Add adm18

examples/AltCLIP-m18/altclip_evaluation.py

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+# Copyright © 2022 BAAI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License")
+import torch
+from flagai.auto_model.auto_loader import AutoLoader
+import zeroshot_classification
+import json 
+import os 
+from torchvision.datasets import CIFAR10
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+maxlen = 256
+
+dataset_root = "./clip_benchmark_datasets/"
+dataset_name = "cifar10"
+
+auto_loader = AutoLoader(
+    task_name="txt_img_matching",
+    model_dir="./checkpoints/",
+    model_name="AltCLIP-XLMR-L-m18"   # Load the checkpoints from Modelhub(model.baai.ac.cn/models)
+)
+
+model = auto_loader.get_model()
+model.to(device)
+model.eval()
+tokenizer = auto_loader.get_tokenizer()
+transform = auto_loader.get_transform()
+
+dataset = CIFAR10(root=os.path.join(dataset_root, dataset_name), 
+                transform=transform,   
+                download=True)
+batch_size = 128
+num_workers = 4
+
+template = {"cifar10": [
+        "a photo of a {c}.",
+        "a blurry photo of a {c}.",
+        "a black and white photo of a {c}.",
+        "a low contrast photo of a {c}.",
+        "a high contrast photo of a {c}.",
+        "a bad photo of a {c}.",
+        "a good photo of a {c}.",
+        "a photo of a small {c}.",
+        "a photo of a big {c}.",
+        "a photo of the {c}.",
+        "a blurry photo of the {c}.",
+        "a black and white photo of the {c}.",
+        "a low contrast photo of the {c}.",
+        "a high contrast photo of the {c}.",
+        "a bad photo of the {c}.",
+        "a good photo of the {c}.",
+        "a photo of the small {c}.",
+        "a photo of the big {c}."
+    ],
+}
+def evaluate():
+    if dataset:
+        dataloader = torch.utils.data.DataLoader(
+            dataset,
+            batch_size=batch_size,
+            shuffle=False,
+            num_workers=num_workers,
+        )
+
+        zeroshot_templates = template["cifar10"]
+        classnames = dataset.classes if hasattr(dataset, "classes") else None
+
+        metrics = zeroshot_classification.evaluate(
+            model,
+            dataloader,
+            tokenizer,
+            classnames, 
+            zeroshot_templates,
+            device=device,
+            amp=True,
+        )
+
+        dump = {
+            "dataset": dataset_name,
+            "metrics": metrics
+        }
+
+        print(dump)
+        with open("./result.txt", "w") as f:
+            json.dump(dump, f)
+        return metrics
+
+if __name__ == "__main__":
+    evaluate()

examples/AltCLIP-m18/altclip_finetuning.py

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+# Copyright © 2022 BAAI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License")
+import torch
+from flagai.auto_model.auto_loader import AutoLoader
+import os 
+from flagai.trainer import Trainer
+from torchvision.datasets import (
+    CIFAR10
+)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+dataset_root = "./clip_benchmark_datasets"
+dataset_name = "cifar10"
+
+batch_size = 4
+classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
+
+auto_loader = AutoLoader(
+    task_name="txt_img_matching",
+    model_dir="./checkpoints",
+    model_name="AltCLIP-XLMR-L-m18"   # Load the checkpoints from Modelhub(model.baai.ac.cn/models)
+)
+
+model = auto_loader.get_model()
+model.to(device)
+model.eval()
+tokenizer = auto_loader.get_tokenizer()
+transform = auto_loader.get_transform()
+
+trainer = Trainer(env_type="pytorch",
+                pytorch_device=device,
+                experiment_name="clip_finetuning",
+                batch_size=4,
+                lr=1e-4,
+                epochs=10,
+                log_interval=10)
+
+dataset = CIFAR10(root=os.path.join(dataset_root, dataset_name), 
+                transform=transform,   
+                download=True)
+
+def cifar10_collate_fn(batch):
+    # image shape is (batch, 3, 224, 224)
+    images = torch.tensor([b[0]["pixel_values"][0] for b in batch])
+    # text_id shape is (batch, n)
+    input_ids = torch.tensor([tokenizer(f"a photo of a {b[1]}",
+                                padding=True,
+                                truncation=True,
+                                max_length=77)["input_ids"] for b in batch])    
+
+    attention_mask = torch.tensor([tokenizer(f"a photo of a {b[1]}",
+                                padding=True,
+                                truncation=True,
+                                max_length=77)["attention_mask"] for b in batch])
+
+    return {
+        "pixel_values": images,
+        "input_ids": input_ids,
+        "attention_mask": attention_mask,
+    }
+
+if __name__ == "__main__":
+    trainer.train(model=model, train_dataset=dataset, collate_fn=cifar10_collate_fn)

examples/AltCLIP-m18/altclip_inference.py

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+import torch
+from PIL import Image
+from flagai.auto_model.auto_loader import AutoLoader
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+loader = AutoLoader(
+    task_name="txt_img_matching",
+    model_name="AltCLIP-XLMR-L-m18",   # Load the checkpoints from Modelhub(model.baai.ac.cn/models)
+    model_dir="./checkpoints"
+)
+
+model = loader.get_model()
+
+tokenizer = loader.get_tokenizer()
+transform = loader.get_transform()
+
+model.eval()
+model.to(device)
+tokenizer = loader.get_tokenizer()
+
+def inference():
+    image = Image.open("./examples/AltCLIP-m18//dog.jpeg")
+    image = transform(image)
+    image = torch.tensor(image["pixel_values"]).to(device)
+    tokenizer_out = tokenizer(["a rat", "a dog", "a cat"], 
+                                padding=True,
+                                truncation=True,
+                                max_length=77,
+                                return_tensors='pt')
+
+    text = tokenizer_out["input_ids"].to(device)
+    attention_mask = tokenizer_out["attention_mask"].to(device)
+    with torch.no_grad():
+        image_features = model.get_image_features(image)
+        text_features = model.get_text_features(text, attention_mask=attention_mask)
+        text_probs = (image_features @ text_features.T).softmax(dim=-1)
+
+    print(text_probs.cpu().numpy()[0].tolist())
+
+if __name__=="__main__":
+    inference()