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| base_model.py: import os import numpy as np import tensorflow as tf import pickle from tqdm import tqdm from utils.nn import NN from utils.misc import ImageLoader, CaptionData, TopN
class BaseModel(object): def __init__(self, config): self.config = config self.is_train = False self.train_cnn = self.is_train and config.train_cnn self.image_loader = ImageLoader('./utils/ilsvrc_2012_mean.npy') self.image_shape = [224, 224, 3] self.nn = NN(config) self.global_step = tf.Variable(0, name='global_step', trainable=False) self.build()
def build(self): raise NotImplementedError()
def beam_search(self, sess, image_files, vocabulary): """Use beam search to generate the captions for a batch of images.""" # Feed in the images to get the contexts and the initial LSTM states config = self.config images = self.image_loader.load_images(image_files) contexts, initial_memory, initial_output = sess.run( [self.conv_feats, self.initial_memory, self.initial_output], feed_dict={self.images: images})
partial_caption_data = [] complete_caption_data = [] for k in range(config.batch_size): initial_beam = CaptionData(sentence=[], memory=initial_memory[k], output=initial_output[k], score=1.0) partial_caption_data.append(TopN(config.beam_size)) partial_caption_data[-1].push(initial_beam) complete_caption_data.append(TopN(config.beam_size))
# Run beam search for idx in range(config.max_caption_length): partial_caption_data_lists = [] for k in range(config.batch_size): data = partial_caption_data[k].extract() partial_caption_data_lists.append(data) partial_caption_data[k].reset()
num_steps = 1 if idx == 0 else config.beam_size for b in range(num_steps): if idx == 0: last_word = np.zeros((config.batch_size), np.int32) else: last_word = np.array([pcl[b].sentence[-1] for pcl in partial_caption_data_lists], np.int32)
last_memory = np.array([pcl[b].memory for pcl in partial_caption_data_lists], np.float32) last_output = np.array([pcl[b].output for pcl in partial_caption_data_lists], np.float32)
memory, output, scores = sess.run( [self.memory, self.output, self.probs], feed_dict={self.contexts: contexts, self.last_word: last_word, self.last_memory: last_memory, self.last_output: last_output})
# Find the beam_size most probable next words for k in range(config.batch_size): caption_data = partial_caption_data_lists[k][b] words_and_scores = list(enumerate(scores[k])) words_and_scores.sort(key=lambda x: -x[1]) words_and_scores = words_and_scores[0:config.beam_size + 1]
# Append each of these words to the current partial caption for w, s in words_and_scores: sentence = caption_data.sentence + [w] score = caption_data.score * s beam = CaptionData(sentence, memory[k], output[k], score) if vocabulary.words[w] == '.': complete_caption_data[k].push(beam) else: partial_caption_data[k].push(beam)
results = [] for k in range(config.batch_size): if complete_caption_data[k].size() == 0: complete_caption_data[k] = partial_caption_data[k] results.append(complete_caption_data[k].extract(sort=True))
return results
def load(self, sess, model_file=None): """ Load the model. """ config = self.config if model_file is not None: save_path = model_file else: info_path = os.path.join(config.save_dir, "config.pickle") info_file = open(info_path, "rb") config = pickle.load(info_file) global_step = config.global_step info_file.close() save_path = os.path.join(config.save_dir, str(global_step) + ".npy")
print("Loading the model from %s..." % save_path) data_dict = np.load(save_path, allow_pickle=True, encoding="bytes").item() count = 0 for v in tqdm(tf.compat.v1.global_variables()): if v.name in data_dict.keys(): sess.run(v.assign(data_dict[v.name])) count += 1 print("%d tensors loaded." % count) generator.py: import tensorflow as tf from base_model import BaseModel
class CaptionGenerator(BaseModel): def build(self): """ Build the model. """ self.build_cnn() self.build_rnn() if self.is_train: self.build_optimizer() self.build_summary()
def build_cnn(self): """ Build the CNN. """ print("Building the CNN...") if self.config.cnn == 'vgg16': self.build_vgg16() else: self.build_resnet50() print("CNN built.")
