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eval.py
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eval.py
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import torch
import torch.nn as nn
from torchvision import models
from torch.nn.utils.rnn import pack_padded_sequence
from models import Encoder, Decoder
from utils import collate_fn
from vocab import build_vocab
import pickle
import argparse
import os
from torchvision import transforms
from dataset import ImageDataset
import numpy as np
import json
from pycocotools.coco import COCO
from coco_eval import COCOEvalCap
def main(args):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Evaluating on {device}")
with open(args.vocab_path, 'rb') as f:
vocab_object = pickle.load(f)
print(f"Loaded the vocabulary object from {args.vocab_path}, total size={len(vocab_object)}")
if args.glove_embed_path is not None:
with open(args.glove_embed_path, 'rb') as f:
glove_embeddings = pickle.load(f)
print(f"Loaded the glove embeddings from {args.glove_embed_path}, total size={len(glove_embeddings)}")
# We are using 300d glove embeddings
args.embed_size = 300
weights_matrix = np.zeros((len(vocab_object), args.embed_size))
for word, index in vocab_object.word2index.items():
if word in glove_embeddings:
weights_matrix[index] = glove_embeddings[word]
else:
weights_matrix[index] = np.random.normal(scale=0.6, size=(args.embed_size, ))
weights_matrix = torch.from_numpy(weights_matrix).float().to(device)
else:
weights_matrix = None
img_transforms = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
val_dataset = ImageDataset(args.image_root, img_transforms)
val_dataloader = torch.utils.data.DataLoader(
dataset=val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
encoder = Encoder(args.resnet_size, (3, 224, 224), args.embed_size)
encoder = encoder.eval().to(device)
decoder = Decoder(args.rnn_type, weights_matrix, len(vocab_object), args.embed_size, args.hidden_size)
decoder = decoder.eval().to(device)
model_ckpt = torch.load(args.eval_ckpt_path, map_location=lambda storage, loc: storage)
encoder.load_state_dict(model_ckpt['encoder'])
decoder.load_state_dict(model_ckpt['decoder'])
print(f"Loaded model from {args.eval_ckpt_path}")
val_results = []
total_examples = len(val_dataloader)
for i, (images, image_ids) in enumerate(val_dataloader):
images = images.to(device)
with torch.no_grad():
image_embeddings = encoder(images)
captions_wid = decoder.sample_batch(image_embeddings, args.caption_maxlen)
captions_wid = captions_wid.cpu().numpy()
captions = []
for caption_wid in captions_wid:
caption_words = []
for word_id in caption_wid:
word = vocab_object.index2word[word_id]
caption_words.append(word)
if word == '<end>':
break
captions.append(' '.join(caption_words[1:-2]))
image_ids = image_ids.tolist()
for image_id, caption in zip(image_ids, captions):
val_results.append({'image_id': image_id, 'caption': caption})
with open(args.results_json_path,'w') as f:
json.dump(val_results, f)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--vocab_path', type=str, help='vocabulary pickle path', required=True)
parser.add_argument('--image_root', type=str, default='val2014')
parser.add_argument('--eval_ckpt_path', type=str, required=True)
parser.add_argument('--glove_embed_path', type=str, default=None)
parser.add_argument('--results_json_path', type=str, required=True)
parser.add_argument('--annotations_path', type=str, default='annotations/captions_val2014.json')
parser.add_argument('--batch_size', type=int, default=256)
parser.add_argument('--num_workers', type=int, default=8)
parser.add_argument('--embed_size', type=int, default=512)
parser.add_argument('--hidden_size', type=int, default=512)
parser.add_argument('--rnn_type', type=str, default='lstm')
parser.add_argument('--resnet_size', type=int, choices=[18, 34, 50, 101, 152], default=50)
parser.add_argument('--caption_maxlen', type=int, default=15)
args = parser.parse_args()
print(args)
main(args)