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import os
import torch
from PIL import Image
from accelerate import Accelerator
from omegaconf import OmegaConf
from torch.nn.utils.rnn import pad_sequence
from torchmetrics.multimodal.clip_score import CLIPScore as CLIP_TScore
from tqdm import tqdm
from app_model import AppModel
from app_utils import randomize_seed_fn
from eval.clip_score import CLIPIScore as CLIP_IScore
from eval.clip_score import CLIPTScore as CLIP_TScore
from eval.dino_score import DINOScore as DINO_Score
from eval.dreambench_prompts import *
from fairseq import options
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
class Image_Dataset(torch.utils.data.Dataset):
def __init__(self, args, files):
self.args = args
self.files = files
def __len__(self):
return len(self.files)
def __getitem__(self, index):
image_path = self.files[index]
object_name, object_id, image_id, prompt = image_path.split('/')[-1].split('.')[0].split('+')
image = Image.open(image_path).convert('RGB')
real_image = Image.open(os.path.join(self.args.data_dir, object_name, object_id + '.jpg')).convert('RGB')
return image, real_image, prompt
def image_collate_fn(batch):
image = [x[0] for x in batch]
real_image = [x[1] for x in batch]
prompt = [x[2] for x in batch]
return image, real_image, prompt
class DreamBench_Dataset(torch.utils.data.Dataset):
def __init__(self, args, preprocess_fn):
self.args = args
self.preprocess_fn = preprocess_fn
# Traverse all images in the dataset
self.image_paths = []
for root, dirs, files in os.walk(args.data_dir):
for file in files:
if file.endswith(".jpg"):
self.image_paths.append(os.path.join(root, file))
def __len__(self):
return len(self.image_paths) * 25
def __getitem__(self, index):
image_path = self.image_paths[index // 25]
real_image = Image.open(image_path).convert('RGB')
object_id = image_path.split('/')[-1].split('.')[0]
object_name = image_path.split('/')[-2]
if object_name in OBJECT:
object_class = OBJECT[object_name]
prompt = OBJECT_PROMPTS[index % 25]
input_prompt = KOSMOSG_OBJECT_PROMPTS[index % 25]
else:
object_class = LIVE_OBJECT[object_name]
prompt = LIVE_OBJECT_PROMPTS[index % 25]
input_prompt = KOSMOSG_LIVE_OBJECT_PROMPTS[index % 25]
prompt = prompt.format(object_class)
input_prompt = input_prompt.format('<i>' if self.args.drop_object else (object_class + ' <i>'))
src_tokens, gpt_img_src_tokens, img_gpt_input_mask, negative_tokens = \
self.preprocess_fn(input_prompt,
"" if self.args.negative_prompt else "",
real_image, single_batch=False)
return src_tokens, gpt_img_src_tokens, img_gpt_input_mask, negative_tokens, object_name, object_id, \
real_image, prompt
def dreambench_collate_fn(batch):
src_tokens = [x[0] for x in batch]
gpt_img_src_tokens = torch.cat([x[1] for x in batch])
img_gpt_input_mask = [x[2] for x in batch]
negative_tokens = batch[0][3].unsqueeze(0)
src_tokens = pad_sequence(src_tokens, batch_first=True, padding_value=1)
img_gpt_input_mask = pad_sequence(img_gpt_input_mask, batch_first=True, padding_value=0)
object_name = [x[4] for x in batch]
object_id = [x[5] for x in batch]
real_image = [x[6] for x in batch]
prompt = [x[7] for x in batch]
return src_tokens, gpt_img_src_tokens, img_gpt_input_mask, negative_tokens, object_name, object_id, real_image, prompt
def main(cfg):
cfg.model.pretrained_ckpt_path = "/path/to/checkpoint_final.pt"
args = OmegaConf.create()
args.data_dir = "/path/to/dreambench/dreambooth/dataset"
args.batch_size = 5
args.num_workers = 4
args.scheduler = "dpms" # ['ddim', 'pndm', 'dpms']
args.num_inference_steps = 100
args.guidance_scale = 7.5
args.num_images_per_prompt = 4
args.seed = 0
args.negative_prompt = False
args.drop_object = True
args.output_dir = "/path/to/output-dir/" + cfg.model.pretrained_ckpt_path.split('/')[-2] + '_' \
+ cfg.model.pretrained_ckpt_path.split('/')[-1].split('.')[0].split('_')[-1] + '_' \
+ args.scheduler + '_' + str(args.num_inference_steps) + '_' + str(args.guidance_scale) \
+ '_' + str(args.negative_prompt) + '_' + str(args.drop_object)