def build_vgg16(self): """ Build the VGG16 net. """ config = self.config
images = tf.compat.v1.placeholder( dtype=tf.float32, shape=[config.batch_size] + self.image_shape)
conv1_1_feats = self.nn.conv2d(images, 64, name='conv1_1') conv1_2_feats = self.nn.conv2d(conv1_1_feats, 64, name='conv1_2') pool1_feats = self.nn.max_pool2d(conv1_2_feats, name='pool1')
conv2_1_feats = self.nn.conv2d(pool1_feats, 128, name='conv2_1') conv2_2_feats = self.nn.conv2d(conv2_1_feats, 128, name='conv2_2') pool2_feats = self.nn.max_pool2d(conv2_2_feats, name='pool2')
conv3_1_feats = self.nn.conv2d(pool2_feats, 256, name='conv3_1') conv3_2_feats = self.nn.conv2d(conv3_1_feats, 256, name='conv3_2') conv3_3_feats = self.nn.conv2d(conv3_2_feats, 256, name='conv3_3') pool3_feats = self.nn.max_pool2d(conv3_3_feats, name='pool3')
conv4_1_feats = self.nn.conv2d(pool3_feats, 512, name='conv4_1') conv4_2_feats = self.nn.conv2d(conv4_1_feats, 512, name='conv4_2') conv4_3_feats = self.nn.conv2d(conv4_2_feats, 512, name='conv4_3') pool4_feats = self.nn.max_pool2d(conv4_3_feats, name='pool4')
conv5_1_feats = self.nn.conv2d(pool4_feats, 512, name='conv5_1') conv5_2_feats = self.nn.conv2d(conv5_1_feats, 512, name='conv5_2') conv5_3_feats = self.nn.conv2d(conv5_2_feats, 512, name='conv5_3')
reshaped_conv5_3_feats = tf.reshape(conv5_3_feats, [config.batch_size, 196, 512])
self.conv_feats = reshaped_conv5_3_feats self.num_ctx = 196 self.dim_ctx = 512 self.images = images
def build_resnet50(self): """ Build the ResNet50. """ config = self.config
images = tf.placeholder( dtype=tf.float32, shape=[config.batch_size] + self.image_shape)
conv1_feats = self.nn.conv2d(images, filters=64, kernel_size=(7, 7), strides=(2, 2), activation=None, name='conv1') conv1_feats = self.nn.batch_norm(conv1_feats, 'bn_conv1') conv1_feats = tf.nn.relu(conv1_feats) pool1_feats = self.nn.max_pool2d(conv1_feats, pool_size=(3, 3), strides=(2, 2), name='pool1')
res2a_feats = self.resnet_block(pool1_feats, 'res2a', 'bn2a', 64, 1) res2b_feats = self.resnet_block2(res2a_feats, 'res2b', 'bn2b', 64) res2c_feats = self.resnet_block2(res2b_feats, 'res2c', 'bn2c', 64)
res3a_feats = self.resnet_block(res2c_feats, 'res3a', 'bn3a', 128) res3b_feats = self.resnet_block2(res3a_feats, 'res3b', 'bn3b', 128) res3c_feats = self.resnet_block2(res3b_feats, 'res3c', 'bn3c', 128) res3d_feats = self.resnet_block2(res3c_feats, 'res3d', 'bn3d', 128)
res4a_feats = self.resnet_block(res3d_feats, 'res4a', 'bn4a', 256) res4b_feats = self.resnet_block2(res4a_feats, 'res4b', 'bn4b', 256) res4c_feats = self.resnet_block2(res4b_feats, 'res4c', 'bn4c', 256) res4d_feats = self.resnet_block2(res4c_feats, 'res4d', 'bn4d', 256) res4e_feats = self.resnet_block2(res4d_feats, 'res4e', 'bn4e', 256) res4f_feats = self.resnet_block2(res4e_feats, 'res4f', 'bn4f', 256)
res5a_feats = self.resnet_block(res4f_feats, 'res5a', 'bn5a', 512) res5b_feats = self.resnet_block2(res5a_feats, 'res5b', 'bn5b', 512) res5c_feats = self.resnet_block2(res5b_feats, 'res5c', 'bn5c', 512)
reshaped_res5c_feats = tf.reshape(res5c_feats, [config.batch_size, 49, 2048])
self.conv_feats = reshaped_res5c_feats self.num_ctx = 49 self.dim_ctx = 2048 self.images = images
def resnet_block(self, inputs, name1, name2, c, s=2): """ A basic block of ResNet. """ branch1_feats = self.nn.conv2d(inputs, filters=4 * c, kernel_size=(1, 1), strides=(s, s), activation=None, use_bias=False, name=name1 + '_branch1') branch1_feats = self.nn.batch_norm(branch1_feats, name2 + '_branch1')
