451 lines
15 KiB
Python
451 lines
15 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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from ..configuration_utils import ConfigMixin, register_to_config
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from ..modeling_utils import ModelMixin
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from .unet_blocks import UNetMidBlock2D, get_down_block, get_up_block
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class Encoder(nn.Module):
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def __init__(
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self,
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in_channels=3,
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out_channels=3,
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down_block_types=("DownEncoderBlock2D",),
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block_out_channels=(64,),
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layers_per_block=2,
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act_fn="silu",
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double_z=True,
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):
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super().__init__()
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self.layers_per_block = layers_per_block
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self.conv_in = torch.nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1)
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self.mid_block = None
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self.down_blocks = nn.ModuleList([])
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# down
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output_channel = block_out_channels[0]
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for i, down_block_type in enumerate(down_block_types):
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input_channel = output_channel
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output_channel = block_out_channels[i]
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is_final_block = i == len(block_out_channels) - 1
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down_block = get_down_block(
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down_block_type,
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num_layers=self.layers_per_block,
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in_channels=input_channel,
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out_channels=output_channel,
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add_downsample=not is_final_block,
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resnet_eps=1e-6,
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downsample_padding=0,
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resnet_act_fn=act_fn,
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attn_num_head_channels=None,
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temb_channels=None,
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)
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self.down_blocks.append(down_block)
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# mid
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self.mid_block = UNetMidBlock2D(
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in_channels=block_out_channels[-1],
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resnet_eps=1e-6,
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resnet_act_fn=act_fn,
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output_scale_factor=1,
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resnet_time_scale_shift="default",
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attn_num_head_channels=None,
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resnet_groups=32,
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temb_channels=None,
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)
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# out
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num_groups_out = 32
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self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=num_groups_out, eps=1e-6)
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self.conv_act = nn.SiLU()
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conv_out_channels = 2 * out_channels if double_z else out_channels
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self.conv_out = nn.Conv2d(block_out_channels[-1], conv_out_channels, 3, padding=1)
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def forward(self, x):
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sample = x
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sample = self.conv_in(sample)
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# down
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for down_block in self.down_blocks:
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sample = down_block(sample)
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# middle
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sample = self.mid_block(sample)
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# post-process
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sample = self.conv_norm_out(sample)
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sample = self.conv_act(sample)
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sample = self.conv_out(sample)
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return sample
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class Decoder(nn.Module):
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def __init__(
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self,
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in_channels=3,
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out_channels=3,
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up_block_types=("UpDecoderBlock2D",),
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block_out_channels=(64,),
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layers_per_block=2,
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act_fn="silu",
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):
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super().__init__()
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self.layers_per_block = layers_per_block
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self.conv_in = nn.Conv2d(in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1)
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self.mid_block = None
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self.up_blocks = nn.ModuleList([])
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# mid
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self.mid_block = UNetMidBlock2D(
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in_channels=block_out_channels[-1],
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resnet_eps=1e-6,
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resnet_act_fn=act_fn,
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output_scale_factor=1,
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resnet_time_scale_shift="default",
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attn_num_head_channels=None,
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resnet_groups=32,
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temb_channels=None,
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)
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# up
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reversed_block_out_channels = list(reversed(block_out_channels))
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output_channel = reversed_block_out_channels[0]
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for i, up_block_type in enumerate(up_block_types):
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prev_output_channel = output_channel
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output_channel = reversed_block_out_channels[i]
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is_final_block = i == len(block_out_channels) - 1
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up_block = get_up_block(
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up_block_type,
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num_layers=self.layers_per_block + 1,
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in_channels=prev_output_channel,
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out_channels=output_channel,
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prev_output_channel=None,
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add_upsample=not is_final_block,
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resnet_eps=1e-6,
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resnet_act_fn=act_fn,
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attn_num_head_channels=None,
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temb_channels=None,
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)
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self.up_blocks.append(up_block)
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prev_output_channel = output_channel
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# out
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num_groups_out = 32
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self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=1e-6)
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self.conv_act = nn.SiLU()
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self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
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def forward(self, z):
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sample = z
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sample = self.conv_in(sample)
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# middle
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sample = self.mid_block(sample)
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# up
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for up_block in self.up_blocks:
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sample = up_block(sample)
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# post-process
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sample = self.conv_norm_out(sample)
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sample = self.conv_act(sample)
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sample = self.conv_out(sample)
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return sample
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class VectorQuantizer(nn.Module):
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"""
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Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix
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multiplications and allows for post-hoc remapping of indices.
