identity()
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elif self.overwrite_for_ldm:
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dims = 2
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channels = in_channels
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emb_channels = temb_channels
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use_scale_shift_norm = False
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self.in_layers = nn.Sequential(
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normalization(channels, swish=1.0),
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nn.Identity(),
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conv_nd(dims, channels, self.out_channels, 3, padding=1),
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)
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self.emb_layers = nn.Sequential(
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nn.SiLU(),
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linear(
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emb_channels,
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2 * self.out_channels if self.time_embedding_norm == "scale_shift" else self.out_channels,
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),
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)
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self.out_layers = nn.Sequential(
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normalization(self.out_channels, swish=0.0 if use_scale_shift_norm else 1.0),
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nn.SiLU() if use_scale_shift_norm else nn.Identity(),
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nn.Dropout(p=dropout),
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zero_module(conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)),
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)
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if self.out_channels == in_channels:
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self.skip_connection = nn.Identity()
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else:
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self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
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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 forward(self, x, temb, 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_ldm and not self.is_overwritten:
|
||||
self.set_weights_ldm()
|
||||
self.is_overwritten = True
|
||||
|
||||
h = x
|
||||
h = h * mask
|
||||
if self.pre_norm:
|
||||
h = self.norm1(h)
|
||||
h = self.nonlinearity(h)
|
||||
|
||||
if self.up or self.down:
|
||||
x = self.x_upd(x)
|
||||
h = self.h_upd(h)
|
||||
|
||||
h = self.conv1(h)
|
||||
|
||||
if not self.pre_norm:
|
||||
h = self.norm1(h)
|
||||
h = self.nonlinearity(h)
|
||||
h = h * mask
|
||||
|
||||
temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
|
||||
|
||||
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.in_channels != self.out_channels:
|
||||
x = self.nin_shortcut(x)
|
||||
|
||||
return x + h
|
||||
|
||||
|
||||
# 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()
|
||||
)
|
||||
|
||||
|
||||
# unet_score_estimation.py
|
||||
class ResnetBlockBigGANpp(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
act,
|
||||
in_ch,
|
||||
out_ch=None,
|
||||
temb_dim=None,
|
||||
up=False,
|
||||
down=False,
|
||||
dropout=0.1,
|
||||
fir_kernel=(1, 3, 3, 1),
|
||||
skip_rescale=True,
|
||||
init_scale=0.0,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
out_ch = out_ch if out_ch else in_ch
|
||||
self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
|
||||
self.up = up
|
||||
self.down = down
|
||||
self.fir_kernel = fir_kernel
|
||||
|
||||
self.Conv_0 = 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)
|
||||
self.Dense_0.weight.data = variance_scaling()(self.Dense_0.weight.shape)
|
||||
nn.init.zeros_(self.Dense_0.bias)
|
||||
|
||||
self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
|
||||
self.Dropout_0 = nn.Dropout(dropout)
|
||||
self.Conv_1 = conv2d(out_ch, out_ch, init_scale=init_scale, kernel_size=3, padding=1)
|
||||
if in_ch != out_ch or up or down:
