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AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/build_tfrecord.py
python
_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.
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.
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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 range(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()
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/build_tfrecord.py#L170-L225
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/build_tfrecord.py
python
_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.
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.
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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.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 range(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 range(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))
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/build_tfrecord.py#L228-L270
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/build_tfrecord.py
python
_create_vocab
(captions)
return vocab
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.
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.
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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
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/build_tfrecord.py#L273-L304
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/build_tfrecord.py
python
_process_caption_jieba
(caption)
return tokenized_caption
Processes a Chinese caption string into a list of tonenized words. Args: caption: A string caption. Returns: A list of strings; the tokenized caption.
Processes a Chinese caption string into a list of tonenized words. Args: caption: A string caption. Returns: A list of strings; the tokenized caption.
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def _process_caption_jieba(caption): """Processes a Chinese 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(jieba.cut(caption, cut_all=False)) tokenized_caption.append(FLAGS.end_word) return tokenized_caption
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/build_tfrecord.py#L307-L317
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/build_tfrecord.py
python
_load_and_process_metadata
(captions_file, image_dir)
return image_metadata
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.
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.
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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. """ image_id = set([]) id_to_captions = {} with open(captions_file, 'r') as f: caption_data = json.load(f) for data in caption_data: image_name = data['image_id'].split('.')[0] descriptions = data['caption'] if image_name not in image_id: id_to_captions.setdefault(image_name, []) image_id.add(image_name) caption_num = len(descriptions) for i in range(caption_num): caption_temp = descriptions[i].strip().strip("。").replace('\n', '') if caption_temp != '': id_to_captions[image_name].append(caption_temp) print("Loaded caption metadata for %d images from %s and image_id num is %s" % (len(id_to_captions), captions_file, len(image_id))) # Process the captions and combine the data into a list of ImageMetadata. print("Proccessing captions.") image_metadata = [] num_captions = 0 id = 0 for base_filename in image_id: filename = os.path.join(image_dir, base_filename + '.jpg') # captions = [_process_caption(c) for c in id_to_captions[base_filename]] captions = [_process_caption_jieba(c) for c in id_to_captions[base_filename]] image_metadata.append(ImageMetadata(id, filename, captions)) id = id + 1 num_captions += len(captions) print("Finished processing %d captions for %d images in %s" % (num_captions, len(id_to_captions), captions_file)) return image_metadata
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/build_tfrecord.py#L320-L361
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/build_tfrecord.py
python
Vocabulary.__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.
Initializes the vocabulary. Args: vocab: A dictionary of word to word_id. unk_id: Id of the special 'unknown' word.
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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
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/build_tfrecord.py#L79-L86
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/build_tfrecord.py
python
Vocabulary.word_to_id
(self, word)
Returns the integer id of a word string.
Returns the integer id of a word string.
[ "Returns", "the", "integer", "id", "of", "a", "word", "string", "." ]
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
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/build_tfrecord.py#L88-L93
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/configuration.py
python
ModelConfig.__init__
(self)
Sets the default model hyperparameters.
Sets the default model hyperparameters.
[ "Sets", "the", "default", "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 <UNK>). # 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 = 20000 # 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
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/configuration.py#L26-L78
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/configuration.py
python
TrainingConfig.__init__
(self)
Sets the default training hyperparameters.
Sets the default training hyperparameters.
[ "Sets", "the", "default", "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
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/configuration.py#L84-L104
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py
python
ShowAndTellModel.__init__
(self, config, mode, train_inception=False)
Basic setup. Args: config: Object containing configuration parameters. mode: "train", "eval" or "inference". train_inception: Whether the inception submodel variables are trainable.
Basic setup.
[ "Basic", "setup", "." ]
def __init__(self, config, mode, train_inception=False): """Basic setup. Args: config: Object containing configuration parameters. mode: "train", "eval" or "inference". train_inception: Whether the inception submodel variables are trainable. """ assert mode in ["train", "eval", "inference"] self.config = config self.mode = mode self.train_inception = train_inception # Reader for the input data. self.reader = tf.TFRecordReader() # To match the "Show and Tell" paper we initialize all variables with a # random uniform initializer. self.initializer = tf.random_uniform_initializer( minval=-self.config.initializer_scale, maxval=self.config.initializer_scale) # A float32 Tensor with shape [batch_size, height, width, channels]. self.images = None # An int32 Tensor with shape [batch_size, padded_length]. self.input_seqs = None # An int32 Tensor with shape [batch_size, padded_length]. self.target_seqs = None # An int32 0/1 Tensor with shape [batch_size, padded_length]. self.input_mask = None # A float32 Tensor with shape [batch_size, embedding_size]. self.image_embeddings = None # A float32 Tensor with shape [batch_size, padded_length, embedding_size]. self.seq_embeddings = None # A float32 scalar Tensor; the total loss for the trainer to optimize. self.total_loss = None # A float32 Tensor with shape [batch_size * padded_length]. self.target_cross_entropy_losses = None # A float32 Tensor with shape [batch_size * padded_length]. self.target_cross_entropy_loss_weights = None # Collection of variables from the inception submodel. self.inception_variables = [] # Function to restore the inception submodel from checkpoint. self.init_fn = None # Global step Tensor. self.global_step = None
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py#L41-L97
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py
python
ShowAndTellModel.is_training
(self)
return self.mode == "train"
Returns true if the model is built for training mode.
Returns true if the model is built for training mode.
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def is_training(self): """Returns true if the model is built for training mode.""" return self.mode == "train"
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py#L99-L101
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py
python
ShowAndTellModel.process_image
(self, encoded_image, thread_id=0)
return image_processing.process_image(encoded_image, is_training=self.is_training(), height=self.config.image_height, width=self.config.image_width, thread_id=thread_id, image_format=self.config.image_format)
Decodes and processes an image string. Args: encoded_image: A scalar string Tensor; the encoded image. thread_id: Preprocessing thread id used to select the ordering of color distortions. Returns: A float32 Tensor of shape [height, width, 3]; the processed image.
Decodes and processes an image string.
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def process_image(self, encoded_image, thread_id=0): """Decodes and processes an image string. Args: encoded_image: A scalar string Tensor; the encoded image. thread_id: Preprocessing thread id used to select the ordering of color distortions. Returns: A float32 Tensor of shape [height, width, 3]; the processed image. """ return image_processing.process_image(encoded_image, is_training=self.is_training(), height=self.config.image_height, width=self.config.image_width, thread_id=thread_id, image_format=self.config.image_format)
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py#L103-L119
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py
python
ShowAndTellModel.build_inputs
(self)
Input prefetching, preprocessing and batching. Outputs: self.images self.input_seqs self.target_seqs (training and eval only) self.input_mask (training and eval only)
Input prefetching, preprocessing and batching.
[ "Input", "prefetching", "preprocessing", "and", "batching", "." ]
def build_inputs(self): """Input prefetching, preprocessing and batching. Outputs: self.images self.input_seqs self.target_seqs (training and eval only) self.input_mask (training and eval only) """ if self.mode == "inference": # In inference mode, images and inputs are fed via placeholders. image_feed = tf.placeholder(dtype=tf.string, shape=[], name="image_feed") input_feed = tf.placeholder(dtype=tf.int64, shape=[None], # batch_size name="input_feed") # Process image and insert batch dimensions. images = tf.expand_dims(self.process_image(image_feed), 0) input_seqs = tf.expand_dims(input_feed, 1) # No target sequences or input mask in inference mode. target_seqs = None input_mask = None else: # Prefetch serialized SequenceExample protos. input_queue = input_ops.prefetch_input_data( self.reader, self.config.input_file_pattern, is_training=self.is_training(), batch_size=self.config.batch_size, values_per_shard=self.config.values_per_input_shard, input_queue_capacity_factor=self.config.input_queue_capacity_factor, num_reader_threads=self.config.num_input_reader_threads) # Image processing and random distortion. Split across multiple threads # with each thread applying a slightly different distortion. assert self.config.num_preprocess_threads % 2 == 0 images_and_captions = [] for thread_id in range(self.config.num_preprocess_threads): serialized_sequence_example = input_queue.dequeue() encoded_image, caption = input_ops.parse_sequence_example( serialized_sequence_example, image_feature=self.config.image_feature_name, caption_feature=self.config.caption_feature_name) image = self.process_image(encoded_image, thread_id=thread_id) images_and_captions.append([image, caption]) # Batch inputs. queue_capacity = (2 * self.config.num_preprocess_threads * self.config.batch_size) images, input_seqs, target_seqs, input_mask = ( input_ops.batch_with_dynamic_pad(images_and_captions, batch_size=self.config.batch_size, queue_capacity=queue_capacity)) self.images = images self.input_seqs = input_seqs self.target_seqs = target_seqs self.input_mask = input_mask
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py#L121-L179
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py
python
ShowAndTellModel.build_image_embeddings
(self)
Builds the image model subgraph and generates image embeddings. Inputs: self.images Outputs: self.image_embeddings
Builds the image model subgraph and generates image embeddings.
[ "Builds", "the", "image", "model", "subgraph", "and", "generates", "image", "embeddings", "." ]
def build_image_embeddings(self): """Builds the image model subgraph and generates image embeddings. Inputs: self.images Outputs: self.image_embeddings """ inception_output = image_embedding.inception_v3( self.images, trainable=self.train_inception, is_training=self.is_training()) self.inception_variables = tf.get_collection( tf.GraphKeys.GLOBAL_VARIABLES, scope="InceptionV3") # Map inception output into embedding space. with tf.variable_scope("image_embedding") as scope: image_embeddings = tf.contrib.layers.fully_connected( inputs=inception_output, num_outputs=self.config.embedding_size, activation_fn=None, weights_initializer=self.initializer, biases_initializer=None, scope=scope) # Save the embedding size in the graph. tf.constant(self.config.embedding_size, name="embedding_size") self.image_embeddings = image_embeddings
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py#L181-L210
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py
python
ShowAndTellModel.build_seq_embeddings
(self)
Builds the input sequence embeddings. Inputs: self.input_seqs Outputs: self.seq_embeddings
Builds the input sequence embeddings.
[ "Builds", "the", "input", "sequence", "embeddings", "." ]
def build_seq_embeddings(self): """Builds the input sequence embeddings. Inputs: self.input_seqs Outputs: self.seq_embeddings """ with tf.variable_scope("seq_embedding"), tf.device("/cpu:0"): embedding_map = tf.get_variable( name="map", shape=[self.config.vocab_size, self.config.embedding_size], initializer=self.initializer) seq_embeddings = tf.nn.embedding_lookup(embedding_map, self.input_seqs) self.seq_embeddings = seq_embeddings
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py#L212-L228
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py
python
ShowAndTellModel.build_model
(self)
Builds the model. Inputs: self.image_embeddings self.seq_embeddings self.target_seqs (training and eval only) self.input_mask (training and eval only) Outputs: self.total_loss (training and eval only) self.target_cross_entropy_losses (training and eval only) self.target_cross_entropy_loss_weights (training and eval only)
Builds the model.
[ "Builds", "the", "model", "." ]
def build_model(self): """Builds the model. Inputs: self.image_embeddings self.seq_embeddings self.target_seqs (training and eval only) self.input_mask (training and eval only) Outputs: self.total_loss (training and eval only) self.target_cross_entropy_losses (training and eval only) self.target_cross_entropy_loss_weights (training and eval only) """ # This LSTM cell has biases and outputs tanh(new_c) * sigmoid(o), but the # modified LSTM in the "Show and Tell" paper has no biases and outputs # new_c * sigmoid(o). lstm_cell = tf.contrib.rnn.BasicLSTMCell( num_units=self.config.num_lstm_units, state_is_tuple=True) if self.mode == "train": lstm_cell = tf.contrib.rnn.DropoutWrapper( lstm_cell, input_keep_prob=self.config.lstm_dropout_keep_prob, output_keep_prob=self.config.lstm_dropout_keep_prob) with tf.variable_scope("lstm", initializer=self.initializer) as lstm_scope: # Feed the image embeddings to set the initial LSTM state. zero_state = lstm_cell.zero_state( batch_size=self.image_embeddings.get_shape()[0], dtype=tf.float32) _, initial_state = lstm_cell(self.image_embeddings, zero_state) # Allow the LSTM variables to be reused. lstm_scope.reuse_variables() if self.mode == "inference": # In inference mode, use concatenated states for convenient feeding and # fetching. tf.concat(initial_state, 1, name="initial_state") # Placeholder for feeding a batch of concatenated states. state_feed = tf.placeholder(dtype=tf.float32, shape=[None, sum(lstm_cell.state_size)], name="state_feed") state_tuple = tf.split(value=state_feed, num_or_size_splits=2, axis=1) # Run a single LSTM step. lstm_outputs, state_tuple = lstm_cell( inputs=tf.squeeze(self.seq_embeddings, squeeze_dims=[1]), state=state_tuple) # Concatentate the resulting state. tf.concat(state_tuple, 1, name="state") else: # Run the batch of sequence embeddings through the LSTM. sequence_length = tf.reduce_sum(self.input_mask, 1) lstm_outputs, _ = tf.nn.dynamic_rnn(cell=lstm_cell, inputs=self.seq_embeddings, sequence_length=sequence_length, initial_state=initial_state, dtype=tf.float32, scope=lstm_scope) # Stack batches vertically. lstm_outputs = tf.reshape(lstm_outputs, [-1, lstm_cell.output_size]) with tf.variable_scope("logits") as logits_scope: logits = tf.contrib.layers.fully_connected( inputs=lstm_outputs, num_outputs=self.config.vocab_size, activation_fn=None, weights_initializer=self.initializer, scope=logits_scope) if self.mode == "inference": tf.nn.softmax(logits, name="softmax") else: targets = tf.reshape(self.target_seqs, [-1]) weights = tf.to_float(tf.reshape(self.input_mask, [-1])) # Compute losses. losses = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=targets, logits=logits) batch_loss = tf.div(tf.reduce_sum(tf.multiply(losses, weights)), tf.reduce_sum(weights), name="batch_loss") tf.losses.add_loss(batch_loss) total_loss = tf.losses.get_total_loss() # Add summaries. tf.summary.scalar("losses/batch_loss", batch_loss) tf.summary.scalar("losses/total_loss", total_loss) for var in tf.trainable_variables(): tf.summary.histogram("parameters/" + var.op.name, var) self.total_loss = total_loss self.target_cross_entropy_losses = losses # Used in evaluation. self.target_cross_entropy_loss_weights = weights
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py#L230-L326
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py
python
ShowAndTellModel.setup_inception_initializer
(self)
Sets up the function to restore inception variables from checkpoint.
Sets up the function to restore inception variables from checkpoint.
[ "Sets", "up", "the", "function", "to", "restore", "inception", "variables", "from", "checkpoint", "." ]
def setup_inception_initializer(self): """Sets up the function to restore inception variables from checkpoint.""" if self.mode != "inference": # Restore inception variables only. saver = tf.train.Saver(self.inception_variables) def restore_fn(sess): tf.logging.info("Restoring Inception variables from checkpoint file %s", self.config.inception_checkpoint_file) saver.restore(sess, self.config.inception_checkpoint_file) self.init_fn = restore_fn
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py#L328-L339
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py
python
ShowAndTellModel.setup_global_step
(self)
Sets up the global step Tensor.
Sets up the global step Tensor.
[ "Sets", "up", "the", "global", "step", "Tensor", "." ]
def setup_global_step(self): """Sets up the global step Tensor.""" global_step = tf.Variable( initial_value=0, name="global_step", trainable=False, collections=[tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.GLOBAL_VARIABLES]) self.global_step = global_step
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py#L341-L349
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py
python
ShowAndTellModel.build
(self)
Creates all ops for training and evaluation.
Creates all ops for training and evaluation.
[ "Creates", "all", "ops", "for", "training", "and", "evaluation", "." ]
def build(self): """Creates all ops for training and evaluation.""" self.build_inputs() self.build_image_embeddings() self.build_seq_embeddings() self.build_model() self.setup_inception_initializer() self.setup_global_step()
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/show_and_tell_model.py#L351-L358
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/ops/image_embedding.py
python
inception_v3
(images, trainable=True, is_training=True, weight_decay=0.00004, stddev=0.1, dropout_keep_prob=0.8, use_batch_norm=True, batch_norm_params=None, add_summaries=True, scope="InceptionV3")
return net
Builds an Inception V3 subgraph for image embeddings. Args: images: A float32 Tensor of shape [batch, height, width, channels]. trainable: Whether the inception submodel should be trainable or not. is_training: Boolean indicating training mode or not. weight_decay: Coefficient for weight regularization. stddev: The standard deviation of the trunctated normal weight initializer. dropout_keep_prob: Dropout keep probability. use_batch_norm: Whether to use batch normalization. batch_norm_params: Parameters for batch normalization. See tf.contrib.layers.batch_norm for details. add_summaries: Whether to add activation summaries. scope: Optional Variable scope. Returns: end_points: A dictionary of activations from inception_v3 layers.
Builds an Inception V3 subgraph for image embeddings.
[ "Builds", "an", "Inception", "V3", "subgraph", "for", "image", "embeddings", "." ]
def inception_v3(images, trainable=True, is_training=True, weight_decay=0.00004, stddev=0.1, dropout_keep_prob=0.8, use_batch_norm=True, batch_norm_params=None, add_summaries=True, scope="InceptionV3"): """Builds an Inception V3 subgraph for image embeddings. Args: images: A float32 Tensor of shape [batch, height, width, channels]. trainable: Whether the inception submodel should be trainable or not. is_training: Boolean indicating training mode or not. weight_decay: Coefficient for weight regularization. stddev: The standard deviation of the trunctated normal weight initializer. dropout_keep_prob: Dropout keep probability. use_batch_norm: Whether to use batch normalization. batch_norm_params: Parameters for batch normalization. See tf.contrib.layers.batch_norm for details. add_summaries: Whether to add activation summaries. scope: Optional Variable scope. Returns: end_points: A dictionary of activations from inception_v3 layers. """ # Only consider the inception model to be in training mode if it's trainable. is_inception_model_training = trainable and is_training if use_batch_norm: # Default parameters for batch normalization. if not batch_norm_params: batch_norm_params = { "is_training": is_inception_model_training, "trainable": trainable, # Decay for the moving averages. "decay": 0.9997, # Epsilon to prevent 0s in variance. "epsilon": 0.001, # Collection containing the moving mean and moving variance. "variables_collections": { "beta": None, "gamma": None, "moving_mean": ["moving_vars"], "moving_variance": ["moving_vars"], } } else: batch_norm_params = None if trainable: weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay) else: weights_regularizer = None with tf.variable_scope(scope, "InceptionV3", [images]) as scope: with slim.arg_scope( [slim.conv2d, slim.fully_connected], weights_regularizer=weights_regularizer, trainable=trainable): with slim.arg_scope( [slim.conv2d], weights_initializer=tf.truncated_normal_initializer(stddev=stddev), activation_fn=tf.nn.relu, normalizer_fn=slim.batch_norm, normalizer_params=batch_norm_params): net, end_points = inception_v3_base(images, scope=scope) with tf.variable_scope("logits"): shape = net.get_shape() net = slim.avg_pool2d(net, shape[1:3], padding="VALID", scope="pool") net = slim.dropout( net, keep_prob=dropout_keep_prob, is_training=is_inception_model_training, scope="dropout") net = slim.flatten(net, scope="flatten") # Add summaries. if add_summaries: for v in end_points.values(): tf.contrib.layers.summaries.summarize_activation(v) return net
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/ops/image_embedding.py#L30-L114
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/ops/image_processing.py
python
distort_image
(image, thread_id)
return image
Perform random distortions on an image. Args: image: A float32 Tensor of shape [height, width, 3] with values in [0, 1). thread_id: Preprocessing thread id used to select the ordering of color distortions. There should be a multiple of 2 preprocessing threads. Returns: distorted_image: A float32 Tensor of shape [height, width, 3] with values in [0, 1].
Perform random distortions on an image.
[ "Perform", "random", "distortions", "on", "an", "image", "." ]
def distort_image(image, thread_id): """Perform random distortions on an image. Args: image: A float32 Tensor of shape [height, width, 3] with values in [0, 1). thread_id: Preprocessing thread id used to select the ordering of color distortions. There should be a multiple of 2 preprocessing threads. Returns: distorted_image: A float32 Tensor of shape [height, width, 3] with values in [0, 1]. """ # Randomly flip horizontally. with tf.name_scope("flip_horizontal", values=[image]): image = tf.image.random_flip_left_right(image) # Randomly distort the colors based on thread id. color_ordering = thread_id % 2 with tf.name_scope("distort_color", values=[image]): if color_ordering == 0: image = tf.image.random_brightness(image, max_delta=32. / 255.) image = tf.image.random_saturation(image, lower=0.5, upper=1.5) image = tf.image.random_hue(image, max_delta=0.032) image = tf.image.random_contrast(image, lower=0.5, upper=1.5) elif color_ordering == 1: image = tf.image.random_brightness(image, max_delta=32. / 255.) image = tf.image.random_contrast(image, lower=0.5, upper=1.5) image = tf.image.random_saturation(image, lower=0.5, upper=1.5) image = tf.image.random_hue(image, max_delta=0.032) # The random_* ops do not necessarily clamp. image = tf.clip_by_value(image, 0.0, 1.0) return image
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/ops/image_processing.py#L26-L59
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/ops/image_processing.py
python
process_image
(encoded_image, is_training, height, width, resize_height=346, resize_width=346, thread_id=0, image_format="jpeg")
return image
Decode an image, resize and apply random distortions. In training, images are distorted slightly differently depending on thread_id. Args: encoded_image: String Tensor containing the image. is_training: Boolean; whether preprocessing for training or eval. height: Height of the output image. width: Width of the output image. resize_height: If > 0, resize height before crop to final dimensions. resize_width: If > 0, resize width before crop to final dimensions. thread_id: Preprocessing thread id used to select the ordering of color distortions. There should be a multiple of 2 preprocessing threads. image_format: "jpeg" or "png". Returns: A float32 Tensor of shape [height, width, 3] with values in [-1, 1]. Raises: ValueError: If image_format is invalid.
Decode an image, resize and apply random distortions.
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def process_image(encoded_image, is_training, height, width, resize_height=346, resize_width=346, thread_id=0, image_format="jpeg"): """Decode an image, resize and apply random distortions. In training, images are distorted slightly differently depending on thread_id. Args: encoded_image: String Tensor containing the image. is_training: Boolean; whether preprocessing for training or eval. height: Height of the output image. width: Width of the output image. resize_height: If > 0, resize height before crop to final dimensions. resize_width: If > 0, resize width before crop to final dimensions. thread_id: Preprocessing thread id used to select the ordering of color distortions. There should be a multiple of 2 preprocessing threads. image_format: "jpeg" or "png". Returns: A float32 Tensor of shape [height, width, 3] with values in [-1, 1]. Raises: ValueError: If image_format is invalid. """ # Helper function to log an image summary to the visualizer. Summaries are # only logged in thread 0. def image_summary(name, image): if not thread_id: tf.summary.image(name, tf.expand_dims(image, 0)) # Decode image into a float32 Tensor of shape [?, ?, 3] with values in [0, 1). with tf.name_scope("decode", values=[encoded_image]): if image_format == "jpeg": image = tf.image.decode_jpeg(encoded_image, channels=3) elif image_format == "png": image = tf.image.decode_png(encoded_image, channels=3) else: raise ValueError("Invalid image format: %s" % image_format) image = tf.image.convert_image_dtype(image, dtype=tf.float32) image_summary("original_image", image) # Resize image. assert (resize_height > 0) == (resize_width > 0) if resize_height: image = tf.image.resize_images(image, size=[resize_height, resize_width], method=tf.image.ResizeMethod.BILINEAR) # Crop to final dimensions. if is_training: image = tf.random_crop(image, [height, width, 3]) else: # Central crop, assuming resize_height > height, resize_width > width. image = tf.image.resize_image_with_crop_or_pad(image, height, width) image_summary("resized_image", image) # Randomly distort the image. if is_training: image = distort_image(image, thread_id) image_summary("final_image", image) # Rescale to [-1,1] instead of [0, 1] image = tf.subtract(image, 0.5) image = tf.multiply(image, 2.0) return image
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/ops/image_processing.py#L62-L133
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/ops/inputs.py
python
parse_sequence_example
(serialized, image_feature, caption_feature)
return encoded_image, caption
Parses a tensorflow.SequenceExample into an image and caption. Args: serialized: A scalar string Tensor; a single serialized SequenceExample. image_feature: Name of SequenceExample context feature containing image data. caption_feature: Name of SequenceExample feature list containing integer captions. Returns: encoded_image: A scalar string Tensor containing a JPEG encoded image. caption: A 1-D uint64 Tensor with dynamically specified length.
Parses a tensorflow.SequenceExample into an image and caption.
[ "Parses", "a", "tensorflow", ".", "SequenceExample", "into", "an", "image", "and", "caption", "." ]
def parse_sequence_example(serialized, image_feature, caption_feature): """Parses a tensorflow.SequenceExample into an image and caption. Args: serialized: A scalar string Tensor; a single serialized SequenceExample. image_feature: Name of SequenceExample context feature containing image data. caption_feature: Name of SequenceExample feature list containing integer captions. Returns: encoded_image: A scalar string Tensor containing a JPEG encoded image. caption: A 1-D uint64 Tensor with dynamically specified length. """ context, sequence = tf.parse_single_sequence_example( serialized, context_features={ image_feature: tf.FixedLenFeature([], dtype=tf.string) }, sequence_features={ caption_feature: tf.FixedLenSequenceFeature([], dtype=tf.int64), }) encoded_image = context[image_feature] caption = sequence[caption_feature] return encoded_image, caption
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/ops/inputs.py#L26-L51
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/ops/inputs.py
python
prefetch_input_data
(reader, file_pattern, is_training, batch_size, values_per_shard, input_queue_capacity_factor=16, num_reader_threads=1, shard_queue_name="filename_queue", value_queue_name="input_queue")
return values_queue
Prefetches string values from disk into an input queue. In training the capacity of the queue is important because a larger queue means better mixing of training examples between shards. The minimum number of values kept in the queue is values_per_shard * input_queue_capacity_factor, where input_queue_memory factor should be chosen to trade-off better mixing with memory usage. Args: reader: Instance of tf.ReaderBase. file_pattern: Comma-separated list of file patterns (e.g. /tmp/train_data-?????-of-00100). is_training: Boolean; whether prefetching for training or eval. batch_size: Model batch size used to determine queue capacity. values_per_shard: Approximate number of values per shard. input_queue_capacity_factor: Minimum number of values to keep in the queue in multiples of values_per_shard. See comments above. num_reader_threads: Number of reader threads to fill the queue. shard_queue_name: Name for the shards filename queue. value_queue_name: Name for the values input queue. Returns: A Queue containing prefetched string values.
Prefetches string values from disk into an input queue.
[ "Prefetches", "string", "values", "from", "disk", "into", "an", "input", "queue", "." ]
def prefetch_input_data(reader, file_pattern, is_training, batch_size, values_per_shard, input_queue_capacity_factor=16, num_reader_threads=1, shard_queue_name="filename_queue", value_queue_name="input_queue"): """Prefetches string values from disk into an input queue. In training the capacity of the queue is important because a larger queue means better mixing of training examples between shards. The minimum number of values kept in the queue is values_per_shard * input_queue_capacity_factor, where input_queue_memory factor should be chosen to trade-off better mixing with memory usage. Args: reader: Instance of tf.ReaderBase. file_pattern: Comma-separated list of file patterns (e.g. /tmp/train_data-?????-of-00100). is_training: Boolean; whether prefetching for training or eval. batch_size: Model batch size used to determine queue capacity. values_per_shard: Approximate number of values per shard. input_queue_capacity_factor: Minimum number of values to keep in the queue in multiples of values_per_shard. See comments above. num_reader_threads: Number of reader threads to fill the queue. shard_queue_name: Name for the shards filename queue. value_queue_name: Name for the values input queue. Returns: A Queue containing prefetched string values. """ data_files = [] for pattern in file_pattern.split(","): data_files.extend(tf.gfile.Glob(pattern)) if not data_files: tf.logging.fatal("Found no input files matching %s", file_pattern) else: tf.logging.info("Prefetching values from %d files matching %s", len(data_files), file_pattern) if is_training: filename_queue = tf.train.string_input_producer( data_files, shuffle=True, capacity=16, name=shard_queue_name) min_queue_examples = values_per_shard * input_queue_capacity_factor capacity = min_queue_examples + 100 * batch_size values_queue = tf.RandomShuffleQueue( capacity=capacity, min_after_dequeue=min_queue_examples, dtypes=[tf.string], name="random_" + value_queue_name) else: filename_queue = tf.train.string_input_producer( data_files, shuffle=False, capacity=1, name=shard_queue_name) capacity = values_per_shard + 3 * batch_size values_queue = tf.FIFOQueue( capacity=capacity, dtypes=[tf.string], name="fifo_" + value_queue_name) enqueue_ops = [] for _ in range(num_reader_threads): _, value = reader.read(filename_queue) enqueue_ops.append(values_queue.enqueue([value])) tf.train.queue_runner.add_queue_runner(tf.train.queue_runner.QueueRunner( values_queue, enqueue_ops)) tf.summary.scalar( "queue/%s/fraction_of_%d_full" % (values_queue.name, capacity), tf.cast(values_queue.size(), tf.float32) * (1. / capacity)) return values_queue
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/ops/inputs.py#L54-L123
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/ops/inputs.py
python
batch_with_dynamic_pad
(images_and_captions, batch_size, queue_capacity, add_summaries=True)
return images, input_seqs, target_seqs, mask
Batches input images and captions. This function splits the caption into an input sequence and a target sequence, where the target sequence is the input sequence right-shifted by 1. Input and target sequences are batched and padded up to the maximum length of sequences in the batch. A mask is created to distinguish real words from padding words. Example: Actual captions in the batch ('-' denotes padded character): [ [ 1 2 5 4 5 ], [ 1 2 3 4 - ], [ 1 2 3 - - ], ] input_seqs: [ [ 1 2 3 4 ], [ 1 2 3 - ], [ 1 2 - - ], ] target_seqs: [ [ 2 3 4 5 ], [ 2 3 4 - ], [ 2 3 - - ], ] mask: [ [ 1 1 1 1 ], [ 1 1 1 0 ], [ 1 1 0 0 ], ] Args: images_and_captions: A list of pairs [image, caption], where image is a Tensor of shape [height, width, channels] and caption is a 1-D Tensor of any length. Each pair will be processed and added to the queue in a separate thread. batch_size: Batch size. queue_capacity: Queue capacity. add_summaries: If true, add caption length summaries. Returns: images: A Tensor of shape [batch_size, height, width, channels]. input_seqs: An int32 Tensor of shape [batch_size, padded_length]. target_seqs: An int32 Tensor of shape [batch_size, padded_length]. mask: An int32 0/1 Tensor of shape [batch_size, padded_length].
Batches input images and captions.
[ "Batches", "input", "images", "and", "captions", "." ]
def batch_with_dynamic_pad(images_and_captions, batch_size, queue_capacity, add_summaries=True): """Batches input images and captions. This function splits the caption into an input sequence and a target sequence, where the target sequence is the input sequence right-shifted by 1. Input and target sequences are batched and padded up to the maximum length of sequences in the batch. A mask is created to distinguish real words from padding words. Example: Actual captions in the batch ('-' denotes padded character): [ [ 1 2 5 4 5 ], [ 1 2 3 4 - ], [ 1 2 3 - - ], ] input_seqs: [ [ 1 2 3 4 ], [ 1 2 3 - ], [ 1 2 - - ], ] target_seqs: [ [ 2 3 4 5 ], [ 2 3 4 - ], [ 2 3 - - ], ] mask: [ [ 1 1 1 1 ], [ 1 1 1 0 ], [ 1 1 0 0 ], ] Args: images_and_captions: A list of pairs [image, caption], where image is a Tensor of shape [height, width, channels] and caption is a 1-D Tensor of any length. Each pair will be processed and added to the queue in a separate thread. batch_size: Batch size. queue_capacity: Queue capacity. add_summaries: If true, add caption length summaries. Returns: images: A Tensor of shape [batch_size, height, width, channels]. input_seqs: An int32 Tensor of shape [batch_size, padded_length]. target_seqs: An int32 Tensor of shape [batch_size, padded_length]. mask: An int32 0/1 Tensor of shape [batch_size, padded_length]. """ enqueue_list = [] for image, caption in images_and_captions: caption_length = tf.shape(caption)[0] input_length = tf.expand_dims(tf.subtract(caption_length, 1), 0) input_seq = tf.slice(caption, [0], input_length) target_seq = tf.slice(caption, [1], input_length) indicator = tf.ones(input_length, dtype=tf.int32) enqueue_list.append([image, input_seq, target_seq, indicator]) images, input_seqs, target_seqs, mask = tf.train.batch_join( enqueue_list, batch_size=batch_size, capacity=queue_capacity, dynamic_pad=True, name="batch_and_pad") if add_summaries: lengths = tf.add(tf.reduce_sum(mask, 1), 1) tf.summary.scalar("caption_length/batch_min", tf.reduce_min(lengths)) tf.summary.scalar("caption_length/batch_max", tf.reduce_max(lengths)) tf.summary.scalar("caption_length/batch_mean", tf.reduce_mean(lengths)) return images, input_seqs, target_seqs, mask
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/ops/inputs.py#L126-L204
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/vocabulary.py
python
Vocabulary.__init__
(self, vocab_file, start_word="<S>", end_word="</S>", unk_word="<UNK>")
Initializes the vocabulary. Args: vocab_file: File containing the vocabulary, where the words are the first whitespace-separated token on each line (other tokens are ignored) and the word ids are the corresponding line numbers. start_word: Special word denoting sentence start. end_word: Special word denoting sentence end. unk_word: Special word denoting unknown words.
Initializes the vocabulary.
[ "Initializes", "the", "vocabulary", "." ]
def __init__(self, vocab_file, start_word="<S>", end_word="</S>", unk_word="<UNK>"): """Initializes the vocabulary. Args: vocab_file: File containing the vocabulary, where the words are the first whitespace-separated token on each line (other tokens are ignored) and the word ids are the corresponding line numbers. start_word: Special word denoting sentence start. end_word: Special word denoting sentence end. unk_word: Special word denoting unknown words. """ if not tf.gfile.Exists(vocab_file): tf.logging.fatal("Vocab file %s not found.", vocab_file) tf.logging.info("Initializing vocabulary from file: %s", vocab_file) with tf.gfile.GFile(vocab_file, mode="r") as f: reverse_vocab = list(f.readlines()) reverse_vocab = [line.split()[0] for line in reverse_vocab] assert start_word in reverse_vocab assert end_word in reverse_vocab if unk_word not in reverse_vocab: reverse_vocab.append(unk_word) vocab = dict([(x, y) for (y, x) in enumerate(reverse_vocab)]) tf.logging.info("Created vocabulary with %d words" % len(vocab)) self.vocab = vocab # vocab[word] = id self.reverse_vocab = reverse_vocab # reverse_vocab[id] = word # Save special word ids. self.start_id = vocab[start_word] self.end_id = vocab[end_word] self.unk_id = vocab[unk_word]
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/vocabulary.py#L28-L64
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/vocabulary.py
python
Vocabulary.word_to_id
(self, word)
Returns the integer word id of a word string.
Returns the integer word id of a word string.
[ "Returns", "the", "integer", "word", "id", "of", "a", "word", "string", "." ]
def word_to_id(self, word): """Returns the integer word id of a word string.""" if word in self.vocab: return self.vocab[word] else: return self.unk_id
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/vocabulary.py#L66-L71
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/vocabulary.py
python
Vocabulary.id_to_word
(self, word_id)
Returns the word string of an integer word id.
Returns the word string of an integer word id.
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def id_to_word(self, word_id): """Returns the word string of an integer word id.""" if word_id >= len(self.reverse_vocab): return self.reverse_vocab[self.unk_id] else: return self.reverse_vocab[word_id]
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/vocabulary.py#L73-L78
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/inference_wrapper_base.py
python
InferenceWrapperBase.build_model
(self, model_config)
Builds the model for inference. Args: model_config: Object containing configuration for building the model. Returns: model: The model object.
Builds the model for inference.
[ "Builds", "the", "model", "for", "inference", "." ]
def build_model(self, model_config): """Builds the model for inference. Args: model_config: Object containing configuration for building the model. Returns: model: The model object. """ tf.logging.fatal("Please implement build_model in subclass")
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/inference_wrapper_base.py#L62-L71
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/inference_wrapper_base.py
python
InferenceWrapperBase._create_restore_fn
(self, checkpoint_path, saver)
return _restore_fn
Creates a function that restores a model from checkpoint. Args: checkpoint_path: Checkpoint file or a directory containing a checkpoint file. saver: Saver for restoring variables from the checkpoint file. Returns: restore_fn: A function such that restore_fn(sess) loads model variables from the checkpoint file. Raises: ValueError: If checkpoint_path does not refer to a checkpoint file or a directory containing a checkpoint file.
Creates a function that restores a model from checkpoint.
[ "Creates", "a", "function", "that", "restores", "a", "model", "from", "checkpoint", "." ]
def _create_restore_fn(self, checkpoint_path, saver): """Creates a function that restores a model from checkpoint. Args: checkpoint_path: Checkpoint file or a directory containing a checkpoint file. saver: Saver for restoring variables from the checkpoint file. Returns: restore_fn: A function such that restore_fn(sess) loads model variables from the checkpoint file. Raises: ValueError: If checkpoint_path does not refer to a checkpoint file or a directory containing a checkpoint file. """ if tf.gfile.IsDirectory(checkpoint_path): checkpoint_path = tf.train.latest_checkpoint(checkpoint_path) if not checkpoint_path: raise ValueError("No checkpoint file found in: %s" % checkpoint_path) def _restore_fn(sess): tf.logging.info("Loading model from checkpoint: %s", checkpoint_path) saver.restore(sess, checkpoint_path) tf.logging.info("Successfully loaded checkpoint: %s", os.path.basename(checkpoint_path)) return _restore_fn
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/inference_wrapper_base.py#L73-L100
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/inference_wrapper_base.py
python
InferenceWrapperBase.build_graph_from_config
(self, model_config, checkpoint_path)
return self._create_restore_fn(checkpoint_path, saver)
Builds the inference graph from a configuration object. Args: model_config: Object containing configuration for building the model. checkpoint_path: Checkpoint file or a directory containing a checkpoint file. Returns: restore_fn: A function such that restore_fn(sess) loads model variables from the checkpoint file.
Builds the inference graph from a configuration object.
[ "Builds", "the", "inference", "graph", "from", "a", "configuration", "object", "." ]
def build_graph_from_config(self, model_config, checkpoint_path): """Builds the inference graph from a configuration object. Args: model_config: Object containing configuration for building the model. checkpoint_path: Checkpoint file or a directory containing a checkpoint file. Returns: restore_fn: A function such that restore_fn(sess) loads model variables from the checkpoint file. """ tf.logging.info("Building model.") self.build_model(model_config) saver = tf.train.Saver() return self._create_restore_fn(checkpoint_path, saver)
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/inference_wrapper_base.py#L102-L118
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/inference_wrapper_base.py
python
InferenceWrapperBase.build_graph_from_proto
(self, graph_def_file, saver_def_file, checkpoint_path)
return self._create_restore_fn(checkpoint_path, saver)
Builds the inference graph from serialized GraphDef and SaverDef protos. Args: graph_def_file: File containing a serialized GraphDef proto. saver_def_file: File containing a serialized SaverDef proto. checkpoint_path: Checkpoint file or a directory containing a checkpoint file. Returns: restore_fn: A function such that restore_fn(sess) loads model variables from the checkpoint file.
Builds the inference graph from serialized GraphDef and SaverDef protos.
[ "Builds", "the", "inference", "graph", "from", "serialized", "GraphDef", "and", "SaverDef", "protos", "." ]
def build_graph_from_proto(self, graph_def_file, saver_def_file, checkpoint_path): """Builds the inference graph from serialized GraphDef and SaverDef protos. Args: graph_def_file: File containing a serialized GraphDef proto. saver_def_file: File containing a serialized SaverDef proto. checkpoint_path: Checkpoint file or a directory containing a checkpoint file. Returns: restore_fn: A function such that restore_fn(sess) loads model variables from the checkpoint file. """ # Load the Graph. tf.logging.info("Loading GraphDef from file: %s", graph_def_file) graph_def = tf.GraphDef() with tf.gfile.FastGFile(graph_def_file, "rb") as f: graph_def.ParseFromString(f.read()) tf.import_graph_def(graph_def, name="") # Load the Saver. tf.logging.info("Loading SaverDef from file: %s", saver_def_file) saver_def = tf.train.SaverDef() with tf.gfile.FastGFile(saver_def_file, "rb") as f: saver_def.ParseFromString(f.read()) saver = tf.train.Saver(saver_def=saver_def) return self._create_restore_fn(checkpoint_path, saver)
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/inference_wrapper_base.py#L120-L148
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/inference_wrapper_base.py
python
InferenceWrapperBase.feed_image
(self, sess, encoded_image)
Feeds an image and returns the initial model state. See comments at the top of file. Args: sess: TensorFlow Session object. encoded_image: An encoded image string. Returns: state: A numpy array of shape [1, state_size].
Feeds an image and returns the initial model state.
[ "Feeds", "an", "image", "and", "returns", "the", "initial", "model", "state", "." ]
def feed_image(self, sess, encoded_image): """Feeds an image and returns the initial model state. See comments at the top of file. Args: sess: TensorFlow Session object. encoded_image: An encoded image string. Returns: state: A numpy array of shape [1, state_size]. """ tf.logging.fatal("Please implement feed_image in subclass")
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/inference_wrapper_base.py#L150-L162
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/inference_wrapper_base.py
python
InferenceWrapperBase.inference_step
(self, sess, input_feed, state_feed)
Runs one step of inference. Args: sess: TensorFlow Session object. input_feed: A numpy array of shape [batch_size]. state_feed: A numpy array of shape [batch_size, state_size]. Returns: softmax_output: A numpy array of shape [batch_size, vocab_size]. new_state: A numpy array of shape [batch_size, state_size]. metadata: Optional. If not None, a string containing metadata about the current inference step (e.g. serialized numpy array containing activations from a particular model layer.).
Runs one step of inference.
[ "Runs", "one", "step", "of", "inference", "." ]
def inference_step(self, sess, input_feed, state_feed): """Runs one step of inference. Args: sess: TensorFlow Session object. input_feed: A numpy array of shape [batch_size]. state_feed: A numpy array of shape [batch_size, state_size]. Returns: softmax_output: A numpy array of shape [batch_size, vocab_size]. new_state: A numpy array of shape [batch_size, state_size]. metadata: Optional. If not None, a string containing metadata about the current inference step (e.g. serialized numpy array containing activations from a particular model layer.). """ tf.logging.fatal("Please implement inference_step in subclass")
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/inference_wrapper_base.py#L164-L179
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py
python
Caption.__init__
(self, sentence, state, logprob, score, metadata=None)
Initializes the Caption. Args: sentence: List of word ids in the caption. state: Model state after generating the previous word. logprob: Log-probability of the caption. score: Score of the caption. metadata: Optional metadata associated with the partial sentence. If not None, a list of strings with the same length as 'sentence'.
Initializes the Caption.
[ "Initializes", "the", "Caption", "." ]
def __init__(self, sentence, state, logprob, score, metadata=None): """Initializes the Caption. Args: sentence: List of word ids in the caption. state: Model state after generating the previous word. logprob: Log-probability of the caption. score: Score of the caption. metadata: Optional metadata associated with the partial sentence. If not None, a list of strings with the same length as 'sentence'. """ self.sentence = sentence self.state = state self.logprob = logprob self.score = score self.metadata = metadata
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py#L31-L46
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py
python
Caption.__cmp__
(self, other)
Compares Captions by score.
Compares Captions by score.
[ "Compares", "Captions", "by", "score", "." ]
def __cmp__(self, other): """Compares Captions by score.""" assert isinstance(other, Caption) if self.score == other.score: return 0 elif self.score < other.score: return -1 else: return 1
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py#L48-L56
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py
python
TopN.push
(self, x)
Pushes a new element.
Pushes a new element.
[ "Pushes", "a", "new", "element", "." ]
def push(self, x): """Pushes a new element.""" assert self._data is not None if len(self._data) < self._n: heapq.heappush(self._data, x) else: heapq.heappushpop(self._data, x)
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py#L80-L86
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py
python
TopN.extract
(self, sort=False)
return data
Extracts all elements from the TopN. This is a destructive operation. The only method that can be called immediately after extract() is reset(). Args: sort: Whether to return the elements in descending sorted order. Returns: A list of data; the top n elements provided to the set.
Extracts all elements from the TopN. This is a destructive operation.
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def extract(self, sort=False): """Extracts all elements from the TopN. This is a destructive operation. The only method that can be called immediately after extract() is reset(). Args: sort: Whether to return the elements in descending sorted order. Returns: A list of data; the top n elements provided to the set. """ assert self._data is not None data = self._data self._data = None if sort: data.sort(reverse=True) return data
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py#L88-L104
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py
python
TopN.reset
(self)
Returns the TopN to an empty state.
Returns the TopN to an empty state.
[ "Returns", "the", "TopN", "to", "an", "empty", "state", "." ]
def reset(self): """Returns the TopN to an empty state.""" self._data = []
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py#L106-L108
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py
python
CaptionGenerator.__init__
(self, model, vocab, beam_size=3, max_caption_length=20, length_normalization_factor=0.0)
Initializes the generator. Args: model: Object encapsulating a trained image-to-text model. Must have methods feed_image() and inference_step(). For example, an instance of InferenceWrapperBase. vocab: A Vocabulary object. beam_size: Beam size to use when generating captions. max_caption_length: The maximum caption length before stopping the search. length_normalization_factor: If != 0, a number x such that captions are scored by logprob/length^x, rather than logprob. This changes the relative scores of captions depending on their lengths. For example, if x > 0 then longer captions will be favored.
Initializes the generator.
[ "Initializes", "the", "generator", "." ]
def __init__(self, model, vocab, beam_size=3, max_caption_length=20, length_normalization_factor=0.0): """Initializes the generator. Args: model: Object encapsulating a trained image-to-text model. Must have methods feed_image() and inference_step(). For example, an instance of InferenceWrapperBase. vocab: A Vocabulary object. beam_size: Beam size to use when generating captions. max_caption_length: The maximum caption length before stopping the search. length_normalization_factor: If != 0, a number x such that captions are scored by logprob/length^x, rather than logprob. This changes the relative scores of captions depending on their lengths. For example, if x > 0 then longer captions will be favored. """ self.vocab = vocab self.model = model self.beam_size = beam_size self.max_caption_length = max_caption_length self.length_normalization_factor = length_normalization_factor
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py#L114-L139
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py
python
CaptionGenerator.beam_search
(self, sess, encoded_image)
return complete_captions.extract(sort=True)
Runs beam search caption generation on a single image. Args: sess: TensorFlow Session object. encoded_image: An encoded image string. Returns: A list of Caption sorted by descending score.
Runs beam search caption generation on a single image.
[ "Runs", "beam", "search", "caption", "generation", "on", "a", "single", "image", "." ]
def beam_search(self, sess, encoded_image): """Runs beam search caption generation on a single image. Args: sess: TensorFlow Session object. encoded_image: An encoded image string. Returns: A list of Caption sorted by descending score. """ # Feed in the image to get the initial state. initial_state = self.model.feed_image(sess, encoded_image) initial_beam = Caption( sentence=[self.vocab.start_id], state=initial_state[0], logprob=0.0, score=0.0, metadata=[""]) partial_captions = TopN(self.beam_size) partial_captions.push(initial_beam) complete_captions = TopN(self.beam_size) # Run beam search. for _ in range(self.max_caption_length - 1): partial_captions_list = partial_captions.extract() partial_captions.reset() input_feed = np.array([c.sentence[-1] for c in partial_captions_list]) state_feed = np.array([c.state for c in partial_captions_list]) softmax, new_states, metadata = self.model.inference_step(sess, input_feed, state_feed) for i, partial_caption in enumerate(partial_captions_list): word_probabilities = softmax[i] state = new_states[i] # For this partial caption, get the beam_size most probable next words. words_and_probs = list(enumerate(word_probabilities)) words_and_probs.sort(key=lambda x: -x[1]) words_and_probs = words_and_probs[0:self.beam_size] # Each next word gives a new partial caption. for w, p in words_and_probs: if p < 1e-12: continue # Avoid log(0). sentence = partial_caption.sentence + [w] logprob = partial_caption.logprob + math.log(p) score = logprob if metadata: metadata_list = partial_caption.metadata + [metadata[i]] else: metadata_list = None if w == self.vocab.end_id: if self.length_normalization_factor > 0: score /= len(sentence)**self.length_normalization_factor beam = Caption(sentence, state, logprob, score, metadata_list) complete_captions.push(beam) else: beam = Caption(sentence, state, logprob, score, metadata_list) partial_captions.push(beam) if partial_captions.size() == 0: # We have run out of partial candidates; happens when beam_size = 1. break # If we have no complete captions then fall back to the partial captions. # But never output a mixture of complete and partial captions because a # partial caption could have a higher score than all the complete captions. if not complete_captions.size(): complete_captions = partial_captions return complete_captions.extract(sort=True)
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/caption_baseline/im2txt/im2txt/inference_utils/caption_generator.py#L141-L211
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py
python
enable_parallel
(processnum=None)
Change the module's `cut` and `cut_for_search` functions to the parallel version. Note that this only works using dt, custom Tokenizer instances are not supported.
Change the module's `cut` and `cut_for_search` functions to the parallel version.
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def enable_parallel(processnum=None): """ Change the module's `cut` and `cut_for_search` functions to the parallel version. Note that this only works using dt, custom Tokenizer instances are not supported. """ global pool, dt, cut, cut_for_search from multiprocessing import cpu_count if os.name == 'nt': raise NotImplementedError( "jieba: parallel mode only supports posix system") else: from multiprocessing import Pool dt.check_initialized() if processnum is None: processnum = cpu_count() pool = Pool(processnum) cut = _pcut cut_for_search = _pcut_for_search
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py#L566-L586
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py
python
Tokenizer.cut
(self, sentence, cut_all=False, HMM=True)
The main function that segments an entire sentence that contains Chinese characters into seperated words. Parameter: - sentence: The str(unicode) to be segmented. - cut_all: Model type. True for full pattern, False for accurate pattern. - HMM: Whether to use the Hidden Markov Model.
The main function that segments an entire sentence that contains Chinese characters into seperated words.
[ "The", "main", "function", "that", "segments", "an", "entire", "sentence", "that", "contains", "Chinese", "characters", "into", "seperated", "words", "." ]
def cut(self, sentence, cut_all=False, HMM=True): ''' The main function that segments an entire sentence that contains Chinese characters into seperated words. Parameter: - sentence: The str(unicode) to be segmented. - cut_all: Model type. True for full pattern, False for accurate pattern. - HMM: Whether to use the Hidden Markov Model. ''' sentence = strdecode(sentence) if cut_all: re_han = re_han_cut_all re_skip = re_skip_cut_all else: re_han = re_han_default re_skip = re_skip_default if cut_all: cut_block = self.__cut_all elif HMM: cut_block = self.__cut_DAG else: cut_block = self.__cut_DAG_NO_HMM blocks = re_han.split(sentence) for blk in blocks: if not blk: continue if re_han.match(blk): for word in cut_block(blk): yield word else: tmp = re_skip.split(blk) for x in tmp: if re_skip.match(x): yield x elif not cut_all: for xx in x: yield xx else: yield x
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py#L272-L312
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py
python
Tokenizer.cut_for_search
(self, sentence, HMM=True)
Finer segmentation for search engines.
Finer segmentation for search engines.
[ "Finer", "segmentation", "for", "search", "engines", "." ]
def cut_for_search(self, sentence, HMM=True): """ Finer segmentation for search engines. """ words = self.cut(sentence, HMM=HMM) for w in words: if len(w) > 2: for i in xrange(len(w) - 1): gram2 = w[i:i + 2] if self.FREQ.get(gram2): yield gram2 if len(w) > 3: for i in xrange(len(w) - 2): gram3 = w[i:i + 3] if self.FREQ.get(gram3): yield gram3 yield w
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py#L314-L330
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py
python
Tokenizer.load_userdict
(self, f)
Load personalized dict to improve detect rate. Parameter: - f : A plain text file contains words and their ocurrences. Can be a file-like object, or the path of the dictionary file, whose encoding must be utf-8. Structure of dict file: word1 freq1 word_type1 word2 freq2 word_type2 ... Word type may be ignored
Load personalized dict to improve detect rate.
[ "Load", "personalized", "dict", "to", "improve", "detect", "rate", "." ]
def load_userdict(self, f): ''' Load personalized dict to improve detect rate. Parameter: - f : A plain text file contains words and their ocurrences. Can be a file-like object, or the path of the dictionary file, whose encoding must be utf-8. Structure of dict file: word1 freq1 word_type1 word2 freq2 word_type2 ... Word type may be ignored ''' self.check_initialized() if isinstance(f, string_types): f_name = f f = open(f, 'rb') else: f_name = resolve_filename(f) for lineno, ln in enumerate(f, 1): line = ln.strip() if not isinstance(line, text_type): try: line = line.decode('utf-8').lstrip('\ufeff') except UnicodeDecodeError: raise ValueError('dictionary file %s must be utf-8' % f_name) if not line: continue # match won't be None because there's at least one character word, freq, tag = re_userdict.match(line).groups() if freq is not None: freq = freq.strip() if tag is not None: tag = tag.strip() self.add_word(word, freq, tag)
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py#L356-L392
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py
python
Tokenizer.add_word
(self, word, freq=None, tag=None)
Add a word to dictionary. freq and tag can be omitted, freq defaults to be a calculated value that ensures the word can be cut out.
Add a word to dictionary.
[ "Add", "a", "word", "to", "dictionary", "." ]
def add_word(self, word, freq=None, tag=None): """ Add a word to dictionary. freq and tag can be omitted, freq defaults to be a calculated value that ensures the word can be cut out. """ self.check_initialized() word = strdecode(word) freq = int(freq) if freq is not None else self.suggest_freq(word, False) self.FREQ[word] = freq self.total += freq if tag: self.user_word_tag_tab[word] = tag for ch in xrange(len(word)): wfrag = word[:ch + 1] if wfrag not in self.FREQ: self.FREQ[wfrag] = 0 if freq == 0: finalseg.add_force_split(word)
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py#L394-L413
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py
python
Tokenizer.del_word
(self, word)
Convenient function for deleting a word.
Convenient function for deleting a word.
[ "Convenient", "function", "for", "deleting", "a", "word", "." ]
def del_word(self, word): """ Convenient function for deleting a word. """ self.add_word(word, 0)
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py#L415-L419
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py
python
Tokenizer.suggest_freq
(self, segment, tune=False)
return freq
Suggest word frequency to force the characters in a word to be joined or splitted. Parameter: - segment : The segments that the word is expected to be cut into, If the word should be treated as a whole, use a str. - tune : If True, tune the word frequency. Note that HMM may affect the final result. If the result doesn't change, set HMM=False.
Suggest word frequency to force the characters in a word to be joined or splitted.
[ "Suggest", "word", "frequency", "to", "force", "the", "characters", "in", "a", "word", "to", "be", "joined", "or", "splitted", "." ]
def suggest_freq(self, segment, tune=False): """ Suggest word frequency to force the characters in a word to be joined or splitted. Parameter: - segment : The segments that the word is expected to be cut into, If the word should be treated as a whole, use a str. - tune : If True, tune the word frequency. Note that HMM may affect the final result. If the result doesn't change, set HMM=False. """ self.check_initialized() ftotal = float(self.total) freq = 1 if isinstance(segment, string_types): word = segment for seg in self.cut(word, HMM=False): freq *= self.FREQ.get(seg, 1) / ftotal freq = max(int(freq * self.total) + 1, self.FREQ.get(word, 1)) else: segment = tuple(map(strdecode, segment)) word = ''.join(segment) for seg in segment: freq *= self.FREQ.get(seg, 1) / ftotal freq = min(int(freq * self.total), self.FREQ.get(word, 0)) if tune: add_word(word, freq) return freq
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py#L421-L450
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py
python
Tokenizer.tokenize
(self, unicode_sentence, mode="default", HMM=True)
Tokenize a sentence and yields tuples of (word, start, end) Parameter: - sentence: the str(unicode) to be segmented. - mode: "default" or "search", "search" is for finer segmentation. - HMM: whether to use the Hidden Markov Model.
Tokenize a sentence and yields tuples of (word, start, end)
[ "Tokenize", "a", "sentence", "and", "yields", "tuples", "of", "(", "word", "start", "end", ")" ]
def tokenize(self, unicode_sentence, mode="default", HMM=True): """ Tokenize a sentence and yields tuples of (word, start, end) Parameter: - sentence: the str(unicode) to be segmented. - mode: "default" or "search", "search" is for finer segmentation. - HMM: whether to use the Hidden Markov Model. """ if not isinstance(unicode_sentence, text_type): raise ValueError("jieba: the input parameter should be unicode.") start = 0 if mode == 'default': for w in self.cut(unicode_sentence, HMM=HMM): width = len(w) yield (w, start, start + width) start += width else: for w in self.cut(unicode_sentence, HMM=HMM): width = len(w) if len(w) > 2: for i in xrange(len(w) - 1): gram2 = w[i:i + 2] if self.FREQ.get(gram2): yield (gram2, start + i, start + i + 2) if len(w) > 3: for i in xrange(len(w) - 2): gram3 = w[i:i + 3] if self.FREQ.get(gram3): yield (gram3, start + i, start + i + 3) yield (w, start, start + width) start += width
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/__init__.py#L452-L483
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/analyse/tfidf.py
python
TFIDF.extract_tags
(self, sentence, topK=20, withWeight=False, allowPOS=(), withFlag=False)
Extract keywords from sentence using TF-IDF algorithm. Parameter: - topK: return how many top keywords. `None` for all possible words. - withWeight: if True, return a list of (word, weight); if False, return a list of words. - allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v','nr']. if the POS of w is not in this list,it will be filtered. - withFlag: only work with allowPOS is not empty. if True, return a list of pair(word, weight) like posseg.cut if False, return a list of words
Extract keywords from sentence using TF-IDF algorithm. Parameter: - topK: return how many top keywords. `None` for all possible words. - withWeight: if True, return a list of (word, weight); if False, return a list of words. - allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v','nr']. if the POS of w is not in this list,it will be filtered. - withFlag: only work with allowPOS is not empty. if True, return a list of pair(word, weight) like posseg.cut if False, return a list of words
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def extract_tags(self, sentence, topK=20, withWeight=False, allowPOS=(), withFlag=False): """ Extract keywords from sentence using TF-IDF algorithm. Parameter: - topK: return how many top keywords. `None` for all possible words. - withWeight: if True, return a list of (word, weight); if False, return a list of words. - allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v','nr']. if the POS of w is not in this list,it will be filtered. - withFlag: only work with allowPOS is not empty. if True, return a list of pair(word, weight) like posseg.cut if False, return a list of words """ if allowPOS: allowPOS = frozenset(allowPOS) words = self.postokenizer.cut(sentence) else: words = self.tokenizer.cut(sentence) freq = {} for w in words: if allowPOS: if w.flag not in allowPOS: continue elif not withFlag: w = w.word wc = w.word if allowPOS and withFlag else w if len(wc.strip()) < 2 or wc.lower() in self.stop_words: continue freq[w] = freq.get(w, 0.0) + 1.0 total = sum(freq.values()) for k in freq: kw = k.word if allowPOS and withFlag else k freq[k] *= self.idf_freq.get(kw, self.median_idf) / total if withWeight: tags = sorted(freq.items(), key=itemgetter(1), reverse=True) else: tags = sorted(freq, key=freq.__getitem__, reverse=True) if topK: return tags[:topK] else: return tags
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/analyse/tfidf.py#L75-L116
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/analyse/textrank.py
python
TextRank.textrank
(self, sentence, topK=20, withWeight=False, allowPOS=('ns', 'n', 'vn', 'v'), withFlag=False)
Extract keywords from sentence using TextRank algorithm. Parameter: - topK: return how many top keywords. `None` for all possible words. - withWeight: if True, return a list of (word, weight); if False, return a list of words. - allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v']. if the POS of w is not in this list, it will be filtered. - withFlag: if True, return a list of pair(word, weight) like posseg.cut if False, return a list of words
Extract keywords from sentence using TextRank algorithm. Parameter: - topK: return how many top keywords. `None` for all possible words. - withWeight: if True, return a list of (word, weight); if False, return a list of words. - allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v']. if the POS of w is not in this list, it will be filtered. - withFlag: if True, return a list of pair(word, weight) like posseg.cut if False, return a list of words
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def textrank(self, sentence, topK=20, withWeight=False, allowPOS=('ns', 'n', 'vn', 'v'), withFlag=False): """ Extract keywords from sentence using TextRank algorithm. Parameter: - topK: return how many top keywords. `None` for all possible words. - withWeight: if True, return a list of (word, weight); if False, return a list of words. - allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v']. if the POS of w is not in this list, it will be filtered. - withFlag: if True, return a list of pair(word, weight) like posseg.cut if False, return a list of words """ self.pos_filt = frozenset(allowPOS) g = UndirectWeightedGraph() cm = defaultdict(int) words = tuple(self.tokenizer.cut(sentence)) for i, wp in enumerate(words): if self.pairfilter(wp): for j in xrange(i + 1, i + self.span): if j >= len(words): break if not self.pairfilter(words[j]): continue if allowPOS and withFlag: cm[(wp, words[j])] += 1 else: cm[(wp.word, words[j].word)] += 1 for terms, w in cm.items(): g.addEdge(terms[0], terms[1], w) nodes_rank = g.rank() if withWeight: tags = sorted(nodes_rank.items(), key=itemgetter(1), reverse=True) else: tags = sorted(nodes_rank, key=nodes_rank.__getitem__, reverse=True) if topK: return tags[:topK] else: return tags
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/analyse/textrank.py#L69-L108
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/posseg/__init__.py
python
cut
(sentence, HMM=True)
Global `cut` function that supports parallel processing. Note that this only works using dt, custom POSTokenizer instances are not supported.
Global `cut` function that supports parallel processing.
[ "Global", "cut", "function", "that", "supports", "parallel", "processing", "." ]
def cut(sentence, HMM=True): """ Global `cut` function that supports parallel processing. Note that this only works using dt, custom POSTokenizer instances are not supported. """ global dt if jieba.pool is None: for w in dt.cut(sentence, HMM=HMM): yield w else: parts = strdecode(sentence).splitlines(True) if HMM: result = jieba.pool.map(_lcut_internal, parts) else: result = jieba.pool.map(_lcut_internal_no_hmm, parts) for r in result: for w in r: yield w
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Baselines/translation_and_interpretation_baseline/train/prepare_data/jieba/posseg/__init__.py#L272-L291
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/keypoint_eval/keypoint_eval.py
python
load_annotations
(anno_file, return_dict)
return annotations
Convert annotation JSON file.
Convert annotation JSON file.
[ "Convert", "annotation", "JSON", "file", "." ]
def load_annotations(anno_file, return_dict): """Convert annotation JSON file.""" annotations = dict() annotations['image_ids'] = set([]) annotations['annos'] = dict() annotations['delta'] = 2*np.array([0.01388152, 0.01515228, 0.01057665, 0.01417709, \ 0.01497891, 0.01402144, 0.03909642, 0.03686941, 0.01981803, \ 0.03843971, 0.03412318, 0.02415081, 0.01291456, 0.01236173]) try: annos = json.load(open(anno_file, 'r')) except Exception: return_dict['error'] = 'Annotation file does not exist or is an invalid JSON file.' exit(return_dict['error']) for anno in annos: annotations['image_ids'].add(anno['image_id']) annotations['annos'][anno['image_id']] = dict() annotations['annos'][anno['image_id']]['human_annos'] = anno['human_annotations'] annotations['annos'][anno['image_id']]['keypoint_annos'] = anno['keypoint_annotations'] return annotations
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/keypoint_eval/keypoint_eval.py#L44-L65
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/keypoint_eval/keypoint_eval.py
python
load_predictions
(prediction_file, return_dict)
return predictions
Convert prediction JSON file.
Convert prediction JSON file.
[ "Convert", "prediction", "JSON", "file", "." ]
def load_predictions(prediction_file, return_dict): """Convert prediction JSON file.""" predictions = dict() predictions['image_ids'] = [] predictions['annos'] = dict() id_set = set([]) try: preds = json.load(open(prediction_file, 'r')) except Exception: return_dict['error'] = 'Prediction file does not exist or is an invalid JSON file.' exit(return_dict['error']) for pred in preds: if 'image_id' not in pred.keys(): return_dict['warning'].append('There is an invalid annotation info, \ likely missing key \'image_id\'.') continue if 'keypoint_annotations' not in pred.keys(): return_dict['warning'].append(pred['image_id']+\ ' does not have key \'keypoint_annotations\'.') continue image_id = pred['image_id'].split('.')[0] if image_id in id_set: return_dict['warning'].append(pred['image_id']+\ ' is duplicated in prediction JSON file.') else: id_set.add(image_id) predictions['image_ids'].append(image_id) predictions['annos'][pred['image_id']] = dict() predictions['annos'][pred['image_id']]['keypoint_annos'] = pred['keypoint_annotations'] return predictions
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/keypoint_eval/keypoint_eval.py#L68-L101
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/keypoint_eval/keypoint_eval.py
python
compute_oks
(anno, predict, delta)
return oks
Compute oks matrix (size gtN*pN).
Compute oks matrix (size gtN*pN).
[ "Compute", "oks", "matrix", "(", "size", "gtN", "*", "pN", ")", "." ]
def compute_oks(anno, predict, delta): """Compute oks matrix (size gtN*pN).""" anno_count = len(anno['keypoint_annos'].keys()) predict_count = len(predict.keys()) oks = np.zeros((anno_count, predict_count)) if predict_count == 0: return oks.T # for every human keypoint annotation for i in range(anno_count): anno_key = anno['keypoint_annos'].keys()[i] anno_keypoints = np.reshape(anno['keypoint_annos'][anno_key], (14, 3)) visible = anno_keypoints[:, 2] == 1 bbox = anno['human_annos'][anno_key] scale = np.float32((bbox[3]-bbox[1])*(bbox[2]-bbox[0])) if np.sum(visible) == 0: for j in range(predict_count): oks[i, j] = 0 else: # for every predicted human for j in range(predict_count): predict_key = predict.keys()[j] predict_keypoints = np.reshape(predict[predict_key], (14, 3)) dis = np.sum((anno_keypoints[visible, :2] \ - predict_keypoints[visible, :2])**2, axis=1) oks[i, j] = np.mean(np.exp(-dis/2/delta[visible]**2/(scale+1))) return oks
[ "def", "compute_oks", "(", "anno", ",", "predict", ",", "delta", ")", ":", "anno_count", "=", "len", "(", "anno", "[", "'keypoint_annos'", "]", ".", "keys", "(", ")", ")", "predict_count", "=", "len", "(", "predict", ".", "keys", "(", ")", ")", "oks", "=", "np", ".", "zeros", "(", "(", "anno_count", ",", "predict_count", ")", ")", "if", "predict_count", "==", "0", ":", "return", "oks", ".", "T", "# for every human keypoint annotation", "for", "i", "in", "range", "(", "anno_count", ")", ":", "anno_key", "=", "anno", "[", "'keypoint_annos'", "]", ".", "keys", "(", ")", "[", "i", "]", "anno_keypoints", "=", "np", ".", "reshape", "(", "anno", "[", "'keypoint_annos'", "]", "[", "anno_key", "]", ",", "(", "14", ",", "3", ")", ")", "visible", "=", "anno_keypoints", "[", ":", ",", "2", "]", "==", "1", "bbox", "=", "anno", "[", "'human_annos'", "]", "[", "anno_key", "]", "scale", "=", "np", ".", "float32", "(", "(", "bbox", "[", "3", "]", "-", "bbox", "[", "1", "]", ")", "*", "(", "bbox", "[", "2", "]", "-", "bbox", "[", "0", "]", ")", ")", "if", "np", ".", "sum", "(", "visible", ")", "==", "0", ":", "for", "j", "in", "range", "(", "predict_count", ")", ":", "oks", "[", "i", ",", "j", "]", "=", "0", "else", ":", "# for every predicted human", "for", "j", "in", "range", "(", "predict_count", ")", ":", "predict_key", "=", "predict", ".", "keys", "(", ")", "[", "j", "]", "predict_keypoints", "=", "np", ".", "reshape", "(", "predict", "[", "predict_key", "]", ",", "(", "14", ",", "3", ")", ")", "dis", "=", "np", ".", "sum", "(", "(", "anno_keypoints", "[", "visible", ",", ":", "2", "]", "-", "predict_keypoints", "[", "visible", ",", ":", "2", "]", ")", "**", "2", ",", "axis", "=", "1", ")", "oks", "[", "i", ",", "j", "]", "=", "np", ".", "mean", "(", "np", ".", "exp", "(", "-", "dis", "/", "2", "/", "delta", "[", "visible", "]", "**", "2", "/", "(", "scale", "+", "1", ")", ")", ")", "return", "oks" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/keypoint_eval/keypoint_eval.py#L104-L131
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/keypoint_eval/keypoint_eval.py
python
keypoint_eval
(predictions, annotations, return_dict)
return return_dict
Evaluate predicted_file and return mAP.
Evaluate predicted_file and return mAP.
[ "Evaluate", "predicted_file", "and", "return", "mAP", "." ]
def keypoint_eval(predictions, annotations, return_dict): """Evaluate predicted_file and return mAP.""" oks_all = np.zeros((0)) oks_num = 0 # Construct set to speed up id searching. prediction_id_set = set(predictions['image_ids']) # for every annotation in our test/validation set for image_id in annotations['image_ids']: # if the image in the predictions, then compute oks if image_id in prediction_id_set: oks = compute_oks(anno=annotations['annos'][image_id], \ predict=predictions['annos'][image_id]['keypoint_annos'], \ delta=annotations['delta']) # view pairs with max OKSs as match ones, add to oks_all oks_all = np.concatenate((oks_all, np.max(oks, axis=1)), axis=0) # accumulate total num by max(gtN,pN) oks_num += np.max(oks.shape) else: # otherwise report warning return_dict['warning'].append(image_id+' is not in the prediction JSON file.') # number of humen in ground truth annotations gt_n = len(annotations['annos'][image_id]['human_annos'].keys()) # fill 0 in oks scores oks_all = np.concatenate((oks_all, np.zeros((gt_n))), axis=0) # accumulate total num by ground truth number oks_num += gt_n # compute mAP by APs under different oks thresholds average_precision = [] for threshold in np.linspace(0.5, 0.95, 10): average_precision.append(np.sum(oks_all > threshold)/np.float32(oks_num)) return_dict['score'] = np.mean(average_precision) return return_dict
[ "def", "keypoint_eval", "(", "predictions", ",", "annotations", ",", "return_dict", ")", ":", "oks_all", "=", "np", ".", "zeros", "(", "(", "0", ")", ")", "oks_num", "=", "0", "# Construct set to speed up id searching.", "prediction_id_set", "=", "set", "(", "predictions", "[", "'image_ids'", "]", ")", "# for every annotation in our test/validation set", "for", "image_id", "in", "annotations", "[", "'image_ids'", "]", ":", "# if the image in the predictions, then compute oks", "if", "image_id", "in", "prediction_id_set", ":", "oks", "=", "compute_oks", "(", "anno", "=", "annotations", "[", "'annos'", "]", "[", "image_id", "]", ",", "predict", "=", "predictions", "[", "'annos'", "]", "[", "image_id", "]", "[", "'keypoint_annos'", "]", ",", "delta", "=", "annotations", "[", "'delta'", "]", ")", "# view pairs with max OKSs as match ones, add to oks_all", "oks_all", "=", "np", ".", "concatenate", "(", "(", "oks_all", ",", "np", ".", "max", "(", "oks", ",", "axis", "=", "1", ")", ")", ",", "axis", "=", "0", ")", "# accumulate total num by max(gtN,pN)", "oks_num", "+=", "np", ".", "max", "(", "oks", ".", "shape", ")", "else", ":", "# otherwise report warning", "return_dict", "[", "'warning'", "]", ".", "append", "(", "image_id", "+", "' is not in the prediction JSON file.'", ")", "# number of humen in ground truth annotations", "gt_n", "=", "len", "(", "annotations", "[", "'annos'", "]", "[", "image_id", "]", "[", "'human_annos'", "]", ".", "keys", "(", ")", ")", "# fill 0 in oks scores", "oks_all", "=", "np", ".", "concatenate", "(", "(", "oks_all", ",", "np", ".", "zeros", "(", "(", "gt_n", ")", ")", ")", ",", "axis", "=", "0", ")", "# accumulate total num by ground truth number", "oks_num", "+=", "gt_n", "# compute mAP by APs under different oks thresholds", "average_precision", "=", "[", "]", "for", "threshold", "in", "np", ".", "linspace", "(", "0.5", ",", "0.95", ",", "10", ")", ":", "average_precision", ".", "append", "(", "np", ".", "sum", "(", "oks_all", ">", "threshold", ")", "/", "np", ".", "float32", "(", "oks_num", ")", ")", "return_dict", "[", "'score'", "]", "=", "np", ".", "mean", "(", "average_precision", ")", "return", "return_dict" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/keypoint_eval/keypoint_eval.py#L134-L170
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/keypoint_eval/keypoint_eval.py
python
main
()
The evaluator.
The evaluator.
[ "The", "evaluator", "." ]
def main(): """The evaluator.""" # Arguments parser parser = argparse.ArgumentParser() parser.add_argument('--submit', help='prediction json file', type=str, default='keypoint_predictions_example.json') parser.add_argument('--ref', help='annotation json file', type=str, default='keypoint_annotations_example.json') args = parser.parse_args() # Initialize return_dict return_dict = dict() return_dict['error'] = None return_dict['warning'] = [] return_dict['score'] = None # Load annotation JSON file start_time = time.time() annotations = load_annotations(anno_file=args.ref, return_dict=return_dict) print 'Complete reading annotation JSON file in %.2f seconds.' %(time.time() - start_time) # Load prediction JSON file start_time = time.time() predictions = load_predictions(prediction_file=args.submit, return_dict=return_dict) print 'Complete reading prediction JSON file in %.2f seconds.' %(time.time() - start_time) # Keypoint evaluation start_time = time.time() return_dict = keypoint_eval(predictions=predictions, annotations=annotations, return_dict=return_dict) print 'Complete evaluation in %.2f seconds.' %(time.time() - start_time) # Print return_dict and final score pprint.pprint(return_dict) print 'Score: ', '%.8f' % return_dict['score']
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/keypoint_eval/keypoint_eval.py#L173-L211
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/run_evaluations.py
python
compute_m1
(json_predictions_file, reference_file)
return m1_score
Compute m1_score
Compute m1_score
[ "Compute", "m1_score" ]
def compute_m1(json_predictions_file, reference_file): """Compute m1_score""" m1_score = {} m1_score['error'] = 0 try: coco = COCO(reference_file) coco_res = coco.loadRes(json_predictions_file) # create coco_eval object. coco_eval = COCOEvalCap(coco, coco_res) # evaluate results coco_eval.evaluate() except Exception: m1_score['error'] = 1 else: # print output evaluation scores for metric, score in coco_eval.eval.items(): print '%s: %.3f'%(metric, score) m1_score[metric] = score return m1_score
[ "def", "compute_m1", "(", "json_predictions_file", ",", "reference_file", ")", ":", "m1_score", "=", "{", "}", "m1_score", "[", "'error'", "]", "=", "0", "try", ":", "coco", "=", "COCO", "(", "reference_file", ")", "coco_res", "=", "coco", ".", "loadRes", "(", "json_predictions_file", ")", "# create coco_eval object.", "coco_eval", "=", "COCOEvalCap", "(", "coco", ",", "coco_res", ")", "# evaluate results", "coco_eval", ".", "evaluate", "(", ")", "except", "Exception", ":", "m1_score", "[", "'error'", "]", "=", "1", "else", ":", "# print output evaluation scores", "for", "metric", ",", "score", "in", "coco_eval", ".", "eval", ".", "items", "(", ")", ":", "print", "'%s: %.3f'", "%", "(", "metric", ",", "score", ")", "m1_score", "[", "metric", "]", "=", "score", "return", "m1_score" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/run_evaluations.py#L29-L49
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/run_evaluations.py
python
main
()
The evaluator.
The evaluator.
[ "The", "evaluator", "." ]
def main(): """The evaluator.""" parser = argparse.ArgumentParser() parser.add_argument("-submit", "--submit", type=str, required=True, help=' JSON containing submit sentences.') parser.add_argument("-ref", "--ref", type=str, help=' JSON references.') args = parser.parse_args() json_predictions_file = args.submit reference_file = args.ref print compute_m1(json_predictions_file, reference_file)
[ "def", "main", "(", ")", ":", "parser", "=", "argparse", ".", "ArgumentParser", "(", ")", "parser", ".", "add_argument", "(", "\"-submit\"", ",", "\"--submit\"", ",", "type", "=", "str", ",", "required", "=", "True", ",", "help", "=", "' JSON containing submit sentences.'", ")", "parser", ".", "add_argument", "(", "\"-ref\"", ",", "\"--ref\"", ",", "type", "=", "str", ",", "help", "=", "' JSON references.'", ")", "args", "=", "parser", ".", "parse_args", "(", ")", "json_predictions_file", "=", "args", ".", "submit", "reference_file", "=", "args", ".", "ref", "print", "compute_m1", "(", "json_predictions_file", ",", "reference_file", ")" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/run_evaluations.py#L52-L63
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py
python
precook
(s, n=4, out=False)
return counts
Takes a string as input and returns an object that can be given to either cook_refs or cook_test. This is optional: cook_refs and cook_test can take string arguments as well. :param s: string : sentence to be converted into ngrams :param n: int : number of ngrams for which representation is calculated :return: term frequency vector for occuring ngrams
Takes a string as input and returns an object that can be given to either cook_refs or cook_test. This is optional: cook_refs and cook_test can take string arguments as well. :param s: string : sentence to be converted into ngrams :param n: int : number of ngrams for which representation is calculated :return: term frequency vector for occuring ngrams
[ "Takes", "a", "string", "as", "input", "and", "returns", "an", "object", "that", "can", "be", "given", "to", "either", "cook_refs", "or", "cook_test", ".", "This", "is", "optional", ":", "cook_refs", "and", "cook_test", "can", "take", "string", "arguments", "as", "well", ".", ":", "param", "s", ":", "string", ":", "sentence", "to", "be", "converted", "into", "ngrams", ":", "param", "n", ":", "int", ":", "number", "of", "ngrams", "for", "which", "representation", "is", "calculated", ":", "return", ":", "term", "frequency", "vector", "for", "occuring", "ngrams" ]
def precook(s, n=4, out=False): """ Takes a string as input and returns an object that can be given to either cook_refs or cook_test. This is optional: cook_refs and cook_test can take string arguments as well. :param s: string : sentence to be converted into ngrams :param n: int : number of ngrams for which representation is calculated :return: term frequency vector for occuring ngrams """ words = s.split() counts = defaultdict(int) for k in xrange(1,n+1): for i in xrange(len(words)-k+1): ngram = tuple(words[i:i+k]) counts[ngram] += 1 return counts
[ "def", "precook", "(", "s", ",", "n", "=", "4", ",", "out", "=", "False", ")", ":", "words", "=", "s", ".", "split", "(", ")", "counts", "=", "defaultdict", "(", "int", ")", "for", "k", "in", "xrange", "(", "1", ",", "n", "+", "1", ")", ":", "for", "i", "in", "xrange", "(", "len", "(", "words", ")", "-", "k", "+", "1", ")", ":", "ngram", "=", "tuple", "(", "words", "[", "i", ":", "i", "+", "k", "]", ")", "counts", "[", "ngram", "]", "+=", "1", "return", "counts" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py#L11-L26
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py
python
cook_refs
(refs, n=4)
return [precook(ref, n) for ref in refs]
Takes a list of reference sentences for a single segment and returns an object that encapsulates everything that BLEU needs to know about them. :param refs: list of string : reference sentences for some image :param n: int : number of ngrams for which (ngram) representation is calculated :return: result (list of dict)
Takes a list of reference sentences for a single segment and returns an object that encapsulates everything that BLEU needs to know about them. :param refs: list of string : reference sentences for some image :param n: int : number of ngrams for which (ngram) representation is calculated :return: result (list of dict)
[ "Takes", "a", "list", "of", "reference", "sentences", "for", "a", "single", "segment", "and", "returns", "an", "object", "that", "encapsulates", "everything", "that", "BLEU", "needs", "to", "know", "about", "them", ".", ":", "param", "refs", ":", "list", "of", "string", ":", "reference", "sentences", "for", "some", "image", ":", "param", "n", ":", "int", ":", "number", "of", "ngrams", "for", "which", "(", "ngram", ")", "representation", "is", "calculated", ":", "return", ":", "result", "(", "list", "of", "dict", ")" ]
def cook_refs(refs, n=4): ## lhuang: oracle will call with "average" '''Takes a list of reference sentences for a single segment and returns an object that encapsulates everything that BLEU needs to know about them. :param refs: list of string : reference sentences for some image :param n: int : number of ngrams for which (ngram) representation is calculated :return: result (list of dict) ''' return [precook(ref, n) for ref in refs]
[ "def", "cook_refs", "(", "refs", ",", "n", "=", "4", ")", ":", "## lhuang: oracle will call with \"average\"", "return", "[", "precook", "(", "ref", ",", "n", ")", "for", "ref", "in", "refs", "]" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py#L28-L36
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py
python
cook_test
(test, n=4)
return precook(test, n, True)
Takes a test sentence and returns an object that encapsulates everything that BLEU needs to know about it. :param test: list of string : hypothesis sentence for some image :param n: int : number of ngrams for which (ngram) representation is calculated :return: result (dict)
Takes a test sentence and returns an object that encapsulates everything that BLEU needs to know about it. :param test: list of string : hypothesis sentence for some image :param n: int : number of ngrams for which (ngram) representation is calculated :return: result (dict)
[ "Takes", "a", "test", "sentence", "and", "returns", "an", "object", "that", "encapsulates", "everything", "that", "BLEU", "needs", "to", "know", "about", "it", ".", ":", "param", "test", ":", "list", "of", "string", ":", "hypothesis", "sentence", "for", "some", "image", ":", "param", "n", ":", "int", ":", "number", "of", "ngrams", "for", "which", "(", "ngram", ")", "representation", "is", "calculated", ":", "return", ":", "result", "(", "dict", ")" ]
def cook_test(test, n=4): '''Takes a test sentence and returns an object that encapsulates everything that BLEU needs to know about it. :param test: list of string : hypothesis sentence for some image :param n: int : number of ngrams for which (ngram) representation is calculated :return: result (dict) ''' return precook(test, n, True)
[ "def", "cook_test", "(", "test", ",", "n", "=", "4", ")", ":", "return", "precook", "(", "test", ",", "n", ",", "True", ")" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py#L38-L45
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py
python
CiderScorer.copy
(self)
return new
copy the refs.
copy the refs.
[ "copy", "the", "refs", "." ]
def copy(self): ''' copy the refs.''' new = CiderScorer(n=self.n) new.ctest = copy.copy(self.ctest) new.crefs = copy.copy(self.crefs) return new
[ "def", "copy", "(", "self", ")", ":", "new", "=", "CiderScorer", "(", "n", "=", "self", ".", "n", ")", "new", ".", "ctest", "=", "copy", ".", "copy", "(", "self", ".", "ctest", ")", "new", ".", "crefs", "=", "copy", ".", "copy", "(", "self", ".", "crefs", ")", "return", "new" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py#L51-L56
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py
python
CiderScorer.__init__
(self, test=None, refs=None, n=4, sigma=6.0)
singular instance
singular instance
[ "singular", "instance" ]
def __init__(self, test=None, refs=None, n=4, sigma=6.0): ''' singular instance ''' self.n = n self.sigma = sigma self.crefs = [] self.ctest = [] self.document_frequency = defaultdict(float) self.cook_append(test, refs) self.ref_len = None
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py#L58-L66
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py
python
CiderScorer.cook_append
(self, test, refs)
called by constructor and __iadd__ to avoid creating new instances.
called by constructor and __iadd__ to avoid creating new instances.
[ "called", "by", "constructor", "and", "__iadd__", "to", "avoid", "creating", "new", "instances", "." ]
def cook_append(self, test, refs): '''called by constructor and __iadd__ to avoid creating new instances.''' if refs is not None: self.crefs.append(cook_refs(refs)) if test is not None: self.ctest.append(cook_test(test)) ## N.B.: -1 else: self.ctest.append(None)
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py#L68-L76
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py
python
CiderScorer.__iadd__
(self, other)
return self
add an instance (e.g., from another sentence).
add an instance (e.g., from another sentence).
[ "add", "an", "instance", "(", "e", ".", "g", ".", "from", "another", "sentence", ")", "." ]
def __iadd__(self, other): '''add an instance (e.g., from another sentence).''' if type(other) is tuple: ## avoid creating new CiderScorer instances self.cook_append(other[0], other[1]) else: self.ctest.extend(other.ctest) self.crefs.extend(other.crefs) return self
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py#L82-L92
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py
python
CiderScorer.compute_doc_freq
(self)
Compute term frequency for reference data. This will be used to compute idf (inverse document frequency later) The term frequency is stored in the object :return: None
Compute term frequency for reference data. This will be used to compute idf (inverse document frequency later) The term frequency is stored in the object :return: None
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def compute_doc_freq(self): ''' Compute term frequency for reference data. This will be used to compute idf (inverse document frequency later) The term frequency is stored in the object :return: None ''' for refs in self.crefs: # refs, k ref captions of one image for ngram in set([ngram for ref in refs for (ngram,count) in ref.iteritems()]): self.document_frequency[ngram] += 1
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider_scorer.py#L93-L103
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider.py
python
Cider.compute_score
(self, gts, res)
return score, scores
Main function to compute CIDEr score :param hypo_for_image (dict) : dictionary with key <image> and value <tokenized hypothesis / candidate sentence> ref_for_image (dict) : dictionary with key <image> and value <tokenized reference sentence> :return: cider (float) : computed CIDEr score for the corpus
Main function to compute CIDEr score :param hypo_for_image (dict) : dictionary with key <image> and value <tokenized hypothesis / candidate sentence> ref_for_image (dict) : dictionary with key <image> and value <tokenized reference sentence> :return: cider (float) : computed CIDEr score for the corpus
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def compute_score(self, gts, res): """ Main function to compute CIDEr score :param hypo_for_image (dict) : dictionary with key <image> and value <tokenized hypothesis / candidate sentence> ref_for_image (dict) : dictionary with key <image> and value <tokenized reference sentence> :return: cider (float) : computed CIDEr score for the corpus """ assert(gts.keys() == res.keys()) imgIds = gts.keys() cider_scorer = CiderScorer(n=self._n, sigma=self._sigma) for id in imgIds: hypo = res[id] ref = gts[id] # Sanity check. assert(type(hypo) is list) assert(len(hypo) == 1) assert(type(ref) is list) assert(len(ref) > 0) cider_scorer += (hypo[0], ref) (score, scores) = cider_scorer.compute_score() return score, scores
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/cider/cider.py#L24-L51
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py
python
precook
(s, n=4, out=False)
return (len(words), counts)
Takes a string as input and returns an object that can be given to either cook_refs or cook_test. This is optional: cook_refs and cook_test can take string arguments as well.
Takes a string as input and returns an object that can be given to either cook_refs or cook_test. This is optional: cook_refs and cook_test can take string arguments as well.
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def precook(s, n=4, out=False): """Takes a string as input and returns an object that can be given to either cook_refs or cook_test. This is optional: cook_refs and cook_test can take string arguments as well.""" words = s.split() counts = defaultdict(int) for k in xrange(1,n+1): for i in xrange(len(words)-k+1): ngram = tuple(words[i:i+k]) counts[ngram] += 1 return (len(words), counts)
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py#L23-L33
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py
python
cook_refs
(refs, eff=None, n=4)
return (reflen, maxcounts)
Takes a list of reference sentences for a single segment and returns an object that encapsulates everything that BLEU needs to know about them.
Takes a list of reference sentences for a single segment and returns an object that encapsulates everything that BLEU needs to know about them.
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def cook_refs(refs, eff=None, n=4): ## lhuang: oracle will call with "average" '''Takes a list of reference sentences for a single segment and returns an object that encapsulates everything that BLEU needs to know about them.''' reflen = [] maxcounts = {} for ref in refs: rl, counts = precook(ref, n) reflen.append(rl) for (ngram,count) in counts.iteritems(): maxcounts[ngram] = max(maxcounts.get(ngram,0), count) # Calculate effective reference sentence length. if eff == "shortest": reflen = min(reflen) elif eff == "average": reflen = float(sum(reflen))/len(reflen) ## lhuang: N.B.: leave reflen computaiton to the very end!! ## lhuang: N.B.: in case of "closest", keep a list of reflens!! (bad design) return (reflen, maxcounts)
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py#L35-L58
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py
python
cook_test
(test, (reflen, refmaxcounts), eff=None, n=4)
return result
Takes a test sentence and returns an object that encapsulates everything that BLEU needs to know about it.
Takes a test sentence and returns an object that encapsulates everything that BLEU needs to know about it.
[ "Takes", "a", "test", "sentence", "and", "returns", "an", "object", "that", "encapsulates", "everything", "that", "BLEU", "needs", "to", "know", "about", "it", "." ]
def cook_test(test, (reflen, refmaxcounts), eff=None, n=4): '''Takes a test sentence and returns an object that encapsulates everything that BLEU needs to know about it.''' testlen, counts = precook(test, n, True) result = {} # Calculate effective reference sentence length. if eff == "closest": result["reflen"] = min((abs(l-testlen), l) for l in reflen)[1] else: ## i.e., "average" or "shortest" or None result["reflen"] = reflen result["testlen"] = testlen result["guess"] = [max(0,testlen-k+1) for k in xrange(1,n+1)] result['correct'] = [0]*n for (ngram, count) in counts.iteritems(): result["correct"][len(ngram)-1] += min(refmaxcounts.get(ngram,0), count) return result
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py#L60-L83
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py
python
BleuScorer.copy
(self)
return new
copy the refs.
copy the refs.
[ "copy", "the", "refs", "." ]
def copy(self): ''' copy the refs.''' new = BleuScorer(n=self.n) new.ctest = copy.copy(self.ctest) new.crefs = copy.copy(self.crefs) new._score = None return new
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py#L92-L98
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py
python
BleuScorer.__init__
(self, test=None, refs=None, n=4, special_reflen=None)
singular instance
singular instance
[ "singular", "instance" ]
def __init__(self, test=None, refs=None, n=4, special_reflen=None): ''' singular instance ''' self.n = n self.crefs = [] self.ctest = [] self.cook_append(test, refs) self.special_reflen = special_reflen
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py#L100-L107
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py
python
BleuScorer.cook_append
(self, test, refs)
called by constructor and __iadd__ to avoid creating new instances.
called by constructor and __iadd__ to avoid creating new instances.
[ "called", "by", "constructor", "and", "__iadd__", "to", "avoid", "creating", "new", "instances", "." ]
def cook_append(self, test, refs): '''called by constructor and __iadd__ to avoid creating new instances.''' if refs is not None: self.crefs.append(cook_refs(refs)) if test is not None: cooked_test = cook_test(test, self.crefs[-1]) self.ctest.append(cooked_test) ## N.B.: -1 else: self.ctest.append(None) # lens of crefs and ctest have to match self._score = None
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py#L109-L120
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py
python
BleuScorer.score_ratio
(self, option=None)
return (self.fscore(option=option), self.ratio(option=option))
return (bleu, len_ratio) pair
return (bleu, len_ratio) pair
[ "return", "(", "bleu", "len_ratio", ")", "pair" ]
def score_ratio(self, option=None): '''return (bleu, len_ratio) pair''' return (self.fscore(option=option), self.ratio(option=option))
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py#L126-L128
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py
python
BleuScorer.rescore
(self, new_test)
return self.retest(new_test).compute_score()
replace test(s) with new test(s), and returns the new score.
replace test(s) with new test(s), and returns the new score.
[ "replace", "test", "(", "s", ")", "with", "new", "test", "(", "s", ")", "and", "returns", "the", "new", "score", "." ]
def rescore(self, new_test): ''' replace test(s) with new test(s), and returns the new score.''' return self.retest(new_test).compute_score()
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py#L152-L155
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py
python
BleuScorer.__iadd__
(self, other)
return self
add an instance (e.g., from another sentence).
add an instance (e.g., from another sentence).
