Publish pinned visual components with exact source payloads and native numerical validation

21d1ec3 verified 4 days ago

16.3 kB

import torch
import torch.nn as nn
import numpy as np


def nonlinearity(x):
    # swish
    return x * torch.sigmoid(x)


def Normalize(in_channels, num_groups=32):
    return torch.nn.GroupNorm(
        num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
    )


class Upsample(nn.Module):
    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            self.conv = torch.nn.Conv2d(
                in_channels, in_channels, kernel_size=3, stride=1, padding=1
            )

    def forward(self, x):
        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
        if self.with_conv:
            x = self.conv(x)
        return x


class Downsample(nn.Module):
    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            # no asymmetric padding in torch conv, must do it ourselves
            self.conv = torch.nn.Conv2d(
                in_channels, in_channels, kernel_size=3, stride=2, padding=0
            )

    def forward(self, x):
        if self.with_conv:
            pad = (0, 1, 0, 1)
            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
            x = self.conv(x)
        else:
            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
        return x


class ResnetBlock(nn.Module):
    def __init__(
        self,
        *,
        in_channels,
        out_channels=None,
        conv_shortcut=False,
        dropout,
        temb_channels=512,
    ):
        super().__init__()
        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.norm1 = Normalize(in_channels)
        self.conv1 = torch.nn.Conv2d(
            in_channels, out_channels, kernel_size=3, stride=1, padding=1
        )
        if temb_channels > 0:
            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
        self.norm2 = Normalize(out_channels)
        self.dropout = torch.nn.Dropout(dropout)
        self.conv2 = torch.nn.Conv2d(
            out_channels, out_channels, kernel_size=3, stride=1, padding=1
        )
        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                self.conv_shortcut = torch.nn.Conv2d(
                    in_channels, out_channels, kernel_size=3, stride=1, padding=1
                )
            else:
                self.nin_shortcut = torch.nn.Conv2d(
                    in_channels, out_channels, kernel_size=1, stride=1, padding=0
                )

    def forward(self, x, temb):
        h = x
        h = self.norm1(h)
        h = nonlinearity(h)
        h = self.conv1(h)

        if temb is not None:
            h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]

        h = self.norm2(h)
        h = nonlinearity(h)
        h = self.dropout(h)
        h = self.conv2(h)

        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                x = self.conv_shortcut(x)
            else:
                x = self.nin_shortcut(x)

        return x + h


class AttnBlock(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.k = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.v = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.proj_out = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )

    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b, c, h, w = q.shape
        q = q.reshape(b, c, h * w)
        q = q.permute(0, 2, 1)  # b,hw,c
        k = k.reshape(b, c, h * w)  # b,c,hw
        w_ = torch.bmm(q, k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
        w_ = w_ * (int(c) ** (-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = v.reshape(b, c, h * w)
        w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
        h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
        h_ = h_.reshape(b, c, h, w)

        h_ = self.proj_out(h_)

        return x + h_


class Encoder(nn.Module):
    def __init__(
        self,
        *,
        ch=128,
        out_ch=3,
        ch_mult=(1, 1, 2, 2, 4),
        num_res_blocks=2,
        attn_resolutions=(16,),
        dropout=0.0,
        resamp_with_conv=True,
        in_channels=3,
        resolution=256,
        z_channels=16,
        double_z=True,
        **ignore_kwargs,
    ):
        super().__init__()
        self.ch = ch
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels

        # downsampling
        self.conv_in = torch.nn.Conv2d(
            in_channels, self.ch, kernel_size=3, stride=1, padding=1
        )

        curr_res = resolution
        in_ch_mult = (1,) + tuple(ch_mult)
        self.down = nn.ModuleList()
        for i_level in range(self.num_resolutions):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_in = ch * in_ch_mult[i_level]
            block_out = ch * ch_mult[i_level]
            for i_block in range(self.num_res_blocks):
                block.append(
                    ResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(AttnBlock(block_in))
            down = nn.Module()
            down.block = block
            down.attn = attn
            if i_level != self.num_resolutions - 1:
                down.downsample = Downsample(block_in, resamp_with_conv)
                curr_res = curr_res // 2
            self.down.append(down)

