Merge branch 'main' of https://github.com/victorchall/EveryDream2trainer into main
This commit is contained in:
Victor Hall
commit
fdf230634e
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caption_fl.py
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caption_fl.py
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"""
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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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from PIL import Image
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import argparse
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import requests
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from transformers import Blip2Processor, Blip2ForConditionalGeneration, GitProcessor, GitForCausalLM, AutoModel, AutoProcessor
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from huggingface_hub import hf_hub_download
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from open_flamingo import create_model_and_transforms
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import torch
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from pynvml import *
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import time
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from colorama import Fore, Style
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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_duplicates(string):
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words = string.split(', ') # Split the string into individual words
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unique_words = []
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for word in words:
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if word not in unique_words:
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unique_words.append(word)
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else:
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break # Stop appending words once a duplicate is found
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return ', '.join(unique_words)
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def get_examples(example_root, image_processor):
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examples = []
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for root, dirs, files in os.walk(example_root):
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for file in files:
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ext = os.path.splitext(file)[-1].lower()
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if ext in SUPPORTED_EXT:
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#get .txt file of same base name
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txt_file = os.path.splitext(file)[0] + ".txt"
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with open(os.path.join(root, txt_file), 'r') as f:
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caption = f.read()
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image = Image.open(os.path.join(root, file))
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vision_x = [image_processor(image).unsqueeze(0)]
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#vision_x = torch.cat(vision_x, dim=0)
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#vision_x = vision_x.unsqueeze(1).unsqueeze(0)
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examples.append((caption, vision_x))
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for x in examples:
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print(f" ** Example: {x[0]}")
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return examples
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def get_dtype_for_cuda_device(device):
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# check compute capability
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compute_capability = torch.cuda.get_device_capability()
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if compute_capability[0] >= 8:
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dtype = torch.bfloat16
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else:
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dtype = torch.float16
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return dtype
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def main(args):
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device = "cuda" if torch.cuda.is_available() and not args.force_cpu else "cpu"
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dtype = get_dtype_for_cuda_device(device) if device == "cuda" else torch.float32
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if args.prompt:
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prompt = args.prompt
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else:
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prompt = "<image>: "
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print(f" using prompt: {prompt}")
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if "mpt7b" in args.model:
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lang_encoder_path="anas-awadalla/mpt-7b"
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tokenizer_path="anas-awadalla/mpt-7b"
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elif "mpt1b" in args.model:
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lang_encoder_path="anas-awadalla/mpt-1b-redpajama-200b"
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tokenizer_path="anas-awadalla/mpt-1b-redpajama-200b"
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model, image_processor, tokenizer = create_model_and_transforms(
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clip_vision_encoder_path="ViT-L-14",
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clip_vision_encoder_pretrained="openai",
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lang_encoder_path=lang_encoder_path,
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tokenizer_path=tokenizer_path,
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cross_attn_every_n_layers=1,
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)
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tokenizer.padding_side = "left"
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checkpoint_path = hf_hub_download(args.model, "checkpoint.pt")
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model.load_state_dict(torch.load(checkpoint_path), strict=False)
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print(f"GPU memory used, before loading model: {get_gpu_memory_map()} MB")
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model.to(0, dtype=dtype)
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print(f"GPU memory used, after loading model: {get_gpu_memory_map()} MB")
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# examples give few shot learning for captioning the novel image
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examples = get_examples(args.example_root, image_processor)
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prompt = ""
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output_prompt = "Output:"
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per_image_prompt = "<image> " + output_prompt
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for example in iter(examples):
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prompt += f"{per_image_prompt}{example[0]}<|endofchunk|>"
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prompt += per_image_prompt # prepare for novel example
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prompt = prompt.replace("\n", "") # in case captions had newlines
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print(f" \n** Final full prompt with example pairs: {prompt}")
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# os.walk all files in args.data_root recursively
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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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vision_x = [vx[1][0] for vx in examples]
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vision_x.append(image_processor(image).unsqueeze(0))
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vision_x = torch.cat(vision_x, dim=0)
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vision_x = vision_x.unsqueeze(1).unsqueeze(0)
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vision_x = vision_x.to(device, dtype=dtype)
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lang_x = tokenizer(
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[prompt], # blank for image captioning
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return_tensors="pt",
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)
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lang_x.to(device)
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input_ids = lang_x["input_ids"].to(device)
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with torch.cuda.amp.autocast(dtype=dtype), torch.no_grad():
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generated_text = model.generate(
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vision_x=vision_x,
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lang_x=input_ids,
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attention_mask=lang_x["attention_mask"],
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max_new_tokens=args.max_new_tokens,
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min_new_tokens=args.min_new_tokens,
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num_beams=args.num_beams,
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temperature=args.temperature,
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top_k=args.top_k,
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top_p=args.top_p,
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repetition_penalty=args.repetition_penalty,
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)
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del vision_x
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del lang_x
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# trim and clean
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generated_text = tokenizer.decode(generated_text[0][len(input_ids[0]):], skip_special_tokens=True)
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generated_text = generated_text.split(output_prompt)[0]
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generated_text = remove_duplicates(generated_text)
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exec_time = time.time() - start_time
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print(f"* Caption: {generated_text}")
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print(f" Time for last caption: {exec_time} sec. GPU memory used: {get_gpu_memory_map()} MB")
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name = os.path.splitext(full_file_path)[0]
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if not os.path.exists(name):
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with open(f"{name}.txt", "w") as f:
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f.write(generated_text)
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print("Done!")
