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100755 py-bottom-up-attention/tools/plain_train_net.py create mode 100755 py-bottom-up-attention/tools/train_net.py create mode 100755 py-bottom-up-attention/tools/visualize_data.py create mode 100755 py-bottom-up-attention/tools/visualize_json_results.py create mode 100644 requirement.txt diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f3563ad --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +.DS_Store/ +.idea/ \ No newline at end of file diff --git a/Im2txt/.gitignore b/Im2txt/.gitignore new file mode 100644 index 0000000..9cc7e1f --- /dev/null +++ b/Im2txt/.gitignore @@ -0,0 +1,9 @@ +.DS_Store +.idea + +checkpoint +*.data-00000-of-00001 +*.index +*.meta + +__pychache__ diff --git a/Im2txt/README.md b/Im2txt/README.md new file mode 100644 index 0000000..06e249f --- /dev/null +++ b/Im2txt/README.md @@ -0,0 +1,84 @@ +# Note: +This repo aims to provide a **Ready-to-Go** setup with TensorFlow environment for **Image Captioning Inference** using pre-trained model. For training from scratch or funetuning, please refer to [Tensorflow Model Repo](https://github.com/tensorflow/models/tree/master/research/im2txt). + + +# Contents +* [Model Overview](#model-overview) + * [Introduction](#introduction) + * [Architecture](#architecture) +* [Requirement](#getting-started) + * [Install](#install-required-packages) + * [Get Pre-trained Model](#get-pre-trained-model) +* [Generating Captions](#generating-captions) +* [Issue](#encoutering-issue) + +## Model Overview + +### Introduction +The *Show and Tell* model is a deep neural network that learns how to describe +the content of images. For example: + +![Example captions](g3doc/example_captions.jpg) + +*Show and Tell: A Neural Image Caption Generator* + +A TensorFlow implementation of the image-to-text model described in the paper: + +"Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning +Challenge." + +Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan. + +*IEEE transactions on pattern analysis and machine intelligence (2016).* + +Full text available at: http://arxiv.org/abs/1609.06647 + +### Architecture +Please refer to the original [Tensorflow Model Repo](https://github.com/tensorflow/models/tree/master/research/im2txt). + +## Requirement + +### Install Required Packages +I strongly suggest that you run `pip install -r requirement.txt` in your CLI +to get all packages needed. + +OR you could opt for manually installing the required packages below: + +* **TensorFlow** 1.0 or greater ([instructions](https://www.tensorflow.org/install/)) +* **NumPy** ([instructions](http://www.scipy.org/install.html)) +* **Natural Language Toolkit (NLTK)**: + * First install NLTK ([instructions](http://www.nltk.org/install.html)) + * Then install the NLTK data package "punkt" ([instructions](http://www.nltk.org/data.html)) + +### Get Pre-trained Model +Download [inceptionv3 finetuned parameters over 1M](https://drive.google.com/open?id=1r4-9FEIbOUyBSvA-fFVFgvhFpgee6sF5) and you will get 4 files, and make sure to put them all into this path `im2txt/model/Hugh/train/` +* **newmodel.ckpt-2000000.data-00000-of-00001** +* **newmodel.ckpt-2000000.index** +* **newmodel.ckpt-2000000.meta** +* **checkpoint** + +## Generating Captions +Your downloaded *Show and Tell* model can generate captions for any JPEG image! The +following command line will generate captions for such an image. +``` +python im2txt/run_inference.py --checkpoint_path="im2txt/model/Hugh/train/newmodel.ckpt-2000000" --vocab_file="im2txt/data +/Hugh/word_counts.txt" --input_files="im2txt/data/images/test.jpg" +``` + +Example output: +``` +Captions for image test.jpg: + 0) a young boy wearing a hat and tie . (p=0.000195) + 1) a young