update

tool/verify_loss.py

@@ -0,0 +1,69 @@
+import torch
+from torch.utils.data import DataLoader
+from ignite.utils import convert_tensor
+from omegaconf import OmegaConf
+
+from data.dataset import SingleFolderDataset
+from loss.I2I.perceptual_loss import PerceptualLoss
+
+import ignite.distributed as idist
+
+CONFIG = """
+loss:
+    perceptual:
+        layer_weights:
+          "1": 0.03125
+          "6": 0.0625
+          "11": 0.125
+          "20": 0.25
+          "29": 1
+        criterion: 'NL2'
+        style_loss: False
+        perceptual_loss: True
+match_data:
+    root: "/tmp/generated/"
+    pipeline:
+        - Load
+        - ToTensor
+        - Normalize:
+            mean: [ 0.5, 0.5, 0.5 ]
+            std: [ 0.5, 0.5, 0.5 ]
+not_match_data:
+    root: "/data/i2i/selfie2anime/trainB/"
+    pipeline:
+        - Load
+        - ToTensor
+        - Normalize:
+            mean: [ 0.5, 0.5, 0.5 ]
+            std: [ 0.5, 0.5, 0.5 ]
+"""
+
+config = OmegaConf.create(CONFIG)
+dataset = SingleFolderDataset(**config.match_data)
+data_loader = DataLoader(dataset, 1, False, num_workers=1)
+
+perceptual_loss = PerceptualLoss(**config.loss.perceptual).to("cuda:0")
+
+pls = []
+for batch in data_loader:
+    with torch.no_grad():
+        batch = convert_tensor(batch, "cuda:0")
+        x, t = torch.chunk(batch, 2, -1)
+        pl, _ = perceptual_loss(x, t)
+        print(pl)
+        pls.append(pl)
+
+torch.save(torch.stack(pls).cpu(), "verify_loss.match.pt")
+
+dataset = SingleFolderDataset(**config.not_match_data)
+data_loader = DataLoader(dataset, 4, False, num_workers=1)
+pls = []
+for batch in data_loader:
+    with torch.no_grad():
+        batch = convert_tensor(batch, "cuda:0")
+        for i, j in [(0, 1), (1, 2), (2, 3), (3, 0)]:
+            x, t = batch[i].unsqueeze(dim=0), batch[j].unsqueeze(dim=0)
+            pl, _ = perceptual_loss(x, t)
+            print(pl)
+            pls.append(pl)
+torch.save(torch.stack(pls).cpu(), "verify_loss.not_match.pt")