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if __name__ == "__main__":
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print(f"Available models:")
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print(f" openflamingo/OpenFlamingo-9B-vitl-mpt7b (default)")
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print(f" openflamingo/OpenFlamingo-3B-vitl-mpt1b")
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print(f" openflamingo/OpenFlamingo-4B-vitl-rpj3b")
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parser = argparse.ArgumentParser()
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parser.add_argument("--data_root", type=str, default="input", help="Path to images")
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parser.add_argument("--example_root", type=str, default="examples", help="Path to 2-3 precaptioned images to guide generation")
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parser.add_argument("--model", type=str, default="openflamingo/OpenFlamingo-9B-vitl-mpt7b", help="Model name or path")
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parser.add_argument("--force_cpu", action="store_true", default=False, help="force using CPU even if GPU is available")
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parser.add_argument("--min_new_tokens", type=int, default=20, help="minimum number of tokens to generate")
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parser.add_argument("--max_new_tokens", type=int, default=50, help="maximum number of tokens to generate")
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parser.add_argument("--num_beams", type=int, default=8, help="number of beams, more is more accurate but slower")
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parser.add_argument("--prompt", type=str, default="Output: ", help="prompt to use for generation, default is 'Output: '")
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parser.add_argument("--temperature", type=float, default=1.0, help="temperature for sampling, 1.0 is default")
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parser.add_argument("--top_k", type=int, default=0, help="top_k sampling, 0 is default")
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parser.add_argument("--top_p", type=float, default=1.0, help="top_p sampling, 1.0 is default")
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parser.add_argument("--repetition_penalty", type=float, default=1.0, help="repetition penalty, 1.0 is default")
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parser.add_argument("--length_penalty", type=float, default=1.0, help="length penalty, 1.0 is default")
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args = parser.parse_args()
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print(f"** OPEN-FLAMINGO ** Captioning files in: {args.data_root}")
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print(f"** Using model: {args.model}")
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main(args)
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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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inputs = processor(text=GROUNDING+args.prompt, images=image, return_tensors="pt")
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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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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:
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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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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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@ -1,34 +1,27 @@
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# Captioning tools
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## Open-Flamingo
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## CogVLM
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#### Note: Open-Flamingo currently only works on Torch 2.0.1. If you want to use it, you will have to backdate your torch installation, which will break features in the trainer. I recommend making a separate environment for Open Flamingo captioning instead. You can run through normal install, then `pip install open-flamingo` in the separate envirment to back date torch and make that install open-flamingo only.
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[CogVLM](https://github.com/THUDM/CogVLM) is, so far, the best model for generating synthetic captions. The script for Cog is enhanced, so read the [CogVLM README](CAPTION_COG.md) for more information.
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`python caption_fl.py --data_root input --min_new_tokens 20 --max_new_tokens 30 --num_beams 3 --model "openflamingo/OpenFlamingo-9B-vitl-mpt7b"`
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## Kosmos-2
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This script uses two example image/caption pairs located in the `/example` folder to prime the system to caption, then captions the images in the input folder. It will save a `.txt` file of the same base filename with the caption in the same folder.