boy wearing a blue shirt and tie . (p=0.000100) + 2) a young boy wearing a blue shirt and a tie . (p=0.000045) +``` + +Note: you may get different results. Some variation between different models is +expected. + +Here is the image: + +![ME](im2txt/data/images/test.jpg) + +## Encoutering Issue +First, check out on this [thread](https://github.com/tensorflow/models/issues/466) and it's likely that you find answer there. Otherwise, open an issue and I will try to help you. diff --git a/Im2txt/WORKSPACE b/Im2txt/WORKSPACE new file mode 100644 index 0000000..af626e6 --- /dev/null +++ b/Im2txt/WORKSPACE @@ -0,0 +1 @@ +workspace(name = "Im2txt") diff --git a/Im2txt/conda/init_im2txt_ubuntu.sh b/Im2txt/conda/init_im2txt_ubuntu.sh new file mode 100644 index 0000000..28bc7dd --- /dev/null +++ b/Im2txt/conda/init_im2txt_ubuntu.sh @@ -0,0 +1,10 @@ +conda create -n im2txt python=3.6 pip --yes +source ~/anaconda3/etc/profile.d/conda.sh +conda activate im2txt + +cd /path/to/Im2txt + +# Install python libraries +pip install -r requirement.txt + +echo "Done" diff --git a/Im2txt/g3doc/COCO_val2014_000000224477.jpg b/Im2txt/g3doc/COCO_val2014_000000224477.jpg new file mode 100644 index 0000000000000000000000000000000000000000..8976fa84b40b04c5bf1205a49c8d236b747f8f9b GIT binary patch literal 194898 zcmb5VWmFu^6ED2D2MO*$f)hMwaMxW}9D=*MYk*)2EKYD)U~z&&@Zb{MgR{XM9xS>1 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All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Image-to-text model and training configurations.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +class ModelConfig(object): + """Wrapper class for model hyperparameters.""" + + def __init__(self): + """Sets the default model hyperparameters.""" + # File pattern of sharded TFRecord file containing SequenceExample protos. + # Must be provided in training and evaluation modes. + self.input_file_pattern = None + + # Image format ("jpeg" or "png"). + self.image_format = "jpeg" + + # Approximate number of values per input shard. Used to ensure sufficient + # mixing between shards in training. + self.values_per_input_shard = 2300 + # Minimum number of shards to keep in the input queue. + self.input_queue_capacity_factor = 2 + # Number of threads for prefetching SequenceExample protos. + self.num_input_reader_threads = 1 + + # Name of the SequenceExample context feature containing image data. + self.image_feature_name = "image/data" + # Name of the SequenceExample feature list containing integer captions. + self.caption_feature_name = "image/caption_ids" + + # Number of unique words in the vocab (plus 1, for ). + # The default value is larger than the expected actual vocab size to allow + # for differences between tokenizer versions used in preprocessing. There is + # no harm in using a value greater than the actual vocab size, but using a + # value less than the actual vocab size will result in an error. + self.vocab_size = 12000 + + # Number of threads for image preprocessing. Should be a multiple of 2. + self.num_preprocess_threads = 4 + + # Batch size. + self.batch_size = 32 + + # File containing an Inception v3 checkpoint to initialize the variables + # of the Inception model. Must be provided when starting training for the + # first time. + self.inception_checkpoint_file = None + + # Dimensions of Inception v3 input images. + self.image_height = 299 + self.image_width = 299 + + # Scale used to initialize model variables. + self.initializer_scale = 0.08 + + # LSTM input and output dimensionality, respectively. + self.embedding_size = 512 + self.num_lstm_units = 512 + + # If < 1.0, the dropout keep probability applied to LSTM variables. + self.lstm_dropout_keep_prob = 0.7 + + +class TrainingConfig(object): + """Wrapper class for