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )
        self.mid.attn_1 = AttnBlock(block_in)
        self.mid.block_2 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv2d(
            block_in,
            2 * z_channels if double_z else z_channels,
            kernel_size=3,
            stride=1,
            padding=1,
        )

    def forward(self, x):
        # assert x.shape[2] == x.shape[3] == self.resolution, "{}, {}, {}".format(x.shape[2], x.shape[3], self.resolution)

        # timestep embedding
        temb = None

        # downsampling
        hs = [self.conv_in(x)]
        for i_level in range(self.num_resolutions):
            for i_block in range(self.num_res_blocks):
                h = self.down[i_level].block[i_block](hs[-1], temb)
                if len(self.down[i_level].attn) > 0:
                    h = self.down[i_level].attn[i_block](h)
                hs.append(h)
            if i_level != self.num_resolutions - 1:
                hs.append(self.down[i_level].downsample(hs[-1]))

        # middle
        h = hs[-1]
        h = self.mid.block_1(h, temb)
        h = self.mid.attn_1(h)
        h = self.mid.block_2(h, temb)

        # end
        h = self.norm_out(h)
        h = nonlinearity(h)
        h = self.conv_out(h)
        return h


class Decoder(nn.Module):
    def __init__(
        self,
        *,
        ch=128,
        out_ch=3,
        ch_mult=(1, 1, 2, 2, 4),
        num_res_blocks=2,
        attn_resolutions=(),
        dropout=0.0,
        resamp_with_conv=True,
        in_channels=3,
        resolution=256,
        z_channels=16,
        give_pre_end=False,
        **ignore_kwargs,
    ):
        super().__init__()
        self.ch = ch
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels
        self.give_pre_end = give_pre_end

        # compute in_ch_mult, block_in and curr_res at lowest res
        in_ch_mult = (1,) + tuple(ch_mult)
        block_in = ch * ch_mult[self.num_resolutions - 1]
        curr_res = resolution // 2 ** (self.num_resolutions - 1)
        self.z_shape = (1, z_channels, curr_res, curr_res)
        print(
            "Working with z of shape {} = {} dimensions.".format(
                self.z_shape, np.prod(self.z_shape)
            )
        )

        # z to block_in
        self.conv_in = torch.nn.Conv2d(
            z_channels, block_in, kernel_size=3, stride=1, padding=1
        )

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )
        self.mid.attn_1 = AttnBlock(block_in)
        self.mid.block_2 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )

        # upsampling
        self.up = nn.ModuleList()
        for i_level in reversed(range(self.num_resolutions)):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_out = ch * ch_mult[i_level]
            for i_block in range(self.num_res_blocks + 1):
                block.append(
                    ResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(AttnBlock(block_in))
            up = nn.Module()
            up.block = block
            up.attn = attn
            if i_level != 0:
                up.upsample = Upsample(block_in, resamp_with_conv)
                curr_res = curr_res * 2
            self.up.insert(0, up)  # prepend to get consistent order

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv2d(
            block_in, out_ch, kernel_size=3, stride=1, padding=1
        )

    def forward(self, z):
        # assert z.shape[1:] == self.z_shape[1:]
        self.last_z_shape = z.shape

        # timestep embedding
        temb = None

        # z to block_in
        h = self.conv_in(z)

        # middle
        h = self.mid.block_1(h, temb)
        h = self.mid.attn_1(h)
        h = self.mid.block_2(h, temb)

        # upsampling
        for i_level in reversed(range(self.num_resolutions)):
            for i_block in range(self.num_res_blocks + 1):
                h = self.up[i_level].block[i_block](h, temb)
                if len(self.up[i_level].attn) > 0:
                    h = self.up[i_level].attn[i_block](h)
            if i_level != 0:
                h = self.up[i_level].upsample(h)

