145 lines
6.4 KiB
Python
145 lines
6.4 KiB
Python
"""
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Copyright [2022-2023] Victor C Hall
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Licensed under the GNU Affero General Public License;
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You may not use this code except in compliance with the License.
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You may obtain a copy of the License at
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https://www.gnu.org/licenses/agpl-3.0.en.html
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import os
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import io
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import argparse
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import time
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import torch
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from PIL import Image
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from pynvml import *
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from transformers import AutoProcessor, AutoModelForVision2Seq
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import colorama
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GROUNDING = "<grounding>"
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SUPPORTED_EXT = [".jpg", ".png", ".jpeg", ".bmp", ".jfif", ".webp"]
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def get_gpu_memory_map():
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nvmlInit()
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handle = nvmlDeviceGetHandleByIndex(0)
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info = nvmlDeviceGetMemoryInfo(handle)
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nvmlShutdown()
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return info.used/1024/1024
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def remove_starting_string(a, b):
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if b.startswith(a):
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return b[len(a):] # Remove string A from the beginning of string B
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elif b.strip().startswith(a.strip()):
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return b.strip()[len(a.strip()):]
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else:
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return b
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def main(args):
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model = AutoModelForVision2Seq.from_pretrained("microsoft/kosmos-2-patch14-224")
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processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
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dtype=torch.float32
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if not args.cpu:
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if args.dtype == "fp16":
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dtype=torch.float16
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elif args.dtype == "bf16":
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dtype=torch.bfloat16
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elif args.dtype == "fp32":
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dtype=torch.float32
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model = model.to(dtype=dtype).cuda()
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print(f"Using cuda, model dtype: {model.dtype}")
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else:
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print(f"Using cpu, model dtype: {model.dtype}")
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for root, dirs, files in os.walk(args.data_root):
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for file in files:
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#get file extension
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ext = os.path.splitext(file)[1]
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if ext.lower() in SUPPORTED_EXT:
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start_time = time.time()
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full_file_path = os.path.join(root, file)
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image = Image.open(full_file_path)
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full_file_path = os.path.join(root, file)
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image = Image.open(full_file_path)
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if args.phrase_mode:
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text = GROUNDING + "".join(["<phrase>" + x.strip() + "</phrase>" for x in args.prompt.split(",")])
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else:
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text = GROUNDING + args.prompt
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inputs = processor(text=text, images=image, return_tensors="pt")
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with torch.cuda.amp.autocast(enabled=args.dtype != "fp32", dtype=dtype):
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generated_ids = model.generate(
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pixel_values=inputs["pixel_values"].cuda() if not args.cpu else inputs["pixel_values"],
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input_ids=inputs["input_ids"].cuda() if not args.cpu else inputs["input_ids"],
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attention_mask=inputs["attention_mask"].cuda() if not args.cpu else inputs["attention_mask"],
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image_embeds=None,
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image_embeds_position_mask=inputs["image_embeds_position_mask"].cuda() if not args.cpu else inputs["image_embeds_position_mask"],
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use_cache=True,
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max_new_tokens=args.max_new_tokens,
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)
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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processed_text, entities = processor.post_process_generation(generated_text) # remove remaining special tokens to get just the caption and entities
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if not args.keep_prompt:
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processed_text = remove_starting_string(args.prompt, processed_text)
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print(f"File: {full_file_path}, Generated caption: {processed_text}")
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name = os.path.splitext(full_file_path)[0]
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if (not os.path.exists(f"{name}.txt") or args.overwrite) and not args.save_entities_only:
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with open(f"{name}.txt", "w") as f:
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f.write(processed_text)
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if args.save_entities and (not os.path.exists(f"{name}.ent") or args.overwrite):
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with open(f"{name}.ent", "w") as entities_file:
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entities_file.write(str(entities))
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gpu_mb_used = get_gpu_memory_map()
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print(f"gpu usage: {gpu_mb_used:.1f} mb, time taken: {time.time()-start_time:.2f} seconds")
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if __name__ == "__main__":
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print("Kosmos-2 captioning script")
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parser = argparse.ArgumentParser()
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parser.description = "Kosmos-2 captioning script"
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parser.add_argument("--data_root", type=str, default="input", help="Path to folder of images to caption")
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parser.add_argument("--prompt", type=str, default="Describe this image in detail: ", help="Prompt for generating caption")
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parser.add_argument("--phrase_mode", action="store_true", default=False, help="uses 'phrase mode' grounding, interprets prompt as csv list of phrases to ground.")
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parser.add_argument("--keep_prompt", action="store_true", default=False, help="will keep the prompt at the start of the caption when saved")
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parser.add_argument("--max_new_tokens", type=int, default=128, help="Maximum number of tokens to generate")
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parser.add_argument("--save_entities", action="store_true", default=False, help="Save coord box with entities to a separate .ent file")
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parser.add_argument("--save_entities_only", action="store_true", default=False, help="Only save coord box with entities to a separate .ent file, do not write caption .txt")
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parser.add_argument("--overwrite", action="store_true", default=False, help="will overwrite .txt and .ent files if they exist")
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parser.add_argument("--cpu", action="store_true", default=False, help="use cpu instead of cuda")
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parser.add_argument("--dtype", type=str, default="fp16", help="force a different dtype if using GPU (fp16, bf16, fp32) (default: fp16)")
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args = parser.parse_args()
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parser.print_help()
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if args.save_entities_only:
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args.save_entities = True
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if not args.prompt.startswith(" "):
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args.prompt = " " + args.prompt
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print(f"Captioning images in {args.data_root} with prompt: {args.prompt}")
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print(f"Ideas for prompts:")
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print(f" Describe this image in detail: (default)")
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print(f" An image of ")
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print(f" A two sentence description of this image:")
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print()
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main(args) |