training hyperparameters.""" + + def __init__(self): + """Sets the default training hyperparameters.""" + # Number of examples per epoch of training data. + self.num_examples_per_epoch = 586363 + + # Optimizer for training the model. + self.optimizer = "SGD" + + # Learning rate for the initial phase of training. + self.initial_learning_rate = 2.0 + self.learning_rate_decay_factor = 0.5 + self.num_epochs_per_decay = 8.0 + + # Learning rate when fine tuning the Inception v3 parameters. + self.train_inception_learning_rate = 0.0005 + + # If not None, clip gradients to this value. + self.clip_gradients = 5.0 + + # How many model checkpoints to keep. + self.max_checkpoints_to_keep = 5 diff --git a/Im2txt/im2txt/data/Hugh/word_counts.txt b/Im2txt/im2txt/data/Hugh/word_counts.txt new file mode 100644 index 0000000..dd83d2f --- /dev/null +++ b/Im2txt/im2txt/data/Hugh/word_counts.txt @@ -0,0 +1,11519 @@ +a 969108 + 586368 + 586368 +. 440479 +on 213612 +of 202290 +the 196219 +in 182598 +with 152984 +and 139109 +is 97322 +man 72712 +to 67506 +sitting 52259 +an 49463 +two 47993 +, 43921 +standing 42264 +at 42204 +people 41672 +are 40768 +next 36794 +white 35874 +woman 33849 +street 30173 +table 29681 +that 27969 +holding 27648 +it 26574 +person 24540 +large 24417 +some 24130 +down 22912 +top 21994 +group 21582 +up 20742 +field 20614 +small 19884 +tennis 19485 +near 19450 +his 19304 +front 19296 +black 19187 +train 18217 +dog 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+asain 4 +establishments 4 +clue 4 +sheltered 4 +fastening 4 +seperating 4 +65th 4 +curbed 4 +harsh 4 +scalloped 4 +teriyaki 4 +porridge 4 +dixie 4 +alright 4 +alighting 4 +stone-paved 4 +yello 4 +shaven 4 +spam 4 +whispering 4 +baggies 4 +scaling 4 +volcano 4 +stylist 4 +bohemian 4 +handcuffs 4 +currency 4 +refg 4 +mustached 4 +nautical 4 +drunk 4 +drip 4 +semi-formal 4 +soot 4 +scoring 4 +weaves 4 +mrs. 4 +zipped 4 +heir 4 +beck 4 +ranchers 4 +8th 4 +onit 4 +unbrella 4 +oom 4 +kabobs 4 +toucan 4 +bruises 4 +dozing 4 +barbecuing 4 +suticase 4 +parched 4 +cascading 4 +garish 4 +clasps 4 +interrupted 4 +claw-foot 4 +shipped 4 +nations 4 +igloo 4 +gearing 4 +grafiti 4 +undecorated 4 +sprawls 4 +improve 4 +atlas 4 +offloaded 4 +rooting 4 +behinds 4 +hippie 4 +aluminium 4 +phase 4 +freestyle 4 +acrobat 4 +plate.. 4 +software 4 +witb 4 +ratty 4 +coffees 4 +projecting 4 +warrior 4 +hopefully 4 +harmony 4 +sissors 4 +woodpeckers 4 +fear 4 +scrambles 4 +brilliantly 4 +cloak 4 +spongebob 4 +tufts 4 +intimidating 4 +believe 4 +knack 4 +agricultural 4 +zealand 4 +hwy 4 +supplied 4 +permit 4 +post-it 4 +areal 4 +mighty 4 +caped 4 +pinwheels 4 +rinsing 4 +tick 4 +chariots 4 +demo 4 +ayi 4 +astronomical 4 +sculpting 4 +laboratory 4 +dignitaries 4 +gibson 4 +seatbelt 4 +varies 4 +ewes 4 +fore 4 +bart 4 +peddle 4 +snow-packed 4 +tshirts 4 +frock 4 +federer 4 +innings 4 +montrose 4 +marches 4 +dormant 4 +vampire 4 +braking 4 +seasonal 4 +novels 4 +pecan 4 +hurls 4 +vain 4 +badger 4 +paradise 4 +coordinated 4 +frequently 4 +railyard 4 +dressings 4 +bitty 4 +voting 4 +gargoyle 4 +pasties 4 +aviator 4 +fuselage 4 +marlboro 4 +leaks 4 +decades 4 +asians 4 +greeted 4 +spaceship 4 +melt 4 +hawaii 4 +scare 4 +weekend 4 +staining 4 +altogether 4 +crazing 4 +locust 4 +usaf 4 +evidence 4 +woo 4 +dishwashers 4 +traditionally 4 +vanities 4 +clearance 4 +cased 4 +awe 4 +drove 4 +brocclie 4 +corrugated 4 +amphibious 4 +parkland 4 +ignoring 4 +swining 4 +living-room 4 +modes 4 +deciduous 4 +entitled 4 +tudor 4 +experienced 4 +entertained 4 +gordon 4 +roomy 4 +hurrying 4 +sanitation 4 +bask 4 +hairstyle 4 +diversion 4 +impersonators 4 +trudging 4 \ No newline at end of file diff --git a/Im2txt/im2txt/data/build_mscoco_data.py b/Im2txt/im2txt/data/build_mscoco_data.py