[ "add", "an", "instance", "(", "e", ".", "g", ".", "from", "another", "sentence", ")", "." ]
def __iadd__(self, other): '''add an instance (e.g., from another sentence).''' if type(other) is tuple: ## avoid creating new BleuScorer instances self.cook_append(other[0], other[1]) else: assert self.compatible(other), "incompatible BLEUs." self.ctest.extend(other.ctest) self.crefs.extend(other.crefs) self._score = None ## need to recompute return self
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/bleu/bleu_scorer.py#L161-L173
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/rouge/rouge.py
python
my_lcs
(string, sub)
return lengths[len(string)][len(sub)]
Calculates longest common subsequence for a pair of tokenized strings :param string : list of str : tokens from a string split using whitespace :param sub : list of str : shorter string, also split using whitespace :returns: length (list of int): length of the longest common subsequence between the two strings Note: my_lcs only gives length of the longest common subsequence, not the actual LCS
Calculates longest common subsequence for a pair of tokenized strings :param string : list of str : tokens from a string split using whitespace :param sub : list of str : shorter string, also split using whitespace :returns: length (list of int): length of the longest common subsequence between the two strings
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def my_lcs(string, sub): """ Calculates longest common subsequence for a pair of tokenized strings :param string : list of str : tokens from a string split using whitespace :param sub : list of str : shorter string, also split using whitespace :returns: length (list of int): length of the longest common subsequence between the two strings Note: my_lcs only gives length of the longest common subsequence, not the actual LCS """ if(len(string)< len(sub)): sub, string = string, sub lengths = [[0 for i in range(0,len(sub)+1)] for j in range(0,len(string)+1)] for j in range(1,len(sub)+1): for i in range(1,len(string)+1): if(string[i-1] == sub[j-1]): lengths[i][j] = lengths[i-1][j-1] + 1 else: lengths[i][j] = max(lengths[i-1][j] , lengths[i][j-1]) return lengths[len(string)][len(sub)]
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/rouge/rouge.py#L13-L34
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/rouge/rouge.py
python
Rouge.calc_score
(self, candidate, refs)
return score
Compute ROUGE-L score given one candidate and references for an image :param candidate: str : candidate sentence to be evaluated :param refs: list of str : COCO reference sentences for the particular image to be evaluated :returns score: int (ROUGE-L score for the candidate evaluated against references)
Compute ROUGE-L score given one candidate and references for an image :param candidate: str : candidate sentence to be evaluated :param refs: list of str : COCO reference sentences for the particular image to be evaluated :returns score: int (ROUGE-L score for the candidate evaluated against references)
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def calc_score(self, candidate, refs): """ Compute ROUGE-L score given one candidate and references for an image :param candidate: str : candidate sentence to be evaluated :param refs: list of str : COCO reference sentences for the particular image to be evaluated :returns score: int (ROUGE-L score for the candidate evaluated against references) """ assert(len(candidate)==1) assert(len(refs)>0) prec = [] rec = [] # split into tokens token_c = candidate[0].split(" ") for reference in refs: # split into tokens token_r = reference.split(" ") # compute the longest common subsequence lcs = my_lcs(token_r, token_c) prec.append(lcs/float(len(token_c))) rec.append(lcs/float(len(token_r))) prec_max = max(prec) rec_max = max(rec) if(prec_max!=0 and rec_max !=0): score = ((1 + self.beta**2)*prec_max*rec_max)/float(rec_max + self.beta**2*prec_max) else: score = 0.0 return score
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/rouge/rouge.py#L45-L75
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxevalcap/rouge/rouge.py
python
Rouge.compute_score
(self, gts, res)
return average_score, np.array(score)
Computes Rouge-L score given a set of reference and candidate sentences for the dataset Invoked by evaluate_captions.py :param hypo_for_image: dict : candidate / test sentences with "image name" key and "tokenized sentences" as values :param ref_for_image: dict : reference MS-COCO sentences with "image name" key and "tokenized sentences" as values :returns: average_score: float (mean ROUGE-L score computed by averaging scores for all the images)
Computes Rouge-L score given a set of reference and candidate sentences for the dataset Invoked by evaluate_captions.py :param hypo_for_image: dict : candidate / test sentences with "image name" key and "tokenized sentences" as values :param ref_for_image: dict : reference MS-COCO sentences with "image name" key and "tokenized sentences" as values :returns: average_score: float (mean ROUGE-L score computed by averaging scores for all the images)
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def compute_score(self, gts, res): """ Computes Rouge-L score given a set of reference and candidate sentences for the dataset Invoked by evaluate_captions.py :param hypo_for_image: dict : candidate / test sentences with "image name" key and "tokenized sentences" as values :param ref_for_image: dict : reference MS-COCO sentences with "image name" key and "tokenized sentences" as values :returns: average_score: float (mean ROUGE-L score computed by averaging scores for all the images) """ assert(gts.keys() == res.keys()) imgIds = gts.keys() score = [] for id in imgIds: hypo = res[id] ref = gts[id] score.append(self.calc_score(hypo, ref)) # Sanity check. assert(type(hypo) is list) assert(len(hypo) == 1) assert(type(ref) is list) assert(len(ref) > 0) average_score = np.mean(np.array(score)) return average_score, np.array(score)
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxevalcap/rouge/rouge.py#L77-L102
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.__init__
(self, annotation_file=None)
Constructor of Microsoft COCO helper class for reading and visualizing annotations. :param annotation_file (str): location of annotation file :param image_folder (str): location to the folder that hosts images. :return:
Constructor of Microsoft COCO helper class for reading and visualizing annotations. :param annotation_file (str): location of annotation file :param image_folder (str): location to the folder that hosts images. :return:
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def __init__(self, annotation_file=None): """ Constructor of Microsoft COCO helper class for reading and visualizing annotations. :param annotation_file (str): location of annotation file :param image_folder (str): location to the folder that hosts images. :return: """ # load dataset self.dataset = {} self.anns = [] self.imgToAnns = {} self.catToImgs = {} self.imgs = [] self.cats = [] self.image2hash = {} if not annotation_file == None: print('loading annotations into memory...') time_t = datetime.datetime.utcnow() dataset = json.load(open(annotation_file, 'r')) print( datetime.datetime.utcnow() - time_t) self.dataset = dataset self.createIndex()
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L65-L87
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.info
(self)
Print information about the annotation file. :return:
Print information about the annotation file. :return:
[ "Print", "information", "about", "the", "annotation", "file", ".", ":", "return", ":" ]
def info(self): """ Print information about the annotation file. :return: """ for key, value in self.datset['info'].items(): print( '%s: %s'%(key, value))
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L129-L135
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.getAnnIds
(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None)
return ids
Get ann ids that satisfy given filter conditions. default skips that filter :param imgIds (int array) : get anns for given imgs catIds (int array) : get anns for given cats areaRng (float array) : get anns for given area range (e.g. [0 inf]) iscrowd (boolean) : get anns for given crowd label (False or True) :return: ids (int array) : integer array of ann ids
Get ann ids that satisfy given filter conditions. default skips that filter :param imgIds (int array) : get anns for given imgs catIds (int array) : get anns for given cats areaRng (float array) : get anns for given area range (e.g. [0 inf]) iscrowd (boolean) : get anns for given crowd label (False or True) :return: ids (int array) : integer array of ann ids
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def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None): """ Get ann ids that satisfy given filter conditions. default skips that filter :param imgIds (int array) : get anns for given imgs catIds (int array) : get anns for given cats areaRng (float array) : get anns for given area range (e.g. [0 inf]) iscrowd (boolean) : get anns for given crowd label (False or True) :return: ids (int array) : integer array of ann ids """ imgIds = imgIds if type(imgIds) == list else [imgIds] catIds = catIds if type(catIds) == list else [catIds] if len(imgIds) == len(catIds) == len(areaRng) == 0: anns = self.dataset['annotations'] else: if not len(imgIds) == 0: anns = sum([self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns],[]) else: anns = self.dataset['annotations'] anns = anns if len(catIds) == 0 else [ann for ann in anns if ann['category_id'] in catIds] anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]] if self.dataset['type'] == 'instances': if not iscrowd == None: ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd] else: ids = [ann['id'] for ann in anns] else: ids = [ann['id'] for ann in anns] return ids
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L137-L165
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.getCatIds
(self, catNms=[], supNms=[], catIds=[])
return ids
filtering parameters. default skips that filter. :param catNms (str array) : get cats for given cat names :param supNms (str array) : get cats for given supercategory names :param catIds (int array) : get cats for given cat ids :return: ids (int array) : integer array of cat ids
filtering parameters. default skips that filter. :param catNms (str array) : get cats for given cat names :param supNms (str array) : get cats for given supercategory names :param catIds (int array) : get cats for given cat ids :return: ids (int array) : integer array of cat ids
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def getCatIds(self, catNms=[], supNms=[], catIds=[]): """ filtering parameters. default skips that filter. :param catNms (str array) : get cats for given cat names :param supNms (str array) : get cats for given supercategory names :param catIds (int array) : get cats for given cat ids :return: ids (int array) : integer array of cat ids """ catNms = catNms if type(catNms) == list else [catNms] supNms = supNms if type(supNms) == list else [supNms] catIds = catIds if type(catIds) == list else [catIds] if len(catNms) == len(supNms) == len(catIds) == 0: cats = self.dataset['categories'] else: cats = self.dataset['categories'] cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name'] in catNms] cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms] cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id'] in catIds] ids = [cat['id'] for cat in cats] return ids
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L167-L187
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.getImgIds
(self, imgIds=[], catIds=[])
return list(ids)
Get img ids that satisfy given filter conditions. :param imgIds (int array) : get imgs for given ids :param catIds (int array) : get imgs with all given cats :return: ids (int array) : integer array of img ids
Get img ids that satisfy given filter conditions. :param imgIds (int array) : get imgs for given ids :param catIds (int array) : get imgs with all given cats :return: ids (int array) : integer array of img ids
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def getImgIds(self, imgIds=[], catIds=[]): ''' Get img ids that satisfy given filter conditions. :param imgIds (int array) : get imgs for given ids :param catIds (int array) : get imgs with all given cats :return: ids (int array) : integer array of img ids ''' imgIds = imgIds if type(imgIds) == list else [imgIds] catIds = catIds if type(catIds) == list else [catIds] if len(imgIds) == len(catIds) == 0: ids = self.imgs.keys() else: ids = set(imgIds) for catId in catIds: if len(ids) == 0: ids = set(self.catToImgs[catId]) else: ids &= set(self.catToImgs[catId]) return list(ids)
[ "def", "getImgIds", "(", "self", ",", "imgIds", "=", "[", "]", ",", "catIds", "=", "[", "]", ")", ":", "imgIds", "=", "imgIds", "if", "type", "(", "imgIds", ")", "==", "list", "else", "[", "imgIds", "]", "catIds", "=", "catIds", "if", "type", "(", "catIds", ")", "==", "list", "else", "[", "catIds", "]", "if", "len", "(", "imgIds", ")", "==", "len", "(", "catIds", ")", "==", "0", ":", "ids", "=", "self", ".", "imgs", ".", "keys", "(", ")", "else", ":", "ids", "=", "set", "(", "imgIds", ")", "for", "catId", "in", "catIds", ":", "if", "len", "(", "ids", ")", "==", "0", ":", "ids", "=", "set", "(", "self", ".", "catToImgs", "[", "catId", "]", ")", "else", ":", "ids", "&=", "set", "(", "self", ".", "catToImgs", "[", "catId", "]", ")", "return", "list", "(", "ids", ")" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L189-L208
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.loadAnns
(self, ids=[])
Load anns with the specified ids. :param ids (int array) : integer ids specifying anns :return: anns (object array) : loaded ann objects
Load anns with the specified ids. :param ids (int array) : integer ids specifying anns :return: anns (object array) : loaded ann objects
[ "Load", "anns", "with", "the", "specified", "ids", ".", ":", "param", "ids", "(", "int", "array", ")", ":", "integer", "ids", "specifying", "anns", ":", "return", ":", "anns", "(", "object", "array", ")", ":", "loaded", "ann", "objects" ]
def loadAnns(self, ids=[]): """ Load anns with the specified ids. :param ids (int array) : integer ids specifying anns :return: anns (object array) : loaded ann objects """ if type(ids) == list: return [self.anns[id] for id in ids] elif type(ids) == int: return [self.anns[ids]]
[ "def", "loadAnns", "(", "self", ",", "ids", "=", "[", "]", ")", ":", "if", "type", "(", "ids", ")", "==", "list", ":", "return", "[", "self", ".", "anns", "[", "id", "]", "for", "id", "in", "ids", "]", "elif", "type", "(", "ids", ")", "==", "int", ":", "return", "[", "self", ".", "anns", "[", "ids", "]", "]" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L210-L219
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.loadCats
(self, ids=[])
Load cats with the specified ids. :param ids (int array) : integer ids specifying cats :return: cats (object array) : loaded cat objects
Load cats with the specified ids. :param ids (int array) : integer ids specifying cats :return: cats (object array) : loaded cat objects
[ "Load", "cats", "with", "the", "specified", "ids", ".", ":", "param", "ids", "(", "int", "array", ")", ":", "integer", "ids", "specifying", "cats", ":", "return", ":", "cats", "(", "object", "array", ")", ":", "loaded", "cat", "objects" ]
def loadCats(self, ids=[]): """ Load cats with the specified ids. :param ids (int array) : integer ids specifying cats :return: cats (object array) : loaded cat objects """ if type(ids) == list: return [self.cats[id] for id in ids] elif type(ids) == int: return [self.cats[ids]]
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L221-L230
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.loadImgs
(self, ids=[])
Load anns with the specified ids. :param ids (int array) : integer ids specifying img :return: imgs (object array) : loaded img objects
Load anns with the specified ids. :param ids (int array) : integer ids specifying img :return: imgs (object array) : loaded img objects
[ "Load", "anns", "with", "the", "specified", "ids", ".", ":", "param", "ids", "(", "int", "array", ")", ":", "integer", "ids", "specifying", "img", ":", "return", ":", "imgs", "(", "object", "array", ")", ":", "loaded", "img", "objects" ]
def loadImgs(self, ids=[]): """ Load anns with the specified ids. :param ids (int array) : integer ids specifying img :return: imgs (object array) : loaded img objects """ if type(ids) == list: return [self.imgs[id] for id in ids] elif type(ids) == int: return [self.imgs[ids]]
[ "def", "loadImgs", "(", "self", ",", "ids", "=", "[", "]", ")", ":", "if", "type", "(", "ids", ")", "==", "list", ":", "return", "[", "self", ".", "imgs", "[", "id", "]", "for", "id", "in", "ids", "]", "elif", "type", "(", "ids", ")", "==", "int", ":", "return", "[", "self", ".", "imgs", "[", "ids", "]", "]" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L232-L241
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.showAnns
(self, anns)
Display the specified annotations. :param anns (array of object): annotations to display :return: None
Display the specified annotations. :param anns (array of object): annotations to display :return: None
[ "Display", "the", "specified", "annotations", ".", ":", "param", "anns", "(", "array", "of", "object", ")", ":", "annotations", "to", "display", ":", "return", ":", "None" ]
def showAnns(self, anns): """ Display the specified annotations. :param anns (array of object): annotations to display :return: None """ if len(anns) == 0: return 0 if self.dataset['type'] == 'instances': ax = plt.gca() polygons = [] color = [] for ann in anns: c = np.random.random((1, 3)).tolist()[0] if type(ann['segmentation']) == list: # polygon for seg in ann['segmentation']: poly = np.array(seg).reshape((len(seg)/2, 2)) polygons.append(Polygon(poly, True,alpha=0.4)) color.append(c) else: # mask mask = COCO.decodeMask(ann['segmentation']) img = np.ones( (mask.shape[0], mask.shape[1], 3) ) if ann['iscrowd'] == 1: color_mask = np.array([2.0,166.0,101.0])/255 if ann['iscrowd'] == 0: color_mask = np.random.random((1, 3)).tolist()[0] for i in range(3): img[:,:,i] = color_mask[i] ax.imshow(np.dstack( (img, mask*0.5) )) p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4) ax.add_collection(p) if self.dataset['type'] == 'captions': for ann in anns: print( ann['caption'])