        # end
        if self.give_pre_end:
            return h

        h = self.norm_out(h)
        h = nonlinearity(h)
        h = self.conv_out(h)
        return h


class DiagonalGaussianDistribution(object):
    def __init__(self, parameters, deterministic=False):
        self.parameters = parameters
        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
        self.deterministic = deterministic
        self.std = torch.exp(0.5 * self.logvar)
        self.var = torch.exp(self.logvar)
        if self.deterministic:
            self.var = self.std = torch.zeros_like(self.mean).to(
                device=self.parameters.device
            )

    def sample(self):
        x = self.mean + self.std * torch.randn(self.mean.shape).to(
            device=self.parameters.device
        )
        return x

    def kl(self, other=None):
        if self.deterministic:
            return torch.Tensor([0.0])
        else:
            if other is None:
                return 0.5 * torch.sum(
                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
                    dim=[1, 2, 3],
                )
            else:
                return 0.5 * torch.sum(
                    torch.pow(self.mean - other.mean, 2) / other.var
                    + self.var / other.var
                    - 1.0
                    - self.logvar
                    + other.logvar,
                    dim=[1, 2, 3],
                )

    def nll(self, sample, dims=[1, 2, 3]):
        if self.deterministic:
            return torch.Tensor([0.0])
        logtwopi = np.log(2.0 * np.pi)
        return 0.5 * torch.sum(
            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
            dim=dims,
        )

    def mode(self):
        return self.mean


class AutoencoderKL(nn.Module):
    def __init__(self, embed_dim, ch_mult, use_variational=True, ckpt_path=None):
        super().__init__()
        self.encoder = Encoder(ch_mult=ch_mult, z_channels=embed_dim)
        self.decoder = Decoder(ch_mult=ch_mult, z_channels=embed_dim)
        self.use_variational = use_variational
        mult = 2 if self.use_variational else 1
        self.quant_conv = torch.nn.Conv2d(2 * embed_dim, mult * embed_dim, 1)
        self.post_quant_conv = torch.nn.Conv2d(embed_dim, embed_dim, 1)
        self.embed_dim = embed_dim
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path)

    def init_from_ckpt(self, path):
        sd = torch.load(path, map_location="cpu")["model"]
        msg = self.load_state_dict(sd, strict=False)
        print("Loading pre-trained KL-VAE")
        print("Missing keys:")
        print(msg.missing_keys)
        print("Unexpected keys:")
        print(msg.unexpected_keys)
        print(f"Restored from {path}")

    def encode(self, x):
        h = self.encoder(x)
        moments = self.quant_conv(h)
        if not self.use_variational:
            moments = torch.cat((moments, torch.ones_like(moments)), 1)
        posterior = DiagonalGaussianDistribution(moments)
        return posterior

    def decode(self, z):
        z = self.post_quant_conv(z)
        dec = self.decoder(z)
        return dec

    def forward(self, inputs, disable=True, train=True, optimizer_idx=0):
        if train:
            return self.training_step(inputs, disable, optimizer_idx)
        else:
            return self.validation_step(inputs, disable)


if __name__ == "__main__":
    from PIL import Image
    import torch.nn.functional as F

    vae = AutoencoderKL(
        embed_dim=16, ch_mult=(1, 1, 2, 2, 4),
        ckpt_path='checkpoints/kl16.ckpt')

    image = Image.open('data/ILSVRC2012_val_00023344.JPEG')
    image = torch.from_numpy(np.array(image))
    image = image.permute(2, 0, 1).float() / 255
    image = 2 * image - 1

    x = F.interpolate(image[None], size=(256, 256), mode='bilinear', align_corners=True)

    print(x.shape)

    with torch.no_grad():
        z = vae.encode(x).sample()
        print(z.shape)
        x_rec = vae.decode(z)[0]