new file mode 100644 index 0000000..2c3e9d9 --- /dev/null +++ b/Im2txt/im2txt/data/build_mscoco_data.py @@ -0,0 +1,483 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Converts MSCOCO data to TFRecord file format with SequenceExample protos. + +The MSCOCO images are expected to reside in JPEG files located in the following +directory structure: + + train_image_dir/COCO_train2014_000000000151.jpg + train_image_dir/COCO_train2014_000000000260.jpg + ... + +and + + val_image_dir/COCO_val2014_000000000042.jpg + val_image_dir/COCO_val2014_000000000073.jpg + ... + +The MSCOCO annotations JSON files are expected to reside in train_captions_file +and val_captions_file respectively. + +This script converts the combined MSCOCO data into sharded data files consisting +of 256, 4 and 8 TFRecord files, respectively: + + output_dir/train-00000-of-00256 + output_dir/train-00001-of-00256 + ... + output_dir/train-00255-of-00256 + +and + + output_dir/val-00000-of-00004 + ... + output_dir/val-00003-of-00004 + +and + + output_dir/test-00000-of-00008 + ... + output_dir/test-00007-of-00008 + +Each TFRecord file contains ~2300 records. Each record within the TFRecord file +is a serialized SequenceExample proto consisting of precisely one image-caption +pair. Note that each image has multiple captions (usually 5) and therefore each +image is replicated multiple times in the TFRecord files. + +The SequenceExample proto contains the following fields: + + context: + image/image_id: integer MSCOCO image identifier + image/data: string containing JPEG encoded image in RGB colorspace + + feature_lists: + image/caption: list of strings containing the (tokenized) caption words + image/caption_ids: list of integer ids corresponding to the caption words + +The captions are tokenized using the NLTK (http://www.nltk.org/) word tokenizer. +The vocabulary of word identifiers is constructed from the sorted list (by +descending frequency) of word tokens in the training set. Only tokens appearing +at least 4 times are considered; all other words get the "unknown" word id. + +NOTE: This script will consume around 100GB of disk space because each image +in the MSCOCO dataset is replicated ~5 times (once per caption) in the output. +This is done for two reasons: + 1. In order to better shuffle the training data. + 2. It makes it easier to perform asynchronous preprocessing of each image in + TensorFlow. + +Running this script using 16 threads may take around 1 hour on a HP Z420. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from collections import Counter +from collections import namedtuple +from datetime import datetime +import json +import os.path +import random +import sys +import threading + + + +import nltk.tokenize +import numpy as np +from six.moves import xrange +import tensorflow as tf + +tf.flags.DEFINE_string("train_image_dir", "/tmp/train2014/", + "Training image directory.") +tf.flags.DEFINE_string("val_image_dir", "/tmp/val2014", + "Validation image directory.") + +tf.flags.DEFINE_string("train_captions_file", "/tmp/captions_train2014.json", + "Training captions JSON file.") +tf.flags.DEFINE_string("val_captions_file", "/tmp/captions_val2014.json", + "Validation captions JSON file.") + +tf.flags.DEFINE_string("output_dir", "/tmp/", "Output data directory.") + +tf.flags.DEFINE_integer("train_shards", 256, + "Number of shards in training TFRecord files.") +tf.flags.DEFINE_integer("val_shards", 4, + "Number of shards in validation TFRecord files.") +tf.flags.DEFINE_integer("test_shards", 8, + "Number of shards in testing TFRecord files.") + +tf.flags.DEFINE_string("start_word", "", + "Special word added to the beginning of each sentence.") +tf.flags.DEFINE_string("end_word", "", + "Special word