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Update my_model/captioner/image_captioning.py
Browse files
my_model/captioner/image_captioning.py
CHANGED
@@ -41,7 +41,7 @@ class ImageCaptioningModel:
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torch_dtype=self.torch_dtype,
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device_map=self.device_map
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)
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-
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self.model = InstructBlipForConditionalGeneration.from_pretrained(self.model_path,
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load_in_8bit=self.load_in_8bit,
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load_in_4bit=self.load_in_4bit,
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@@ -50,7 +50,9 @@ class ImageCaptioningModel:
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device_map=self.device_map
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)
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def resize_image(self, image, max_image_size=None):
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if max_image_size is None:
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max_image_size = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
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@@ -66,7 +68,8 @@ class ImageCaptioningModel:
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def generate_caption(self, image_path):
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if isinstance(image_path, str) or isinstance(image_path, io.IOBase):
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# If it's a file path or file-like object, open it as a PIL Image
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image = Image.open(image_path)
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@@ -78,7 +81,8 @@ class ImageCaptioningModel:
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inputs = self.processor(image, self.prompt, return_tensors="pt").to("cuda", self.torch_dtype)
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outputs = self.model.generate(**inputs, min_length=self.min_length, max_new_tokens=self.max_new_tokens)
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caption = self.processor.decode(outputs[0], skip_special_tokens=self.skip_secial_tokens).strip()
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return caption
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def generate_captions_for_multiple_images(self, image_paths):
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@@ -88,12 +92,11 @@ class ImageCaptioningModel:
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def get_caption(img):
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captioner = ImageCaptioningModel()
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captioner.load_model()
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caption = captioner.generate_caption(img)
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return caption
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if __name__ == "__main__":
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pass
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torch_dtype=self.torch_dtype,
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device_map=self.device_map
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)
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free_gpu_resources()
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self.model = InstructBlipForConditionalGeneration.from_pretrained(self.model_path,
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load_in_8bit=self.load_in_8bit,
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load_in_4bit=self.load_in_4bit,
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device_map=self.device_map
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)
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free_gpu_resources()
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+
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def resize_image(self, image, max_image_size=None):
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if max_image_size is None:
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max_image_size = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
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def generate_caption(self, image_path):
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free_gpu_resources()
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free_gpu_resources()
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if isinstance(image_path, str) or isinstance(image_path, io.IOBase):
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# If it's a file path or file-like object, open it as a PIL Image
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image = Image.open(image_path)
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inputs = self.processor(image, self.prompt, return_tensors="pt").to("cuda", self.torch_dtype)
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outputs = self.model.generate(**inputs, min_length=self.min_length, max_new_tokens=self.max_new_tokens)
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caption = self.processor.decode(outputs[0], skip_special_tokens=self.skip_secial_tokens).strip()
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free_gpu_resources()
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free_gpu_resources()
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return caption
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def generate_captions_for_multiple_images(self, image_paths):
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def get_caption(img):
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captioner = ImageCaptioningModel()
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free_gpu_resources()
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captioner.load_model()
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free_gpu_resources()
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caption = captioner.generate_caption(img)
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free_gpu_resources()
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return caption
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