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Microsoft's [Kosmos-2](https://huggingface.co/microsoft/kosmos-2-patch14-224) is significantly lighter weight than Cog, using <5GB of VRAM and generating captions in under a second on a RTX 3090.
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This script currently requires an AMPERE or newer GPU due to using bfloat16.
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It has the capability to output grounding bounding boxes.
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**Trying out different example image/caption pairs will influence how the system captions the input images.** Adding more examples slows processing.
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Run `python caption_kosmos2.py --help` to get a list of options.
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Supported models:
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### _Kosmos-2 grounding_
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* `openflamingo/OpenFlamingo-3B-vitl-mpt1b` Small model, requires 8 GB VRAM a num_beams 3, or 12 GB at num_beams 16
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* `openflamingo/OpenFlamingo-9B-vitl-mpt7b` Large model, requires 24 GB VRAM at num_beams 3, or 36.7gb at num_beams 32
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Kosmos-2can generate bounding boxes for the "grounding" of the caption. This is useful for identifying specific objects in the image in 2D space, which can be useful in later piplines.
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The small model with more beams (ex. 16) performs well with details and should not be immediately discounted.
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It's worth reading the documentation [here](https://huggingface.co/microsoft/kosmos-2-patch14-224) to understand the grounding output.
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The larger model is more accurate with proper names (i.e. identifying well-known celebrities, objects, or locations) and seems to exhibit a larger vocabulary.
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`--save_entities` outputs a '.ent' file with bounding box information. The entities identified will be based on what caption is produced.
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Primary params:
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`--phrase_mode` This modifies how the model is called, wrapping phrases in \<phrase> tags. This also interprets your prompt as a CSV, wrapping each item in a phrase tag. You might use it with `--prompt "dog,cat,tree"` for instance. *This is not a gaurantee your phrases will be found and output into the grounding output file.*
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* `--num_beams 3` increasing uses more VRAM and runs slower, may improve detail, but can increase hallicunations
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* `--min_new_tokens 20` and `--max_new_tokens 35` control the length of the caption
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`--save_entities_only` This will not attempt to write the caption into the .txt file at all. **This is recommended with `--phrase_mode`**. Using this option forces `--save_entities`.
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Other settings:
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* `--force_cpu` forces to use CPU even if a CUDA device is present
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* `--temperature 1.0` relates to randomness used for next token chosen
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* `--repetition_penalty 1.0` penalizes repeating tokens/words, can adjust up if you see repeated terms
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* `--length_penalty 1.0` penalizes longer captions
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There is a trivial/dumb UI for viewing the grounding in the scripts folder. Launch it with `python scripts/grounding_ui.py` and it will open a window allowing you to select a directory, and it will display the images and bounding boxes.
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@ -120,7 +120,7 @@ I would recommend not setting any of these and leave the default values until yo
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`--no_repeat_ngram_size 3` prevents the same n-gram (successive token sequence) from being repeated in the output. Can help prevent the model from repeating itself.
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`--bad_words "foo,bar"` Attempts to prevent the model from using these words in the output caption. Comma-delimited.
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`--bad_words "foo,bar"` Attempts to prevent the model from using these words in the output caption. Comma-delimited. Very useful, consider trying `"depicts,poses,posing,showcases,appears,suggests"` to get more concise phrasing in captions. This is not a guarantee, due to [different tokenizations](https://github.com/huggingface/transformers/issues/17504) being possible for a given bad_word.
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`--force_word "photograph,Spain"` Attempts to force the model to include the words in the output caption. Comma-delimited.
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@ -128,7 +128,7 @@ I would recommend not setting any of these and leave the default values until yo
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`--max_new_tokens 120` Truncates output after n tokens. May cut off captions abruptly.
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`--no_repeat_ngram_size 3` prevents the same n-gram from being repeated in the output. Default is 0, which means no n-gram is prevented from repeating. Setting this to 2 or 3 can help prevent the model from repeating itself.
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`--no_repeat_ngram_size 3` prevents the same n-gram (sequence of size n-tokens) from being repeated in the output. Default is 0, which means no n-gram is prevented from repeating. Setting this to 2 or 3 can help prevent the model from repeating itself.
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`--min_new_tokens 5` Force the model to produce at least n tokens.
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