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import os
import json
import shutil
from tqdm import tqdm
from PIL import Image
import natsort
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from config import config
from src.open_clip import create_model_and_transforms
class loading_img(Dataset):
def __init__(self, img_list):
self.img_list = img_list
def __len__(self):
return len(self.img_list)
def __getitem__(self, idx):
return self.img_list[idx].squeeze(0)
class CustomDataset(Dataset):
def __init__(self, questions, clippy, preprocess_val, clip_size, base_dir):
self.questions = questions
self.clippy = clippy
self.clip_size = clip_size
self.preprocess_val = preprocess_val
self.device = next(clippy.parameters()).device
self.base_dir = base_dir
def __getitem__(self, index):
line = self.questions[index]
images_dir = f"{line['q_uid']}"
if line["Activity"] == "" or ("Activity" not in line): ref1 = []
else:
if isinstance(line["Activity"], list): ref1 = line["Activity"]
else: ref1 = line["Activity"].split(', ')
keywords = ref1
clip_size = self.clip_size
clippy = self.clippy
preprocess_val = self.preprocess_val
images = []
timelines = []
timelines_int = []
img_names = []
image_list = []
nframes_paths = line["filepath"]
total_len = len(nframes_paths)
nframes_paths = natsort.natsorted(nframes_paths)
img_paths = []
for img_path in nframes_paths:
img_path = self.base_dir + "/" + "/".join(img_path.split("/")[-4:])
img_paths.append(img_path)
img_names.append(img_path.split('/')[-1].split('.')[0])
cur_img = Image.open(img_path).resize(clip_size)
image_list.append(preprocess_val(cur_img))
timeline = f"{img_names[-1].split('_')[-2]}.{img_names[-1].split('_')[-1]} seconds"
timeline_int = float(f"{img_names[-1].split('_')[-2]}.{img_names[-1].split('_')[-1]}")
timelines.append(timeline)
timelines_int.append(timeline_int)
return image_list, img_paths, timelines, timelines_int, keywords, img_names
def __len__(self):
return len(self.questions)
def disable_torch_init():
setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
def SortSimilarity(q_uid, simmat, keywords, nimgtokens, nframes_paths, maximgslen):
sort_simmat, sort_idx = torch.sort(simmat, dim=-1, descending=True)
sort_idx = torch.floor(sort_idx/nimgtokens).to(int)
curimgslen = 0
imgidx_kw_dict = dict()
numrow, numcol = sort_simmat.shape
row_col_list = [0 for _ in range(numrow)]
token = True
while token:
j = 0
while j < numrow:
k = 0
i = row_col_list[j]
while k < numcol-i:
col_idx = i+k
k += 1
simvalue = sort_simmat[j, col_idx].item()
img_idx = sort_idx[j, col_idx].item()
curr_keyword = keywords[j]
curr_kfpath = nframes_paths[img_idx]
if img_idx in imgidx_kw_dict: continue
else:
imgidx_kw_dict[img_idx] = {"kw": curr_keyword, "simvalue": simvalue, "kf_path": curr_kfpath, "kw_others": []}
curimgslen += 1
row_col_list[j] = col_idx + 1
if curimgslen == maximgslen: return imgidx_kw_dict
else: break
j += 1
if sum(row_col_list) >= numrow*(numcol-1): token = False
def create_data_loader(questions, clippy, preprocess_val, clip_size, base_dir, batch_size=1, num_workers=16):
assert batch_size == 1, "batch_size must be 1"
dataset = CustomDataset(questions, clippy, preprocess_val, clip_size, base_dir)
data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
return data_loader
def eval_model():
disable_torch_init()
question_path, maximgslen, base_dir, concatname, modelpath, answerpath, concatdir = config.question_path, config.maximgslen, config.base_dir, config.concatname, config.modelpath, config.answerpath, config.concatdir
pretrained_ckpt = f"{modelpath}"
clippy, preprocess_train, preprocess_val = create_model_and_transforms(
"clippy-B-16",
device="cuda",
pretrained=pretrained_ckpt
)
clip_size = (224,224)
device = next(clippy.parameters()).device
questions = [json.loads(q) for q in open(os.path.expanduser(question_path), "r")]
answer_path = f"{answerpath}"
print(f"\nquestion_path:{question_path}\nanswer_path:{answer_path}")
