fine tune decoder with mask

9cf79cf over 2 years ago

22.6 kB

# pytorch_diffusion + derived encoder decoder
import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
import torch.nn.functional as F


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):
    return torch.nn.GroupNorm(num_groups=32, 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,
                                     bias=False)
        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,
                                     bias=False)
        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                self.conv_shortcut = torch.nn.Conv2d(out_channels,
                                                     out_channels,
                                                     kernel_size=3,
                                                     stride=1,
                                                     padding=1,
                                                     bias=False)
            else:
                self.nin_shortcut = torch.nn.Conv2d(out_channels,
                                                    out_channels,
                                                    kernel_size=1,
                                                    stride=1,
                                                    padding=0,
                                                    bias=False)

    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(h)
            else:
                x = self.nin_shortcut(h)

        return x+h


class Encoder(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=False, in_channels,
                 resolution, z_channels, 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,
                                       bias=False)

        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()
            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
            down = nn.Module()
            down.block = block
            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.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=1,
                                        stride=1,
                                        padding=0)


    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)
                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.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=(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, **ignorekwargs):
        super().__init__()
        self.ch = ch    # 128
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult) # 4
        self.num_res_blocks = num_res_blocks # 2
        self.resolution = resolution    # 256
        self.in_channels = in_channels  # 3
        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)    # 没有用
        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,  # 0
                                       dropout=dropout) # 0.0
        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)):   # 4个
            block = nn.ModuleList()
            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))
                # print("i_level=", i_level, "block_in=", block_in, "block_out=", block_out)
                block_in = block_out
            up = nn.Module()
            up.block = block
            if i_level != 0:
                up.upsample = Upsample(block_in, resamp_with_conv)  # Ture
                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.block_2(h, temb)

        # upsampling
        for i_level in reversed(range(self.num_resolutions)):
            for i_block in range(self.num_res_blocks):
                h = self.up[i_level].block[i_block](h, temb)
            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

def init_weights_zero(m):
    if isinstance(m, nn.Conv2d):
        nn.init.constant_(m.weight, 0)
        if m.bias is not None:
            nn.init.constant_(m.bias, 0)

def init_weights_kaiming(m):
    if isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight)
        if m.bias is not None:
            nn.init.constant_(m.bias, 0)

##########################################################################
##---------- Prompt Gen Module -----------------------
class PromptGenBlock(nn.Module):
    def __init__(self, prompt_dim=128, prompt_size=96, prompt_len=1):
        super(PromptGenBlock,self).__init__()
        self.prompt_param = nn.Parameter(torch.rand(1, prompt_len, prompt_dim, prompt_size, prompt_size)) # (1, 1, 128, 96, 96)
        self.conv3x3 = nn.Conv2d(prompt_dim, prompt_dim, kernel_size=3, stride=1, padding=1, bias=False)

    def init_weights_zero(self, m):
        if isinstance(m, nn.Conv2d):
            nn.init.constant_(m.weight, 0)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        B, C, H, W = x.shape
        prompt = self.prompt_param.unsqueeze(0).repeat(B,1,1,1,1,1).squeeze(1)
        prompt = torch.sum(prompt, dim=1)    # (B, prompt_dim, prompt_size, prompt_size)
        prompt = F.interpolate(prompt, (H, W), mode="bilinear")    # 
        prompt = self.conv3x3(prompt)   # (B, prompt_dim, H, W)
        return prompt

class Attention(nn.Module):
    def __init__(self, dim, hidden_dim, bias, prompt_dim=192):
        super(Attention, self).__init__()
        self.shared_mlp = nn.Sequential(
            nn.Conv2d(prompt_dim, hidden_dim, kernel_size=3, stride=1, padding=1, bias=bias)
        )
        self.mlp_gama = nn.Conv2d(hidden_dim, dim, kernel_size=3, stride=1, padding=1, bias=bias)
        self.mlp_beta = nn.Conv2d(hidden_dim, dim, kernel_size=3, stride=1, padding=1, bias=bias)