added to the end of each sentence.") +tf.flags.DEFINE_string("unknown_word", "", + "Special word meaning 'unknown'.") +tf.flags.DEFINE_integer("min_word_count", 4, + "The minimum number of occurrences of each word in the " + "training set for inclusion in the vocabulary.") +tf.flags.DEFINE_string("word_counts_output_file", "/tmp/word_counts.txt", + "Output vocabulary file of word counts.") + +tf.flags.DEFINE_integer("num_threads", 8, + "Number of threads to preprocess the images.") + +FLAGS = tf.flags.FLAGS + +ImageMetadata = namedtuple("ImageMetadata", + ["image_id", "filename", "captions"]) + + +class Vocabulary(object): + """Simple vocabulary wrapper.""" + + def __init__(self, vocab, unk_id): + """Initializes the vocabulary. + + Args: + vocab: A dictionary of word to word_id. + unk_id: Id of the special 'unknown' word. + """ + self._vocab = vocab + self._unk_id = unk_id + + def word_to_id(self, word): + """Returns the integer id of a word string.""" + if word in self._vocab: + return self._vocab[word] + else: + return self._unk_id + + +class ImageDecoder(object): + """Helper class for decoding images in TensorFlow.""" + + def __init__(self): + # Create a single TensorFlow Session for all image decoding calls. + self._sess = tf.Session() + + # TensorFlow ops for JPEG decoding. + self._encoded_jpeg = tf.placeholder(dtype=tf.string) + self._decode_jpeg = tf.image.decode_jpeg(self._encoded_jpeg, channels=3) + + def decode_jpeg(self, encoded_jpeg): + image = self._sess.run(self._decode_jpeg, + feed_dict={self._encoded_jpeg: encoded_jpeg}) + assert len(image.shape) == 3 + assert image.shape[2] == 3 + return image + + +def _int64_feature(value): + """Wrapper for inserting an int64 Feature into a SequenceExample proto.""" + return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) + + +def _bytes_feature(value): + """Wrapper for inserting a bytes Feature into a SequenceExample proto.""" + return tf.train.Feature(bytes_list=tf.train.BytesList(value=[str(value)])) + + +def _int64_feature_list(values): + """Wrapper for inserting an int64 FeatureList into a SequenceExample proto.""" + return tf.train.FeatureList(feature=[_int64_feature(v) for v in values]) + + +def _bytes_feature_list(values): + """Wrapper for inserting a bytes FeatureList into a SequenceExample proto.""" + return tf.train.FeatureList(feature=[_bytes_feature(v) for v in values]) + + +def _to_sequence_example(image, decoder, vocab): + """Builds a SequenceExample proto for an image-caption pair. + + Args: + image: An ImageMetadata object. + decoder: An ImageDecoder object. + vocab: A Vocabulary object. + + Returns: + A SequenceExample proto. + """ + with tf.gfile.FastGFile(image.filename, "r") as f: + encoded_image = f.read() + + try: + decoder.decode_jpeg(encoded_image) + except (tf.errors.InvalidArgumentError, AssertionError): + print("Skipping file with invalid JPEG data: %s" % image.filename) + return + + context = tf.train.Features(feature={ + "image/image_id": _int64_feature(image.image_id), + "image/data": _bytes_feature(encoded_image), + }) + + assert len(image.captions) == 1 + caption = image.captions[0] + caption_ids = [vocab.word_to_id(word) for word in caption] + feature_lists = tf.train.FeatureLists(feature_list={ + "image/caption": _bytes_feature_list(caption), + "image/caption_ids": _int64_feature_list(caption_ids) + }) + sequence_example = tf.train.SequenceExample( + context=context, feature_lists=feature_lists) + + return sequence_example + + +def _process_image_files(thread_index, ranges, name, images, decoder, vocab, + num_shards): + """Processes and saves a subset of images as TFRecord files in one thread. + + Args: + thread_index: Integer thread identifier within [0, len(ranges)]. + ranges: A list of pairs