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import sys
import json
import os.path
import logging
import argparse
from tqdm import tqdm
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import clip
from collections import defaultdict
from PIL import Image
import faiss
import os
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cudnn.benchmark = True
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed(0)
import gc
class ClipRetrieval():
def __init__(self, index_name):
self.datastore = faiss.read_index(index_name)
#self.datastore.nprobe=25
def get_nns(self, query_img, k=20):
#get k nearest image
D, I = self.datastore.search(query_img, k)
return D, I[:,:k]
class EvalDataset():
def __init__(self, dataset_splits, images_dir, images_names, clip_retrieval_processor, eval_split="val_images"):
super().__init__()
with open(dataset_splits) as f:
self.split = json.load(f)
self.split = self.split[eval_split]
self.images_dir= images_dir
with open(args.images_names) as f:
self.images_names = json.load(f)
self.clip_retrieval_processor = clip_retrieval_processor
def __getitem__(self, i):
coco_id = self.split[i]
image_filename= self.images_dir+self.images_names[coco_id]
img_open = Image.open(image_filename).copy()
img = np.array(img_open)
if len(img.shape) ==2 or img.shape[-1]!=3: #convert grey or CMYK to RGB
img_open = img_open.convert('RGB')
gc.collect()
print("img_open",np.array(img_open).shape)
#inputs_features_retrieval = self.clip_retrieval_processor(img_open).unsqueeze(0)
return self.clip_retrieval_processor(img_open).unsqueeze(0), coco_id
def __len__(self):
return len(self.split)
def evaluate(args):
#load data of the datastore (i.e., captions)
with open(args.index_captions) as f:
data_datastore = json.load(f)
datastore = ClipRetrieval(args.datastore_path)
datastore_name = args.datastore_path.split("/")[-1]
#load clip to encode the images that we want to retrieve captions for
clip_retrieval_model, clip_retrieval_feature_extractor = clip.load("RN50x64", device=device)
clip_retrieval_model.eval()
#data_loader to get images that we want to retrieve captions for
data_loader = torch.utils.data.DataLoader(
EvalDataset(
args.dataset_splits,
args.images_dir,
args.images_names,
clip_retrieval_feature_extractor,
args.split),
batch_size=1,
shuffle=True,
num_workers=1,
pin_memory=True
)
print("device",device)
nearest_caps={}
for data in tqdm(data_loader):
inputs_features_retrieval, coco_id = data
coco_id = coco_id[0]
#normalize images to retrieve (since datastore has also normalized captions)
inputs_features_retrieval = inputs_features_retrieval.to(device)
image_retrieval_features = clip_retrieval_model.encode_image(inputs_features_retrieval[0])
image_retrieval_features /= image_retrieval_features.norm(dim=-1, keepdim=True)
image_retrieval_features=image_retrieval_features.detach().cpu().numpy().astype(np.float32)
print("inputs_features_retrieval",inputs_features_retrieval.size())
print("image_retrieval_features",image_retrieval_features.shape)
D, nearest_ids=datastore.get_nns(image_retrieval_features, k=5)
print("D size", D.shape)
print("nea", nearest_ids.shape)
gc.collect()
#Since at inference batch is 1
D=D[0]
nearest_ids=nearest_ids[0]
list_of_similar_caps=defaultdict(list)
for index in range(len(nearest_ids)):
nearest_id = str(nearest_ids[index])
nearest_cap=data_datastore[nearest_id]
if len(nearest_cap.split()) > args.max_caption_len:
print("retrieve cap too big" )
continue
#distance=D[index]
#list_of_similar_caps[datastore_name].append((nearest_cap, str(distance)))
#list_of_similar_caps[datastore_name].append(nearest_cap)
#nearest_caps[str(coco_id)]=list_of_similar_caps
#save results
outputs_dir = os.path.join(args.output_path, "retrieved_caps")
if not os.path.exists(outputs_dir):
os.makedirs(outputs_dir)
data_name=dataset_splits.split("/")[-1]
name = "nearest_caps_"+data_name +"_w_"+datastore_name + "_"+ args.split
results_output_file_name = os.path.join(outputs_dir, name + ".json")
json.dump(nearest_caps, open(results_output_file_name, "w"))
def check_args(args):
parser = argparse.ArgumentParser()
#Info of the dataset to evaluate on (vizwiz, flick30k, msr-vtt)
parser.add_argument("--images_dir",help="Folder where the preprocessed image data is located", default="data/vizwiz/images")
parser.add_argument("--dataset_splits",help="File containing the dataset splits", default="data/vizwiz/dataset_splits.json")
parser.add_argument("--images_names",help="File containing the images names per id", default="data/vizwiz/images_names.json")
parser.add_argument("--split", default="val_images", choices=["val_images", "test_images"])
parser.add_argument("--max-caption-len", type=int, default=25)
#Which datastore to use (web, human)
parser.add_argument("--datastore_path", type=str, default="datastore2/vizwiz/vizwiz")
parser.add_argument("--index_captions",
help="File containing the captions of the datastore per id", default="datastore2/vizwiz/vizwiz.json")
parser.add_argument("--output-path",help="Folder where to store outputs", default="eval_vizwiz_with_datastore_from_vizwiz.json")
parsed_args = parser.parse_args(args)
return parsed_args
if __name__ == "__main__":
args = check_args(sys.argv[1:])
logging.basicConfig(
format='%(levelname)s: %(message)s', level=logging.INFO)
logging.info(args)
evaluate(args)
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