        self.mlp_gama.apply(init_weights_zero)
        self.mlp_beta.apply(init_weights_zero)


    def init_weights_zero(self, m):
        if isinstance(m, nn.Conv2d):
            nn.init.constant_(m.weight, 0)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
    
    def forward(self, x, prompt):
        b, c, h, w = x.shape
        prompt = self.shared_mlp(prompt)
        prompt = prompt.expand(b, -1, -1, -1)
        # prompt = F.adaptive_avg_pool2d(prompt, x.size()[2:])
        gama = self.mlp_gama(prompt)
        beta = self.mlp_beta(prompt)
        # gama, beta = prompt.chunk(2, dim=1)
        x = x *( 1 + gama) + beta
        return x

class DepthwiseSeparableConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
        super(DepthwiseSeparableConv, self).__init__()

        # Depthwise convolution
        self.depthwise_conv = nn.Conv2d(
            in_channels,
            in_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            groups=in_channels,
            bias=False
        )

        # Pointwise convolution
        self.pointwise_conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=False
        )

    def forward(self, x):
        out = self.depthwise_conv(x)
        out = self.pointwise_conv(out)
        return out

class SFT(nn.Module):
    def __init__(self, x_dim, prompt_dim=192, ks=3, nhidden=128):
        super(SFT, self).__init__()
        pw = ks // 2
        self.mlp_shared = nn.Sequential(
            nn.Conv2d(prompt_dim, nhidden, kernel_size=1),
            nn.ReLU()
        )
        self.mlp_gama = DepthwiseSeparableConv(nhidden, x_dim, kernel_size=ks, padding=pw)
        self.mlp_beta = DepthwiseSeparableConv(nhidden, x_dim, kernel_size=ks, padding=pw)

        # self.mlp_shared.apply(init_weights_zero)  # Initialize shared_mlp
        # self.mlp_gama.apply(init_weights_zero)
        # self.mlp_beta.apply(init_weights_zero)

    def forward(self, x, prompt):
        actv = self.mlp_shared(prompt)
        gama = self.mlp_gama(actv)
        beta = self.mlp_beta(actv)
        # print("gama_max=", gama.max())
        # print("beta_max=", beta.max())
        # print("gama_min=", gama.min())
        # print("beta_min=", beta.min())
        out = x * (1 + gama) + beta
        return out

def init_weights_gama(m):
    if isinstance(m, nn.Conv2d):
        nn.init.constant_(m.weight, 0)
        center = m.kernel_size[0] // 2
        if m.groups == m.in_channels and m.in_channels == m.out_channels:  # depthwise conv
            nn.init.constant_(m.weight[:, :, center, center], 1)
        else:  # pointwise conv
            nn.init.constant_(m.weight, 1)
        if m.bias is not None:
            nn.init.constant_(m.bias, 0)

class Decoder_w_Prompt(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, **ignorekwargs):
        super().__init__()
        self.ch = ch    # 128
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult) # 4
        self.num_res_blocks = num_res_blocks # 2
        self.resolution = resolution    # 256
        self.in_channels = in_channels  # 3
        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)    # 没有用
        block_in = ch*ch_mult[self.num_resolutions-1]   # 128 * 4
        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,  # 0
                                       dropout=dropout) # 0.0
        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)):   # 4,3,2,1,0
            block = nn.ModuleList()
            block_out = ch*ch_mult[i_level] # ch*8
            for i_block in range(self.num_res_blocks):
                block.append(ResnetBlock(in_channels=block_in,  # 512
                                         out_channels=block_out,    # 
                                         temb_channels=self.temb_ch,
                                         dropout=dropout))
                # print("i_level=", i_level, "block_in=", block_in, "block_out=", block_out)
                block_in = block_out
            up = nn.Module()
            up.block = block
            if i_level != 0:
                if i_level == 1:
                    up.prompt = PromptGenBlock(prompt_dim=64, prompt_size=16)
                    up.prompt_attn = Attention(dim=128, hidden_dim=64, bias=True, prompt_dim=64)
                    # up.prompt_sft = SFT(x_dim=128, prompt_dim=128)
                elif i_level == 2:
                    up.prompt = PromptGenBlock(prompt_dim=128, prompt_size=16)
                    # up.prompt_sft = SFT(x_dim=256, prompt_dim=256)
                    up.prompt_attn = Attention(dim=256, hidden_dim=128, bias=True, prompt_dim=128)
                elif i_level == 3:
                    up.prompt = PromptGenBlock(prompt_dim=128, prompt_size=32)
                    # up.prompt_sft = SFT(x_dim=256, prompt_dim=256)
                    up.prompt_attn = Attention(dim=256, hidden_dim=128, bias=True, prompt_dim=128)
                elif i_level == 4:
                    up.prompt = PromptGenBlock(prompt_dim=256, prompt_size=32)
                    # up.prompt_sft = SFT(x_dim=512, prompt_dim=512)
                    up.prompt_attn = Attention(dim=512, hidden_dim=192, bias=True, prompt_dim=256)
                up.upsample = Upsample(block_in, resamp_with_conv)  # Ture
                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) # 256->512

