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https://github.com/yunjey/pytorch-tutorial.git
synced 2025-07-27 20:13:33 +08:00
captioning modules are edited
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yunjey
@ -13,12 +13,13 @@ from pycocotools.coco import COCO
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class CocoDataset(data.Dataset):
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"""COCO Custom Dataset compatible with torch.utils.data.DataLoader."""
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def __init__(self, root, json, vocab, transform=None):
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"""
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"""Set the path for images, captions and vocabulary wrapper.
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Args:
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root: image directory.
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json: coco annotation file path.
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vocab: vocabulary wrapper.
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transform: transformer for image.
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transform: image transformer
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"""
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self.root = root
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self.coco = COCO(json)
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@ -27,7 +28,7 @@ class CocoDataset(data.Dataset):
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self.transform = transform
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def __getitem__(self, index):
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"""This function should return one data pair(image and caption)."""
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"""Returns one data pair (image and caption)."""
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coco = self.coco
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vocab = self.vocab
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ann_id = self.ids[index]
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@ -53,9 +54,10 @@ class CocoDataset(data.Dataset):
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def collate_fn(data):
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"""Build mini-batch tensors from a list of (image, caption) tuples.
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"""Creates mini-batch tensors from the list of tuples (image, caption).
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Args:
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data: list of (image, caption) tuple.
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data: list of tuple (image, caption).
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- image: torch tensor of shape (3, 256, 256).
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- caption: torch tensor of shape (?); variable length.
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@ -68,10 +70,10 @@ def collate_fn(data):
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data.sort(key=lambda x: len(x[1]), reverse=True)
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images, captions = zip(*data)
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# Merge images (convert tuple of 3D tensor to 4D tensor)
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# Merge images (from tuple of 3D tensor to 4D tensor)
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images = torch.stack(images, 0)
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# Merget captions (convert tuple of 1D tensor to 2D tensor)
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# Merge captions (from tuple of 1D tensor to 2D tensor)
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lengths = [len(cap) for cap in captions]
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targets = torch.zeros(len(captions), max(lengths)).long()
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for i, cap in enumerate(captions):
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@ -80,18 +82,18 @@ def collate_fn(data):
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return images, targets, lengths
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def get_loader(root, json, vocab, transform, batch_size=100, shuffle=True, num_workers=2):
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def get_data_loader(root, json, vocab, transform, batch_size, shuffle, num_workers):
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"""Returns torch.utils.data.DataLoader for custom coco dataset."""
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# COCO custom dataset
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# COCO dataset
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coco = CocoDataset(root=root,
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json=json,
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vocab = vocab,
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transform=transform)
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# Data loader
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# Data loader for COCO dataset
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data_loader = torch.utils.data.DataLoader(dataset=coco,
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batch_size=batch_size,
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shuffle=True,
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shuffle=shuffle,
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num_workers=num_workers,
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collate_fn=collate_fn)
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return data_loader
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@ -7,43 +7,44 @@ from torch.autograd import Variable
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class EncoderCNN(nn.Module):
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def __init__(self, embed_size):
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"""Load pretrained ResNet-152 and replace top fc layer."""
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"""Loads the pretrained ResNet-152 and replace top fc layer."""
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super(EncoderCNN, self).__init__()
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self.resnet = models.resnet152(pretrained=True)
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# For efficient memory usage.
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for param in self.resnet.parameters():
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param.requires_grad = False
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self.resnet.fc = nn.Linear(self.resnet.fc.in_features, embed_size)
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self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)
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self.init_weights()
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def init_weights(self):
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self.resnet.fc.weight.data.uniform_(-0.1, 0.1)
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"""Initialize weights."""
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self.resnet.fc.weight.data.normal_(0.0, 0.02)
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self.resnet.fc.bias.data.fill_(0)
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def forward(self, images):
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"""Extract image feature vectors."""
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"""Extracts the image feature vectors."""
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features = self.resnet(images)
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features = self.bn(features)
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return features
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class DecoderRNN(nn.Module):
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def __init__(self, embed_size, hidden_size, vocab_size, num_layers):
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"""Set hyper-parameters and build layers."""
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"""Set the hyper-parameters and build the layers."""
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super(DecoderRNN, self).__init__()
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self.embed_size = embed_size
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self.hidden_size = hidden_size
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self.vocab_size = vocab_size
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self.embed = nn.Embedding(vocab_size, embed_size)
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self.lstm = nn.LSTM(embed_size, hidden_size, num_layers)
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self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
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self.linear = nn.Linear(hidden_size, vocab_size)
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self.init_weights()
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def init_weights(self):
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"""Initialize weights."""