    x_rec = (x_rec + 1.0) * 255 / 2
    x_rec = torch.clamp(x_rec, min=0, max=255)
    x_rec = x_rec.to(torch.uint8)

    x_rec = x_rec.permute(1, 2, 0)

    x_rec = Image.fromarray(x_rec.numpy())

    x_rec.show()

	import torch
	import torch.nn as nn
	import numpy as np


	def nonlinearity(x):
	# swish
	return x * torch.sigmoid(x)


	def Normalize(in_channels, num_groups=32):
	return torch.nn.GroupNorm(
	num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
	)


	class Upsample(nn.Module):
	def __init__(self, in_channels, with_conv):
	super().__init__()
	self.with_conv = with_conv
	if self.with_conv:
	self.conv = torch.nn.Conv2d(
	in_channels, in_channels, kernel_size=3, stride=1, padding=1
	)

	def forward(self, x):
	x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
	if self.with_conv:
	x = self.conv(x)
	return x


	class Downsample(nn.Module):
	def __init__(self, in_channels, with_conv):
	super().__init__()
	self.with_conv = with_conv
	if self.with_conv:
	# no asymmetric padding in torch conv, must do it ourselves
	self.conv = torch.nn.Conv2d(
	in_channels, in_channels, kernel_size=3, stride=2, padding=0
	)

	def forward(self, x):
	if self.with_conv:
	pad = (0, 1, 0, 1)
	x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
	x = self.conv(x)
	else:
	x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
	return x


	class ResnetBlock(nn.Module):
	def __init__(
	self,
	*,
	in_channels,
	out_channels=None,
	conv_shortcut=False,
	dropout,
	temb_channels=512,
	):
	super().__init__()
	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.norm1 = Normalize(in_channels)
	self.conv1 = torch.nn.Conv2d(
	in_channels, out_channels, kernel_size=3, stride=1, padding=1
	)
	if temb_channels > 0:
	self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
	self.norm2 = Normalize(out_channels)
	self.dropout = torch.nn.Dropout(dropout)
	self.conv2 = torch.nn.Conv2d(
	out_channels, out_channels, kernel_size=3, stride=1, padding=1
	)
	if self.in_channels != self.out_channels:
	if self.use_conv_shortcut:
	self.conv_shortcut = torch.nn.Conv2d(
	in_channels, out_channels, kernel_size=3, stride=1, padding=1
	)
	else:
	self.nin_shortcut = torch.nn.Conv2d(
	in_channels, out_channels, kernel_size=1, stride=1, padding=0
	)

	def forward(self, x, temb):
	h = x
	h = self.norm1(h)
	h = nonlinearity(h)
	h = self.conv1(h)

	if temb is not None:
	h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]

	h = self.norm2(h)
	h = nonlinearity(h)
	h = self.dropout(h)
	h = self.conv2(h)

	if self.in_channels != self.out_channels:
	if self.use_conv_shortcut:
	x = self.conv_shortcut(x)
	else:
	x = self.nin_shortcut(x)

	return x + h


	class AttnBlock(nn.Module):
	def __init__(self, in_channels):
	super().__init__()
	self.in_channels = in_channels

	self.norm = Normalize(in_channels)
	self.q = torch.nn.Conv2d(
	in_channels, in_channels, kernel_size=1, stride=1, padding=0
	)
	self.k = torch.nn.Conv2d(
	in_channels, in_channels, kernel_size=1, stride=1, padding=0
	)
	self.v = torch.nn.Conv2d(
	in_channels, in_channels, kernel_size=1, stride=1, padding=0
	)
	self.proj_out = torch.nn.Conv2d(
	in_channels, in_channels, kernel_size=1, stride=1, padding=0
	)

	def forward(self, x):
	h_ = x
	h_ = self.norm(h_)
	q = self.q(h_)
	k = self.k(h_)
	v = self.v(h_)

