def hard_sampler(y_none, y_pred):
# We obtain the similarity matrix and its diagonal
v, c, y_true = tf.split(y_pred, [2048, 2048, 768], axis=1)
S = cos_similarity(v, c)
St = tf.transpose(S)
diagonal = tf.linalg.diag_part(S)
#print(diagonal)
reshaped = tf.expand_dims(diagonal,
axis=1) #tf.reshape(diagonal,(s[0],1))
# Set the diagonal to -1 so it is never chosen as the next best candidate
S = tf.linalg.set_diag(S, -tf.math.pow(diagonal, 0))
St = tf.linalg.set_diag(St, -tf.math.pow(diagonal, 0))
#print(reshaped.shape)
# Proceed to substract the diagonal to the sims matrix
values_s = tf.math.top_k(S, k=n)[0]
vid_contrast = values_s - reshaped #+ margin
values_st = tf.math.top_k(St, k=n)[0]
sen_contrast = values_st - reshaped #+ margin
b_loss = tf.maximum(0.0, vid_contrast + margin) + tf.maximum(
0.0, sen_contrast + margin)
b_sum = tf.reduce_sum(b_loss, axis=-1) # Should be mean
return tf.reduce_mean(b_sum)
def gen_topics_embedding(corpus, num_topics, dictionary, word_dim, w2v_model):
lda_model = train_lda(True, corpus, num_topics, dictionary)
temp = dictionary[0] # This is only to "load" the dictionary.
topics_emb = np.zeros((num_topics, word_dim))
for topic_id in range(num_topics):
topic = lda_model.get_topic_terms(topicid=topic_id, topn=10)
topic_emb = np.zeros(word_dim)
weights = []
vecs = []
for idx in range(len(topic)):
word = dictionary.id2token[topic[idx][0]]
weights.append(topic[idx][1])
vecs.append(w2v_model[word])
weights = np.array(weights)
weights = weights / weights.sum()
for idx in range(len(topic)):
topic_emb += weights[idx] * vecs[idx]
topics_emb[topic_id] = topic_emb
# print(topic_emb)
sim_mat = np.zeros((num_topics, num_topics))
for i in range(num_topics):
for j in range(num_topics):
sim_mat[i][j] = cos_similarity(topics_emb[i], topics_emb[j])
print("Topics similarity matrix:\n{}".format(sim_mat))
return topics_emb
def validate_classification(train_idxs, test_idxs, raw_tasks, topics_emb, net,
theta, dataset):
train_ids = [raw_tasks[train_idx] for train_idx in train_idxs]
test_ids = [raw_tasks[test_idx] for test_idx in test_idxs]
train_embs = [topics_emb[train_idx] for train_idx in train_idxs]
test_embs = [topics_emb[test_idx] for test_idx in test_idxs]
train_emb_distrib = compute_mix_emb(train_ids, train_embs)
support_shots = num_training_samples_per_class
query_shots = num_classes_per_task
# support_shots = num_training_samples_per_class * sample_rate
# query_shots = num_classes_per_task * sample_rate
metrics = []
# total_val_samples_per_task = num_val_samples_per_class * num_classes_per_task
for idx in range(len(test_ids)):
task_ids = test_ids[idx]
task_embs = test_embs[idx]
net.load_weights(INNER_MODEL_PATH)
# task_s, task_q = get_support_query_data(task, num_training_samples_per_class)
# task_sample_ids = sample_task_from_raw_task(task_ids, support_shots, query_shots)
task_sample_ids = sample_task_from_raw_task_by_label(
task_ids, support_shots, query_shots, False)
task_s = load_struct_data([task_sample_ids['s']], dataset)[0]
task_q = load_struct_data([task_sample_ids['q']], dataset)[0]
zeta = cos_similarity(train_emb_distrib, task_embs)
drop_theta(zeta, theta)
# w_task = adapt_to_task(task=task_s, inner_model=net, w0=theta)
adapt_to_task(task_s, net, theta)
WX_q, X_q, cnnX_q, MX_q, y_q = parse_task_data(task_q)