accelerator = Accelerator()
if accelerator.is_main_process and not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
dino_score = DINO_Score(model_name_or_path='dino_vits16')
clip_i_score = CLIP_IScore(model_name_or_path='openai/clip-vit-base-patch32')
clip_t_score = CLIP_TScore(model_name_or_path='openai/clip-vit-base-patch32')
dino_score = accelerator.prepare_model(dino_score, evaluation_mode=True)
clip_i_score = accelerator.prepare_model(clip_i_score, evaluation_mode=True)
clip_t_score = accelerator.prepare_model(clip_t_score, evaluation_mode=True)
# stat existing images in output_dir
image_paths = list()
for root, dirs, files in os.walk(args.output_dir):
for file in files:
if file.endswith(".png"):
image_paths.append(os.path.join(root, file))
if len(image_paths) >= 3000:
accelerator.print("Already generated enough images")
dataset = Image_Dataset(args, image_paths)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, num_workers=args.num_workers,
shuffle=False, pin_memory=True, drop_last=False,
persistent_workers=True, collate_fn=image_collate_fn)
dataloader = accelerator.prepare(dataloader)
accelerator.print("Number of Images: ", len(dataset))
for batch in tqdm(dataloader):
images, real_images, prompts = batch
dino_score.update(images, real_images)
clip_i_score.update(images, real_images)
clip_t_score.update(images, prompts)
accelerator.print("Computing Scores...")
accelerator.print("DINO Score: ", dino_score.compute())
accelerator.print("CLIP Image Score: ", clip_i_score.compute())
accelerator.print("CLIP Text Score: ", clip_t_score.compute())
return
else:
# clear all existing images
if accelerator.is_main_process:
for root, dirs, files in os.walk(args.output_dir):
for file in files:
if file.endswith(".png"):
os.remove(os.path.join(root, file))
model = AppModel(cfg)
model.set_ckpt_scheduler_fn(cfg.model.pretrained_ckpt_path, args.scheduler)
dataset = DreamBench_Dataset(args, model.kosmosg_preprocess)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,
num_workers=args.num_workers, shuffle=False, pin_memory=True,
drop_last=False, persistent_workers=True,
collate_fn=dreambench_collate_fn)
accelerator.print("Number of Images: ", len(dataset))
model, dataloader = accelerator.prepare(model, dataloader)
kwargs = {
'num_inference_steps': args.num_inference_steps,
'text_guidance_scale': args.guidance_scale,
'num_images_per_prompt': args.num_images_per_prompt,
'lora_scale': 0.0,
}
for batch_id, batch in tqdm(enumerate(dataloader), total=len(dataloader)):
src_tokens, gpt_img_src_tokens, img_gpt_input_mask, negative_tokens, object_name, object_id, real_image, prompt = batch
# generate images
randomize_seed_fn(args.seed, False)
images = model.model.sample(src_tokens, gpt_img_src_tokens, img_gpt_input_mask, negative_tokens, **kwargs)
# save image
for image_id, image in enumerate(images):
pos = batch_id * accelerator.num_processes * args.batch_size * args.num_images_per_prompt + \
image_id * accelerator.num_processes + accelerator.process_index
name = '+'.join([
object_name[image_id % args.batch_size],
object_id[image_id % args.batch_size],
str(pos),
prompt[image_id % args.batch_size]
])
images[image_id].save(os.path.join(args.output_dir, "{}.png".format(name)))
real_image = real_image * args.num_images_per_prompt
dino_score.update(images, real_image)
clip_i_score.update(images, real_image)
clip_t_score.update(images, prompt * args.num_images_per_prompt)
accelerator.print("Number of Samples: ", (dino_score.n_samples * accelerator.num_processes).item())
accelerator.print("DINO Score: ", (dino_score.compute()).item())
accelerator.print("CLIP Image Score: ", (clip_i_score.compute()).item())
accelerator.print("CLIP Text Score: ", (clip_t_score.compute()).item())
if __name__ == "__main__":
parser = options.get_training_parser()
cfg = options.parse_args_and_arch(parser, modify_parser=None)
cfg = convert_namespace_to_omegaconf(cfg)
main(cfg)
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