From 01b77f295e42a5e6b81355cfb81e4f818e88da7c Mon Sep 17 00:00:00 2001 From: Victor Hall Date: Thu, 29 Jun 2023 18:12:52 -0400 Subject: [PATCH] flamingo --- caption_fl.py | 198 ++++++++++++++++++ ...lue 'Underground' sign found in London.png | Bin 0 -> 324936 bytes ...lue 'Underground' sign found in London.txt | 1 + ...my hummus placed on a white countertop.jpg | Bin 0 -> 42730 bytes ...my hummus placed on a white countertop.txt | 1 + plugins/{base_plugin.py => plugins.py} | 0 requirements.txt | 2 + windows_setup.cmd | 2 + 8 files changed, 204 insertions(+) create mode 100644 caption_fl.py create mode 100755 examples/a red and blue 'Underground' sign found in London.png create mode 100644 examples/a red and blue 'Underground' sign found in London.txt create mode 100755 examples/a white bowl filled with creamy hummus placed on a white countertop.jpg create mode 100644 examples/a white bowl filled with creamy hummus placed on a white countertop.txt rename plugins/{base_plugin.py => plugins.py} (100%) diff --git a/caption_fl.py b/caption_fl.py new file mode 100644 index 0000000..baec83e --- /dev/null +++ b/caption_fl.py @@ -0,0 +1,198 @@ +""" +Copyright [2022-2023] Victor C Hall + +Licensed under the GNU Affero General Public License; +You may not use this code except in compliance with the License. +You may obtain a copy of the License at + + https://www.gnu.org/licenses/agpl-3.0.en.html + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import os + +from PIL import Image +import argparse +import requests +from transformers import Blip2Processor, Blip2ForConditionalGeneration, GitProcessor, GitForCausalLM, AutoModel, AutoProcessor +from huggingface_hub import hf_hub_download +from open_flamingo import create_model_and_transforms + +import torch +from pynvml import * + +import time +from colorama import Fore, Style + + +SUPPORTED_EXT = [".jpg", ".png", ".jpeg", ".bmp", ".jfif", ".webp"] + +def get_gpu_memory_map(): + nvmlInit() + handle = nvmlDeviceGetHandleByIndex(0) + info = nvmlDeviceGetMemoryInfo(handle) + nvmlShutdown() + return info.used/1024/1024 + +def remove_duplicates(string): + words = string.split(', ') # Split the string into individual words + unique_words = [] + + for word in words: + if word not in unique_words: + unique_words.append(word) + else: + break # Stop appending words once a duplicate is found + + return ', '.join(unique_words) + +def get_examples(example_root, image_processor): + examples = [] + for root, dirs, files in os.walk(example_root): + for file in files: + ext = os.path.splitext(file)[-1].lower() + if ext in SUPPORTED_EXT: + #get .txt file of same base name + txt_file = os.path.splitext(file)[0] + ".txt" + with open(os.path.join(root, txt_file), 'r') as f: + caption = f.read() + image = Image.open(os.path.join(root, file)) + vision_x = [image_processor(image).unsqueeze(0)] + #vision_x = torch.cat(vision_x, dim=0) + #vision_x = vision_x.unsqueeze(1).unsqueeze(0) + examples.append((caption, vision_x)) + for x in examples: + print(f" ** Example: {x[0]}") + return examples + +def main(args): + device = "cuda" if torch.cuda.is_available() and not args.force_cpu else "cpu" + dtype = torch.bfloat16 if device == "cuda" else torch.float32 + + if args.prompt: + prompt = args.prompt + else: + prompt = ": " + print(f" using prompt: {prompt}") + + if "mpt7b" in args.model: + lang_encoder_path="anas-awadalla/mpt-7b" + tokenizer_path="anas-awadalla/mpt-7b" + elif "mpt1b" in args.model: + lang_encoder_path="anas-awadalla/mpt-1b" + tokenizer_path="anas-awadalla/mpt-1b" + + model, image_processor, tokenizer = create_model_and_transforms( + clip_vision_encoder_path="ViT-L-14", + clip_vision_encoder_pretrained="openai", + lang_encoder_path=lang_encoder_path, + tokenizer_path=tokenizer_path, + cross_attn_every_n_layers=1, + ) + + tokenizer.padding_side = "left" + + checkpoint_path = hf_hub_download(args.model, "checkpoint.pt") + model.load_state_dict(torch.load(checkpoint_path), strict=False) + print(f"GPU memory used, before loading model: {get_gpu_memory_map()} MB") + model.to(0, dtype=dtype) + print(f"GPU memory used, after loading model: {get_gpu_memory_map()} MB") + + # examples give few shot learning for captioning the novel image + examples = get_examples(args.example_root, image_processor) + + prompt = "" + output_prompt = "Output:" + per_image_prompt = " " + output_prompt + + for example in iter(examples): + prompt += f"{per_image_prompt}{example[0]}<|endofchunk|>" + prompt += per_image_prompt # prepare for novel example + prompt = prompt.replace("\n", "") # in case captions had newlines + print(f" \n** Final full prompt with example pairs: {prompt}") + + # os.walk all files in args.data_root recursively + for root, dirs, files in os.walk(args.data_root): + for file in files: + #get file extension + ext = os.path.splitext(file)[1] + if ext.lower() in SUPPORTED_EXT: + start_time = time.time() + + full_file_path = os.path.join(root, file) + image = Image.open(full_file_path) + + vision_x = [vx[1][0] for vx in examples] + vision_x.append(image_processor(image).unsqueeze(0)) + vision_x = torch.cat(vision_x, dim=0) + vision_x = vision_x.unsqueeze(1).unsqueeze(0) + vision_x = vision_x.to(device, dtype=dtype) + + lang_x = tokenizer( + [prompt], # blank for image captioning + return_tensors="pt", + ) + lang_x.to(device) + + input_ids = lang_x["input_ids"].to(device) + + with torch.cuda.amp.autocast(dtype=dtype): + generated_text = model.generate( + vision_x=vision_x, + lang_x=input_ids, + attention_mask=lang_x["attention_mask"], + max_new_tokens=args.max_new_tokens, + min_new_tokens=args.min_new_tokens, + num_beams=args.num_beams, + temperature=args.temperature, + top_k=args.top_k, + top_p=args.top_p, + repetition_penalty=args.repetition_penalty, + ) + del vision_x + del lang_x + + # trim and clean + generated_text = tokenizer.decode(generated_text[0][len(input_ids[0]):], skip_special_tokens=True) + generated_text = generated_text.split(output_prompt)[0] + generated_text = remove_duplicates(generated_text) + + exec_time = time.time() - start_time + print(f"* Caption: {generated_text}") + + print(f" Time for last caption: {exec_time} sec. GPU memory used: {get_gpu_memory_map()} MB") + + name = os.path.splitext(full_file_path)[0] + if not os.path.exists(name): + with open(f"{name}.txt", "w") as f: + f.write(generated_text) + +if __name__ == "__main__": + print(f"Available models:") + print(f" openflamingo/OpenFlamingo-9B-vitl-mpt7b (default)") + print(f" openflamingo/OpenFlamingo-3B-vitl-mpt1b") + print(f" openflamingo/OpenFlamingo-4B-vitl-rpj3b") + parser = argparse.ArgumentParser() + parser.add_argument("--data_root", type=str, default="input", help="Path to images") + parser.add_argument("--example_root", type=str, default="examples", help="Path to 2-3 precaptioned images to guide generation") + parser.add_argument("--model", type=str, default="openflamingo/OpenFlamingo-9B-vitl-mpt7b", help="Model name or path") + parser.add_argument("--force_cpu", action="store_true", default=False, help="force using CPU even if GPU is available") + parser.add_argument("--min_new_tokens", type=int, default=20, help="minimum number of tokens to generate") + parser.add_argument("--max_new_tokens", type=int, default=50, help="maximum number of tokens to generate") + parser.add_argument("--num_beams", type=int, default=8, help="number of beams, more is more accurate but slower") + parser.add_argument("--prompt", type=str, default="Output: ", help="prompt to use for generation, default is 'Output: '") + parser.add_argument("--temperature", type=float, default=1.0, help="temperature for sampling, 1.0 is default") + parser.add_argument("--top_k", type=int, default=0, help="top_k sampling, 0 is default") + parser.add_argument("--top_p", type=float, default=0.9, help="top_p sampling, 0.9 is default") + parser.add_argument("--repetition_penalty", type=float, default=1.0, help="repetition penalty, 1.0 is default") + parser.add_argument("--length_penalty", type=float, default=1.0, help="length penalty, 1.0 is default") + args = parser.parse_args() + + print(f"** OPEN-FLAMINGO ** Captioning files in: {args.data_root}") + 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+open-flamingo==2.0.0 \ No newline at end of file diff --git a/windows_setup.cmd b/windows_setup.cmd index 572174d..52958d9 100644 --- a/windows_setup.cmd +++ b/windows_setup.cmd @@ -21,6 +21,8 @@ pip install numpy==1.23.5 pip install lion-pytorch pip install compel~=1.1.3 pip install dadaptation +pip install safetensors +pip install open-flamingo==2.0.0 python utils/get_yamls.py GOTO :eof