# # ------------------------------------------------------------------------------
# # print output of each layer
# w_layer_names = ['multi_graph_cnn_1', 'embedding_1', 'dense_1',\
# 'conv2d_1', 'multi_graph_cnn_2', 'gru_1', \
# 'dense_2', 'gru_2', 'cocnnattention_1', 'coattention_1',\
# 'batch_normalization_3', 'batch_normalization_2',\
# 'batch_normalization_1', 'dense_3', 'dense_4', 'dense_5',\
# 'dense_6', 'dense_7']
# for name in w_layer_names:
# print_layer(name, net, WX_q, X_q, cnnX_q, MX_q)
# # ------------------------------------------------------------------------------
scores = net.evaluate([WX_q, X_q, cnnX_q, MX_q], y_q, verbose=0)
y_pred = net.predict([WX_q, X_q, cnnX_q, MX_q])
print("y_q:{} y_pred:{}".format(y_q.reshape(-1), y_pred.reshape(-1)))
metrics.append(scores)
# if not train_flag:
# sys.stdout.write('\033[F')
return metrics
def proxy_sampler_2(y_true, y_pred):
proxy = cos_similarity(y_true, y_true)
v, c = tf.split(y_pred, 2, axis=1)
S = cos_similarity(v, c)
St = tf.transpose(S)
s = tf.shape(S)
diagonal = tf.linalg.diag_part(S)
#print(diagonal)
reshaped = tf.expand_dims(diagonal,
axis=1) #tf.reshape(diagonal,(s[0],1))
print(reshaped.shape)
# Proceed to substract the diagonal to the sims matrix
vid_contrast = S - reshaped + margin
sen_contrast = St - reshaped + margin
b_loss = tf.maximum(0.0, vid_contrast) + tf.maximum(0.0, sen_contrast)
b_sum = tf.reduce_sum(b_loss, axis=-1) # Should be mean
return tf.reduce_mean(b_sum)
def task_metalearn(inp, reuse=True):
""" Perform gradient descent for one task in the meta-batch. """
img, label, features = inp
task_outputbs, task_lossesb = [], []
task_accuraciesb = []
if FLAGS.dropout_ratio > 0:
dropout_indices = tf.random.shuffle(tf.range(FLAGS.dict_size))[:int((1-FLAGS.dropout_ratio)*FLAGS.dict_size)]
else:
dropout_indices = tf.range(FLAGS.dict_size)
k_dropped = tf.gather(memo_weights['k'], indices=dropout_indices, axis=0) if 'train' in prefix else memo_weights['k']
v_dropped = tf.gather(memo_weights['v'], indices=dropout_indices, axis=0) if 'train' in prefix else memo_weights['v']
sim = cos_similarity(features, k_dropped, memo_weights['alpha'])
task_outputa = self.fc_forward(k_dropped, weights)
task_lossa = self.loss_func_weighted(task_outputa, v_dropped, sim)
grads = tf.gradients(task_lossa, list(weights.values()))
gradients = dict(zip(weights.keys(), grads))
fast_weights = dict()
for key in weights.keys():
if key in ['w1','b1']:
if FLAGS.scalar_lr:
fast_weights[key] = weights[key] - memo_weights['adaptive_lr']*gradients[key]
else:
fast_weights[key] = weights[key] - tf.matmul(memo_weights['adaptive_lr_diag'],gradients[key])
else:
fast_weights[key] = weights[key]
output = self.fc_forward(features, fast_weights)
task_outputbs.append(output)
task_lossesb.append(self.loss_func(output, label))
for j in range(num_updates - 1):
loss = self.loss_func_weighted(self.fc_forward(k_dropped, fast_weights), v_dropped, sim)
grads = tf.gradients(loss, list(fast_weights.values()))
gradients = dict(zip(fast_weights.keys(), grads))
for key in fast_weights.keys():
if key in ['w1','b1']:
if FLAGS.scalar_lr:
fast_weights[key] = fast_weights[key] - memo_weights['adaptive_lr']*gradients[key]
else:
fast_weights[key] = fast_weights[key] - tf.matmul(memo_weights['adaptive_lr_diag'],gradients[key])
else:
fast_weights[key] = fast_weights[key]
output = self.fc_forward(features, fast_weights)
task_outputbs.append(output)
task_lossesb.append(self.loss_func(output, label))
task_output = [task_outputa, task_outputbs, task_lossa, task_lossesb]