of integers specifying the ranges of the dataset to + process in parallel. + name: Unique identifier specifying the dataset. + images: List of ImageMetadata. + decoder: An ImageDecoder object. + vocab: A Vocabulary object. + num_shards: Integer number of shards for the output files. + """ + # Each thread produces N shards where N = num_shards / num_threads. For + # instance, if num_shards = 128, and num_threads = 2, then the first thread + # would produce shards [0, 64). + num_threads = len(ranges) + assert not num_shards % num_threads + num_shards_per_batch = int(num_shards / num_threads) + + shard_ranges = np.linspace(ranges[thread_index][0], ranges[thread_index][1], + num_shards_per_batch + 1).astype(int) + num_images_in_thread = ranges[thread_index][1] - ranges[thread_index][0] + + counter = 0 + for s in xrange(num_shards_per_batch): + # Generate a sharded version of the file name, e.g. 'train-00002-of-00010' + shard = thread_index * num_shards_per_batch + s + output_filename = "%s-%.5d-of-%.5d" % (name, shard, num_shards) + output_file = os.path.join(FLAGS.output_dir, output_filename) + writer = tf.python_io.TFRecordWriter(output_file) + + shard_counter = 0 + images_in_shard = np.arange(shard_ranges[s], shard_ranges[s + 1], dtype=int) + for i in images_in_shard: + image = images[i] + + sequence_example = _to_sequence_example(image, decoder, vocab) + if sequence_example is not None: + writer.write(sequence_example.SerializeToString()) + shard_counter += 1 + counter += 1 + + if not counter % 1000: + print("%s [thread %d]: Processed %d of %d items in thread batch." % + (datetime.now(), thread_index, counter, num_images_in_thread)) + sys.stdout.flush() + + writer.close() + print("%s [thread %d]: Wrote %d image-caption pairs to %s" % + (datetime.now(), thread_index, shard_counter, output_file)) + sys.stdout.flush() + shard_counter = 0 + print("%s [thread %d]: Wrote %d image-caption pairs to %d shards." % + (datetime.now(), thread_index, counter, num_shards_per_batch)) + sys.stdout.flush() + + +def _process_dataset(name, images, vocab, num_shards): + """Processes a complete data set and saves it as a TFRecord. + + Args: + name: Unique identifier specifying the dataset. + images: List of ImageMetadata. + vocab: A Vocabulary object. + num_shards: Integer number of shards for the output files. + """ + # Break up each image into a separate entity for each caption. + images = [ImageMetadata(image.image_id, image.filename, [caption]) + for image in images for caption in image.captions] + + # Shuffle the ordering of images. Make the randomization repeatable. + random.seed(12345) + random.shuffle(images) + + # Break the images into num_threads batches. Batch i is defined as + # images[ranges[i][0]:ranges[i][1]]. + num_threads = min(num_shards, FLAGS.num_threads) + spacing = np.linspace(0, len(images), num_threads + 1).astype(np.int) + ranges = [] + threads = [] + for i in xrange(len(spacing) - 1): + ranges.append([spacing[i], spacing[i + 1]]) + + # Create a mechanism for monitoring when all threads are finished. + coord = tf.train.Coordinator() + + # Create a utility for decoding JPEG images to run sanity checks. + decoder = ImageDecoder() + + # Launch a thread for each batch. + print("Launching %d threads for spacings: %s" % (num_threads, ranges)) + for thread_index in xrange(len(ranges)): + args = (thread_index, ranges, name, images, decoder, vocab, num_shards) + t = threading.Thread(target=_process_image_files, args=args) + t.start() + threads.append(t) + + # Wait for all the threads to terminate. + coord.join(threads) + print("%s: Finished processing all %d image-caption pairs in data set '%s'." % + (datetime.now(), len(images), name)) + + +def _create_vocab(captions): + """Creates the