sat/sgm/modules/autoencoding/vqvae/movq_dec_3d.py [10:233]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def cast_tuple(t, length=1):
    return t if isinstance(t, tuple) else ((t,) * length)


def divisible_by(num, den):
    return (num % den) == 0


def is_odd(n):
    return not divisible_by(n, 2)


def get_timestep_embedding(timesteps, embedding_dim):
    """
    This matches the implementation in Denoising Diffusion Probabilistic Models:
    From Fairseq.
    Build sinusoidal embeddings.
    This matches the implementation in tensor2tensor, but differs slightly
    from the description in Section 3.5 of "Attention Is All You Need".
    """
    assert len(timesteps.shape) == 1

    half_dim = embedding_dim // 2
    emb = math.log(10000) / (half_dim - 1)
    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
    emb = emb.to(device=timesteps.device)
    emb = timesteps.float()[:, None] * emb[None, :]
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
    if embedding_dim % 2 == 1:  # zero pad
        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
    return emb


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


class SpatialNorm3D(nn.Module):
    def __init__(
        self,
        f_channels,
        zq_channels,
        norm_layer=nn.GroupNorm,
        freeze_norm_layer=False,
        add_conv=False,
        pad_mode="constant",
        **norm_layer_params,
    ):
        super().__init__()
        self.norm_layer = norm_layer(num_channels=f_channels, **norm_layer_params)
        if freeze_norm_layer:
            for p in self.norm_layer.parameters:
                p.requires_grad = False
        self.add_conv = add_conv
        if self.add_conv:
            self.conv = CausalConv3d(zq_channels, zq_channels, kernel_size=3, pad_mode=pad_mode)
        self.conv_y = CausalConv3d(zq_channels, f_channels, kernel_size=1, pad_mode=pad_mode)
        self.conv_b = CausalConv3d(zq_channels, f_channels, kernel_size=1, pad_mode=pad_mode)

    def forward(self, f, zq):
        if zq.shape[2] > 1:
            f_first, f_rest = f[:, :, :1], f[:, :, 1:]
            f_first_size, f_rest_size = f_first.shape[-3:], f_rest.shape[-3:]
            zq_first, zq_rest = zq[:, :, :1], zq[:, :, 1:]
            zq_first = torch.nn.functional.interpolate(zq_first, size=f_first_size, mode="nearest")
            zq_rest = torch.nn.functional.interpolate(zq_rest, size=f_rest_size, mode="nearest")
            zq = torch.cat([zq_first, zq_rest], dim=2)
        else:
            zq = torch.nn.functional.interpolate(zq, size=f.shape[-3:], mode="nearest")
        if self.add_conv:
            zq = self.conv(zq)
        norm_f = self.norm_layer(f)
        new_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
        return new_f


def Normalize3D(in_channels, zq_ch, add_conv):
    return SpatialNorm3D(
        in_channels,
        zq_ch,
        norm_layer=nn.GroupNorm,
        freeze_norm_layer=False,
        add_conv=add_conv,
        num_groups=32,
        eps=1e-6,
        affine=True,
    )


class ResnetBlock3D(nn.Module):
    def __init__(
        self,
        *,
        in_channels,
        out_channels=None,
        conv_shortcut=False,
        dropout,
        temb_channels=512,
        zq_ch=None,
        add_conv=False,
        pad_mode="constant",
    ):
        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 = Normalize3D(in_channels, zq_ch, add_conv=add_conv)
        self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, pad_mode=pad_mode)
        if temb_channels > 0:
            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
        self.norm2 = Normalize3D(out_channels, zq_ch, add_conv=add_conv)
        self.dropout = torch.nn.Dropout(dropout)
        self.conv2 = CausalConv3d(out_channels, out_channels, kernel_size=3, pad_mode=pad_mode)
        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                self.conv_shortcut = CausalConv3d(in_channels, out_channels, kernel_size=3, pad_mode=pad_mode)
            else:
                self.nin_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)

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

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

        h = self.norm2(h, zq)
        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 AttnBlock2D(nn.Module):
    def __init__(self, in_channels, zq_ch=None, add_conv=False):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize3D(in_channels, zq_ch, add_conv=add_conv)
        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, zq):
        h_ = x
        h_ = self.norm(h_, zq)

        t = h_.shape[2]
        h_ = rearrange(h_, "b c t h w -> (b t) c h w")

        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_)

        h_ = rearrange(h_, "(b t) c h w -> b c t h w", t=t)

        return x + h_


class MOVQDecoder3D(nn.Module):
    def __init__(
        self,
        *,
        ch,
        out_ch,
        ch_mult=(1, 2, 4, 8),
        num_res_blocks,
        attn_resolutions,
        dropout=0.0,
        resamp_with_conv=True,
        in_channels,
        resolution,
        z_channels,
        give_pre_end=False,
        zq_ch=None,
        add_conv=False,
        pad_mode="first",
        temporal_compress_times=4,
        **ignorekwargs,
    ):
        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

        # log2 of temporal_compress_times
        self.temporal_compress_level = int(np.log2(temporal_compress_times))

        if zq_ch is None:
            zq_ch = z_channels
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



sat/sgm/modules/autoencoding/vqvae/movq_dec_3d_dev.py [15:257]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def cast_tuple(t, length=1):
    return t if isinstance(t, tuple) else ((t,) * length)


def divisible_by(num, den):
    return (num % den) == 0


def is_odd(n):
    return not divisible_by(n, 2)


def get_timestep_embedding(timesteps, embedding_dim):
    """
    This matches the implementation in Denoising Diffusion Probabilistic Models:
    From Fairseq.
    Build sinusoidal embeddings.
    This matches the implementation in tensor2tensor, but differs slightly
    from the description in Section 3.5 of "Attention Is All You Need".
    """
    assert len(timesteps.shape) == 1

    half_dim = embedding_dim // 2
    emb = math.log(10000) / (half_dim - 1)
    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
    emb = emb.to(device=timesteps.device)
    emb = timesteps.float()[:, None] * emb[None, :]
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
    if embedding_dim % 2 == 1:  # zero pad
        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
    return emb