task_accuracya = tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(task_outputa), 1), tf.argmax(v_dropped, 1))
for j in range(num_updates):
task_accuraciesb.append(tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(task_outputbs[j]), 1), tf.argmax(label, 1)))
task_output.extend([task_accuracya, task_accuraciesb])
if FLAGS.visualize:
task_output.extend([memo_weights['v'], memo_weights['k'], sim, features])
return task_output
def naive_max_ranking_roll(y_none, y_pred):
vidp, senp, y_true = tf.split(y_pred, [2048, 2048, 768], axis=1)
vidn = tf.roll(vidp, shift=1, axis=0)
senn = tf.roll(senp, shift=1, axis=0)
# It's called Roll because treats its neighbor as negative
vp_sn = cos_similarity(vidp, senn)
d_vp_sn = tf.linalg.diag_part(vp_sn)
r_vp_sn = tf.expand_dims(d_vp_sn, axis=1)
vp_sp = cos_similarity(vidp, senp)
d_vp_sp = tf.linalg.diag_part(vp_sp)
r_vp_sp = tf.expand_dims(d_vp_sp, axis=1)
vn_sp = cos_similarity(vidn, senp)
d_vn_sp = tf.linalg.diag_part(vn_sp)
r_vn_sp = tf.expand_dims(d_vn_sp, axis=1)
# Max ranking loss
loss = tf.maximum(0.0, margin + r_vp_sn - r_vp_sp) + tf.maximum(
0.0, margin + r_vn_sp - r_vp_sp)
loss = tf.reduce_mean(loss) + 1e-12
return loss
def proxy_sampler_3(y_none, y_pred):
v, c, y_true = tf.split(y_pred, [2048, 2048, 768], axis=1)
C = cos_similarity(y_true, y_true)
values_p, indices_p = tf.math.top_k(-C, k=n)
S = cos_similarity(v, c)
St = tf.transpose(S)
diagonal = tf.linalg.diag_part(S)
reshaped = tf.expand_dims(diagonal, axis=1)
values_s = tf.gather(S, indices_p, batch_dims=1)
values_st = tf.gather(St, indices_p, batch_dims=1)
vid_contrast = values_s - reshaped + margin
sen_contrast = values_st - reshaped + margin
b_loss = tf.maximum(0.0, vid_contrast) + tf.maximum(0.0, sen_contrast)
b_sum = tf.reduce_sum(b_loss, axis=-1)
return b_sum
def proxy_sampler(y_true, y_pred):
# We obtain the similarity matrix of sentences and its diagonal
C = cos_similarity(y_true, y_true)
# Here C is negative to obtain the position of the most dissimilar sentences
indices_p = tf.math.top_k(-C, k=n)[1]
# Now we start with v and c similarity
v, c = tf.split(y_pred, 2, axis=1)
S = cos_similarity(v, c)
St = tf.transpose(S)
diagonal = tf.linalg.diag_part(S)
#print(diagonal)
reshaped = tf.expand_dims(diagonal,
axis=1) #tf.reshape(diagonal,(s[0],1))
# Extract from the v-c sim matrix the positions obtained by the proxy
values_s = tf.gather(S, indices_p, batch_dims=1)
values_st = tf.gather(St, indices_p, batch_dims=1)
vid_contrast = values_s - reshaped #+ margin
sen_contrast = values_st - reshaped #+ margin
b_loss = tf.maximum(0.0, vid_contrast + margin) + tf.maximum(
0.0, sen_contrast + margin)
b_sum = tf.reduce_sum(b_loss, axis=-1) # Should be mean
return tf.reduce_mean(b_sum)
def hard_sampler_full(y_none, y_pred):
# We obtain the similarity matrix and its diagonal
v, c, y_true = tf.split(y_pred, [2048, 2048, 768], axis=1)
S = cos_similarity(v, c)
St = tf.transpose(S)
diagonal = tf.linalg.diag_part(S)
#print(diagonal)
reshaped = tf.expand_dims(diagonal,
axis=1) #tf.reshape(diagonal,(s[0],1))
#print(reshaped.shape)
# Proceed to substract the diagonal to the sims matrix
vid_contrast = S - reshaped + margin
sen_contrast = St - reshaped + margin
b_loss = tf.maximum(0.0, vid_contrast) + tf.maximum(0.0, sen_contrast)
b_sum = tf.reduce_sum(b_loss, axis=-1) # Should be mean
return tf.reduce_mean(b_sum)
def iterative_loss(y_true, y_p):
# v and c are the respective video and captions tensors from the batch, shape (b, [repr. dim.])
# n is the number of samples per pair
# t type of pairs to sample
# For each datapoint find the 3 most distant
v, c = tf.split(y_p, 2, axis=1)
# D is supposed to be b x b
D = cos_similarity(v, c)
print(D, tf.shape(D))
# vp_cp is just the repetition for n pairs
s = tf.shape(D)
# Convert the matrix into vector
diagonal = tf.linalg.diag_part(D)
reshaped = tf.reshape(diagonal, (s[0], 1))
# Repeat each item into n columns
repeated = tf.tile(reshaped, [1, n])
# Again convert matrix into vector
vp_cp = tf.reshape(repeated, [n * s[0]])
# Change dialect, rows represent video
# Here we eliminate the diagonal to only choose negatives by ranking the respective rows and columns
rows = D - tf.linalg.diag(tf.linalg.diag_part(D)) - tf.eye(s[0])
# Columns represent captions
columns = tf.transpose(rows)
values_r = tf.math.top_k(rows, k=n)[0]
#values_r = -values_r
s_r = tf.shape(values_r)
values_c = tf.math.top_k(columns, k=n)[0]
#values_c = -values_c
s_c = tf.shape(values_c)
vp_cn = tf.reshape(values_r, [n * s_r[0]])
vn_cp = tf.reshape(values_c, [n * s_c[0]])
loss = tf.maximum(0.0, margin + vp_cn - vp_cp) + tf.maximum(
0.0, margin + vn_cp - vp_cp)
loss = tf.reduce_mean(loss) + 1e-12
return loss
def rank_matrix(a, b):
sm = cos_similarity(a, b)
return tf.argsort(sm, direction="DESCENDING")
def meta_train(train_idxs, val_idxs, test_idxs, raw_tasks, topics_emb, net,
theta, dataset):
train_ids = [raw_tasks[train_idx] for train_idx in train_idxs]
val_ids = [raw_tasks[val_idx] for val_idx in val_idxs]
test_ids = [raw_tasks[test_idx] for test_idx in test_idxs]
train_embs = [topics_emb[train_idx] for train_idx in train_idxs]
val_embs = [topics_emb[val_idx] for val_idx in val_idxs]
test_embs = [topics_emb[test_idx] for test_idx in test_idxs]
train_emb_distrib = compute_mix_emb(train_ids, train_embs)
# support_shots = num_training_samples_per_class * num_classes_per_task
# query_shots = num_classes_per_task * num_classes_per_task
support_shots = num_training_samples_per_class * sample_rate
query_shots = num_classes_per_task * sample_rate
for epoch in range(resume_epoch, resume_epoch + num_epochs):
print("Meta epoch_{}:".format(epoch + 1))
# print("theta[0][0]:\n{}".format(theta[0][0]))
task_num = len(train_ids)
# initialize gradients with zero
gradients = []
for item in theta['mean']:
gradients.append(np.zeros(item.shape))
# batch_num = int(task_num/batch_size+0.5)
batch_num = batch_per_epoch
for batch in range(batch_num):
np.random.seed()
batch_idxs = np.random.choice(np.arange(task_num), size=batch_size, \
replace=False, p=None)
batch_ids = [train_ids[batch_idx] for batch_idx in batch_idxs]
batch_embs = [train_embs[batch_idx] for batch_idx in batch_idxs]
for idx in range(len(batch_ids)):
task_ids = batch_ids[idx]
# run a task
# load data of a task
net.load_weights(INNER_MODEL_PATH)
# task_s, task_q = get_support_query_data(train_task, num_training_samples_per_class)
# print(task_ids)
task_sample_ids = sample_task_from_raw_task_by_label(
task_ids, support_shots, query_shots)
task_s = load_struct_data([task_sample_ids['s']], dataset)[0]
task_q = load_struct_data([task_sample_ids['q']], dataset)[0]
zeta = cos_similarity(train_emb_distrib, batch_embs[idx])
drop_theta(zeta, theta)
adapt_to_task(task_s, net, theta)
# we valuate the model for every task
WX_q, X_q, cnnX_q, MX_q, y_q = parse_task_data(task_q)
# y_pred = net.predict([WX_q, X_q, cnnX_q, MX_q])
weights_1 = net.get_weights().copy()
net.fit([WX_q, X_q, cnnX_q, MX_q], y_q, epochs=1)
weights_2 = net.get_weights().copy()
# compute gradients
for idx in range(len(weights_1)):
gradients[idx] += ((weights_1[idx] - weights_2[idx]) /
inner_lr)
# update theta
for idx in range(len(theta['mean'])):
theta['mean'][idx] = theta['mean'][idx] - gradients[idx] * (
meta_lr * pow(lr_decay, epoch)) / batch_size
theta['logSigma'][idx] = theta['logSigma'][idx] - gradients[idx]*(meta_lr*pow(lr_decay, epoch))/batch_size\
*np.exp(theta['logSigma'][idx])
# evaluate the model
metrics = validate_classification(train_idxs, val_idxs, raw_tasks,
topics_emb, net, theta, dataset)
for score in metrics:
print(score)
print("\nVal average: [loss, accuracy, f1_score, precision, recall]\n{}\n".format(\
np.mean(metrics, axis=0)))
with open(log_file, 'a+') as f:
f.write("Val average epoch={}: {}\n".format(\
epoch+1, np.mean(metrics, axis=0)))
# #-------------------------------------------
# print("test_data:")
# fixed_weights = net.get_weights().copy() # advoid updating the meta_model and the inner_model
# metrics = validate_classification(test_data, net, theta, train_flag=False, csv_flag=False)
# net.set_weights(fixed_weights) # advoid updating the meta_model and the inner_model
# for score in metrics:
# print(score)
# #-------------------------------------------
if ((epoch + 1) % num_epochs_save == 0):
checkpoint = {'theta': theta}
print('SAVING WEIGHTS...')
checkpoint_filename = 'Epoch_{0:d}.pt'.format(epoch + 1)
print(checkpoint_filename)
torch.save(checkpoint, os.path.join(dst_folder,
checkpoint_filename))
print()
