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import base64
import io
import multiprocessing
import os
import random
from argparse import ArgumentParser
from multiprocessing import Process
import numpy as np
import requests
import torch
import torch.nn.functional as F
from PIL import Image
from scipy.ndimage import label, find_objects, grey_dilation
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, Blip2Processor, Blip2ForConditionalGeneration, \
CLIPSegProcessor, CLIPSegForImageSegmentation
Image.MAX_IMAGE_PIXELS = 1000000000
INSTRUCTION_KEY = "### Instruction:"
INPUT_KEY = "### Input:"
RESPONSE_KEY = "### Response:"
INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
PROMPT_FOR_GENERATION_FORMAT = """{intro}
{instruction_key}
Extract all objects mentioned in the caption and separate them using commas. Exclude background elements (site, location, environment) and only include foreground objects. Ensure that only nouns are included and exclude adjectives entirely.
{input_key}
{input}
{response_key}
""".format(
intro=INTRO_BLURB,
instruction_key=INSTRUCTION_KEY,
instruction="{instruction}",
input_key=INPUT_KEY,
input="{input}",
response_key=RESPONSE_KEY,
)
@torch.no_grad()
def save_tsv(args, shard_id, shard, device):
os.environ["CUDA_VISIBLE_DEVICES"] = "0, 1, 2, 3, 4, 5, 6, 7"
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.set_device(device)
model_dtype = torch.float16
# blip2
blip2_processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-6.7b")
blip2_model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=model_dtype)
blip2_model.eval().to(device)
# mpt
mpt_config = AutoConfig.from_pretrained('mosaicml/mpt-7b-instruct', trust_remote_code=True)
mpt_config.init_device = device
mpt_config.attn_config['attn_impl'] = args.attn_impl
mpt_tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b')
mpt_tokenizer.pad_token = mpt_tokenizer.eos_token
mpt_tokenizer.padding_side = 'left'
mpt_model = AutoModelForCausalLM.from_pretrained('mosaicml/mpt-7b-instruct', config=mpt_config,
torch_dtype=model_dtype, trust_remote_code=True)
mpt_model.eval()
mpt_generate_kwargs = {
'max_new_tokens': args.max_new_tokens,
'temperature': args.temperature,
'top_p': args.top_p,
'top_k': args.top_k,
'repetition_penalty': args.repetition_penalty,
'no_repeat_ngram_size': args.no_repeat_ngram_size,
'use_cache': args.use_cache,
'do_sample': False if args.temperature == 0 else args.do_sample,
'eos_token_id': mpt_tokenizer.eos_token_id,
'pad_token_id': mpt_tokenizer.pad_token_id,
}
# clipseg
clipseg_processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
clipseg_model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined", torch_dtype=model_dtype)
clipseg_model.eval().to(device)
cnt = 0
for image in tqdm(shard):
if image is None:
continue
if cnt % 1000 == 0:
# close previous file if any
if cnt > 0:
f.close()
f = open(os.path.join(args.output_dir, f"cnt_{args.machine_id}_{shard_id}_{cnt // 1000}.tsv"), "w",
encoding='utf-8')
cnt += 1
blip2_input = blip2_processor(images=image, return_tensors="pt").to(device, model_dtype)
blip2_gen = blip2_model.generate(**blip2_input)
caption = blip2_processor.batch_decode(blip2_gen, skip_special_tokens=True)[0] \
.replace('\t', '').replace('\n', '').strip()
# tag extraction
prompt = PROMPT_FOR_GENERATION_FORMAT.format(input=caption)
# Run HF generate
mpt_input = mpt_tokenizer(prompt, return_tensors='pt', padding=True)
for key, value in mpt_input.items():
mpt_input[key] = value.to(device)
mpt_gen = mpt_model.generate(
input_ids=mpt_input['input_ids'],
attention_mask=mpt_input['attention_mask'],
**mpt_generate_kwargs,
)
tags = mpt_tokenizer.batch_decode(mpt_gen, skip_special_tokens=True)[0][len(prompt):]
if '#' in tags:
continue
tags = tags.split(",")
tags = [tag.replace('\t', '').replace('\n', '').strip() for tag in tags]
tags = [tag for tag in tags if len(tag) > 0 and tag in caption]
if len(tags) == 0:
continue
clipseg_input = clipseg_processor(text=tags, images=[image] * len(tags), padding=True, return_tensors="pt")
for key, value in clipseg_input.items():
clipseg_input[key] = value.to(device)
if value.dtype == torch.float32:
clipseg_input[key] = value.to(device, model_dtype)
# predict
clipseg_gen = clipseg_model(**clipseg_input).logits
if len(tags) == 1:
clipseg_gen = clipseg_gen.unsqueeze(0)
image_size = image.height
# interpolate to original size
clipseg_gen = F.interpolate(clipseg_gen.unsqueeze(1), size=image_size, mode='bilinear')
masks = torch.sigmoid(clipseg_gen).squeeze(1)
masks = masks.cpu().numpy()
sub_images = []
tags_to_keep = []
# save the masked image
for mask_id, mask in enumerate(masks):
image_array = np.array(image)
thresholded_mask = mask > args.threshold
if thresholded_mask.max() == 0:
continue
thresholded_mask = grey_dilation(thresholded_mask, size=(image_size // 100, image_size // 100))
labeled_matrix, num_features = label(thresholded_mask)
regions = find_objects(labeled_matrix)
sizes = [np.sum(thresholded_mask[region]) for region in regions]
max_index = np.argmax(sizes)