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"""
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# NOTE: due to a bug the beta term was applied to the wrong term. for
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# backwards compatibility we use the buggy version by default, but you can
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# specify legacy=False to fix it.
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def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", sane_index_shape=False, legacy=True):
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super().__init__()
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self.n_e = n_e
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self.e_dim = e_dim
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self.beta = beta
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self.legacy = legacy
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self.embedding = nn.Embedding(self.n_e, self.e_dim)
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self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
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self.remap = remap
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if self.remap is not None:
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self.register_buffer("used", torch.tensor(np.load(self.remap)))
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self.re_embed = self.used.shape[0]
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self.unknown_index = unknown_index # "random" or "extra" or integer
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if self.unknown_index == "extra":
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self.unknown_index = self.re_embed
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self.re_embed = self.re_embed + 1
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print(
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f"Remapping {self.n_e} indices to {self.re_embed} indices. "
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f"Using {self.unknown_index} for unknown indices."
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)
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else:
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self.re_embed = n_e
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self.sane_index_shape = sane_index_shape
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def remap_to_used(self, inds):
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ishape = inds.shape
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assert len(ishape) > 1
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inds = inds.reshape(ishape[0], -1)
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used = self.used.to(inds)
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match = (inds[:, :, None] == used[None, None, ...]).long()
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new = match.argmax(-1)
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unknown = match.sum(2) < 1
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if self.unknown_index == "random":
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new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
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else:
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new[unknown] = self.unknown_index
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return new.reshape(ishape)
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def unmap_to_all(self, inds):
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ishape = inds.shape
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assert len(ishape) > 1
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inds = inds.reshape(ishape[0], -1)
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used = self.used.to(inds)
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if self.re_embed > self.used.shape[0]: # extra token
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inds[inds >= self.used.shape[0]] = 0 # simply set to zero
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back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
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return back.reshape(ishape)
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def forward(self, z):
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# reshape z -> (batch, height, width, channel) and flatten
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z = z.permute(0, 2, 3, 1).contiguous()
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z_flattened = z.view(-1, self.e_dim)
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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d = (
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torch.sum(z_flattened**2, dim=1, keepdim=True)
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+ torch.sum(self.embedding.weight**2, dim=1)
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- 2 * torch.einsum("bd,dn->bn", z_flattened, self.embedding.weight.t())
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)
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min_encoding_indices = torch.argmin(d, dim=1)
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z_q = self.embedding(min_encoding_indices).view(z.shape)
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perplexity = None
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min_encodings = None
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# compute loss for embedding
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if not self.legacy:
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loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean((z_q - z.detach()) ** 2)
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else:
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loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
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# preserve gradients
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z_q = z + (z_q - z).detach()
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# reshape back to match original input shape
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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if self.remap is not None:
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min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis
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min_encoding_indices = self.remap_to_used(min_encoding_indices)
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min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
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if self.sane_index_shape:
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min_encoding_indices = min_encoding_indices.reshape(z_q.shape[0], z_q.shape[2], z_q.shape[3])
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return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
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def get_codebook_entry(self, indices, shape):
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# shape specifying (batch, height, width, channel)
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if self.remap is not None:
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indices = indices.reshape(shape[0], -1) # add batch axis
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indices = self.unmap_to_all(indices)
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indices = indices.reshape(-1) # flatten again
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# get quantized latent vectors
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z_q = self.embedding(indices)
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if shape is not None:
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z_q = z_q.view(shape)
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# reshape back to match original input shape
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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return z_q
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class DiagonalGaussianDistribution(object):
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def __init__(self, parameters, deterministic=False):
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self.parameters = parameters
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self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
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self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
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self.deterministic = deterministic
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self.std = torch.exp(0.5 * self.logvar)
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self.var = torch.exp(self.logvar)
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if self.deterministic:
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self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
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def sample(self):
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x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
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return x
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def kl(self, other=None):
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if self.deterministic:
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return torch.Tensor([0.0])
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else:
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if other is None:
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return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3])
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else:
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return 0.5 * torch.sum(
