How to make a test?

[inkzk]

Could you please upload a example script?

[yufengm]

Please refer to https://github.com/yufengm/Adaptive/blob/4c0555af546cdbd49e99ff1bd6e91d1654ae0cd2/train.py#L152 for test on the validation dataset.

[17000432]

Can you share the test code?I tried to write the test section, but there were some problems.

--coding:utf-8 --

import torch import matplotlib.pyplot as plt import numpy as np import argparse import pickle import os from torchvision import transforms from build_vocab import Vocabulary from adaptive import AttentiveCNN ,Decoder ,Encoder2Decoder from PIL import Image

from torch.autograd import Variable

Device configuration

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

device = torch.cuda.set_device(0)

import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = "1" #使用第二块GPU

def load_image(image_path, transform=None): image = Image.open(image_path) image = image.resize([224, 224], Image.LANCZOS)

image = Variable(image)

if transform is not None:
    image = transform(image).unsqueeze(0)
return image

def main(args):

Image preprocessing

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.485, 0.456, 0.406),
                         (0.229, 0.224, 0.225))])

# Load vocabulary wrapper
with open(args.vocab_path, 'rb') as f:
    vocab = pickle.load(f)

# Build models
encoder = AttentiveCNN(args.embed_size, args.hidden_size).eval()  # eval mode (batchnorm uses moving mean/variance)

encoder2decoder = Encoder2Decoder(args.embed_size, len(vocab), args.hidden_size)
# Load the trained model parameters
encoder2decoder.load_state_dict(torch.load(args.encoder2decoder_path))

# Prepare an image
image = load_image(args.image, transform)

# Generate an caption from the image
sampled_ids, attention, Beta = encoder2decoder.sampler(image)
sampled_ids=Variable(torch.LongTensor(sampled_ids))
sampled_ids = sampled_ids[0].cpu().numpy()  # (1, max_seq_length) -> (max_seq_length)
# Convert word_ids to words
sampled_caption = []
for word_id in sampled_ids:
    word = vocab.idx2word[word_id]
    sampled_caption.append(word)
    if word == '<end>':
        break
sentence = ' '.join(sampled_caption)

# Print out the image and the generated caption
print (sentence)
image = Image.open(args.image)
plt.imshow(np.asarray(image))

if name == 'main': parser = argparse.ArgumentParser() parser.add_argument('--image', type=str, required=True, help='input image for generating caption') parser.add_argument('--encoder2decoder_path', type=str, default='models/adaptive-1.pkl', help='path for trained encoder2decoder') parser.add_argument('--vocab_path', type=str, default='data/vocab.pkl', help='path for vocabulary wrapper')

# Model parameters (should be same as paramters in train.py)
parser.add_argument('--embed_size', type=int, default=256, help='dimension of word embedding vectors')
parser.add_argument('--hidden_size', type=int, default=512, help='dimension of lstm hidden states')
parser.add_argument('--num_layers', type=int, default=1, help='number of layers in lstm')
args = parser.parse_args()
main(args)

RuntimeError:Expected object of type torch.LongTensor but found type torch.cuda.LongTensor for argument #3'index'

please help!!!

[17000432]

@inkzk

Did you later write the test module to generate image subtitles?Can you share it with me

[Allenxq]

@inkzk

Did you later write the test module to generate image subtitles?Can you share it with me

Did you solve it?