feat: 在训练时处理高斯模糊

.vscode/settings.json

@@ -0,0 +1,4 @@
+{
+    "python-envs.defaultEnvManager": "ms-python.python:system",
+    "python-envs.defaultPackageManager": "ms-python.python:pip"
+}

data/src/auto.ts

@@ -268,8 +268,8 @@ const labelConfig: IAutoLabelConfig = {
     maxFishCount: 2,
     minEntryCount: 1,
     maxEntryCount: 4,
-    guassainRadius: 2,
+    guassainRadius: 0,
-    heatmapKernel: 3,
+    heatmapKernel: 0,
     ignoreIssues: true,
     customTowerFilter: info => {
         // if (info.name !== 'Apeiria') {

data/src/auto/heatmap.ts

@@ -8,6 +8,7 @@ export function generateHeatmap(
     tokens: Set<number>,
     kernel: number = 5
 ): number[][] {
+    if (kernel === 0) return map.map(v => v.slice());
     if (kernel % 2 !== 1) {
         throw new Error(`Kernal size must be odd.`);
     }
@@ -44,6 +45,7 @@ export function generateHeatmap(
  * @param sigma 标准差
  */
 export function gaussainHeatmap(map: number[][], sigma: number = 1) {
+    if (sigma === 0) return map.map(v => v.slice());
     const radius = sigma * 3;
     const width = map[0].length;
     const height = map.length;

ginka/dataset.py

@@ -1,5 +1,8 @@
 import json
+import random
 import torch
+import cv2
+import numpy as np
 from torch.utils.data import Dataset
 
 def load_data(path: str):
@@ -13,9 +16,11 @@ def load_data(path: str):
     return data_list
 
 class GinkaMaskGITDataset(Dataset):
-    def __init__(self, data_path: str, device):
+    def __init__(self, data_path: str, sigma_rand=0.1, blur_min=3, blur_max=6):
-        self.data = load_data(data_path)  # 自定义数据加载函数
+        self.data = load_data(data_path)
-        self.device = device
+        self.sigma_rand = sigma_rand
+        self.blur_min = blur_min
+        self.blur_max = blur_max
         
     def __len__(self):
         return len(self.data)
@@ -25,7 +30,28 @@ class GinkaMaskGITDataset(Dataset):
         
         target = torch.LongTensor(item['map']) # [H, W]
         cond = torch.FloatTensor(item['val']) # [cond_dim]
-        heatmap = torch.FloatTensor(item['heatmap']) # [heatmap_channel, H, W]
+        heatmap = np.array(item['heatmap'])
+        
+        if random.random() < 0.5:
+            size = random.randint(self.blur_min, self.blur_max)
+            if size % 2 == 0:
+                size = size + 1 if random.random() < 0.5 else size - 1
+            heatmap = cv2.GaussianBlur(heatmap, (size, size), 0)
+        else:
+            sizeX = random.randint(self.blur_min, self.blur_max)
+            sizeY = random.randint(self.blur_min, self.blur_max)
+            if sizeX % 2 == 0:
+                sizeX = sizeX + 1 if random.random() < 0.5 else sizeX - 1
+            if sizeY % 2 == 0:
+                sizeY = sizeY + 1 if random.random() < 0.5 else sizeY - 1
+            heatmap = cv2.GaussianBlur(heatmap, (sizeX, sizeY), 0)
+            
+        heatmap = torch.FloatTensor(heatmap) # [heatmap_channel, H, W]
+        
+        if random.random() < 0.5:
+            sigma = random.random() * self.sigma_rand
+            rand = torch.randn_like(heatmap) * sigma
+            heatmap = heatmap + rand
 
         return {
             "cond": cond,

ginka/train_maskGIT.py

@@ -44,6 +44,8 @@ GENERATE_STEP = 8
 MAP_SIZE = 13 * 13
 HEATMAP_CHANNEL = 9
 LABEL_SMOOTHING = 0
+BLUR_MIN_SIZE = 3
+BLUR_MAX_SIZE = 6
 RAND_RATIO = 0.1
 MASK_PROBS = [0.5, 0.5] # 纯随机，分块随机
 
@@ -78,13 +80,12 @@ def train():
     model = GinkaMaskGIT(num_classes=NUM_CLASSES, heatmap_channel=HEATMAP_CHANNEL).to(device)
     masker = MapMask([0.5, 0.5])
     
-    dataset = GinkaMaskGITDataset(args.train, device)
+    dataset = GinkaMaskGITDataset(args.train, sigma_rand=RAND_RATIO, blur_min=BLUR_MIN_SIZE, blur_max=BLUR_MAX_SIZE)
-    dataset_val = GinkaMaskGITDataset(args.validate, device)
+    dataset_val = GinkaMaskGITDataset(args.validate, sigma_rand=RAND_RATIO, blur_min=BLUR_MIN_SIZE, blur_max=BLUR_MAX_SIZE)
     dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
     dataloader_val = DataLoader(dataset_val, batch_size=BATCH_SIZE // VAL_BATCH_DIVIDER, shuffle=True)
     
     optimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-2)
-    # 自定义调度器允许在 self_prob 提高时重置调度器信息并提高学习率以适应学习
     scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs, eta_min=1e-6)
 
     # 用于生成图片
@@ -105,7 +106,7 @@ def train():
     
         print("Train from loaded state.")
     
-    for epoch in tqdm(range(args.epochs), desc="VAE Training", disable=disable_tqdm):
+    for epoch in tqdm(range(args.epochs), desc="MaskGIT Training", disable=disable_tqdm):
         loss_total = torch.Tensor([0]).to(device)
         
         for batch in tqdm(dataloader, leave=False, desc="Epoch Progress", disable=disable_tqdm):
@@ -115,9 +116,6 @@ def train():
             B, H, W = target_map.shape
 
             target_map = target_map.view(B, H * W)
-            rand = torch.randn_like(heatmap).to(device) * RAND_RATIO
-            if random.random() > 0.5:
-                heatmap = heatmap + rand
             
             mask = np.zeros((B, H * W))
             for i in range(B):
@@ -163,7 +161,7 @@ def train():
                 gap = 5
                 color = (255, 255, 255)  # 白色
                 vline = np.full((416, gap, 3), color, dtype=np.uint8)  # 垂直分割线
-                for batch in tqdm(dataloader_val, desc="Validating generator.", leave=False, disable=disable_tqdm):
+                for batch in tqdm(dataloader_val, desc="Validating", leave=False, disable=disable_tqdm):
                     # 1. 常规生成
                     target_map = batch["target_map"].to(device)
                     cond = batch["cond"].to(device)
@@ -226,7 +224,8 @@ def train():
                             break
                         
                     generated_img = matrix_to_image_cv(map.view(B, H, W)[0].cpu().numpy(), tile_dict)
-                    cv2.imwrite(f"result/transformer_img/g-{idx}.png", generated_img)
+                    img = np.block([[real_img], [vline], [generated_img]])
+                    cv2.imwrite(f"result/transformer_img/g-{idx}.png", img)
                     
             avg_loss_val = val_loss_total.item() / len(dataloader_val)
             tqdm.write(

requirements.txt

@@ -5,3 +5,4 @@ transformers
 scipy
 numpy
 cv2
+perlin-noise