# pytorch_diffusion + derived encoder decoder import math import torch import torch.nn as nn import numpy as np from einops import rearrange from ..basic import CircularConv2d from ...utils.misc_utils import instantiate_from_config from ...modules.attention import LinearAttention 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) def Normalize(in_channels, num_groups=32): return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) UPSAMPLE_STRIDE2KERNEL_DICT = {(1, 2): (1, 5), (1, 4): (1, 7), (2, 1): (5, 1), (2, 2): (3, 3)} UPSAMPLE_STRIDE2PAD_DICT = {(1, 2): (2, 2, 0, 0), (1, 4): (3, 3, 0, 0), (2, 1): (0, 0, 2, 2), (2, 2): (1, 1, 1, 1)} class Upsample(nn.Module): def __init__(self, in_channels, with_conv, stride): super().__init__() self.with_conv = with_conv self.stride = stride if self.with_conv: k, p = UPSAMPLE_STRIDE2KERNEL_DICT[stride], UPSAMPLE_STRIDE2PAD_DICT[stride] self.conv = CircularConv2d(in_channels, in_channels, kernel_size=k, padding=p) def forward(self, x): x = torch.nn.functional.interpolate(x, scale_factor=self.stride, mode='bilinear', align_corners=True) if self.with_conv: x = self.conv(x) return x DOWNSAMPLE_STRIDE2KERNEL_DICT = {(1, 2): (3, 3), (1, 4): (3, 5), (2, 1): (3, 3), (2, 2): (3, 3)} DOWNSAMPLE_STRIDE2PAD_DICT = {(1, 2): (0, 1, 1, 1), (1, 4): (1, 1, 1, 1), (2, 1): (1, 1, 1, 1), (2, 2): (0, 1, 0, 1)} class Downsample(nn.Module): def __init__(self, in_channels, with_conv, stride): super().__init__() self.with_conv = with_conv self.stride = stride if self.with_conv: k, p = DOWNSAMPLE_STRIDE2KERNEL_DICT[stride], DOWNSAMPLE_STRIDE2PAD_DICT[stride] self.conv = CircularConv2d(in_channels, in_channels, kernel_size=k, stride=stride, padding=p) def forward(self, x): if self.with_conv: x = self.conv(x) else: x = torch.nn.functional.avg_pool2d(x, kernel_size=self.stride, stride=self.stride) # modified for lidar return x UNIFORM_KERNEL2PAD_DICT = {(3, 3): (1, 1, 1, 1), (1, 4): (1, 2, 0, 0)} class ResnetBlock(nn.Module): def __init__(self, *, in_channels, out_channels=None, kernel_size=(3, 3), 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 pad = UNIFORM_KERNEL2PAD_DICT[kernel_size] self.norm1 = Normalize(in_channels) self.conv1 = CircularConv2d(in_channels, out_channels, kernel_size=kernel_size, stride=1, padding=pad) 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 = CircularConv2d(out_channels, out_channels, kernel_size=kernel_size, stride=1, padding=pad) if self.in_channels != self.out_channels: if self.use_conv_shortcut: self.conv_shortcut = CircularConv2d(in_channels, out_channels, kernel_size=kernel_size, stride=1, padding=pad) 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 LinAttnBlock(LinearAttention): """to match AttnBlock usage""" def __init__(self, in_channels): super().__init__(dim=in_channels, heads=1, dim_head=in_channels) 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_ def make_attn(in_channels, attn_type="vanilla"): assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown' # print(f"making attention of type '{attn_type}' with {in_channels} in_channels") if attn_type == "vanilla": return AttnBlock(in_channels) elif attn_type == "none": return nn.Identity(in_channels) else: return LinAttnBlock(in_channels) class Encoder(nn.Module): def __init__(self, *, ch, out_ch, ch_mult, strides, num_res_blocks, attn_levels, dropout=0.0, resamp_with_conv=True, in_channels, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", use_mask=False, **ignore_kwargs): super().__init__() if use_mask: assert out_ch == in_channels + 1, 'Set "out_ch = out_ch + 1" for mask prediction.' if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.in_channels = in_channels # downsampling self.conv_in = CircularConv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) in_ch_mult = (1,) + tuple(ch_mult) self.in_ch_mult = in_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 i_level in attn_levels: attn.append(make_attn(block_in, attn_type=attn_type)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions - 1: stride = tuple(strides[i_level]) down.downsample = Downsample(block_in, resamp_with_conv, stride) 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 = make_attn(block_in, attn_type=attn_type) 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 = CircularConv2d(block_in, 2 * z_channels if double_z else z_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): # 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, out_ch, ch_mult, strides, num_res_blocks, attn_levels, dropout=0.0, resamp_with_conv=True, in_channels, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, attn_type="vanilla", use_mask=False, **ignorekwargs): super().