|
||||
# 1x1 convolution with DDPM initialization.
|
||||
self.Conv_2 = conv2d(in_ch, out_ch, kernel_size=1, padding=0)
|
||||
|
||||
self.skip_rescale = skip_rescale
|
||||
self.act = act
|
||||
self.in_ch = in_ch
|
||||
self.out_ch = out_ch
|
||||
|
||||
def forward(self, x, temb=None):
|
||||
h = self.act(self.GroupNorm_0(x))
|
||||
|
||||
if self.up:
|
||||
h = upsample_2d(h, self.fir_kernel, factor=2)
|
||||
x = upsample_2d(x, self.fir_kernel, factor=2)
|
||||
elif self.down:
|
||||
h = downsample_2d(h, self.fir_kernel, factor=2)
|
||||
x = downsample_2d(x, self.fir_kernel, factor=2)
|
||||
|
||||
h = self.Conv_0(h)
|
||||
# Add bias to each feature map conditioned on the time embedding
|
||||
if temb is not None:
|
||||
h += self.Dense_0(self.act(temb))[:, :, None, None]
|
||||
h = self.act(self.GroupNorm_1(h))
|
||||
h = self.Dropout_0(h)
|
||||
h = self.Conv_1(h)
|
||||
|
||||
if self.in_ch != self.out_ch or self.up or self.down:
|
||||
x = self.Conv_2(x)
|
||||
|
||||
if not self.skip_rescale:
|
||||
return x + h
|
||||
else:
|
||||
return (x + h) / np.sqrt(2.0)
|
||||
|
||||
|
||||
# unet_rl.py
|
||||
class ResidualTemporalBlock(nn.Module):
|
||||
def __init__(self, inp_channels, out_channels, embed_dim, horizon, kernel_size=5):
|
||||
super().__init__()
|
||||
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
Conv1dBlock(inp_channels, out_channels, kernel_size),
|
||||
Conv1dBlock(out_channels, out_channels, kernel_size),
|
||||
]
|
||||
)
|
||||
|
||||
self.time_mlp = nn.Sequential(
|
||||
nn.Mish(),
|
||||
nn.Linear(embed_dim, out_channels),
|
||||
RearrangeDim(),
|
||||
# Rearrange("batch t -> batch t 1"),
|
||||
)
|
||||
|
||||
self.residual_conv = (
|
||||
nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
|
||||
)
|
||||
|
||||
def forward(self, x, t):
|
||||
"""
|
||||
x : [ batch_size x inp_channels x horizon ] t : [ batch_size x embed_dim ] returns: out : [ batch_size x
|
||||
out_channels x horizon ]
|
||||
"""
|
||||
out = self.blocks[0](x) + self.time_mlp(t)
|
||||
out = self.blocks[1](out)
|
||||
return out + self.residual_conv(x)
|
||||
|
||||
|
||||
# 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 conv2d(in_planes, out_planes, kernel_size=3, stride=1, bias=True, init_scale=1.0, padding=1):
|
||||
"""nXn convolution with DDPM initialization."""
|
||||
conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias)
|
||||
conv.weight.data = variance_scaling(init_scale)(conv.weight.data.shape)
|
||||
nn.init.zeros_(conv.bias)
|
||||
return conv
|
||||
|
||||
|
||||
def variance_scaling(scale=1.0, in_axis=1, out_axis=0, dtype=torch.float32, device="cpu"):
|
||||
"""Ported from JAX."""
|
||||
scale = 1e-10 if scale == 0 else scale
|
||||
|
||||
def _compute_fans(shape, in_axis=1, out_axis=0):
|
||||
receptive_field_size = np.prod(shape) / shape[in_axis] / shape[out_axis]
|
||||
fan_in = shape[in_axis] * receptive_field_size
|
||||
fan_out = shape[out_axis] * receptive_field_size
|
||||
return fan_in, fan_out
|
||||
|
||||
def init(shape, dtype=dtype, device=device):
|
||||
fan_in, fan_out = _compute_fans(shape, in_axis, out_axis)
|
||||
denominator = (fan_in + fan_out) / 2
|
||||
variance = scale / denominator
|
||||
return (torch.rand(*shape, dtype=dtype, device=device) * 2.0 - 1.0) * np.sqrt(3 * variance)
|
||||
|
||||
return init
|
||||
|
||||
|
||||
def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
|
||||
return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
|
||||
|
||||
|
||||
def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
|
||||
_, 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)
|
||||
|
||||
|
||||
def upsample_2d(x, k=None, factor=2, gain=1):
|
||||
r"""Upsample 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 = _setup_kernel(k) * (gain * (factor**2))
|
||||
p = k.shape[0] - factor
|
||||
return upfirdn2d(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"""Downsample 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 = _setup_kernel(k) * gain
|
||||
p = k.shape[0] - factor
|
||||
return upfirdn2d(x, torch.tensor(k, device=x.device), down=factor, pad=((p + 1) // 2, p // 2))
|
||||
|
||||
|
||||
def _setup_kernel(k):
|
||||
k = np.asarray(k, dtype=np.float32)
|
||||
if k.ndim == 1:
|
||||
k = np.outer(k, k)
|
||||
k /= np.sum(k)
|
||||
assert k.ndim == 2
|
||||
assert k.shape[0] == k.shape[1]
|
||||
return k
|
||||
883
G
883
G
@@ -1,883 +0,0 @@
|
||||
from abc import abstractmethod
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
def avg_pool_nd(dims, *args, **kwargs):
|
||||
"""
|
||||
Create a 1D, 2D, or 3D average pooling module.
