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DLCV HW2

tags: Course

湯濬澤
NTUST_M11015117
HackMD Link：https://hackmd.io/@RTon/SJw676wms

Problem 1 - GAN

1. Architecture of model A & B

Model A - DCGAN

DCGAN_Generator(
  (net): Sequential(
    (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace=True)
    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (8): ReLU(inplace=True)
    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (11): ReLU(inplace=True)
    (12): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (13): Tanh()
  )
)

DCGAN_Discriminator(
  (net): Sequential(
    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): LeakyReLU(negative_slope=0.2, inplace=True)
    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (4): LeakyReLU(negative_slope=0.2, inplace=True)
    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (7): LeakyReLU(negative_slope=0.2, inplace=True)
    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (9): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (10): LeakyReLU(negative_slope=0.2, inplace=True)
    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (12): Sigmoid()
  )
)

Model B - WGAN-GP

WGAN_GP_Generator(
  (net): Sequential(
    (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace=True)
    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (8): ReLU(inplace=True)
    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (11): ReLU(inplace=True)
    (12): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (13): Tanh()
  )
)

WGAN_GP_Discriminator(
  (net): Sequential(
    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): LeakyReLU(negative_slope=0.2, inplace=True)
    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (3): LeakyReLU(negative_slope=0.2, inplace=True)
    (4): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (5): LeakyReLU(negative_slope=0.2, inplace=True)
    (6): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (7): LeakyReLU(negative_slope=0.2, inplace=True)
    (8): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
  )
)

2. Generated images of model A & B

Model A (DCGAN)

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Model B (WGAN-GP)

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Model A 的整體效果較好一些些，不像 WGAN-GP 有時會出現一些糊糊的照片，五官也比較清晰一點。實際用 FID 跑測試，DCGAN 的結果可以到 27.7，但 WGAN-GP 出來卻高達 100 多。

3. What I’ve observed and learned ?

從 Model-A (DCGAN) 與 Model-B (WGAN-GP) 的結果來看，兩者差異其實不大？甚至體感上好像 DCGAN 的表現反而好一點。這部分我的猜測是因為在 DCGAN 中，有嘗試一些增強 GAN 的技巧，例如 Discriminator 訓練多次，但那些技巧在 WGAN-GP 上有時會導致模型爆掉，所以沒有嘗試。而 GAN 本身的概念其實蠻特別的，同時訓練 Discriminator 與 generator，讓兩者互相對抗。並透過加雜訊來訓練模型還原原始圖片，電腦這樣的學習方式還蠻特別的。
而至於兩者之間的差異，其實蠻小的。WGAN 相較於 DCGAN，主要是引入了 Wasserstein distance 來取代 DCGAN 的 loss。而 WGAN-GP 則是多了 gradient penalty，Generator 部分一模一樣。