        # middle
        h = self.mid.block_1(h, temb)   # 512
        h = self.mid.block_2(h, temb)   # 512

        # upsampling
        for i_level in reversed(range(self.num_resolutions)):
            for i_block in range(self.num_res_blocks):
                h = self.up[i_level].block[i_block](h, temb)
            if i_level != 0:
                # prompt & attention
                prompt = self.up[i_level].prompt(h)
                # h = self.up[i_level].prompt_sft(h, prompt)
                h = self.up[i_level].prompt_attn(h, prompt)
                h = self.up[i_level].upsample(h)
        # end
        if self.give_pre_end:   # False
            return h

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

	# pytorch_diffusion + derived encoder decoder
	import math
	import torch
	import torch.nn as nn
	import numpy as np
	from einops import rearrange
	import torch.nn.functional as F


	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):
	return torch.nn.GroupNorm(num_groups=32, 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,
	bias=False)
	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,
	bias=False)
	if self.in_channels != self.out_channels:
	if self.use_conv_shortcut:
	self.conv_shortcut = torch.nn.Conv2d(out_channels,
	out_channels,
	kernel_size=3,
	stride=1,
	padding=1,
	bias=False)
	else:
	self.nin_shortcut = torch.nn.Conv2d(out_channels,
	out_channels,
	kernel_size=1,
	stride=1,
	padding=0,
	bias=False)

	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(h)
	else:
	x = self.nin_shortcut(h)

	return x+h


	class Encoder(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=False, in_channels,
	resolution, z_channels, 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,
	bias=False)

	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()
	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
	down = nn.Module()
	down.block = block
	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.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=1,
	stride=1,
	padding=0)


	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)
	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.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=(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, **ignorekwargs):
	super().__init__()
	self.ch = ch # 128
	self.temb_ch = 0
	self.num_resolutions = len(ch_mult) # 4
	self.num_res_blocks = num_res_blocks # 2
	self.resolution = resolution # 256
	self.in_channels = in_channels # 3
	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) # 没有用
	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, # 0
	dropout=dropout) # 0.0
	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)): # 4个
	block = nn.ModuleList()
	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))
	# print("i_level=", i_level, "block_in=", block_in, "block_out=", block_out)
	block_in = block_out
	up = nn.Module()
	up.block = block
	if i_level != 0:
	up.upsample = Upsample(block_in, resamp_with_conv) # Ture
	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.block_2(h, temb)

	# upsampling
	for i_level in reversed(range(self.num_resolutions)):
	for i_block in range(self.num_res_blocks):
	h = self.up[i_level].block[i_block](h, temb)
	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

	def init_weights_zero(m):
	if isinstance(m, nn.Conv2d):
	nn.init.constant_(m.weight, 0)
	if m.bias is not None:
	nn.init.constant_(m.bias, 0)

	def init_weights_kaiming(m):
	if isinstance(m, nn.Conv2d):
	nn.init.kaiming_normal_(m.weight)
	if m.bias is not None:
	nn.init.constant_(m.bias, 0)