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torch.pow(self.mean - other.mean, 2) / other.var
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+ self.var / other.var
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- 1.0
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- self.logvar
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+ other.logvar,
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dim=[1, 2, 3],
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)
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def nll(self, sample, dims=[1, 2, 3]):
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if self.deterministic:
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return torch.Tensor([0.0])
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logtwopi = np.log(2.0 * np.pi)
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return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims)
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def mode(self):
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return self.mean
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class VQModel(ModelMixin, ConfigMixin):
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@register_to_config
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def __init__(
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self,
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in_channels=3,
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out_channels=3,
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down_block_types=("DownEncoderBlock2D",),
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up_block_types=("UpDecoderBlock2D",),
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block_out_channels=(64,),
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layers_per_block=1,
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act_fn="silu",
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latent_channels=3,
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sample_size=32,
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num_vq_embeddings=256,
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):
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super().__init__()
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# pass init params to Encoder
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self.encoder = Encoder(
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in_channels=in_channels,
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out_channels=latent_channels,
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down_block_types=down_block_types,
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block_out_channels=block_out_channels,
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layers_per_block=layers_per_block,
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act_fn=act_fn,
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double_z=False,
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)
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self.quant_conv = torch.nn.Conv2d(latent_channels, latent_channels, 1)
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self.quantize = VectorQuantizer(
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num_vq_embeddings, latent_channels, beta=0.25, remap=None, sane_index_shape=False
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)
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self.post_quant_conv = torch.nn.Conv2d(latent_channels, latent_channels, 1)
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# pass init params to Decoder
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self.decoder = Decoder(
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in_channels=latent_channels,
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out_channels=out_channels,
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up_block_types=up_block_types,
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block_out_channels=block_out_channels,
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layers_per_block=layers_per_block,
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act_fn=act_fn,
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)
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def encode(self, x):
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h = self.encoder(x)
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h = self.quant_conv(h)
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return h
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def decode(self, h, force_not_quantize=False):
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# also go through quantization layer
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if not force_not_quantize:
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quant, emb_loss, info = self.quantize(h)
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else:
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quant = h
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quant = self.post_quant_conv(quant)
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dec = self.decoder(quant)
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return dec
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def forward(self, sample):
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x = sample
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h = self.encode(x)
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dec = self.decode(h)
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return dec
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class AutoencoderKL(ModelMixin, ConfigMixin):
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@register_to_config
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def __init__(
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self,
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in_channels=3,
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out_channels=3,
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down_block_types=("DownEncoderBlock2D",),
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up_block_types=("UpDecoderBlock2D",),
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block_out_channels=(64,),
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layers_per_block=1,
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act_fn="silu",
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latent_channels=4,
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sample_size=32,
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):
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super().__init__()
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# pass init params to Encoder
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self.encoder = Encoder(
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in_channels=in_channels,
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out_channels=latent_channels,
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down_block_types=down_block_types,
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block_out_channels=block_out_channels,
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layers_per_block=layers_per_block,
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act_fn=act_fn,
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double_z=True,
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)
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# pass init params to Decoder
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self.decoder = Decoder(
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in_channels=latent_channels,
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out_channels=out_channels,
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up_block_types=up_block_types,
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block_out_channels=block_out_channels,
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layers_per_block=layers_per_block,
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act_fn=act_fn,
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)
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self.quant_conv = torch.nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
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self.post_quant_conv = torch.nn.Conv2d(latent_channels, latent_channels, 1)
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def encode(self, x):
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h = self.encoder(x)
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moments = self.quant_conv(h)
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posterior = DiagonalGaussianDistribution(moments)
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return posterior
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def decode(self, z):
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z = self.post_quant_conv(z)
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dec = self.decoder(z)
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return dec
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def forward(self, sample, sample_posterior=False):
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x = sample
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posterior = self.encode(x)
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if sample_posterior:
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z = posterior.sample()
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else:
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z = posterior.mode()
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dec = self.decode(z)
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return dec
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