os.makedirs(os.path.dirname(answer_path), exist_ok=True)
with open(answer_path, "w") as ans_file:
data_loader = create_data_loader(questions, clippy, preprocess_val, clip_size, base_dir)
concatimg_dir_base = f"{concatdir}"
with torch.no_grad():
for (image_list, nframes_paths, timelines, timelines_int, keywords, img_names), line in tqdm(zip(data_loader, questions), total=len(questions)):
q_uid = line["q_uid"]
CA = line["CA"] if "CA" in line else None
option0 = line['option 0']
option1 = line['option 1']
option2 = line['option 2']
option3 = line['option 3']
option4 = line['option 4']
question = line['question']
pastobj = None
past_VLM_path = None
past_VLM_timeline = None
img_embed = []
nframes_paths = [e[0] for e in nframes_paths]
image_set = loading_img(image_list)
image_loader = DataLoader(image_set, batch_size=64, shuffle=False, num_workers=16)
for e in image_loader: img_embed.append(clippy.encode_image(e.to(device), pool=False)[:, 1:])
img_embed = torch.concat(img_embed, dim=0)
limit_keywords = config.limit_keywords
keywords = [e[0] for e in keywords][:limit_keywords]
keyword_embed = clippy.text.encode(keywords, convert_to_tensor=True)
nframe, nimgtokens, channels = img_embed.shape
keyword_embed = keyword_embed.unsqueeze(1)
img_embed = img_embed.flatten(0, 1).unsqueeze(0)
simmat = F.cosine_similarity(keyword_embed, img_embed, dim=-1).to(torch.float)
imgidx_kw_dict = SortSimilarity(q_uid, simmat, keywords, nimgtokens, nframes_paths, maximgslen=maximgslen)
# order of simvalue
simvalue = np.array([e["simvalue"] for e in imgidx_kw_dict.values()])
ordered_idx = np.argsort(simvalue)
simvalue = simvalue[ordered_idx]
kf_paths = np.array([e["kf_path"] for e in imgidx_kw_dict.values()])[ordered_idx]
matchingkw = np.array([e["kw"] for e in imgidx_kw_dict.values()])[ordered_idx]
#order by timeline
time_kf_paths = np.array(kf_paths[:16])
timelines_int = np.array([float(f"{e.replace('.jpg', '').split('/')[-1].split('_')[1]}" + "."+ f"{e.replace('.jpg', '').split('/')[-1].split('_')[2]}") for e in time_kf_paths])
time_ordered_idx = np.argsort(timelines_int)
timelines_int = timelines_int[time_ordered_idx]
time_simvalue = np.array(simvalue[:16])[time_ordered_idx]
time_kf_paths = np.array(time_kf_paths)[time_ordered_idx]
time_matchingkw = np.array(matchingkw[:16])[time_ordered_idx]
simvalue[:16] = time_simvalue
kf_paths[:16] = time_kf_paths
matchingkw[:16] = time_matchingkw
segment_timeline = f"{timelines[0][0].split(' seconds')[0]}-{timelines[-1][0].split(' seconds')[0]}"
imgw, imgh = Image.open(kf_paths[0]).size
redwidth = 20
newimgw, newimgh = (imgw+redwidth) * 4 + redwidth, (imgh+redwidth) * 2 + redwidth
concatimg = np.zeros((newimgh, newimgw, 3), dtype=np.uint8)
concatimg[:, :, 0] = 255
concatimglist = []
concatimg_dir = f"{concatimg_dir_base}/{q_uid}"
for i, cpath in enumerate(kf_paths):
cur_img = np.array(Image.open(cpath))
whole_frame = 8
remainder = i % whole_frame
rowremainder = i % (whole_frame//2)
startwidth = redwidth + (imgw + redwidth)*rowremainder
endwidth = startwidth + imgw
if remainder / whole_frame < 0.5: concatimg[redwidth:redwidth+imgh, startwidth:endwidth, :] = cur_img
else: concatimg[redwidth+imgh+redwidth:newimgh-redwidth, startwidth:endwidth, :] = cur_img
if remainder == whole_frame - 1: concatimglist.append(Image.fromarray(concatimg))
if os.path.exists(concatimg_dir): shutil.rmtree(concatimg_dir)
os.makedirs(f"{concatimg_dir}", exist_ok=True)
for i, img in enumerate(concatimglist): img.save(f"{concatimg_dir}/concat_{i}.jpg")
line["kf_paths"] = kf_paths.tolist()
line["keywords"] = matchingkw.tolist()
line["simvalue"] = simvalue.tolist()
line["imgidx_kw_dict"] = imgidx_kw_dict
line["segment_timeline"] = segment_timeline
line["concatimg_dir"] = concatimg_dir
ans_file.write(json.dumps(line) + "\n")
print(f"question_path:{question_path}\nanswer_path:{answer_path}")
if __name__ == "__main__":
eval_model() |