branch2a_feats = self.nn.conv2d(inputs, filters=c, kernel_size=(1, 1), strides=(s, s), activation=None, use_bias=False, name=name1 + '_branch2a') branch2a_feats = self.nn.batch_norm(branch2a_feats, name2 + '_branch2a') branch2a_feats = tf.nn.relu(branch2a_feats)
branch2b_feats = self.nn.conv2d(branch2a_feats, filters=c, kernel_size=(3, 3), strides=(1, 1), activation=None, use_bias=False, name=name1 + '_branch2b') branch2b_feats = self.nn.batch_norm(branch2b_feats, name2 + '_branch2b') branch2b_feats = tf.nn.relu(branch2b_feats)
branch2c_feats = self.nn.conv2d(branch2b_feats, filters=4 * c, kernel_size=(1, 1), strides=(1, 1), activation=None, use_bias=False, name=name1 + '_branch2c') branch2c_feats = self.nn.batch_norm(branch2c_feats, name2 + '_branch2c')
outputs = branch1_feats + branch2c_feats outputs = tf.nn.relu(outputs) return outputs
def resnet_block2(self, inputs, name1, name2, c): """ Another basic block of ResNet. """ branch2a_feats = self.nn.conv2d(inputs, filters=c, kernel_size=(1, 1), strides=(1, 1), activation=None, use_bias=False, name=name1 + '_branch2a') branch2a_feats = self.nn.batch_norm(branch2a_feats, name2 + '_branch2a') branch2a_feats = tf.nn.relu(branch2a_feats)
branch2b_feats = self.nn.conv2d(branch2a_feats, filters=c, kernel_size=(3, 3), strides=(1, 1), activation=None, use_bias=False, name=name1 + '_branch2b') branch2b_feats = self.nn.batch_norm(branch2b_feats, name2 + '_branch2b') branch2b_feats = tf.nn.relu(branch2b_feats)
branch2c_feats = self.nn.conv2d(branch2b_feats, filters=4 * c, kernel_size=(1, 1), strides=(1, 1), activation=None, use_bias=False, name=name1 + '_branch2c') branch2c_feats = self.nn.batch_norm(branch2c_feats, name2 + '_branch2c')
outputs = inputs + branch2c_feats outputs = tf.nn.relu(outputs) return outputs
def build_rnn(self): """ Build the RNN. """ print("Building the RNN...") config = self.config
# Setup the placeholders if self.is_train: contexts = self.conv_feats sentences = tf.placeholder( dtype=tf.int32, shape=[config.batch_size, config.max_caption_length]) masks = tf.placeholder( dtype=tf.float32, shape=[config.batch_size, config.max_caption_length]) else: contexts = tf.compat.v1.placeholder( dtype=tf.float32, shape=[config.batch_size, self.num_ctx, self.dim_ctx]) last_memory = tf.compat.v1.placeholder( dtype=tf.float32, shape=[config.batch_size, config.num_lstm_units]) last_output = tf.compat.v1.placeholder( dtype=tf.float32, shape=[config.batch_size, config.num_lstm_units]) last_word = tf.compat.v1.placeholder( dtype=tf.int32, shape=[config.batch_size])
# Setup the word embedding with tf.compat.v1.variable_scope("word_embedding"): embedding_matrix = tf.compat.v1.get_variable( name='weights', shape=[config.vocabulary_size, config.dim_embedding], initializer=self.nn.fc_kernel_initializer, regularizer=self.nn.fc_kernel_regularizer, trainable=self.is_train)
# Setup the LSTM lstm = tf.nn.rnn_cell.LSTMCell( config.num_lstm_units, initializer=self.nn.fc_kernel_initializer)
if self.is_train: lstm = tf.nn.rnn_cell.DropoutWrapper( lstm, input_keep_prob=1.0 - config.lstm_drop_rate, output_keep_prob=1.0 - config.lstm_drop_rate, state_keep_prob=1.0 - config.lstm_drop_rate)