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self.embed.weight.data.uniform_(-0.1, 0.1)
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self.linear.weigth.data.uniform_(-0.1, 0.1)
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self.linear.weight.data.uniform_(-0.1, 0.1)
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self.linear.bias.data.fill_(0)
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def forward(self, features, captions, lengths):
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"""Decode image feature vectors and generate caption."""
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"""Decodes image feature vectors and generates captions."""
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embeddings = self.embed(captions)
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embeddings = torch.cat((features.unsqueeze(1), embeddings), 1)
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packed = pack_padded_sequence(embeddings, lengths, batch_first=True)
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@ -51,14 +52,15 @@ class DecoderRNN(nn.Module):
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outputs = self.linear(hiddens[0])
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return outputs
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def sample(self, feature, state):
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"""Sample a caption for given a image feature."""
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def sample(self, features, states):
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"""Samples captions for given image features."""
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sampled_ids = []
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input = feature.unsqueeze(1)
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inputs = features.unsqueeze(1)
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for i in range(20):
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hidden, state = self.lstm(input, state) # (1, 1, hidden_size)
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output = self.linear(hidden.view(-1, self.hidden_size)) # (1, vocab_size)
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predicted = output.max(1)[1]
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hiddens, states = self.lstm(inputs, states) # (batch_size, 1, hidden_size)
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outputs = self.linear(hiddens.unsqueeze()) # (batch_size, vocab_size)
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predicted = outputs.max(1)[1]
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sampled_ids.append(predicted)
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input = self.embed(predicted)
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inputs = self.embed(predicted)
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sampled_ids = torch.cat(sampled_ids, 1) # (batch_size, 20)
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return sampled_ids
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@ -1,34 +1,34 @@
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from PIL import Image
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from configuration import Config
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import os
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def resize_image(image, size):
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"""Resizes an image to the given size."""
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"""Resizes the image to the given size."""
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return image.resize(size, Image.ANTIALIAS)
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def resize_images(image_dir, output_dir, size):
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"""Resizes the images in the image_dir and save into the output_dir."""
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"""Resizes the images in 'image_dir' and save them in 'output_dir'."""
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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images = os.listdir(image_dir)
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num_images = len(images)
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for i, image in enumerate(images):
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with open(os.path.join(image_dir, image), 'r+b') as f:
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with Image.open(f) as img:
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img = resize_image(img, size)
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img.save(
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os.path.join(output_dir, image), img.format)
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img.save(os.path.join(output_dir, image), img.format)
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if i % 100 == 0:
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print ('[%d/%d] Resized the images and saved into %s.'
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print ('[%d/%d] Resized the images and saved them in %s.'
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%(i, num_images, output_dir))
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def main():
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config = Config()
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splits = ['train', 'val']
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for split in splits:
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image_dir = './data/%s2014/' %split
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output_dir = './data/%s2014resized' %split
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resize_images(image_dir, output_dir, (256, 256))
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image_dir = os.path.join(config.image_path, '%s2014/' %split)
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output_dir = os.path.join(config.image_path, '%s2014resized' %split)
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resize_images(image_dir, output_dir, (config.image_size, config.image_size))
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if __name__ == '__main__':
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@ -1,55 +1,57 @@
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from data import get_loader
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from data import get_data_loader
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from vocab import Vocabulary
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from configuration import Config
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from model import EncoderCNN, DecoderRNN
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from torch.autograd import Variable
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from torch.nn.utils.rnn import pack_padded_sequence
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import torch
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import torch.nn as nn
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import numpy as np
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import torchvision.transforms as T
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import numpy as np
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import pickle
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import os
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# Hyper Parameters
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num_epochs = 1
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batch_size = 32
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embed_size = 256
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hidden_size = 512
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crop_size = 224
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num_layers = 1
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learning_rate = 0.001
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train_image_path = './data/train2014resized/'
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train_json_path = './data/annotations/captions_train2014.json'
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# Image Preprocessing
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transform = T.Compose([
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T.RandomCrop(crop_size),
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def main():
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# Configuration for hyper-parameters
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config = Config()
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# Image preprocessing
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transform = T.Compose([
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T.Scale(config.image_size), # no resize
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T.RandomCrop(config.crop_size),
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T.RandomHorizontalFlip(),
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T.ToTensor(),
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T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
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T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
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# Load Vocabulary Wrapper
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with open('./data/vocab.pkl', 'rb') as f:
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# Load vocabulary wrapper
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with open(os.path.join(config.vocab_path, 'vocab.pkl'), 'rb') as f:
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vocab = pickle.load(f)
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# Build Dataset Loader
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train_loader = get_loader(train_image_path, train_json_path, vocab, transform,
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batch_size=batch_size, shuffle=True, num_workers=2)