	# compute attention
	b, c, h, w = q.shape
	q = q.reshape(b, c, h * w)
	q = q.permute(0, 2, 1) # b,hw,c
	k = k.reshape(b, c, h * w) # b,c,hw
	w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
	w_ = w_ * (int(c) ** (-0.5))
	w_ = torch.nn.functional.softmax(w_, dim=2)

	# attend to values
	v = v.reshape(b, c, h * w)
	w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
	h_ = torch.bmm(v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
	h_ = h_.reshape(b, c, h, w)

	h_ = self.proj_out(h_)

	return x + h_


	class Encoder(nn.Module):
	def __init__(
	self,
	*,
	ch=128,
	out_ch=3,
	ch_mult=(1, 1, 2, 2, 4),
	num_res_blocks=2,
	attn_resolutions=(16,),
	dropout=0.0,
	resamp_with_conv=True,
	in_channels=3,
	resolution=256,
	z_channels=16,
	double_z=True,
	**ignore_kwargs,
	):
	super().__init__()
	self.ch = ch
	self.temb_ch = 0
	self.num_resolutions = len(ch_mult)
	self.num_res_blocks = num_res_blocks
	self.resolution = resolution
	self.in_channels = in_channels

	# downsampling
	self.conv_in = torch.nn.Conv2d(
	in_channels, self.ch, kernel_size=3, stride=1, padding=1
	)

	curr_res = resolution
	in_ch_mult = (1,) + tuple(ch_mult)
	self.down = nn.ModuleList()
	for i_level in range(self.num_resolutions):
	block = nn.ModuleList()
	attn = nn.ModuleList()
	block_in = ch * in_ch_mult[i_level]
	block_out = ch * ch_mult[i_level]
	for i_block in range(self.num_res_blocks):
	block.append(
	ResnetBlock(
	in_channels=block_in,
	out_channels=block_out,
	temb_channels=self.temb_ch,
	dropout=dropout,
	)
	)
	block_in = block_out
	if curr_res in attn_resolutions:
	attn.append(AttnBlock(block_in))
	down = nn.Module()
	down.block = block
	down.attn = attn
	if i_level != self.num_resolutions - 1:
	down.downsample = Downsample(block_in, resamp_with_conv)
	curr_res = curr_res // 2
	self.down.append(down)

	# middle
	self.mid = nn.Module()
	self.mid.block_1 = ResnetBlock(
	in_channels=block_in,
	out_channels=block_in,
	temb_channels=self.temb_ch,
	dropout=dropout,
	)
	self.mid.attn_1 = AttnBlock(block_in)
	self.mid.block_2 = ResnetBlock(
	in_channels=block_in,
	out_channels=block_in,
	temb_channels=self.temb_ch,
	dropout=dropout,
	)

	# end
	self.norm_out = Normalize(block_in)
	self.conv_out = torch.nn.Conv2d(
	block_in,
	2 * z_channels if double_z else z_channels,
	kernel_size=3,
	stride=1,
	padding=1,
	)

	def forward(self, x):
	# assert x.shape[2] == x.shape[3] == self.resolution, "{}, {}, {}".format(x.shape[2], x.shape[3], self.resolution)

	# timestep embedding
	temb = None

	# downsampling
	hs = [self.conv_in(x)]
	for i_level in range(self.num_resolutions):
	for i_block in range(self.num_res_blocks):
	h = self.down[i_level].block[i_block](hs[-1], temb)
	if len(self.down[i_level].attn) > 0:
	h = self.down[i_level].attn[i_block](h)
	hs.append(h)
	if i_level != self.num_resolutions - 1:
	hs.append(self.down[i_level].downsample(hs[-1]))