	##########################################################################
	##---------- Prompt Gen Module -----------------------
	class PromptGenBlock(nn.Module):
	def __init__(self, prompt_dim=128, prompt_size=96, prompt_len=1):
	super(PromptGenBlock,self).__init__()
	self.prompt_param = nn.Parameter(torch.rand(1, prompt_len, prompt_dim, prompt_size, prompt_size)) # (1, 1, 128, 96, 96)
	self.conv3x3 = nn.Conv2d(prompt_dim, prompt_dim, kernel_size=3, stride=1, padding=1, bias=False)

	def init_weights_zero(self, m):
	if isinstance(m, nn.Conv2d):
	nn.init.constant_(m.weight, 0)
	if m.bias is not None:
	nn.init.constant_(m.bias, 0)

	def forward(self, x):
	B, C, H, W = x.shape
	prompt = self.prompt_param.unsqueeze(0).repeat(B,1,1,1,1,1).squeeze(1)
	prompt = torch.sum(prompt, dim=1) # (B, prompt_dim, prompt_size, prompt_size)
	prompt = F.interpolate(prompt, (H, W), mode="bilinear") #
	prompt = self.conv3x3(prompt) # (B, prompt_dim, H, W)
	return prompt

	class Attention(nn.Module):
	def __init__(self, dim, hidden_dim, bias, prompt_dim=192):
	super(Attention, self).__init__()
	self.shared_mlp = nn.Sequential(
	nn.Conv2d(prompt_dim, hidden_dim, kernel_size=3, stride=1, padding=1, bias=bias)
	)
	self.mlp_gama = nn.Conv2d(hidden_dim, dim, kernel_size=3, stride=1, padding=1, bias=bias)
	self.mlp_beta = nn.Conv2d(hidden_dim, dim, kernel_size=3, stride=1, padding=1, bias=bias)

	self.mlp_gama.apply(init_weights_zero)
	self.mlp_beta.apply(init_weights_zero)


	def init_weights_zero(self, m):
	if isinstance(m, nn.Conv2d):
	nn.init.constant_(m.weight, 0)
	if m.bias is not None:
	nn.init.constant_(m.bias, 0)

	def forward(self, x, prompt):
	b, c, h, w = x.shape
	prompt = self.shared_mlp(prompt)
	prompt = prompt.expand(b, -1, -1, -1)
	# prompt = F.adaptive_avg_pool2d(prompt, x.size()[2:])
	gama = self.mlp_gama(prompt)
	beta = self.mlp_beta(prompt)
	# gama, beta = prompt.chunk(2, dim=1)
	x = x *( 1 + gama) + beta
	return x

	class DepthwiseSeparableConv(nn.Module):
	def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
	super(DepthwiseSeparableConv, self).__init__()

	# Depthwise convolution
	self.depthwise_conv = nn.Conv2d(
	in_channels,
	in_channels,
	kernel_size=kernel_size,
	stride=stride,
	padding=padding,
	groups=in_channels,
	bias=False
	)

	# Pointwise convolution
	self.pointwise_conv = nn.Conv2d(
	in_channels,
	out_channels,
	kernel_size=1,
	stride=1,
	padding=0,
	bias=False
	)

	def forward(self, x):
	out = self.depthwise_conv(x)
	out = self.pointwise_conv(out)
	return out

	class SFT(nn.Module):
	def __init__(self, x_dim, prompt_dim=192, ks=3, nhidden=128):
	super(SFT, self).__init__()
	pw = ks // 2
	self.mlp_shared = nn.Sequential(
	nn.Conv2d(prompt_dim, nhidden, kernel_size=1),
	nn.ReLU()
	)
	self.mlp_gama = DepthwiseSeparableConv(nhidden, x_dim, kernel_size=ks, padding=pw)
	self.mlp_beta = DepthwiseSeparableConv(nhidden, x_dim, kernel_size=ks, padding=pw)