Problem 2 - Diffusion models

1. Model

Diffusion_UNet_Small(
  (init_conv): Residual_Block(
    (conv): Sequential(
      (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): GroupNorm(1, 64, eps=1e-05, affine=True)
      (2): GELU(approximate=none)
      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (4): GroupNorm(1, 64, eps=1e-05, affine=True)
    )
  )
  (down1): Down_Block(
    (conv): Sequential(
      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (1): Residual_Block(
        (conv): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): GroupNorm(1, 64, eps=1e-05, affine=True)
          (2): GELU(approximate=none)
          (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): GroupNorm(1, 64, eps=1e-05, affine=True)
        )
      )
      (2): Residual_Block(
        (conv): Sequential(
          (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): GroupNorm(1, 128, eps=1e-05, affine=True)
          (2): GELU(approximate=none)
          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): GroupNorm(1, 128, eps=1e-05, affine=True)
        )
      )
    )
    (time_emb_mlp): Sequential(
      (0): SiLU()
      (1): Linear(in_features=256, out_features=128, bias=True)
    )
  )
  (sa_d1): Self_Attention(
    (attention_module): MultiheadAttention(
      (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
    )
    (layer_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
    (ff): Sequential(
      (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
      (1): Linear(in_features=128, out_features=128, bias=True)
      (2): GELU(approximate=none)
      (3): Linear(in_features=128, out_features=128, bias=True)
    )
  )
  (down2): Down_Block(
    (conv): Sequential(
      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (1): Residual_Block(
        (conv): Sequential(
          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): GroupNorm(1, 128, eps=1e-05, affine=True)
          (2): GELU(approximate=none)
          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): GroupNorm(1, 128, eps=1e-05, affine=True)
        )
      )
      (2): Residual_Block(
        (conv): Sequential(
          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): GroupNorm(1, 128, eps=1e-05, affine=True)
          (2): GELU(approximate=none)
          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): GroupNorm(1, 128, eps=1e-05, affine=True)
        )
      )
    )
    (time_emb_mlp): Sequential(
      (0): SiLU()
      (1): Linear(in_features=256, out_features=128, bias=True)
    )
  )
  (sa_d2): Self_Attention(
    (attention_module): MultiheadAttention(
      (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
    )
    (layer_norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
    (ff): Sequential(
      (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
      (1): Linear(in_features=128, out_features=128, bias=True)
      (2): GELU(approximate=none)
      (3): Linear(in_features=128, out_features=128, bias=True)
    )
  )
  (mid1): Residual_Block(
    (conv): Sequential(
      (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): GroupNorm(1, 256, eps=1e-05, affine=True)
      (2): GELU(approximate=none)
      (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (4): GroupNorm(1, 256, eps=1e-05, affine=True)
    )
  )
  (mid2): Residual_Block(
    (conv): Sequential(
      (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): GroupNorm(1, 128, eps=1e-05, affine=True)
      (2): GELU(approximate=none)
      (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (4): GroupNorm(1, 128, eps=1e-05, affine=True)
    )
  )
  (up1): Up_Block(
    (up_conv): Sequential(
      (0): Upsample(scale_factor=2.0, mode=bilinear)
    )
    (conv): Sequential(
      (0): Residual_Block(
        (conv): Sequential(
          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): GroupNorm(1, 256, eps=1e-05, affine=True)
          (2): GELU(approximate=none)
          (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): GroupNorm(1, 256, eps=1e-05, affine=True)
        )
      )
      (1): Residual_Block(
        (conv): Sequential(
          (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): GroupNorm(1, 128, eps=1e-05, affine=True)
          (2): GELU(approximate=none)
          (3): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): GroupNorm(1, 64, eps=1e-05, affine=True)
        )
      )
    )
    (time_emb_mlp): Sequential(
      (0): SiLU()
      (1): Linear(in_features=256, out_features=64, bias=True)
    )
  )
  (sa_u1): Self_Attention(
    (attention_module): MultiheadAttention(
      (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)
    )
    (layer_norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
    (ff): Sequential(
      (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
      (1): Linear(in_features=64, out_features=64, bias=True)
      (2): GELU(approximate=none)
      (3): Linear(in_features=64, out_features=64, bias=True)
    )
  )
  (up2): Up_Block(
    (up_conv): Sequential(
      (0): Upsample(scale_factor=2.0, mode=bilinear)
    )
    (conv): Sequential(
      (0): Residual_Block(
        (conv): Sequential(
          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): GroupNorm(1, 128, eps=1e-05, affine=True)
          (2): GELU(approximate=none)
          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): GroupNorm(1, 128, eps=1e-05, affine=True)
        )
      )
      (1): Residual_Block(
        (conv): Sequential(
          (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): GroupNorm(1, 64, eps=1e-05, affine=True)
          (2): GELU(approximate=none)
          (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): GroupNorm(1, 64, eps=1e-05, affine=True)
        )
      )
    )
    (time_emb_mlp): Sequential(
      (0): SiLU()
      (1): Linear(in_features=256, out_features=64, bias=True)
    )
  )
  (sa_u2): Self_Attention(
    (attention_module): MultiheadAttention(
      (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)
    )
    (layer_norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
    (ff): Sequential(
      (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
      (1): Linear(in_features=64, out_features=64, bias=True)
      (2): GELU(approximate=none)
      (3): Linear(in_features=64, out_features=64, bias=True)
    )
  )
  (out_conv): Conv2d(64, 3, kernel_size=(1, 1), stride=(1, 1))
  (label_emb): Embedding(10, 256)
)

模型部分主要參考自 https://www.youtube.com/watch?v=TBCRlnwJtZU 。不過他的模型是設計來輸出大圖片的，這次作業只需要 28×28 的圖，因此我有將 UNET 的結構簡化，只留了兩個 down 跟兩個 up block，以減少運算時間。Sampling 的方法與網路上的做法都差不多，用 torch.linspac 產出 beta 後，再計算 alpha 與 torch.cumprod(alpha)。最後在每個 time step 中，去更新圖片 x。

在 optimizer 與 loss 的選擇上，選用了 MSELoss 與 AdamW。

optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
criterion = nn.MSELoss()

2. Results (time steps = 1000)

3. Different time steps

t=1 t=50 t=100 t=200 t=400 t=600 t=800 t=1000

4. What I’ve observed and learned ?

雖說 time step 為 1000 時，效果較好，但相對的也要算比較久的時間計算。這次因為要在 15 分鐘內輸出 1000 張圖，因此最終繳交的 .py 檔設定的 time step 只有 500。
而這次作業最難實作的 model 應該就是 diffusion 了，資料少，且架構相對複雜，特別是計算 sample 與 embedding 那部分。不過原本以為 UNET 只能拿來做 segmentation，沒想到竟然能用在 diffusion model 上，還蠻特別的。

Problem 3 - DANN models

1. Results

MNIST-M → SVHN MNIST-M → USPS
Trained on source 0.2769 0.5658
DANN 0.4360 0.7641
Trained on target 0.9127 0.9845

2. t-SNE results

Domain Class
MNIST-M → SVHN
MNIST-M → USPS

3. DANN implementation details

DANN model 部分主要分為三塊 Feature extractor, Class classifier 以及 Domain classifier。當中 Feature extractor 負責將圖片的特徵萃取出來，丟給 class classifier 分類 0~9。domain classifier 則將 feature 做過一遍 Gradient Reverse 後，再辨別其 domain。

訓練時，如果是 DDAN 的方式，會先針對 source dataset 跑正常的 classification，並在 domain classifier training 時給予 label=1。對於 target doamin，只拿 data 跑 feature extraction 與 domain classification。最終將三者的 NLLLoss 合併做 backward。

loss = src_class_loss + src_domain_loss + tgt_domain_loss

Optimizer 部分選擇的是 Adam，並用 MultiStepLR 調整 learning rate。

訓練時遇到最大的問題莫過於 Target dataset 的 accuracy 震盪，loss 雖說趨於穩定，source dataset 的 accuracy 也在 0.9 左右徘徊，就很奇怪。個人猜測可能 target dataset 資料量較少，所以才會導致 accuracy 變化過大？

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