|
||||
"""
|
||||
if dims == 1:
|
||||
return nn.AvgPool1d(*args, **kwargs)
|
||||
elif dims == 2:
|
||||
return nn.AvgPool2d(*args, **kwargs)
|
||||
elif dims == 3:
|
||||
return nn.AvgPool3d(*args, **kwargs)
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
|
||||
def conv_nd(dims, *args, **kwargs):
|
||||
"""
|
||||
Create a 1D, 2D, or 3D convolution module.
|
||||
"""
|
||||
if dims == 1:
|
||||
return nn.Conv1d(*args, **kwargs)
|
||||
elif dims == 2:
|
||||
return nn.Conv2d(*args, **kwargs)
|
||||
elif dims == 3:
|
||||
return nn.Conv3d(*args, **kwargs)
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
|
||||
def conv_transpose_nd(dims, *args, **kwargs):
|
||||
"""
|
||||
Create a 1D, 2D, or 3D convolution module.
|
||||
"""
|
||||
if dims == 1:
|
||||
return nn.ConvTranspose1d(*args, **kwargs)
|
||||
elif dims == 2:
|
||||
return nn.ConvTranspose2d(*args, **kwargs)
|
||||
elif dims == 3:
|
||||
return nn.ConvTranspose3d(*args, **kwargs)
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
|
||||
def Normalize(in_channels, num_groups=32, eps=1e-6):
|
||||
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=eps, affine=True)
|
||||
|
||||
|
||||
def nonlinearity(x, swish=1.0):
|
||||
# swish
|
||||
if swish == 1.0:
|
||||
return F.silu(x)
|
||||
else:
|
||||
return x * F.sigmoid(x * float(swish))
|
||||
|
||||
|
||||
class TimestepBlock(nn.Module):
|
||||
"""
|
||||
Any module where forward() takes timestep embeddings as a second argument.
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def forward(self, x, emb):
|
||||
"""
|
||||
Apply the module to `x` given `emb` timestep embeddings.
|
||||
"""
|
||||
|
||||
|
||||
class Upsample(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, dims=2, out_channels=None):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels or channels
|
||||
self.use_conv = use_conv
|
||||
self.dims = dims
|
||||
self.use_conv_transpose = use_conv_transpose
|
||||
|
||||
if use_conv_transpose:
|
||||
self.conv = conv_transpose_nd(dims, channels, self.out_channels, 4, 2, 1)
|
||||
elif use_conv:
|
||||
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
assert x.shape[1] == self.channels
|
||||
if self.use_conv_transpose:
|
||||
return self.conv(x)
|
||||
|
||||
if self.dims == 3:
|
||||
x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
|
||||
else:
|
||||
x = F.interpolate(x, scale_factor=2.0, mode="nearest")
|
||||
|
||||
if self.use_conv:
|
||||
x = self.conv(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Downsample(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, dims=2, 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.dims = dims
|
||||
self.padding = padding
|
||||
stride = 2 if dims != 3 else (1, 2, 2)
|
||||
self.name = name
|
||||
|
||||
if use_conv:
|
||||
conv = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=padding)
|
||||
else:
|
||||
assert self.channels == self.out_channels
|
||||
conv = avg_pool_nd(dims, kernel_size=stride, stride=stride)
|
||||
|
||||
if name == "conv":
|
||||
self.conv = conv
|
||||
else:
|
||||
self.op = conv
|
||||
|
||||
def forward(self, x):
|
||||
assert x.shape[1] == self.channels
|
||||
if self.use_conv and self.padding == 0 and self.dims == 2:
|
||||
pad = (0, 1, 0, 1)
|
||||
x = F.pad(x, pad, mode="constant", value=0)
|
||||
|
||||
if self.name == "conv":
|
||||
return self.conv(x)
|
||||
else:
|
||||
return self.op(x)
|
||||
|
||||
|
||||
# TODO (patil-suraj): needs test
|
||||
# class Upsample1d(nn.Module):
|
||||
# def __init__(self, dim):
|
||||
# super().__init__()
|
||||
# self.conv = nn.ConvTranspose1d(dim, dim, 4, 2, 1)
|
||||
#
|
||||
# def forward(self, x):
|
||||
# return self.conv(x)
|
||||
|
||||
|
||||
# RESNETS
|
||||
# unet_score_estimation.py
|
||||
class ResnetBlockBigGANppNew(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
act,
|
||||
in_ch,
|
||||
out_ch=None,
|
||||
temb_dim=None,
|
||||
up=False,
|
||||
down=False,
|
||||
dropout=0.1,
|
||||
fir_kernel=(1, 3, 3, 1),
|
||||
skip_rescale=True,
|
||||
init_scale=0.0,
|
||||
overwrite=True,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
out_ch = out_ch if out_ch else in_ch
|
||||
self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
|
||||
self.up = up
|
||||
self.down = down
|
||||
self.fir_kernel = fir_kernel
|
||||
|
||||
self.Conv_0 = 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)
|
||||
self.Dense_0.weight.data = variance_scaling()(self.Dense_0.weight.shape)
|
||||
nn.init.zeros_(self.Dense_0.bias)
|
||||
|
||||
self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
|
||||
self.Dropout_0 = nn.Dropout(dropout)
|
||||
self.Conv_1 = conv2d(out_ch, out_ch, init_scale=init_scale, kernel_size=3, padding=1)
|
||||
if in_ch != out_ch or up or down:
|
||||
# 1x1 convolution with DDPM initialization.
|
||||
self.Conv_2 = conv2d(in_ch, out_ch, kernel_size=1, padding=0)
|
||||
|
||||
self.skip_rescale = skip_rescale
|
||||
self.act = act
|
||||
self.in_ch = in_ch
|
||||
self.out_ch = out_ch
|
||||
|
||||
self.is_overwritten = False
|
||||
self.overwrite = overwrite
|
||||
if overwrite:
|
||||
self.in_channels = in_channels = in_ch
|
||||
self.out_channels = out_channels = out_ch
|
||||
groups = min(in_ch // 4, 32)
|
||||
eps = 1e-6
|
||||
self.pre_norm = True
|
||||
temb_channels = temb_dim
|
||||
non_linearity = "silu"
|
||||
self.time_embedding_norm = time_embedding_norm = "default"
|
||||
|
||||
if self.pre_norm:
|
||||
self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
|
||||
else:
|
||||
self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
|
||||
|
||||
self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
if time_embedding_norm == "default":
|
||||
self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
|
||||
elif time_embedding_norm == "scale_shift":
|
||||
self.temb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
|
||||
|
||||
self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
|
||||
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 = nonlinearity
|
||||
elif non_linearity == "mish":
|
||||
self.nonlinearity = Mish()
|
||||
elif non_linearity == "silu":
|
||||
self.nonlinearity = nn.SiLU()
|
||||
|
||||
if up:
|
||||
self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
|
||||
self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
|
||||
elif down:
|
||||
self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
|
||||
self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
|
||||
|
||||
if self.in_channels != self.out_channels:
|
||||
self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
|
||||
|
||||
def set_weights(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:
|
||||
self.nin_shortcut.weight.data = self.Conv_2.weight.data
|
||||
self.nin_shortcut.bias.data = self.Conv_2.bias.data
|
||||
|
||||
def set_weights_ldm(self):
|
||||
import ipdb; ipdb.set_trace()
|
||||
|
||||
def forward(self, x, temb=None):
|
||||
if self.overwrite and not self.is_overwritten:
|
||||
self.set_weights()
|
||||
self.is_overwritten = True
|
||||
|
||||
orig_x = x
|
||||
h = self.act(self.GroupNorm_0(x))
|
||||
|
||||
if self.up:
|
||||
h = upsample_2d(h, self.fir_kernel, factor=2)
|
||||
x = upsample_2d(x, self.fir_kernel, factor=2)
|
||||
elif self.down:
|
||||
h = downsample_2d(h, self.fir_kernel, factor=2)
|
||||
x = downsample_2d(x, self.fir_kernel, factor=2)
|
||||
|
||||
h = self.Conv_0(h)
|
||||
# Add bias to each feature map conditioned on the time embedding
|
||||
if temb is not None:
|
||||
h += self.Dense_0(self.act(temb))[:, :, None, None]
|
||||
h = self.act(self.GroupNorm_1(h))
|
||||
h = self.Dropout_0(h)
|
||||
h = self.Conv_1(h)
|
||||
|
||||
if self.in_ch != self.out_ch or self.up or self.down:
|
||||
x = self.Conv_2(x)
|
||||
|
||||
if not self.skip_rescale:
|
||||
result = x + h
|
||||
else:
|
||||
result = (x + h) / np.sqrt(2.0)
|
||||
|
||||
result_2 = self.forward_2(orig_x, temb)
|
||||
print("Diff", (result - result_2).abs().sum())
|
||||
import ipdb; ipdb.set_trace()
|
||||
|
||||
return result
|
||||
|
||||
def forward_2(self, x, temb, mask=1.0):
|
||||
h = x
|
||||
h = h * mask
|
||||
if self.pre_norm:
|
||||
h = self.norm1(h)
|
||||
h = self.nonlinearity(h)
|
||||
|
||||
if self.up or self.down:
|
||||
x = self.x_upd(x)
|
||||
h = self.h_upd(h)
|
||||
|
||||
h = self.conv1(h)
|
||||
|
||||
if not self.pre_norm:
|
||||
h = self.norm1(h)
|
||||
h = self.nonlinearity(h)
|
||||
h = h * mask
|
||||
|
||||
temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
|
||||
|
||||
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.in_channels != self.out_channels:
|
||||
x = self.nin_shortcut(x)
|
||||
|
||||
return x + h
|
||||
|
||||
|
||||
# unet.py, unet_grad_tts.py, unet_ldm.py, unet_glide.py
|
||||
class ResnetBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
in_channels,
|
||||
out_channels=None,
|
||||
conv_shortcut=False,
|
||||
dropout=0.0,
|
||||
temb_channels=512,
|
||||
groups=32,
|
||||
pre_norm=True,
|
||||
eps=1e-6,
|
||||
non_linearity="swish",
|
||||
time_embedding_norm="default",
|
||||
up=False,
|
||||
down=False,
|
||||
overwrite_for_grad_tts=False,
|
||||
overwrite_for_ldm=False,
|
||||
overwrite_for_glide=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
|
||||
|
||||
if self.pre_norm:
|
||||
self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
|
||||
else:
|
||||
self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
|
||||
|
||||
self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
if time_embedding_norm == "default":
|
||||
self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
|
||||
elif time_embedding_norm == "scale_shift":
|
||||
self.temb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
|
||||
|
||||
self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
|
||||
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 = nonlinearity
|
||||
elif non_linearity == "mish":
|
||||
self.nonlinearity = Mish()
|
||||
elif non_linearity == "silu":
|
||||
self.nonlinearity = nn.SiLU()
|
||||
|
||||
if up:
|
||||
self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
|
||||
self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
|
||||
elif down:
|
||||
self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
|
||||
self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
|
||||
|
||||
if self.in_channels != self.out_channels:
|
||||
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
|
||||