	# self.mlp_shared.apply(init_weights_zero) # Initialize shared_mlp
	# self.mlp_gama.apply(init_weights_zero)
	# self.mlp_beta.apply(init_weights_zero)

	def forward(self, x, prompt):
	actv = self.mlp_shared(prompt)
	gama = self.mlp_gama(actv)
	beta = self.mlp_beta(actv)
	# print("gama_max=", gama.max())
	# print("beta_max=", beta.max())
	# print("gama_min=", gama.min())
	# print("beta_min=", beta.min())
	out = x * (1 + gama) + beta
	return out

	def init_weights_gama(m):
	if isinstance(m, nn.Conv2d):
	nn.init.constant_(m.weight, 0)
	center = m.kernel_size[0] // 2
	if m.groups == m.in_channels and m.in_channels == m.out_channels: # depthwise conv
	nn.init.constant_(m.weight[:, :, center, center], 1)
	else: # pointwise conv
	nn.init.constant_(m.weight, 1)
	if m.bias is not None:
	nn.init.constant_(m.bias, 0)

	class Decoder_w_Prompt(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, **ignorekwargs):
	super().__init__()
	self.ch = ch # 128
	self.temb_ch = 0
	self.num_resolutions = len(ch_mult) # 4
	self.num_res_blocks = num_res_blocks # 2
	self.resolution = resolution # 256
	self.in_channels = in_channels # 3
	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) # 没有用
	block_in = chch_mult[self.num_resolutions-1] # 128 4
	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, # 0
	dropout=dropout) # 0.0
	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)): # 4,3,2,1,0
	block = nn.ModuleList()
	block_out = chch_mult[i_level] # ch8
	for i_block in range(self.num_res_blocks):
	block.append(ResnetBlock(in_channels=block_in, # 512
	out_channels=block_out, #
	temb_channels=self.temb_ch,
	dropout=dropout))
	# print("i_level=", i_level, "block_in=", block_in, "block_out=", block_out)
	block_in = block_out
	up = nn.Module()
	up.block = block
	if i_level != 0:
	if i_level == 1:
	up.prompt = PromptGenBlock(prompt_dim=64, prompt_size=16)
	up.prompt_attn = Attention(dim=128, hidden_dim=64, bias=True, prompt_dim=64)
	# up.prompt_sft = SFT(x_dim=128, prompt_dim=128)
	elif i_level == 2:
	up.prompt = PromptGenBlock(prompt_dim=128, prompt_size=16)
	# up.prompt_sft = SFT(x_dim=256, prompt_dim=256)
	up.prompt_attn = Attention(dim=256, hidden_dim=128, bias=True, prompt_dim=128)
	elif i_level == 3:
	up.prompt = PromptGenBlock(prompt_dim=128, prompt_size=32)
	# up.prompt_sft = SFT(x_dim=256, prompt_dim=256)
	up.prompt_attn = Attention(dim=256, hidden_dim=128, bias=True, prompt_dim=128)
	elif i_level == 4:
	up.prompt = PromptGenBlock(prompt_dim=256, prompt_size=32)
	# up.prompt_sft = SFT(x_dim=512, prompt_dim=512)
	up.prompt_attn = Attention(dim=512, hidden_dim=192, bias=True, prompt_dim=256)
	up.upsample = Upsample(block_in, resamp_with_conv) # Ture
	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) # 256->512

	# middle
	h = self.mid.block_1(h, temb) # 512
	h = self.mid.block_2(h, temb) # 512

	# upsampling
	for i_level in reversed(range(self.num_resolutions)):
	for i_block in range(self.num_res_blocks):
	h = self.up[i_level].block[i_block](h, temb)
	if i_level != 0:
	# prompt & attention
	prompt = self.up[i_level].prompt(h)
	# h = self.up[i_level].prompt_sft(h, prompt)
	h = self.up[i_level].prompt_attn(h, prompt)
	h = self.up[i_level].upsample(h)
	# end
	if self.give_pre_end: # False
	return h

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