vocabulary of word to word_id. + + The vocabulary is saved to disk in a text file of word counts. The id of each + word in the file is its corresponding 0-based line number. + + Args: + captions: A list of lists of strings. + + Returns: + A Vocabulary object. + """ + print("Creating vocabulary.") + counter = Counter() + for c in captions: + counter.update(c) + print("Total words:", len(counter)) + + # Filter uncommon words and sort by descending count. + word_counts = [x for x in counter.items() if x[1] >= FLAGS.min_word_count] + word_counts.sort(key=lambda x: x[1], reverse=True) + print("Words in vocabulary:", len(word_counts)) + + # Write out the word counts file. + with tf.gfile.FastGFile(FLAGS.word_counts_output_file, "w") as f: + f.write("\n".join(["%s %d" % (w, c) for w, c in word_counts])) + print("Wrote vocabulary file:", FLAGS.word_counts_output_file) + + # Create the vocabulary dictionary. + reverse_vocab = [x[0] for x in word_counts] + unk_id = len(reverse_vocab) + vocab_dict = dict([(x, y) for (y, x) in enumerate(reverse_vocab)]) + vocab = Vocabulary(vocab_dict, unk_id) + + return vocab + + +def _process_caption(caption): + """Processes a caption string into a list of tonenized words. + + Args: + caption: A string caption. + + Returns: + A list of strings; the tokenized caption. + """ + tokenized_caption = [FLAGS.start_word] + tokenized_caption.extend(nltk.tokenize.word_tokenize(caption.lower())) + tokenized_caption.append(FLAGS.end_word) + return tokenized_caption + + +def _load_and_process_metadata(captions_file, image_dir): + """Loads image metadata from a JSON file and processes the captions. + + Args: + captions_file: JSON file containing caption annotations. + image_dir: Directory containing the image files. + + Returns: + A list of ImageMetadata. + """ + with tf.gfile.FastGFile(captions_file, "r") as f: + caption_data = json.load(f) + + # Extract the filenames. + id_to_filename = [(x["id"], x["file_name"]) for x in caption_data["images"]] + + # Extract the captions. Each image_id is associated with multiple captions. + id_to_captions = {} + for annotation in caption_data["annotations"]: + image_id = annotation["image_id"] + caption = annotation["caption"] + id_to_captions.setdefault(image_id, []) + id_to_captions[image_id].append(caption) + + assert len(id_to_filename) == len(id_to_captions) + assert set([x[0] for x in id_to_filename]) == set(id_to_captions.keys()) + print("Loaded caption metadata for %d images from %s" % + (len(id_to_filename), captions_file)) + + # Process the captions and combine the data into a list of ImageMetadata. + print("Processing captions.") + image_metadata = [] + num_captions = 0 + for image_id, base_filename in id_to_filename: + filename = os.path.join(image_dir, base_filename) + captions = [_process_caption(c) for c in id_to_captions[image_id]] + image_metadata.append(ImageMetadata(image_id, filename, captions)) + num_captions += len(captions) + print("Finished processing %d captions for %d images in %s" % + (num_captions, len(id_to_filename), captions_file)) + + return image_metadata + + +def main(unused_argv): + def _is_valid_num_shards(num_shards): + """Returns True if num_shards is compatible with FLAGS.num_threads.""" + return num_shards < FLAGS.num_threads or not num_shards % FLAGS.num_threads + + assert _is_valid_num_shards(FLAGS.train_shards), ( + "Please make the FLAGS.num_threads commensurate with FLAGS.train_shards") + assert _is_valid_num_shards(FLAGS.val_shards), ( + "Please make the FLAGS.num_threads commensurate with FLAGS.val_shards") + assert _is_valid_num_shards(FLAGS.test_shards), ( + "Please make the FLAGS.num_threads commensurate with FLAGS.test_shards") + + if not tf.gfile.IsDirectory(FLAGS.output_dir): + tf.gfile.MakeDirs(FLAGS.output_dir) + + # Load