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L243-L278
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.loadRes
(self, resFile)
return res
change by ZhengHe Load result file and return a result api object. :param resFile (str) : file name of result file :return: res (obj) : result api object
change by ZhengHe Load result file and return a result api object. :param resFile (str) : file name of result file :return: res (obj) : result api object
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def loadRes(self, resFile): """ change by ZhengHe Load result file and return a result api object. :param resFile (str) : file name of result file :return: res (obj) : result api object """ res = COCO() res.dataset['images'] = [img for img in self.dataset['images']] res.dataset['info'] = copy.deepcopy(self.dataset['info']) res.dataset['type'] = copy.deepcopy(self.dataset['type']) res.dataset['licenses'] = copy.deepcopy(self.dataset['licenses']) # str to hex int for image_id imgdict = {} def get_image_dict(img_name): # image_hash = int(int(hashlib.sha256(img_name).hexdigest(), 16) % sys.maxint) image_hash = self.image2hash[img_name] if image_hash in imgdict: assert imgdict[image_hash] == img_name, 'hash colision: {0}: {1}'.format(image_hash, img_name) else: imgdict[image_hash] = img_name image_dict = {"id": image_hash, "width": 0, "height": 0, "file_name": img_name, "license": '', "url": img_name, "date_captured": '', } return image_hash print ('Loading and preparing results... ') time_t = datetime.datetime.utcnow() anns = json.load(open(resFile)) assert type(anns) == list, 'results in not an array of objects' # annsImgIds = [ann['image_id'] for ann in anns] # change by ZhengHe annsImgIds = [] for ann in anns: assert ann['image_id'] != '','image_id must have a name' assert ann['caption'] != '', 'caption must be a string' w = jieba.cut(ann['caption'].strip().replace('。',''), cut_all=False) p = ' '.join(w) ann['caption'] = p ann['image_id'] = get_image_dict(ann['image_id']) annsImgIds.append((ann['image_id'])) assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \ 'Results do not correspond to current coco set' if 'caption' in anns[0]: imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns]) res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds] for id, ann in enumerate(anns): ann['id'] = id elif 'bbox' in anns[0] and not anns[0]['bbox'] == []: res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) for id, ann in enumerate(anns): bb = ann['bbox'] x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]] ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]] ann['area'] = bb[2]*bb[3] ann['id'] = id ann['iscrowd'] = 0 elif 'segmentation' in anns[0]: res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) for id, ann in enumerate(anns): ann['area']=sum(ann['segmentation']['counts'][2:-1:2]) ann['bbox'] = [] ann['id'] = id ann['iscrowd'] = 0 print( 'DONE (t=%0.2fs)'%((datetime.datetime.utcnow() - time_t).total_seconds())) res.dataset['annotations'] = anns res.createIndex() return res
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https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L280-L360
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.decodeMask
(R)
return M.reshape((R['size']), order='F')
Decode binary mask M encoded via run-length encoding. :param R (object RLE) : run-length encoding of binary mask :return: M (bool 2D array) : decoded binary mask
Decode binary mask M encoded via run-length encoding. :param R (object RLE) : run-length encoding of binary mask :return: M (bool 2D array) : decoded binary mask
[ "Decode", "binary", "mask", "M", "encoded", "via", "run", "-", "length", "encoding", ".", ":", "param", "R", "(", "object", "RLE", ")", ":", "run", "-", "length", "encoding", "of", "binary", "mask", ":", "return", ":", "M", "(", "bool", "2D", "array", ")", ":", "decoded", "binary", "mask" ]
def decodeMask(R): """ Decode binary mask M encoded via run-length encoding. :param R (object RLE) : run-length encoding of binary mask :return: M (bool 2D array) : decoded binary mask """ N = len(R['counts']) M = np.zeros( (R['size'][0]*R['size'][1], )) n = 0 val = 1 for pos in range(N): val = not val for c in range(R['counts'][pos]): R['counts'][pos] M[n] = val n += 1 return M.reshape((R['size']), order='F')
[ "def", "decodeMask", "(", "R", ")", ":", "N", "=", "len", "(", "R", "[", "'counts'", "]", ")", "M", "=", "np", ".", "zeros", "(", "(", "R", "[", "'size'", "]", "[", "0", "]", "*", "R", "[", "'size'", "]", "[", "1", "]", ",", ")", ")", "n", "=", "0", "val", "=", "1", "for", "pos", "in", "range", "(", "N", ")", ":", "val", "=", "not", "val", "for", "c", "in", "range", "(", "R", "[", "'counts'", "]", "[", "pos", "]", ")", ":", "R", "[", "'counts'", "]", "[", "pos", "]", "M", "[", "n", "]", "=", "val", "n", "+=", "1", "return", "M", ".", "reshape", "(", "(", "R", "[", "'size'", "]", ")", ",", "order", "=", "'F'", ")" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L364-L380
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.encodeMask
(M)
return {'size': [h, w], 'counts': counts_list , }
Encode binary mask M using run-length encoding. :param M (bool 2D array) : binary mask to encode :return: R (object RLE) : run-length encoding of binary mask
Encode binary mask M using run-length encoding. :param M (bool 2D array) : binary mask to encode :return: R (object RLE) : run-length encoding of binary mask
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def encodeMask(M): """ Encode binary mask M using run-length encoding. :param M (bool 2D array) : binary mask to encode :return: R (object RLE) : run-length encoding of binary mask """ [h, w] = M.shape M = M.flatten(order='F') N = len(M) counts_list = [] pos = 0 # counts counts_list.append(1) diffs = np.logical_xor(M[0:N-1], M[1:N]) for diff in diffs: if diff: pos +=1 counts_list.append(1) else: counts_list[pos] += 1 # if array starts from 1. start with 0 counts for 0 if M[0] == 1: counts_list = [0] + counts_list return {'size': [h, w], 'counts': counts_list , }
[ "def", "encodeMask", "(", "M", ")", ":", "[", "h", ",", "w", "]", "=", "M", ".", "shape", "M", "=", "M", ".", "flatten", "(", "order", "=", "'F'", ")", "N", "=", "len", "(", "M", ")", "counts_list", "=", "[", "]", "pos", "=", "0", "# counts", "counts_list", ".", "append", "(", "1", ")", "diffs", "=", "np", ".", "logical_xor", "(", "M", "[", "0", ":", "N", "-", "1", "]", ",", "M", "[", "1", ":", "N", "]", ")", "for", "diff", "in", "diffs", ":", "if", "diff", ":", "pos", "+=", "1", "counts_list", ".", "append", "(", "1", ")", "else", ":", "counts_list", "[", "pos", "]", "+=", "1", "# if array starts from 1. start with 0 counts for 0", "if", "M", "[", "0", "]", "==", "1", ":", "counts_list", "=", "[", "0", "]", "+", "counts_list", "return", "{", "'size'", ":", "[", "h", ",", "w", "]", ",", "'counts'", ":", "counts_list", ",", "}" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L383-L408
AIChallenger/AI_Challenger_2017
52014e0defbbdd85bf94ab05d308300d5764022f
Evaluation/caption_eval/coco_caption/pycxtools/coco.py
python
COCO.segToMask
( S, h, w )
return M
Convert polygon segmentation to binary mask. :param S (float array) : polygon segmentation mask :param h (int) : target mask height :param w (int) : target mask width :return: M (bool 2D array) : binary mask
Convert polygon segmentation to binary mask. :param S (float array) : polygon segmentation mask :param h (int) : target mask height :param w (int) : target mask width :return: M (bool 2D array) : binary mask
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def segToMask( S, h, w ): """ Convert polygon segmentation to binary mask. :param S (float array) : polygon segmentation mask :param h (int) : target mask height :param w (int) : target mask width :return: M (bool 2D array) : binary mask """ M = np.zeros((h,w), dtype=np.bool) for s in S: N = len(s) rr, cc = polygon(np.array(s[1:N:2]), np.array(s[0:N:2])) # (y, x) M[rr, cc] = 1 return M
[ "def", "segToMask", "(", "S", ",", "h", ",", "w", ")", ":", "M", "=", "np", ".", "zeros", "(", "(", "h", ",", "w", ")", ",", "dtype", "=", "np", ".", "bool", ")", "for", "s", "in", "S", ":", "N", "=", "len", "(", "s", ")", "rr", ",", "cc", "=", "polygon", "(", "np", ".", "array", "(", "s", "[", "1", ":", "N", ":", "2", "]", ")", ",", "np", ".", "array", "(", "s", "[", "0", ":", "N", ":", "2", "]", ")", ")", "# (y, x)", "M", "[", "rr", ",", "cc", "]", "=", "1", "return", "M" ]
https://github.com/AIChallenger/AI_Challenger_2017/blob/52014e0defbbdd85bf94ab05d308300d5764022f/Evaluation/caption_eval/coco_caption/pycxtools/coco.py#L411-L424
Academic-Hammer/SciTSR
79954b5143295162ceaf7e9d9af918a29fe12f55
scitsr/table.py
python
Box.__init__
(self, pos)
pos: (x1, x2, y1, y2)
pos: (x1, x2, y1, y2)
[ "pos", ":", "(", "x1", "x2", "y1", "y2", ")" ]
def __init__(self, pos): """pos: (x1, x2, y1, y2)""" self.set_pos(pos)
[ "def", "__init__", "(", "self", ",", "pos", ")", ":", "self", ".", "set_pos", "(", "pos", ")" ]
https://github.com/Academic-Hammer/SciTSR/blob/79954b5143295162ceaf7e9d9af918a29fe12f55/scitsr/table.py#L30-L32
Academic-Hammer/SciTSR
79954b5143295162ceaf7e9d9af918a29fe12f55
scitsr/eval.py
python
eval_relations
(gt:List[List], res:List[List], cmp_blank=True)
return precision, recall
Evaluate results Args: gt: a list of list of Relation res: a list of list of Relation
Evaluate results
[ "Evaluate", "results" ]
def eval_relations(gt:List[List], res:List[List], cmp_blank=True): """Evaluate results Args: gt: a list of list of Relation res: a list of list of Relation """ #TODO to know how to calculate the total recall and prec assert len(gt) == len(res) tot_prec = 0 tot_recall = 0 total = 0 # print("evaluating result...") # for _gt, _res in tqdm(zip(gt, res)): # for _gt, _res in tqdm(zip(gt, res), total=len(gt), desc='eval'): idx, t = 0, len(gt) for _gt, _res in zip(gt, res): idx += 1 print('Eval %d/%d (%d%%)' % (idx, t, idx / t * 100), ' ' * 45, end='\r') corr = compare_rel(_gt, _res, cmp_blank) precision = corr / len(_res) if len(_res) != 0 else 0 recall = corr / len(_gt) if len(_gt) != 0 else 0 tot_prec += precision tot_recall += recall total += 1 # print() precision = tot_prec / total recall = tot_recall / total # print("Test on %d instances. Precision: %.2f, Recall: %.2f" % ( # total, precision, recall)) return precision, recall
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https://github.com/Academic-Hammer/SciTSR/blob/79954b5143295162ceaf7e9d9af918a29fe12f55/scitsr/eval.py#L30-L64
Academic-Hammer/SciTSR
79954b5143295162ceaf7e9d9af918a29fe12f55
scitsr/eval.py
python
Table2Relations
(t:Table)
return ret
Convert a Table object to a List of Relation.
Convert a Table object to a List of Relation.
[ "Convert", "a", "Table", "object", "to", "a", "List", "of", "Relation", "." ]
def Table2Relations(t:Table): """Convert a Table object to a List of Relation. """ ret = [] cl = t.coo2cell_id # remove duplicates with pair set used = set() # look right for r in range(t.row_n): for cFrom in range(t.col_n - 1): cTo = cFrom + 1 loop = True while loop and cTo < t.col_n: fid, tid = cl[r][cFrom], cl[r][cTo] if fid != -1 and tid != -1 and fid != tid: if (fid, tid) not in used: ret.append(Relation( from_text=t.cells[fid].text, to_text=t.cells[tid].text, direction=DIR_HORIZ, from_id=fid, to_id=tid, no_blanks=cTo - cFrom - 1 )) used.add((fid, tid)) loop = False else: if fid != -1 and tid != -1 and fid == tid: cFrom = cTo cTo += 1 # look down for c in range(t.col_n): for rFrom in range(t.row_n - 1): rTo = rFrom + 1 loop = True while loop and rTo < t.row_n: fid, tid = cl[rFrom][c], cl[rTo][c] if fid != -1 and tid != -1 and fid != tid: if (fid, tid) not in used: ret.append(Relation( from_text=t.cells[fid].text, to_text=t.cells[tid].text, direction=DIR_VERT, from_id=fid, to_id=tid, no_blanks=rTo - rFrom - 1 )) used.add((fid, tid)) loop = False else: if fid != -1 and tid != -1 and fid == tid: rFrom = rTo rTo += 1 return ret
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https://github.com/Academic-Hammer/SciTSR/blob/79954b5143295162ceaf7e9d9af918a29fe12f55/scitsr/eval.py#L89-L145
Academic-Hammer/SciTSR
79954b5143295162ceaf7e9d9af918a29fe12f55
scitsr/eval.py
python
json2Table
(json_obj, tid="", splitted_content=False)
return Table(row_n + 1, col_n + 1, cells, tid)
Construct a Table object from json object Args: json_obj: a json object Returns: a Table object
Construct a Table object from json object
[ "Construct", "a", "Table", "object", "from", "json", "object" ]
def json2Table(json_obj, tid="", splitted_content=False): """Construct a Table object from json object Args: json_obj: a json object Returns: a Table object """ jo = json_obj["cells"] row_n, col_n = 0, 0 cells = [] for co in jo: content = co["content"] if content is None: continue if splitted_content: content = " ".join(content) else: content = content.strip() if content == "": continue start_row = co["start_row"] end_row = co["end_row"] start_col = co["start_col"] end_col = co["end_col"] row_n = max(row_n, end_row) col_n = max(col_n, end_col) cell = Chunk(content, (start_row, end_row, start_col, end_col)) cells.append(cell) return Table(row_n + 1, col_n + 1, cells, tid)
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https://github.com/Academic-Hammer/SciTSR/blob/79954b5143295162ceaf7e9d9af918a29fe12f55/scitsr/eval.py#L147-L174
Academic-Hammer/SciTSR
79954b5143295162ceaf7e9d9af918a29fe12f55
scitsr/model.py
python
Attention.forward
(self, x, y, mask)
return x
Shapes: mask: [nodes/edges, edges/nodes] q: [nodes/edges, h] k: [edges/nodes, h] v: [edges/nodes, h] score: [nodes/edges, edges/nodes] x_atten: [nodes/edges, h]
Shapes: mask: [nodes/edges, edges/nodes] q: [nodes/edges, h] k: [edges/nodes, h] v: [edges/nodes, h] score: [nodes/edges, edges/nodes] x_atten: [nodes/edges, h]
[ "Shapes", ":", "mask", ":", "[", "nodes", "/", "edges", "edges", "/", "nodes", "]", "q", ":", "[", "nodes", "/", "edges", "h", "]", "k", ":", "[", "edges", "/", "nodes", "h", "]", "v", ":", "[", "edges", "/", "nodes", "h", "]", "score", ":", "[", "nodes", "/", "edges", "edges", "/", "nodes", "]", "x_atten", ":", "[", "nodes", "/", "edges", "h", "]" ]
def forward(self, x, y, mask): """ Shapes: mask: [nodes/edges, edges/nodes] q: [nodes/edges, h] k: [edges/nodes, h] v: [edges/nodes, h] score: [nodes/edges, edges/nodes] x_atten: [nodes/edges, h] """ q = self.linear_q(x) k = self.linear_k(y) v = self.linear_v(y) score = torch.mm(q, k.t()) / math.sqrt(self.size) score = self.masked_softmax(score, mask, dim=1) x_atten = torch.mm(score, v) # dropout x_atten = self.dropout(x_atten) x = self.layer_norm_1(x + x_atten) x_linear = self.feed_forward(x) # dropout x_linear = self.dropout(x_linear) x = self.layer_norm_2(x + x_linear) return x
[ "def", "forward", "(", "self", ",", "x", ",", "y", ",", "mask", ")", ":", "q", "=", "self", ".", "linear_q", "(", "x", ")", "k", "=", "self", ".", "linear_k", "(", "y", ")", "v", "=", "self", ".", "linear_v", "(", "y", ")", "score", "=", "torch", ".", "mm", "(", "q", ",", "k", ".", "t", "(", ")", ")", "/", "math", ".", "sqrt", "(", "self", ".", "size", ")", "score", "=", "self", ".", "masked_softmax", "(", "score", ",", "mask", ",", "dim", "=", "1", ")", "x_atten", "=", "torch", ".", "mm", "(", "score", ",", "v", ")", "# dropout", "x_atten", "=", "self", ".", "dropout", "(", "x_atten", ")", "x", "=", "self", ".", "layer_norm_1", "(", "x", "+", "x_atten", ")", "x_linear", "=", "self", ".", "feed_forward", "(", "x", ")", "# dropout", "x_linear", "=", "self", ".", "dropout", "(", "x_linear", ")", "x", "=", "self", ".", "layer_norm_2", "(", "x", "+", "x_linear", ")", "return", "x" ]
https://github.com/Academic-Hammer/SciTSR/blob/79954b5143295162ceaf7e9d9af918a29fe12f55/scitsr/model.py#L38-L61
Academic-Hammer/SciTSR
79954b5143295162ceaf7e9d9af918a29fe12f55
scitsr/graph.py
python
Vertex.__init__
(self, vid: int, chunk: Chunk, tab_h, tab_w)
Args: vid: Vertex id chunk: the chunk to extract features tab_h: height of the table (y-axis) tab_w: width of the table (x-axis)
Args: vid: Vertex id chunk: the chunk to extract features tab_h: height of the table (y-axis) tab_w: width of the table (x-axis)
[ "Args", ":", "vid", ":", "Vertex", "id", "chunk", ":", "the", "chunk", "to", "extract", "features", "tab_h", ":", "height", "of", "the", "table", "(", "y", "-", "axis", ")", "tab_w", ":", "width", "of", "the", "table", "(", "x", "-", "axis", ")" ]
def __init__(self, vid: int, chunk: Chunk, tab_h, tab_w): """ Args: vid: Vertex id chunk: the chunk to extract features tab_h: height of the table (y-axis) tab_w: width of the table (x-axis) """ self.vid = vid self.tab_h = tab_h self.tab_w = tab_w self.chunk = chunk self.features = self.get_features()
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https://github.com/Academic-Hammer/SciTSR/blob/79954b5143295162ceaf7e9d9af918a29fe12f55/scitsr/graph.py#L15-L27
Academic-Hammer/SciTSR
79954b5143295162ceaf7e9d9af918a29fe12f55
scitsr/train.py
python
patch_chunks
(dataset_folder)
return 1
To patch the all chunk files of the train & test dataset that have the problem of duplicate last character of the last cell in all chunk files :param dataset_folder: train dataset path :return: 1
To patch the all chunk files of the train & test dataset that have the problem of duplicate last character of the last cell in all chunk files :param dataset_folder: train dataset path :return: 1
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def patch_chunks(dataset_folder): """ To patch the all chunk files of the train & test dataset that have the problem of duplicate last character of the last cell in all chunk files :param dataset_folder: train dataset path :return: 1 """ import os import shutil from pathlib import Path shutil.move(os.path.join(dataset_folder, "chunk"), os.path.join(dataset_folder, "chunk-old")) dir_ = Path(os.path.join(dataset_folder, "chunk-old")) os.makedirs(os.path.join(dataset_folder, "chunk"), exist_ok=True) for chunk_path in dir_.iterdir(): # print(chunk_path) with open(str(chunk_path), encoding="utf-8") as f: chunks = json.load(f)['chunks'] chunks[-1]['text'] = chunks[-1]['text'][:-1] with open(str(chunk_path).replace("chunk-old", "chunk"), "w", encoding="utf-8") as ofile: json.dump({"chunks": chunks}, ofile) print("Input files patched, ready for the use") return 1
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https://github.com/Academic-Hammer/SciTSR/blob/79954b5143295162ceaf7e9d9af918a29fe12f55/scitsr/train.py#L146-L170