chore: 添加 epoch 之间的检查

ginka/heatmap/model.py

@@ -29,20 +29,20 @@ class GinkaHeatmapModel(nn.Module):
     def forward(self, input: torch.Tensor, cond: torch.Tensor, t: torch.Tensor):
         # input: [B, heatmap_dim, H, W] 噪声
         # cond: [B, heatmap_dim, H, W] 点图
-        # t: [B, 1]
+        # t: [B]
         input = self.input(input, t) # [B, d_model, H, W]
         cond = self.cond(cond, t) # [B, d_model, H, W]
-        B, C, H, W = cond.shape
-        cond_tokens = cond.view(B, C, H * W).permute(0, 2, 1) # [B, H * W, d_model]
-        scale = torch.sigmoid(cond)
-        hidden = input * (1 + scale) + cond
+        B, C, H, W = input.shape
+        scale = torch.sigmoid(cond) # [B, d_model, H, W]
+        hidden = input * (1 + scale) + cond # [B, d_model, H, W]
         hidden = hidden.view(B, C, H * W).permute(0, 2, 1) # [B, H * W, d_model]
-        hidden = hidden + self.pos_embedding
+        hidden = hidden + self.pos_embedding # [B, H * W, d_model]
         hidden = self.transformer(hidden) # [B, H * W, d_model]
-        attn, _ = self.cross_attn(hidden, cond_tokens, cond_tokens)
-        hidden = hidden + attn
+        cond_tokens = cond.view(B, C, H * W).permute(0, 2, 1) # [B, H * W, d_model]
+        attn, _ = self.cross_attn(hidden, cond_tokens, cond_tokens) # [B, H * W, d_model]
+        hidden = hidden + attn # [B, H * W, d_model]
         output = self.output_fc(hidden) # [B, H * W, heatmap_dim]
-        return output.view(B, H, W, self.heatmap_dim).permute(0, 3, 1, 2)
+        return output.view(B, H, W, self.heatmap_dim).permute(0, 3, 1, 2) # [B, heatmap_dim, H, W]
         
 if __name__ == "__main__":
     device = torch.device("cpu")

ginka/train_heatmap.py

@@ -185,7 +185,7 @@ def train():
                     
                     pred_noise = model(x_t, cond_heatmap, t)
                     
-                    loss = F.l1_loss(pred_noise, noise)
+                    loss = F.mse_loss(pred_noise, noise)
                     
                     val_loss_total += loss.detach()
                 
@@ -238,10 +238,11 @@ def get_nms_sampling_count():
 def full_generate(heatmap, maskGIT, cond_heatmap: torch.Tensor, diffusion: Diffusion):
     fake_heatmap_cond = diffusion.sample(heatmap, cond_heatmap)
     fake_heatmap_uncond = diffusion.sample(heatmap, torch.zeros_like(cond_heatmap))
-    fake_heatmap = fake_heatmap_uncond + W * (fake_heatmap_uncond - fake_heatmap_cond)
+    fake_heatmap = fake_heatmap_uncond + W * (fake_heatmap_uncond - fake_heatmap_cond) # [B, C, H, W]
     return maskGIT_generate(maskGIT, cond_heatmap.shape[0], fake_heatmap)
 
 def maskGIT_generate(maskGIT, B: int, heatmap: torch.Tensor):
+    # heatmap: [B, C, H, W]
     map = torch.full((B, MAP_H * MAP_W), MASK_TOKEN).to(device)
     for i in range(GENERATE_STEP):
         # 1. 预测

ginka/train_joint.py

@@ -349,6 +349,7 @@ def train():
                 pred_noise_for_joint = model(x_t, cond_heatmap, t)
 
             generated_heatmap = predict_x0(diffusion, x_t, pred_noise_for_joint, t)
+            print(torch.mean(generated_heatmap), torch.std(generated_heatmap), generated_heatmap.shape)
             maskgit_loss = maskgit_joint_loss(maskgit, generated_heatmap, target_map)
 
             loss = diffusion_loss + CE_WEIGHT * maskgit_loss