	# middle
	h = hs[-1]
	h = self.mid.block_1(h, temb)
	h = self.mid.attn_1(h)
	h = self.mid.block_2(h, temb)

	# end
	h = self.norm_out(h)
	h = nonlinearity(h)
	h = self.conv_out(h)
	return h


	class Decoder(nn.Module):
	def __init__(
	self,
	*,
	ch=128,
	out_ch=3,
	ch_mult=(1, 1, 2, 2, 4),
	num_res_blocks=2,
	attn_resolutions=(),
	dropout=0.0,
	resamp_with_conv=True,
	in_channels=3,
	resolution=256,
	z_channels=16,
	give_pre_end=False,
	**ignore_kwargs,
	):
	super().__init__()
	self.ch = ch
	self.temb_ch = 0
	self.num_resolutions = len(ch_mult)
	self.num_res_blocks = num_res_blocks
	self.resolution = resolution
	self.in_channels = in_channels
	self.give_pre_end = give_pre_end

	# compute in_ch_mult, block_in and curr_res at lowest res
	in_ch_mult = (1,) + tuple(ch_mult)
	block_in = ch * ch_mult[self.num_resolutions - 1]
	curr_res = resolution // 2 ** (self.num_resolutions - 1)
	self.z_shape = (1, z_channels, curr_res, curr_res)
	print(
	"Working with z of shape {} = {} dimensions.".format(
	self.z_shape, np.prod(self.z_shape)
	)
	)

	# z to block_in
	self.conv_in = torch.nn.Conv2d(
	z_channels, block_in, kernel_size=3, stride=1, padding=1
	)

	# middle
	self.mid = nn.Module()
	self.mid.block_1 = ResnetBlock(
	in_channels=block_in,
	out_channels=block_in,
	temb_channels=self.temb_ch,
	dropout=dropout,
	)
	self.mid.attn_1 = AttnBlock(block_in)
	self.mid.block_2 = ResnetBlock(
	in_channels=block_in,
	out_channels=block_in,
	temb_channels=self.temb_ch,
	dropout=dropout,
	)

	# upsampling
	self.up = nn.ModuleList()
	for i_level in reversed(range(self.num_resolutions)):
	block = nn.ModuleList()
	attn = nn.ModuleList()
	block_out = ch * ch_mult[i_level]
	for i_block in range(self.num_res_blocks + 1):
	block.append(
	ResnetBlock(
	in_channels=block_in,
	out_channels=block_out,
	temb_channels=self.temb_ch,
	dropout=dropout,
	)
	)
	block_in = block_out
	if curr_res in attn_resolutions:
	attn.append(AttnBlock(block_in))
	up = nn.Module()
	up.block = block
	up.attn = attn
	if i_level != 0:
	up.upsample = Upsample(block_in, resamp_with_conv)
	curr_res = curr_res * 2
	self.up.insert(0, up) # prepend to get consistent order

	# end
	self.norm_out = Normalize(block_in)
	self.conv_out = torch.nn.Conv2d(
	block_in, out_ch, kernel_size=3, stride=1, padding=1
	)

	def forward(self, z):
	# assert z.shape[1:] == self.z_shape[1:]
	self.last_z_shape = z.shape

	# timestep embedding
	temb = None

	# z to block_in
	h = self.conv_in(z)

	# middle
	h = self.mid.block_1(h, temb)
	h = self.mid.attn_1(h)
	h = self.mid.block_2(h, temb)

	# upsampling
	for i_level in reversed(range(self.num_resolutions)):
	for i_block in range(self.num_res_blocks + 1):
	h = self.up[i_level].block[i_block](h, temb)
	if len(self.up[i_level].attn) > 0:
	h = self.up[i_level].attn[i_block](h)
	if i_level != 0:
	h = self.up[i_level].upsample(h)