# Initialize the LSTM using the mean context with tf.compat.v1.variable_scope("initialize"): context_mean = tf.reduce_mean(self.conv_feats, axis=1) initial_memory, initial_output = self.initialize(context_mean) initial_state = initial_memory, initial_output
# Prepare to run predictions = [] if self.is_train: alphas = [] cross_entropies = [] predictions_correct = [] num_steps = config.max_caption_length last_output = initial_output last_memory = initial_memory last_word = tf.zeros([config.batch_size], tf.int32) else: num_steps = 1 last_state = last_memory, last_output
# Generate the words one by one for idx in range(num_steps): # Attention mechanism with tf.compat.v1.variable_scope("attend"): alpha = self.attend(contexts, last_output) context = tf.reduce_sum(contexts * tf.expand_dims(alpha, 2), axis=1) if self.is_train: tiled_masks = tf.tile(tf.expand_dims(masks[:, idx], 1), [1, self.num_ctx]) masked_alpha = alpha * tiled_masks alphas.append(tf.reshape(masked_alpha, [-1]))
# Embed the last word with tf.compat.v1.variable_scope("word_embedding"): word_embed = tf.nn.embedding_lookup(embedding_matrix, last_word) # Apply the LSTM with tf.compat.v1.variable_scope("lstm"): current_input = tf.concat([context, word_embed], 1) output, state = lstm(current_input, last_state) memory, _ = state
# Decode the expanded output of LSTM into a word with tf.compat.v1.variable_scope("decode"): expanded_output = tf.concat([output, context, word_embed], axis=1) logits = self.decode(expanded_output) probs = tf.nn.softmax(logits) prediction = tf.argmax(logits, 1) predictions.append(prediction)
# Compute the loss for this step, if necessary if self.is_train: cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( labels=sentences[:, idx], logits=logits) masked_cross_entropy = cross_entropy * masks[:, idx] cross_entropies.append(masked_cross_entropy)
ground_truth = tf.cast(sentences[:, idx], tf.int64) prediction_correct = tf.where( tf.equal(prediction, ground_truth), tf.cast(masks[:, idx], tf.float32), tf.cast(tf.zeros_like(prediction), tf.float32)) predictions_correct.append(prediction_correct)
last_output = output last_memory = memory last_state = state last_word = sentences[:, idx]
tf.compat.v1.get_variable_scope().reuse_variables()
# Compute the final loss, if necessary if self.is_train: cross_entropies = tf.stack(cross_entropies, axis=1) cross_entropy_loss = tf.reduce_sum(cross_entropies) \ / tf.reduce_sum(masks)
alphas = tf.stack(alphas, axis=1) alphas = tf.reshape(alphas, [config.batch_size, self.num_ctx, -1]) attentions = tf.reduce_sum(alphas, axis=2) diffs = tf.ones_like(attentions) - attentions attention_loss = config.attention_loss_factor \ * tf.nn.l2_loss(diffs) \ / (config.batch_size * self.num_ctx)
reg_loss = tf.losses.get_regularization_loss()
total_loss = cross_entropy_loss + attention_loss + reg_loss
predictions_correct = tf.stack(predictions_correct, axis=1) accuracy = tf.reduce_sum(predictions_correct) \ / tf.reduce_sum(masks)
self.contexts = contexts if self.is_train: self.sentences = sentences self.masks = masks self.total_loss = total_loss self.cross_entropy_loss = cross_entropy_loss self.attention_loss = attention_loss self.reg_loss = reg_loss self.accuracy = accuracy self.attentions = attentions else: self.initial_memory = initial_memory self.initial_output = initial_output self.last_memory = last_memory self.last_output = last_output self.last_word = last_word self.memory = memory self.output = output self.probs = probs
print("RNN built.")