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:
|
||||
dims = 2
|
||||
channels = in_channels
|
||||
emb_channels = temb_channels
|
||||
use_scale_shift_norm = False
|
||||
|
||||
self.in_layers = nn.Sequential(
|
||||
normalization(channels, swish=1.0),
|
||||
nn.Identity(),
|
||||
conv_nd(dims, 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(conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)),
|
||||
)
|
||||
if self.out_channels == in_channels:
|
||||
self.skip_connection = nn.Identity()
|
||||
else:
|
||||
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
|
||||
|
||||
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 forward(self, x, temb, 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_ldm and not self.is_overwritten:
|
||||
self.set_weights_ldm()
|
||||
self.is_overwritten = True
|
||||
|
||||
h = x
|
||||
h = h * mask
|
||||
if self.pre_norm:
|
||||
h = self.norm1(h)
|
||||
h = self.nonlinearity(h)
|
||||
|
||||
if self.up or self.down:
|
||||
x = self.x_upd(x)
|
||||
h = self.h_upd(h)
|
||||
|
||||
h = self.conv1(h)
|
||||
|
||||
if not self.pre_norm:
|
||||
h = self.norm1(h)
|
||||
h = self.nonlinearity(h)
|
||||
h = h * mask
|
||||
|
||||
temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
|
||||
|
||||
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.in_channels != self.out_channels:
|
||||
x = self.nin_shortcut(x)
|
||||
|
||||
return x + h
|
||||
|
||||
|
||||
# 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()
|
||||
)
|
||||
|
||||
|
||||
# unet_score_estimation.py
|
||||
class ResnetBlockBigGANpp(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
act,
|
||||
in_ch,
|
||||
out_ch=None,
|
||||
temb_dim=None,
|
||||
up=False,
|
||||
down=False,
|
||||
dropout=0.1,
|
||||
fir_kernel=(1, 3, 3, 1),
|
||||
skip_rescale=True,
|
||||
init_scale=0.0,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
out_ch = out_ch if out_ch else in_ch
|
||||
self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
|
||||
self.up = up
|
||||
self.down = down
|
||||
self.fir_kernel = fir_kernel
|
||||
|
||||
self.Conv_0 = 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)
|
||||
self.Dense_0.weight.data = variance_scaling()(self.Dense_0.weight.shape)
|
||||
nn.init.zeros_(self.Dense_0.bias)
|
||||
|
||||
self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
|
||||
self.Dropout_0 = nn.Dropout(dropout)
|
||||
self.Conv_1 = conv2d(out_ch, out_ch, init_scale=init_scale, kernel_size=3, padding=1)
|
||||
if in_ch != out_ch or up or down:
|
||||
# 1x1 convolution with DDPM initialization.
|
||||
self.Conv_2 = conv2d(in_ch, out_ch, kernel_size=1, padding=0)
|
||||
|
||||
self.skip_rescale = skip_rescale
|
||||
self.act = act
|
||||
self.in_ch = in_ch
|
||||
self.out_ch = out_ch
|
||||
|
||||
def forward(self, x, temb=None):
|
||||
h = self.act(self.GroupNorm_0(x))
|
||||
|
||||
if self.up:
|
||||
h = upsample_2d(h, self.fir_kernel, factor=2)
|
||||
x = upsample_2d(x, self.fir_kernel, factor=2)
|
||||
elif self.down:
|
||||
h = downsample_2d(h, self.fir_kernel, factor=2)
|
||||
x = downsample_2d(x, self.fir_kernel, factor=2)
|
||||
|
||||
h = self.Conv_0(h)
|
||||
# Add bias to each feature map conditioned on the time embedding
|
||||
if temb is not None:
|
||||
h += self.Dense_0(self.act(temb))[:, :, None, None]
|
||||
h = self.act(self.GroupNorm_1(h))
|
||||
h = self.Dropout_0(h)
|
||||
h = self.Conv_1(h)
|
||||
|
||||
if self.in_ch != self.out_ch or self.up or self.down:
|
||||
x = self.Conv_2(x)
|
||||
|
||||
if not self.skip_rescale:
|
||||
return x + h
|
||||
else:
|
||||
return (x + h) / np.sqrt(2.0)
|
||||
|
||||
|
||||
# unet_rl.py
|
||||
class ResidualTemporalBlock(nn.Module):
|
||||
def __init__(self, inp_channels, out_channels, embed_dim, horizon, kernel_size=5):
|
||||
super().__init__()
|
||||
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
Conv1dBlock(inp_channels, out_channels, kernel_size),
|
||||
Conv1dBlock(out_channels, out_channels, kernel_size),
|
||||
]
|
||||
)
|
||||
|
||||
self.time_mlp = nn.Sequential(
|
||||
nn.Mish(),
|
||||
nn.Linear(embed_dim, out_channels),
|
||||
RearrangeDim(),
|
||||
# Rearrange("batch t -> batch t 1"),
|
||||
)
|
||||
|
||||
self.residual_conv = (
|
||||
nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
|
||||
)
|
||||
|
||||
def forward(self, x, t):
|
||||
"""
|
||||
x : [ batch_size x inp_channels x horizon ] t : [ batch_size x embed_dim ] returns: out : [ batch_size x
|
||||
out_channels x horizon ]
|
||||
"""
|
||||
out = self.blocks[0](x) + self.time_mlp(t)
|
||||
out = self.blocks[1](out)
|
||||
return out + self.residual_conv(x)
|
||||
|
||||
|
||||
# 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 conv2d(in_planes, out_planes, kernel_size=3, stride=1, bias=True, init_scale=1.0, padding=1):
|
||||
"""nXn convolution with DDPM initialization."""
|
||||
conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias)
|
||||
conv.weight.data = variance_scaling(init_scale)(conv.weight.data.shape)
|
||||
nn.init.zeros_(conv.bias)
|
||||
return conv
|
||||
|
||||
|
||||
def variance_scaling(scale=1.0, in_axis=1, out_axis=0, dtype=torch.float32, device="cpu"):
|
||||
"""Ported from JAX."""
|
||||
scale = 1e-10 if scale == 0 else scale
|
||||
|
||||
def _compute_fans(shape, in_axis=1, out_axis=0):
|
||||
receptive_field_size = np.prod(shape) / shape[in_axis] / shape[out_axis]
|
||||
fan_in = shape[in_axis] * receptive_field_size
|
||||
fan_out = shape[out_axis] * receptive_field_size
|
||||
return fan_in, fan_out
|
||||
|
||||
def init(shape, dtype=dtype, device=device):
|
||||
fan_in, fan_out = _compute_fans(shape, in_axis, out_axis)
|
||||
denominator = (fan_in + fan_out) / 2
|
||||
variance = scale / denominator
|
||||
return (torch.rand(*shape, dtype=dtype, device=device) * 2.0 - 1.0) * np.sqrt(3 * variance)
|
||||
|
||||
return init
|
||||
|
||||
|
||||
def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
|
||||
return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
|
||||
|
||||
|
||||
def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
|
||||
_, 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)
|
||||
|
||||
|
||||
def upsample_2d(x, k=None, factor=2, gain=1):
|
||||
r"""Upsample 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 = _setup_kernel(k) * (gain * (factor**2))
|
||||
p = k.shape[0] - factor
|
||||
return upfirdn2d(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"""Downsample 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 = _setup_kernel(k) * gain
|
||||
p = k.shape[0] - factor
|
||||
return upfirdn2d(x, torch.tensor(k, device=x.device), down=factor, pad=((p + 1) // 2, p // 2))
|
||||
|
||||
|
||||
def _setup_kernel(k):
|
||||
k = np.asarray(k, dtype=np.float32)
|
||||
if k.ndim == 1:
|
||||
k = np.outer(k, k)
|
||||
k /= np.sum(k)
|
||||
assert k.ndim == 2
|
||||
assert k.shape[0] == k.shape[1]
|
||||
return k
|
||||
Reference in New Issue
Block a user