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


class SpatialNorm3D(nn.Module):
    def __init__(
        self,
        f_channels,
        zq_channels,
        norm_layer=nn.GroupNorm,
        freeze_norm_layer=False,
        add_conv=False,
        pad_mode="constant",
        **norm_layer_params,
    ):
        super().__init__()
        self.norm_layer = norm_layer(num_channels=f_channels, **norm_layer_params)
        if freeze_norm_layer:
            for p in self.norm_layer.parameters:
                p.requires_grad = False
        self.add_conv = add_conv
        if self.add_conv:
            # self.conv = nn.Conv3d(zq_channels, zq_channels, kernel_size=3, stride=1, padding=1)
            self.conv = CausalConv3d(zq_channels, zq_channels, kernel_size=3, pad_mode=pad_mode)
        # self.conv_y = nn.Conv3d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
        # self.conv_b = nn.Conv3d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
        self.conv_y = CausalConv3d(zq_channels, f_channels, kernel_size=1, pad_mode=pad_mode)
        self.conv_b = CausalConv3d(zq_channels, f_channels, kernel_size=1, pad_mode=pad_mode)

    def forward(self, f, zq):
        if zq.shape[2] > 1:
            f_first, f_rest = f[:, :, :1], f[:, :, 1:]
            f_first_size, f_rest_size = f_first.shape[-3:], f_rest.shape[-3:]
            zq_first, zq_rest = zq[:, :, :1], zq[:, :, 1:]
            zq_first = torch.nn.functional.interpolate(zq_first, size=f_first_size, mode="nearest")
            zq_rest = torch.nn.functional.interpolate(zq_rest, size=f_rest_size, mode="nearest")
            zq = torch.cat([zq_first, zq_rest], dim=2)
        else:
            zq = torch.nn.functional.interpolate(zq, size=f.shape[-3:], mode="nearest")
        if self.add_conv:
            zq = self.conv(zq)
        norm_f = self.norm_layer(f)
        new_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
        return new_f


def Normalize3D(in_channels, zq_ch, add_conv):
    return SpatialNorm3D(
        in_channels,
        zq_ch,
        norm_layer=nn.GroupNorm,
        freeze_norm_layer=False,
        add_conv=add_conv,
        num_groups=32,
        eps=1e-6,
        affine=True,
    )


class ResnetBlock3D(nn.Module):
    def __init__(
        self,
        *,
        in_channels,
        out_channels=None,
        conv_shortcut=False,
        dropout,
        temb_channels=512,
        zq_ch=None,
        add_conv=False,
        pad_mode="constant",
    ):
        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 = Normalize3D(in_channels, zq_ch, add_conv=add_conv)
        # self.conv1 = torch.nn.Conv3d(in_channels,
        #                              out_channels,
        #                              kernel_size=3,
        #                              stride=1,
        #                              padding=1)
        self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, pad_mode=pad_mode)
        if temb_channels > 0:
            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
        self.norm2 = Normalize3D(out_channels, zq_ch, add_conv=add_conv)
        self.dropout = torch.nn.Dropout(dropout)
        # self.conv2 = torch.nn.Conv3d(out_channels,
        #                              out_channels,
        #                              kernel_size=3,
        #                              stride=1,
        #                              padding=1)
        self.conv2 = CausalConv3d(out_channels, out_channels, kernel_size=3, pad_mode=pad_mode)
        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                # self.conv_shortcut = torch.nn.Conv3d(in_channels,
                #                                      out_channels,
                #                                      kernel_size=3,
                #                                      stride=1,
                #                                      padding=1)
                self.conv_shortcut = CausalConv3d(in_channels, out_channels, kernel_size=3, pad_mode=pad_mode)
            else:
                self.nin_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
                # self.nin_shortcut = CausalConv3d(in_channels, out_channels, kernel_size=1, pad_mode=pad_mode)

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

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

        h = self.norm2(h, zq)
        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 AttnBlock2D(nn.Module):
    def __init__(self, in_channels, zq_ch=None, add_conv=False):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize3D(in_channels, zq_ch, add_conv=add_conv)
        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, zq):
        h_ = x
        h_ = self.norm(h_, zq)

        t = h_.shape[2]
        h_ = rearrange(h_, "b c t h w -> (b t) c h w")

        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_)

        h_ = rearrange(h_, "(b t) c h w -> b c t h w", t=t)

        return x + h_


class MOVQDecoder3D(nn.Module):
    def __init__(
        self,
        *,
        ch,
        out_ch,
        ch_mult=(1, 2, 4, 8),
        num_res_blocks,
        attn_resolutions,
        dropout=0.0,
        resamp_with_conv=True,
        in_channels,
        resolution,
        z_channels,
        give_pre_end=False,
        zq_ch=None,
        add_conv=False,
        pad_mode="first",
        temporal_compress_times=4,
        **ignorekwargs,
    ):
        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

        # log2 of temporal_compress_times
        self.temporal_compress_level = int(np.log2(temporal_compress_times))

        if zq_ch is None:
            zq_ch = z_channels
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -