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"""
****************** COPYRIGHT AND CONFIDENTIALITY INFORMATION ******************
Copyright (c) 2018 [Thomson Licensing]
All Rights Reserved
This program contains proprietary information which is a trade secret/business \
secret of [Thomson Licensing] and is protected, even if unpublished, under \
applicable Copyright laws (including French droit d'auteur) and/or may be \
subject to one or more patent(s).
Recipient is to retain this program in confidence and is not permitted to use \
or make copies thereof other than as permitted in a written agreement with \
[Thomson Licensing] unless otherwise expressly allowed by applicable laws or \
by [Thomson Licensing] under express agreement.
Thomson Licensing is a company of the group TECHNICOLOR
*******************************************************************************
This scripts permits one to reproduce training and experiments of:
Engilberge, M., Chevallier, L., Pérez, P., & Cord, M. (2018, April).
Finding beans in burgers: Deep semantic-visual embedding with localization.
In Proceedings of CVPR (pp. 3984-3993)
Author: Martin Engilberge
"""
import argparse
import re
import time
import numpy as np
from numpy.__config__ import show
import torch
from misc.model import img_embedding, joint_embedding
from torch.utils.data import DataLoader, dataset
from misc.dataset import TextDataset
from misc.utils import collate_fn_cap_padded
from torch.utils.data import DataLoader
from misc.utils import load_obj
from misc.evaluation import recallTopK
from misc.utils import show_imgs
import sys
from misc.dataset import TextEncoder
device = torch.device("cuda")
# device = torch.device("cpu") # uncomment to run with cpu
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Extract embedding representation for images')
parser.add_argument("-p", '--path', dest="model_path", help='Path to the weights of the model to evaluate')
parser.add_argument("-d", '--data', dest="data_path", help='path to the file containing the sentence to embed')
parser.add_argument("-bs", "--batch_size", help="The size of the batches", type=int, default=1)
args = parser.parse_args()
print("Loading model from:", args.model_path)
checkpoint = torch.load(args.model_path, map_location=lambda storage, loc: storage)
join_emb = joint_embedding(checkpoint['args_dict'])
join_emb.load_state_dict(checkpoint["state_dict"])
for param in join_emb.parameters():
param.requires_grad = False
join_emb.to(device)
join_emb.eval()
encoder = TextEncoder()
print("Loading model done")
# (4) design intersection mode.
print("Please input your description of the image that you wanna search >>>")
for line in sys.stdin:
t0 = time.time()
cap_str = line.strip()
# with open(args.data_path, 'w') as cap_file:
# cap_file.writelines(cap_str)
t1 = time.time()
print("text is embedding ...")
dataset = torch.Tensor(encoder.encode(cap_str)).unsqueeze(dim=0)
t111 = time.time()
dataset_loader = DataLoader(dataset, batch_size=args.batch_size, num_workers=1, pin_memory=True, collate_fn=collate_fn_cap_padded)
t11 = time.time()
caps_enc = list()
for i, (caps, length) in enumerate(dataset_loader, 0):
input_caps = caps.to(device)
with torch.no_grad():
_, output_emb = join_emb(None, input_caps, length)
caps_enc.append(output_emb.cpu().data.numpy())
t12 = time.time()
caps_stack = np.vstack(caps_enc)
# print(t11 - t1, t12 - t11, t111 - t1)
t2 = time.time()
print("recall from resources ...")
# (1) load candidate imgs from saved embeding pkl file.
imgs_emb_file_path = "/home/atticus/proj/matching/DSVE/imgs_embed/v20210915_01_9408/allImg"
# imgs_emb(40775, 2400)
imgs_emb, imgs_path = load_obj(imgs_emb_file_path)
# (2) calculate the sim between cap and imgs.
# (3) rank imgs and display the searching result.
recall_imgs = recallTopK(caps_stack, imgs_emb, imgs_path, ks=5)
t3 = time.time()
show_imgs(imgs_path=recall_imgs)
# print("input stage time: {} \n text embedding stage time: {} \n recall stage time: {}".format(t1 - t0, t2 - t1, t3 - t2))
print("======== current epoch done ========")
print("Please input your description of the image that you wanna search >>>")
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