__init__() stride2kernel = {(2, 2): (3, 3), (1, 2): (1, 4)} if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.in_channels = in_channels self.give_pre_end = give_pre_end self.tanh_out = tanh_out # compute in_ch_mult, block_in and curr_res at lowest res block_in = ch * ch_mult[self.num_resolutions - 1] # z to block_in self.conv_in = CircularConv2d(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 = make_attn(block_in, attn_type=attn_type) 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)): stride = tuple(strides[i_level - 1]) if i_level > 0 else None kernel = stride2kernel[stride] if stride is not None else (1, 4) 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, kernel_size=kernel, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if i_level in attn_levels: attn.append(make_attn(block_in, attn_type=attn_type)) up = nn.Module() up.block = block up.attn = attn if stride is not None: up.upsample = Upsample(block_in, resamp_with_conv, stride) self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = Normalize(block_in) self.conv_out = CircularConv2d(block_in, out_ch, kernel_size=(1, 4), stride=1, padding=(1, 2, 0, 0)) def forward(self, z): 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) if self.tanh_out: h = torch.tanh(h) return h class SimpleDecoder(nn.Module): def __init__(self, in_channels, out_channels, *args, **kwargs): super().__init__() self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), ResnetBlock(in_channels=in_channels, out_channels=2 * in_channels, temb_channels=0, dropout=0.0), ResnetBlock(in_channels=2 * in_channels, out_channels=4 * in_channels, temb_channels=0, dropout=0.0), ResnetBlock(in_channels=4 * in_channels, out_channels=2 * in_channels, temb_channels=0, dropout=0.0), nn.Conv2d(2 * in_channels, in_channels, 1), Upsample(in_channels, with_conv=True)]) # end self.norm_out = Normalize(in_channels) self.conv_out = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): for i, layer in enumerate(self.model): if i in [1, 2, 3]: x = layer(x, None) else: x = layer(x) h = self.norm_out(x) h = nonlinearity(h) x = self.conv_out(h) return x class UpsampleDecoder(nn.Module): def __init__(self, in_channels, out_channels, ch, num_res_blocks, ch_mult=(2, 2), dropout=0.0): super().__init__() # upsampling self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks block_in = in_channels self.res_blocks = nn.ModuleList() self.upsample_blocks = nn.ModuleList() for i_level in range(self.num_resolutions): res_block = [] block_out = ch * ch_mult[i_level] for i_block in range(self.num_res_blocks + 1): res_block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out self.res_blocks.append(nn.ModuleList(res_block)) if i_level != self.num_resolutions - 1: self.upsample_blocks.append(Upsample(block_in, True)) # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d(block_in, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): # upsampling h = x for k, i_level in enumerate(range(self.num_resolutions)): for i_block in range(self.num_res_blocks + 1): h = self.res_blocks[i_level][i_block](h, None) if i_level != self.num_resolutions - 1: h = self.upsample_blocks[k](h) h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h class LatentRescaler(nn.Module): def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): super().__init__() # residual block, interpolate, residual block self.factor = factor self.conv_in = nn.Conv2d(in_channels, mid_channels, kernel_size=3, stride=1, padding=1) self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, out_channels=mid_channels, temb_channels=0, dropout=0.0) for _ in range(depth)]) self.attn = AttnBlock(mid_channels) self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, out_channels=mid_channels, temb_channels=0, dropout=0.0) for _ in range(depth)]) self.conv_out = nn.Conv2d(mid_channels, out_channels, kernel_size=1, ) def forward(self, x): x = self.conv_in(x) for block in self.res_block1: x = block(x, None) x = torch.nn.functional.interpolate(x, size=( int(round(x.shape[2] * self.factor)), int(round(x.shape[3] * self.factor)))) x = self.attn(x) for block in self.res_block2: x = block(x, None) x = self.conv_out(x) return x class MergedRescaleEncoder(nn.Module): def __init__(self, in_channels, ch, out_ch, num_res_blocks, attn_levels, dropout=0.0, resamp_with_conv=True, ch_mult=(1, 2, 4, 8), rescale_factor=1.0, rescale_module_depth=1): super().