max_region = regions[max_index]
thresholded_mask[labeled_matrix != (max_index + 1)] = False
tags_to_keep.append(tags[mask_id])
# Determine the dimensions of the region
y_start, y_stop = max_region[0].start, max_region[0].stop
x_start, x_stop = max_region[1].start, max_region[1].stop
height = y_stop - y_start
width = x_stop - x_start
# Calculate the desired side length for a square region
side_length = max(height, width)
# Calculate the center of the region
center_y = (y_start + y_stop) // 2
center_x = (x_start + x_stop) // 2
# Calculate the new boundaries for the region
new_y_start = center_y - (side_length // 2)
new_y_stop = new_y_start + side_length
new_x_start = center_x - (side_length // 2)
new_x_stop = new_x_start + side_length
# Adjust the boundaries if they exceed the image boundaries
if new_y_start < 0:
new_y_start = 0
new_y_stop = side_length
elif new_y_stop > image_array.shape[0]:
new_y_start = image_array.shape[0] - side_length
new_y_stop = image_array.shape[0]
if new_x_start < 0:
new_x_start = 0
new_x_stop = side_length
elif new_x_stop > image_array.shape[1]:
new_x_start = image_array.shape[1] - side_length
new_x_stop = image_array.shape[1]
# Create a new mask with the adjusted boundaries
object_image = image_array[new_y_start:new_y_stop, new_x_start:new_x_stop]
max_region_mask = thresholded_mask[new_y_start:new_y_stop, new_x_start:new_x_stop]
masked_image = object_image.copy()
masked_image[~max_region_mask] = 255
object_image = Image.fromarray(object_image).resize((512, 512))
masked_image = Image.fromarray(masked_image).resize((512, 512))
sub_images.extend([object_image, masked_image])
if len(sub_images) == 0:
continue
image = image.resize((512, 512))
# encode image using base64
buffer = io.BytesIO()
image.save(buffer, format='PNG')
image = base64.b64encode(buffer.getvalue()).decode('utf-8')
for j, im in enumerate(sub_images):
buffer = io.BytesIO()
im.save(buffer, format='PNG')
sub_images[j] = base64.b64encode(buffer.getvalue()).decode('utf-8')
# write to tsv file
f.write('\t'.join([
caption,
','.join(tags_to_keep),
image,
*sub_images
]) + '\n')
class OpenImageDataset(Dataset):
def __init__(self, url_data):
self.data = url_data
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
try:
items = self.data[idx].split(',')
image = Image.open(requests.get(items[2], stream=True).raw).convert('RGB')
# caption
width, height = image.size
shortest_side = min(width, height)
left = (width - shortest_side) // 2
top = (height - shortest_side) // 2
right = left + shortest_side
bottom = top + shortest_side
image = image.crop((left, top, right, bottom))
return image
except:
return None
def collate_fn(batch):
return batch[0] if batch is not None else None
def main():
"""Parse commandline arguments."""
parser = ArgumentParser()
parser.add_argument('--data-dir', type=str,
default='/path/to/image_ids_and_rotation.csv')
parser.add_argument('--output-dir', type=str, default='/path/to/output-dir/')
parser.add_argument('--num-process', type=int, default=8)
parser.add_argument('--cuda-device', type=list, default=[0, 1, 2, 3, 4, 5, 6, 7])
parser.add_argument('--num-machine', type=int, default=1)
parser.add_argument('--machine-id', type=int, default=0)
parser.add_argument('--max-seq-len', type=int, default=None)
parser.add_argument('--max-new-tokens', type=int, default=10)
parser.add_argument('--temperature', type=float, default=1.0)
parser.add_argument('--top-k', type=int, default=50)
parser.add_argument('--top-p', type=float, default=0.95)
parser.add_argument('--repetition-penalty', type=float, default=1.0)
parser.add_argument('--no-repeat-ngram-size', type=int, default=0)
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--do-sample', type=bool, default=True)
parser.add_argument('--use-cache', type=bool, default=True)
parser.add_argument('--trust-remote-code', type=bool, default=True)
parser.add_argument('--attn-impl', type=str, default='torch')
parser.add_argument('--threshold', type=float, default=0.3)
args = parser.parse_args()
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
with open(args.data_dir, 'r', encoding='utf8') as f:
url_data = f.read().strip().split('\n')
# split into 8 machine, and pick the part of machine_id
url_data = url_data[args.machine_id::args.num_machine]
# split url data into shards
url_data = [url_data[i::args.num_process] for i in range(args.num_process)]
dataloaders = [
DataLoader(
OpenImageDataset(url_data[i]),
batch_size=1,
shuffle=False,
num_workers=4,
pin_memory=True,
persistent_workers=True,
drop_last=False,
prefetch_factor=4,
collate_fn=collate_fn
)
for i in range(args.num_process)
]
multiprocessing.set_start_method('spawn')
processes = []
for shard_id, shard in enumerate(dataloaders):
p = Process(
target=save_tsv,
args=(
args,
shard_id,
shard,
torch.device('cuda:{}'.format(args.cuda_device[shard_id % len(args.cuda_device)]))
)
)
p.start()
processes.append(p)
for p in processes:
p.join()
print('Done!')
if __name__ == '__main__':
main()
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