	# end
	if self.give_pre_end:
	return h

	h = self.norm_out(h)
	h = nonlinearity(h)
	h = self.conv_out(h)
	return h


	class DiagonalGaussianDistribution(object):
	def __init__(self, parameters, deterministic=False):
	self.parameters = parameters
	self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
	self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
	self.deterministic = deterministic
	self.std = torch.exp(0.5 * self.logvar)
	self.var = torch.exp(self.logvar)
	if self.deterministic:
	self.var = self.std = torch.zeros_like(self.mean).to(
	device=self.parameters.device
	)

	def sample(self):
	x = self.mean + self.std * torch.randn(self.mean.shape).to(
	device=self.parameters.device
	)
	return x

	def kl(self, other=None):
	if self.deterministic:
	return torch.Tensor([0.0])
	else:
	if other is None:
	return 0.5 * torch.sum(
	torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
	dim=[1, 2, 3],
	)
	else:
	return 0.5 * torch.sum(
	torch.pow(self.mean - other.mean, 2) / other.var
	+ self.var / other.var
	- 1.0
	- self.logvar
	+ other.logvar,
	dim=[1, 2, 3],
	)

	def nll(self, sample, dims=[1, 2, 3]):
	if self.deterministic:
	return torch.Tensor([0.0])
	logtwopi = np.log(2.0 * np.pi)
	return 0.5 * torch.sum(
	logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
	dim=dims,
	)

	def mode(self):
	return self.mean


	class AutoencoderKL(nn.Module):
	def __init__(self, embed_dim, ch_mult, use_variational=True, ckpt_path=None):
	super().__init__()
	self.encoder = Encoder(ch_mult=ch_mult, z_channels=embed_dim)
	self.decoder = Decoder(ch_mult=ch_mult, z_channels=embed_dim)
	self.use_variational = use_variational
	mult = 2 if self.use_variational else 1
	self.quant_conv = torch.nn.Conv2d(2 * embed_dim, mult * embed_dim, 1)
	self.post_quant_conv = torch.nn.Conv2d(embed_dim, embed_dim, 1)
	self.embed_dim = embed_dim
	if ckpt_path is not None:
	self.init_from_ckpt(ckpt_path)

	def init_from_ckpt(self, path):
	sd = torch.load(path, map_location="cpu")["model"]
	msg = self.load_state_dict(sd, strict=False)
	print("Loading pre-trained KL-VAE")
	print("Missing keys:")
	print(msg.missing_keys)
	print("Unexpected keys:")
	print(msg.unexpected_keys)
	print(f"Restored from {path}")

	def encode(self, x):
	h = self.encoder(x)
	moments = self.quant_conv(h)
	if not self.use_variational:
	moments = torch.cat((moments, torch.ones_like(moments)), 1)
	posterior = DiagonalGaussianDistribution(moments)
	return posterior

	def decode(self, z):
	z = self.post_quant_conv(z)
	dec = self.decoder(z)
	return dec

	def forward(self, inputs, disable=True, train=True, optimizer_idx=0):
	if train:
	return self.training_step(inputs, disable, optimizer_idx)
	else:
	return self.validation_step(inputs, disable)


	if __name__ == "__main__":
	from PIL import Image
	import torch.nn.functional as F

	vae = AutoencoderKL(
	embed_dim=16, ch_mult=(1, 1, 2, 2, 4),
	ckpt_path='checkpoints/kl16.ckpt')

	image = Image.open('data/ILSVRC2012_val_00023344.JPEG')
	image = torch.from_numpy(np.array(image))
	image = image.permute(2, 0, 1).float() / 255
	image = 2 * image - 1

	x = F.interpolate(image[None], size=(256, 256), mode='bilinear', align_corners=True)

	print(x.shape)

	with torch.no_grad():
	z = vae.encode(x).sample()
	print(z.shape)
	x_rec = vae.decode(z)[0]

	x_rec = (x_rec + 1.0) * 255 / 2
	x_rec = torch.clamp(x_rec, min=0, max=255)
	x_rec = x_rec.to(torch.uint8)

	x_rec = x_rec.permute(1, 2, 0)

	x_rec = Image.fromarray(x_rec.numpy())

	x_rec.show()