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janus/utils/io.py

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+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import json
+from typing import Dict, List
+
+import PIL.Image
+import torch
+import base64
+import io
+from transformers import AutoModelForCausalLM
+
+from janus.models import MultiModalityCausalLM, VLChatProcessor
+
+
+def load_pretrained_model(model_path: str):
+    vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
+    tokenizer = vl_chat_processor.tokenizer
+
+    vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
+        model_path, trust_remote_code=True
+    )
+    vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
+
+    return tokenizer, vl_chat_processor, vl_gpt
+
+
+def load_pil_images(conversations: List[Dict[str, str]]) -> List[PIL.Image.Image]:
+    """
+
+    Support file path or base64 images.
+
+    Args:
+        conversations (List[Dict[str, str]]): the conversations with a list of messages. An example is :
+            [
+                {
+                    "role": "User",
+                    "content": "<image_placeholder>\nExtract all information from this image and convert them into markdown format.",
+                    "images": ["./examples/table_datasets.png"]
+                },
+                {"role": "Assistant", "content": ""},
+            ]
+
+    Returns:
+        pil_images (List[PIL.Image.Image]): the list of PIL images.
+
+    """
+
+    pil_images = []
+
+    for message in conversations:
+        if "images" not in message:
+            continue
+
+        for image_data in message["images"]:
+            if image_data.startswith("data:image"):
+                # Image data is in base64 format
+                _, image_data = image_data.split(",", 1)
+                image_bytes = base64.b64decode(image_data)
+                pil_img = PIL.Image.open(io.BytesIO(image_bytes))
+            else:
+                # Image data is a file path
+                pil_img = PIL.Image.open(image_data)
+            pil_img = pil_img.convert("RGB")
+            pil_images.append(pil_img)
+
+    return pil_images
+
+
+def load_json(filepath):
+    with open(filepath, "r") as f:
+        data = json.load(f)
+        return data