image metadata from caption files. + mscoco_train_dataset = _load_and_process_metadata(FLAGS.train_captions_file, + FLAGS.train_image_dir) + mscoco_val_dataset = _load_and_process_metadata(FLAGS.val_captions_file, + FLAGS.val_image_dir) + + # Redistribute the MSCOCO data as follows: + # train_dataset = 100% of mscoco_train_dataset + 85% of mscoco_val_dataset. + # val_dataset = 5% of mscoco_val_dataset (for validation during training). + # test_dataset = 10% of mscoco_val_dataset (for final evaluation). + train_cutoff = int(0.85 * len(mscoco_val_dataset)) + val_cutoff = int(0.90 * len(mscoco_val_dataset)) + train_dataset = mscoco_train_dataset + mscoco_val_dataset[0:train_cutoff] + val_dataset = mscoco_val_dataset[train_cutoff:val_cutoff] + test_dataset = mscoco_val_dataset[val_cutoff:] + + # Create vocabulary from the training captions. + train_captions = [c for image in train_dataset for c in image.captions] + vocab = _create_vocab(train_captions) + + _process_dataset("train", train_dataset, vocab, FLAGS.train_shards) + _process_dataset("val", val_dataset, vocab, FLAGS.val_shards) + _process_dataset("test", test_dataset, vocab, FLAGS.test_shards) + + +if __name__ == "__main__": + tf.app.run() diff --git a/Im2txt/im2txt/data/download_and_preprocess_mscoco.sh b/Im2txt/im2txt/data/download_and_preprocess_mscoco.sh new file mode 100755 index 0000000..ab3ff28 --- /dev/null +++ b/Im2txt/im2txt/data/download_and_preprocess_mscoco.sh @@ -0,0 +1,90 @@ +#!/bin/bash +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Script to download and preprocess the MSCOCO data set. +# +# The outputs of this script are sharded TFRecord files containing serialized +# SequenceExample protocol buffers. See build_mscoco_data.py for details of how +# the SequenceExample protocol buffers are constructed. +# +# usage: +# ./download_and_preprocess_mscoco.sh +set -e + +if [ -z "$1" ]; then + echo "usage download_and_preproces_mscoco.sh [data dir]" + exit +fi + +if [ "$(uname)" == "Darwin" ]; then + UNZIP="tar -xf" +else + UNZIP="unzip -nq" +fi + +# Create the output directories. +OUTPUT_DIR="${1%/}" +SCRATCH_DIR="${OUTPUT_DIR}/raw-data" +mkdir -p "${OUTPUT_DIR}" +mkdir -p "${SCRATCH_DIR}" +CURRENT_DIR=$(pwd) +WORK_DIR="$0.runfiles/im2txt/im2txt" + +# Helper function to download and unpack a .zip file. +function download_and_unzip() { + local BASE_URL=${1} + local FILENAME=${2} + + if [ ! -f ${FILENAME} ]; then + echo "Downloading ${FILENAME} to $(pwd)" + wget -nd -c "${BASE_URL}/${FILENAME}" + else + echo "Skipping download of ${FILENAME}" + fi + echo "Unzipping ${FILENAME}" + ${UNZIP} ${FILENAME} +} + +cd ${SCRATCH_DIR} + +# Download the images. +BASE_IMAGE_URL="http://msvocds.blob.core.windows.net/coco2014" + +TRAIN_IMAGE_FILE="train2014.zip" +download_and_unzip ${BASE_IMAGE_URL} ${TRAIN_IMAGE_FILE} +TRAIN_IMAGE_DIR="${SCRATCH_DIR}/train2014" + +VAL_IMAGE_FILE="val2014.zip" +download_and_unzip ${BASE_IMAGE_URL} ${VAL_IMAGE_FILE} +VAL_IMAGE_DIR="${SCRATCH_DIR}/val2014" + +# Download the captions. +BASE_CAPTIONS_URL="http://msvocds.blob.core.windows.net/annotations-1-0-3" +CAPTIONS_FILE="captions_train-val2014.zip" +download_and_unzip ${BASE_CAPTIONS_URL} ${CAPTIONS_FILE} +TRAIN_CAPTIONS_FILE="${SCRATCH_DIR}/annotations/captions_train2014.json" +VAL_CAPTIONS_FILE="${SCRATCH_DIR}/annotations/captions_val2014.json" + +# Build TFRecords of the image data. +cd "${CURRENT_DIR}" +BUILD_SCRIPT="${WORK_DIR}/build_mscoco_data" +"${BUILD_SCRIPT}" \ + --train_image_dir="${TRAIN_IMAGE_DIR}" \ + --val_image_dir="${VAL_IMAGE_DIR}" \ + --train_captions_file="${TRAIN_CAPTIONS_FILE}" \ + --val_captions_file="${VAL_CAPTIONS_FILE}" \ + 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