ArtiAgent - DefectDiffu/engine/DefectDiffu/autoencoder.py

import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
import os
import torchvision.transforms as transforms
from torchvision.utils import save_image
import os
from PIL import Image


os.environ["CUDA_VISIBLE_DEVICES"] = "1"
class LinearAttention(nn.Module):
    def __init__(self, dim, heads=4, dim_head=32):
        super().__init__()
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
        self.to_out = nn.Conv2d(hidden_dim, dim, 1)

    def forward(self, x):
        b, c, h, w = x.shape
        qkv = self.to_qkv(x)
        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
        k = k.softmax(dim=-1)
        context = torch.einsum('bhdn,bhen->bhde', k, v)
        out = torch.einsum('bhde,bhdn->bhen', context, q)
        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
        return self.to_out(out)


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 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=(1,2,4,8), num_res_blocks,
                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
                 resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
                 **ignore_kwargs):
        super().__init__()
        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.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.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 curr_res in attn_resolutions:
                    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:
                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 = 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 = 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):
        # 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=(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, tanh_out=False, use_linear_attn=False,
                 attn_type="vanilla", **ignorekwargs):
        super().__init__()
        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.resolution = resolution
        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
        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 = 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)):
            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(make_attn(block_in, attn_type=attn_type))
            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)
        if self.tanh_out:
            h = torch.tanh(h)
        return h


class FrozenAutoencoderKL(nn.Module):
    def __init__(self, ddconfig, embed_dim, pretrained_path, scale_factor=0.18215):
        super().__init__()
        print(f'Create autoencoder with scale_factor={scale_factor}')
        self.encoder = Encoder(**ddconfig)
        self.decoder = Decoder(**ddconfig)
        assert ddconfig["double_z"]
        self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1)
        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
        self.embed_dim = embed_dim
        self.scale_factor = scale_factor
        m, u = self.load_state_dict(torch.load(pretrained_path, map_location='cpu'))
        assert len(m) == 0 and len(u) == 0
        self.eval()
        self.requires_grad_(False)

    def encode_moments(self, x):
        h = self.encoder(x)
        moments = self.quant_conv(h)
        return moments

    def sample(self, moments):
        mean, logvar = torch.chunk(moments, 2, dim=1)
        logvar = torch.clamp(logvar, -30.0, 20.0)
        std = torch.exp(0.5 * logvar)
        z = mean + std * torch.randn_like(mean)
        z = self.scale_factor * z
        return z

    def encode(self, x):
        moments = self.encode_moments(x)
        z = self.sample(moments)
        return z

    def decode(self, z):
        z = (1. / self.scale_factor) * z
        z = self.post_quant_conv(z)
        dec = self.decoder(z)
        return dec

    def forward(self, inputs, fn):
        if fn == 'encode_moments':
            return self.encode_moments(inputs)
        elif fn == 'encode':
            return self.encode(inputs)
        elif fn == 'decode':
            return self.decode(inputs)
        else:
            raise NotImplementedError


def get_model(pretrained_path, scale_factor=0.18215):
    ddconfig = dict(
        double_z=True,
        z_channels=4,
        resolution=256,
        in_channels=3,
        out_ch=3,
        ch=128,
        ch_mult=[1, 2, 4, 4],
        num_res_blocks=2,
        attn_resolutions=[],
        dropout=0.0
    )
    return FrozenAutoencoderKL(ddconfig, 4, pretrained_path, scale_factor)


def main():
    import torchvision.transforms as transforms
    from torchvision.utils import save_image
    import os
    from PIL import Image

    model = get_model('/data/CASIA/Projects/sqf/Diffsion_Model/self_operate/save_auto/checkpoint/autoencoder_kl.pth')
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model = model.to(device)

    scale_factor = 0.18215
    T = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor()])
    path = '/data/CASIA/Projects/sqf/Diffsion_Model/self_operate/auto_data/test'
    fnames = os.listdir(path)
    for fname in fnames:
        p = os.path.join(path, fname)
        img = Image.open(p)
        img = T(img)
        img = img * 2. - 1
        img = img[None, ...]
        img = img.to(device)

        # with torch.cuda.amp.autocast():
        #     moments = model.encode_moments(img)
        #     mean, logvar = torch.chunk(moments, 2, dim=1)
        #     logvar = torch.clamp(logvar, -30.0, 20.0)
        #     std = torch.exp(0.5 * logvar)
        #     zs = [(mean + std * torch.randn_like(mean)) * scale_factor for _ in range(4)]
        #     recons = [model.decode(z) for z in zs]

        with torch.cuda.amp.autocast():
            print('test encode & decode')
            recons = [model.decode(model.encode(img)) for _ in range(4)]

        out = torch.cat([img, *recons], dim=0)
        out = (out + 1) * 0.5
        save_image(out, '/data/CASIA/Projects/sqf/Diffsion_Model/self_operate/save_auto/img/' + f'recons_{fname}')


from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import ToTensor, Compose, CenterCrop, Resize, RandomCrop, Lambda
from torch.optim import Adam
class Dataset_self(Dataset):
    def __init__(self,img_root, preprocess):    
        self.img_root = img_root
        self.img_process = preprocess
        self.img = []
        for name_img in os.listdir(self.img_root):
            self.img.append(self.img_root + '/' + name_img)

    def __len__(self):
        return len(self.img)

    def __getitem__(self, idx):
        img_path = self.img[idx]
        image = Image.open(img_path).convert('RGB')
        image = self.img_process(image)
        return image

def train():
    model = get_model('assets/stable-diffusion/autoencoder_kl.pth')
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model = model.to(device)
    scale_factor = 0.18215
    path_test = ['/data/CASIA/Projects/sqf/Diffsion_Model/self_operate/auto_data/test/389.png', '/data/CASIA/Projects/sqf/Diffsion_Model/self_operate/auto_data/test/390.png']
    T = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor(), Lambda(lambda t: (t * 2) - 1)])
    dataset = Dataset_self(img_root= '/data/CASIA/Projects/sqf/Diffsion_Model/self_operate/auto_data/train/good', preprocess=T)
    dataloader = DataLoader(dataset, batch_size=4, shuffle=True)
    criterion = nn.MSELoss()
    optimizer = Adam(model.parameters(), lr=1e-5)
    epochs = 100000
    for epoch in range(epochs):
        step = 0
        for batch in dataloader:
            step += 1
            optimizer.zero_grad()
            batch = batch.to(device)
            output_model = model.decode(model.encode(batch))
            loss = criterion(output_model, batch)
            loss.backward()
            optimizer.step()
        print(epoch, loss.item())
        torch.save({
            'model_state_dict': model.state_dict(),
            'optimizer_state_dict': optimizer.state_dict(),
            }, '/data/CASIA/Projects/sqf/Diffsion_Model/self_operate/save_auto/checkpoint/model_hazelnut_last.pth')
        for i in range(2):
            img_path = path_test[i]
            img = Image.open(img_path)
            img = T(img)
            img = img[None, ...]
            img = img.to(device)
            out = model.decode(model.encode(img))
            out = (out + 1) * 0.5
            save_image(out, '/data/CASIA/Projects/sqf/Diffsion_Model/self_operate/save_auto/img/' + f'recons_{epoch}_{i}.png')


if __name__ == "__main__":
    # train()
    main()