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total_step = len(train_loader)
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# Build data loader
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image_path = os.path.join(config.image_path, 'train2014')
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json_path = os.path.join(config.caption_path, 'captions_train2014.json')
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train_loader = get_data_loader(image_path, json_path, vocab,
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transform, config.batch_size,
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shuffle=True, num_workers=config.num_threads)
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total_step = len(train_loader)
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# Build Models
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encoder = EncoderCNN(embed_size)
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decoder = DecoderRNN(embed_size, hidden_size, len(vocab), num_layers)
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encoder.cuda()
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decoder.cuda()
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# Build Models
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encoder = EncoderCNN(config.embed_size)
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decoder = DecoderRNN(config.embed_size, config.hidden_size,
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len(vocab), config.num_layers)
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encoder.cuda()
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decoder.cuda()
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# Loss and Optimizer
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criterion = nn.CrossEntropyLoss()
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params = list(decoder.parameters()) + list(encoder.resnet.fc.parameters())
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optimizer = torch.optim.Adam(params, lr=learning_rate)
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# Loss and Optimizer
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criterion = nn.CrossEntropyLoss()
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params = list(decoder.parameters()) + list(encoder.resnet.fc.parameters())
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optimizer = torch.optim.Adam(params, lr=config.learning_rate)
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# Train the Decoder
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for epoch in range(num_epochs):
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# Train the Models
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for epoch in range(config.num_epochs):
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for i, (images, captions, lengths) in enumerate(train_loader):
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# Set mini-batch dataset
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images = Variable(images).cuda()
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captions = Variable(captions).cuda()
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@ -57,16 +59,26 @@ for epoch in range(num_epochs):
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# Forward, Backward and Optimize
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decoder.zero_grad()
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encoder.zero_grad()
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features = encoder(images)
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outputs = decoder(features, captions, lengths)
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loss = criterion(outputs, targets)
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loss.backward()
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optimizer.step()
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if i % 100 == 0:
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# Print log info
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if i % config.log_step == 0:
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print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f, Perplexity: %5.4f'
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%(epoch, num_epochs, i, total_step, loss.data[0], np.exp(loss.data[0])))
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%(epoch, config.num_epochs, i, total_step,
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loss.data[0], np.exp(loss.data[0])))
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# Save the Model
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torch.save(decoder, 'decoder.pkl')
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torch.save(encoder, 'encoder.pkl')
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# Save the Model
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if (i+1) % config.save_step == 0:
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torch.save(decoder.state_dict(),
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os.path.join(config.model_path,
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'decoder-%d-%d.pkl' %(epoch+1, i+1)))
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torch.save(encoder.state_dict(),
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os.path.join(config.model_path,
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'encoder-%d-%d.pkl' %(epoch+1, i+1)))
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if __name__ == '__main__':
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main()
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@ -1,6 +1,7 @@
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# Create a vocabulary wrapper
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import nltk
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import pickle
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import os
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from configuration import Config
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from collections import Counter
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from pycocotools.coco import COCO
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@ -27,7 +28,7 @@ class Vocabulary(object):
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return len(self.word2idx)
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def build_vocab(json, threshold):
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"""Build a simple vocabulary wrapper."""
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"""Builds a simple vocabulary wrapper."""
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coco = COCO(json)
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counter = Counter()
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ids = coco.anns.keys()
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@ -37,29 +38,31 @@ def build_vocab(json, threshold):
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counter.update(tokens)
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if i % 1000 == 0:
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print("[%d/%d] tokenized the captions." %(i, len(ids)))
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print("[%d/%d] Tokenized the captions." %(i, len(ids)))
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# Discard if the occurrence of the word is less than min_word_cnt.
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# If the word frequency is less than 'threshold', then the word is discarded.
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words = [word for word, cnt in counter.items() if cnt >= threshold]
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# Create a vocab wrapper and add some special tokens.
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# Creates a vocab wrapper and add some special tokens.
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vocab = Vocabulary()
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vocab.add_word('<pad>')
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vocab.add_word('<start>')
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vocab.add_word('<end>')
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vocab.add_word('<unk>')
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# Add words to the vocabulary.
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# Adds the words to the vocabulary.
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for i, word in enumerate(words):
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vocab.add_word(word)
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return vocab
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def main():
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vocab = build_vocab(json='./data/annotations/captions_train2014.json',
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threshold=4)
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with open('./data/vocab.pkl', 'wb') as f:
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config = Config()
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vocab = build_vocab(json=os.path.join(config.caption_path, 'captions_train2014.json'),
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threshold=config.word_count_threshold)
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vocab_path = os.path.join(config.vocab_path, 'vocab.pkl')
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with open(vocab_path, 'wb') as f:
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pickle.dump(vocab, f, pickle.HIGHEST_PROTOCOL)
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print("Saved vocabulary file to ", './data/vocab.pkl')
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print("Saved the vocabulary wrapper to ", vocab_path)
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if __name__ == '__main__':
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main()
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