Update README.md

README.md

@@ -5,4 +5,118 @@ license: apache-2.0
 Finetuned from p1atdev/siglip-tagger-test-3  
 https://huggingface.co/p1atdev/siglip-tagger-test-3  
 
-test work
+test work  
+  
+Usage:  
+```
+import torch
+import torch.nn as nn
+import numpy as np
+from dataclasses import dataclass
+from transformers import SiglipVisionModel, SiglipPreTrainedModel, SiglipVisionConfig, AutoImageProcessor
+from transformers.utils import ModelOutput
+
+@dataclass
+class SiglipForImageClassifierOutput(ModelOutput):
+    loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    pooler_output: torch.FloatTensor | None = None
+    hidden_states: tuple[torch.FloatTensor, ...] | None = None
+    attentions: tuple[torch.FloatTensor, ...] | None = None
+
+class SiglipForImageClassification(SiglipPreTrainedModel):
+    config_class = SiglipVisionConfig
+    main_input_name = "pixel_values"
+
+    def __init__(
+        self,
+        config,
+    ):
+        super().__init__(config)
+
+        # self.num_labels = config.num_labels
+        self.siglip = SiglipVisionModel(config)
+
+        # Classifier head
+        self.classifier = (
+            nn.Linear(config.hidden_size, config.num_labels)
+            if config.num_labels > 0
+            else nn.Identity()
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self, pixel_values: torch.FloatTensor, labels: torch.LongTensor | None = None
+    ):
+        outputs = self.siglip(pixel_values)
+        pooler_output = outputs.pooler_output
+        logits = self.classifier(pooler_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.BCEWithLogitsLoss()
+            loss = loss_fct(logits, labels)
+
+        return SiglipForImageClassifierOutput(
+            loss=loss,
+            logits=logits,
+            pooler_output=outputs.pooler_output,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+# モデル設定のロード
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+config = SiglipVisionConfig.from_pretrained('cella110n/siglip-tagger-FT3ep')
+processor = AutoImageProcessor.from_pretrained("cella110n/siglip-tagger-FT3ep", config=config)
+model = SiglipForImageClassification.from_pretrained('cella110n/siglip-tagger-FT3ep', torch_dtype=torch.bfloat16).to(device)
+
+model.eval()
+print("Model Loaded. device:", model.device)
+
+from PIL import Image
+
+# 入力画像サイズの確認と調整
+img_path =  "path/to/image"
+img = Image.open(img_path).
+
+inputs = processor(images=img, return_tensors="pt")  # 画像をモデルに適した形式に変換
+print("Image processed.")
+
+# inputs.pixel_valuesの画像を表示
+img = inputs.pixel_values[0].permute(1, 2, 0).cpu().numpy()
+plt.imshow(img)
+plt.axis('off')
+plt.show()
+
+# # モデルの予測実行
+with torch.no_grad():
+    logits = (model(
+            **inputs.to(
+            model.device,
+            model.dtype
+            )
+        )
+        .logits.detach()
+        .cpu()
+        .float()
+    )
+
+logits = np.clip(logits, 0.0, 1.0)  # オーバーフローを防ぐためにlogitsをクリップ
+
+prob_cutoff = 0.3  # この確率以上のクラスのみを表示
+
+result = {}
+
+for prediction in logits:
+    for i, prob in enumerate(prediction):
+        if prob.item() > prob_cutoff:
+            result[model.config.id2label[i]] = prob.item()
+
+# resultを、高いほうから表示
+sorted_result = sorted(result.items(), key=lambda x: x[1], reverse=True)
+sorted_result
+```