def initialize(self, context_mean): """ Initialize the LSTM using the mean context. """ config = self.config context_mean = self.nn.dropout(context_mean) if config.num_initalize_layers == 1: # use 1 fc layer to initialize memory = self.nn.dense(context_mean, units=config.num_lstm_units, activation=None, name='fc_a') output = self.nn.dense(context_mean, units=config.num_lstm_units, activation=None, name='fc_b') else: # use 2 fc layers to initialize temp1 = self.nn.dense(context_mean, units=config.dim_initalize_layer, activation=tf.tanh, name='fc_a1') temp1 = self.nn.dropout(temp1) memory = self.nn.dense(temp1, units=config.num_lstm_units, activation=None, name='fc_a2')
temp2 = self.nn.dense(context_mean, units=config.dim_initalize_layer, activation=tf.tanh, name='fc_b1') temp2 = self.nn.dropout(temp2) output = self.nn.dense(temp2, units=config.num_lstm_units, activation=None, name='fc_b2') return memory, output
def attend(self, contexts, output): """ Attention Mechanism. """ config = self.config reshaped_contexts = tf.reshape(contexts, [-1, self.dim_ctx]) reshaped_contexts = self.nn.dropout(reshaped_contexts) output = self.nn.dropout(output) if config.num_attend_layers == 1: # use 1 fc layer to attend logits1 = self.nn.dense(reshaped_contexts, units=1, activation=None, use_bias=False, name='fc_a') logits1 = tf.reshape(logits1, [-1, self.num_ctx]) logits2 = self.nn.dense(output, units=self.num_ctx, activation=None, use_bias=False, name='fc_b') logits = logits1 + logits2 else: # use 2 fc layers to attend temp1 = self.nn.dense(reshaped_contexts, units=config.dim_attend_layer, activation=tf.tanh, name='fc_1a') temp2 = self.nn.dense(output, units=config.dim_attend_layer, activation=tf.tanh, name='fc_1b') temp2 = tf.tile(tf.expand_dims(temp2, 1), [1, self.num_ctx, 1]) temp2 = tf.reshape(temp2, [-1, config.dim_attend_layer]) temp = temp1 + temp2 temp = self.nn.dropout(temp) logits = self.nn.dense(temp, units=1, activation=None, use_bias=False, name='fc_2') logits = tf.reshape(logits, [-1, self.num_ctx]) alpha = tf.nn.softmax(logits) return alpha
def decode(self, expanded_output): """ Decode the expanded output of the LSTM into a word. """ config = self.config expanded_output = self.nn.dropout(expanded_output) if config.num_decode_layers == 1: # use 1 fc layer to decode logits = self.nn.dense(expanded_output, units=config.vocabulary_size, activation=None, name='fc') else: # use 2 fc layers to decode temp = self.nn.dense(expanded_output, units=config.dim_decode_layer, activation=tf.tanh, name='fc_1') temp = self.nn.dropout(temp) logits = self.nn.dense(temp, units=config.vocabulary_size, activation=None, name='fc_2') return logits
def build_optimizer(self): """ Setup the optimizer and training operation. """ config = self.config
learning_rate = tf.constant(config.initial_learning_rate) if config.learning_rate_decay_factor < 1.0: def _learning_rate_decay_fn(learning_rate, global_step): return tf.train.exponential_decay( learning_rate, global_step, decay_steps=config.num_steps_per_decay, decay_rate=config.learning_rate_decay_factor, staircase=True)
learning_rate_decay_fn = _learning_rate_decay_fn else: learning_rate_decay_fn = None
with tf.variable_scope('optimizer', reuse=tf.AUTO_REUSE): if config.optimizer == 'Adam': optimizer = tf.train.AdamOptimizer( learning_rate=config.initial_learning_rate, beta1=config.beta1, beta2=config.beta2, epsilon=config.epsilon ) elif config.optimizer == 'RMSProp': optimizer = tf.train.RMSPropOptimizer( learning_rate=config.initial_learning_rate, decay=config.decay, momentum=config.momentum, centered=config.centered, epsilon=config.epsilon ) elif config.optimizer == 'Momentum': optimizer = tf.train.MomentumOptimizer( learning_rate=config.initial_learning_rate, momentum=config.momentum, use_nesterov=config.use_nesterov ) else: optimizer = tf.train.GradientDescentOptimizer( learning_rate=config.initial_learning_rate )
opt_op = tf.contrib.layers.optimize_loss( loss=self.total_loss, global_step=self.global_step, learning_rate=learning_rate, optimizer=optimizer, clip_gradients=config.clip_gradients, learning_rate_decay_fn=learning_rate_decay_fn)
self.opt_op = opt_op
def build_summary(self): """ Build the summary (for TensorBoard visualization). """ with tf.name_scope("variables"): for var in tf.trainable_variables(): with tf.name_scope(var.name[:var.name.find(":")]): self.variable_summary(var)
with tf.name_scope("metrics"): tf.summary.scalar("cross_entropy_loss", self.cross_entropy_loss) tf.summary.scalar("attention_loss", self.attention_loss) tf.summary.scalar("reg_loss", self.reg_loss) tf.summary.scalar("total_loss", self.total_loss) tf.summary.scalar("accuracy", self.accuracy)
with tf.name_scope("attentions"): self.variable_summary(self.attentions)
self.summary = tf.summary.merge_all()
def variable_summary(self, var): """ Build the summary for a variable. """ mean = tf.reduce_mean(var) tf.summary.scalar('mean', mean) stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) tf.summary.scalar('stddev', stddev) tf.summary.scalar('max', tf.reduce_max(var)) tf.summary.scalar('min', tf.reduce_min(var)) tf.summary.histogram('histogram', var)
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