__init__() intermediate_chn = ch * ch_mult[-1] self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, z_channels=intermediate_chn, double_z=False, attn_levels=attn_levels, dropout=dropout, resamp_with_conv=resamp_with_conv, out_ch=None) self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) def forward(self, x): x = self.encoder(x) x = self.rescaler(x) return x class MergedRescaleDecoder(nn.Module): def __init__(self, z_channels, out_ch, num_res_blocks, attn_levels, ch, ch_mult=(1, 2, 4, 8), dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): super().__init__() tmp_chn = z_channels * ch_mult[-1] self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_levels=attn_levels, dropout=dropout, resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, ch_mult=ch_mult, ch=ch) self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, out_channels=tmp_chn, depth=rescale_module_depth) def forward(self, x): x = self.rescaler(x) x = self.decoder(x) return x class Upsampler(nn.Module): def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): super().__init__() assert out_size >= in_size num_blocks = int(np.log2(out_size // in_size)) + 1 factor_up = 1. + (out_size % in_size) print( f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2 * in_channels, out_channels=in_channels) self.decoder = Decoder(out_ch=out_channels, z_channels=in_channels, num_res_blocks=2, attn_levels=[], in_channels=None, ch=in_channels, ch_mult=[ch_mult for _ in range(num_blocks)]) def forward(self, x): x = self.rescaler(x) x = self.decoder(x) return x class Resize(nn.Module): def __init__(self, in_channels=None, learned=False, mode="bilinear"): super().__init__() self.with_conv = learned self.mode = mode if self.with_conv: print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") raise NotImplementedError() assert in_channels is not None # no asymmetric padding in torch conv, must do it ourselves self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=4, stride=2, padding=1) def forward(self, x, scale_factor=1.0): if scale_factor == 1.0: return x else: x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) return x class FirstStagePostProcessor(nn.Module): def __init__(self, ch_mult: list, in_channels, pretrained_model: nn.Module = None, reshape=False, n_channels=None, dropout=0., pretrained_config=None): super().__init__() if pretrained_config is None: assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' self.pretrained_model = pretrained_model else: assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' self.instantiate_pretrained(pretrained_config) self.do_reshape = reshape if n_channels is None: n_channels = self.pretrained_model.encoder.ch self.proj_norm = Normalize(in_channels, num_groups=in_channels // 2) self.proj = nn.Conv2d(in_channels, n_channels, kernel_size=3, stride=1, padding=1) blocks = [] downs = [] ch_in = n_channels for m in ch_mult: blocks.append(ResnetBlock(in_channels=ch_in, out_channels=m * n_channels, dropout=dropout)) ch_in = m * n_channels downs.append(Downsample(ch_in, with_conv=False)) self.model = nn.ModuleList(blocks) self.downsampler = nn.ModuleList(downs) def instantiate_pretrained(self, config): model = instantiate_from_config(config) self.pretrained_model = model.eval() # self.pretrained_model.train = False for param in self.pretrained_model.parameters(): param.requires_grad = False @torch.no_grad() def encode_with_pretrained(self, x): c = self.pretrained_model.encode(x) if isinstance(c, DiagonalGaussianDistribution): c = c.mode() return c def forward(self, x): z_fs = self.encode_with_pretrained(x) z = self.proj_norm(z_fs) z = self.proj(z) z = nonlinearity(z) for submodel, downmodel in zip(self.model, self.downsampler): z = submodel(z, temb=None) z = downmodel(z) if self.do_reshape: z = rearrange(z, 'b c h w -> b (h w) c') return z