def __init__(self, x, y, num_classes, dtype=tf.float32, learn_rate=1e-3):
x_shape = x.get_shape()
x_size = 1
for ss in x_shape[1:]:
x_size *= int(ss)
x = tf.reshape(x, [-1, x_size])
w_class = weight_variable([x_size, num_classes],
init_method='truncated_normal',
dtype=tf.float32,
init_param={'stddev': 0.01},
name='w_class')
b_class = weight_variable([num_classes],
init_method='constant',
init_param={'val': 0.0},
name='b_class')
logits = tf.matmul(x, w_class) + b_class
xent = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=y)
xent = tf.reduce_mean(xent, name='xent')
cost = xent
cost += self._decay()
self._cost = cost
self._inputs = x
self._labels = y
self._train_op = tf.train.AdamOptimizer(learn_rate).minimize(
cost, var_list=[w_class, b_class])
correct = tf.equal(tf.argmax(logits, axis=1), y)
self._acc = tf.reduce_mean(tf.cast(correct, dtype))
self._prediction = tf.nn.softmax(logits)
def _weight_variable(self,
shape,
init_method=None,
dtype=tf.float32,
init_param=None,
wd=None,
name=None,
trainable=True,
seed=0):
"""Wrapper to declare variables. Default on CPU."""
if self._ext_wts is None:
return weight_variable(shape,
init_method=init_method,
dtype=dtype,
init_param=init_param,
wd=wd,
name=name,
trainable=trainable,
seed=seed)
else:
assert self._slow_bn, "Must enable slow BN"
assert name is not None # Use name to retrieve the variable name
vs = tf.get_variable_scope()
var_name = vs.name + '/' + name
if var_name in self._ext_wts:
log.info(
'Found variable {} in external weights'.format(var_name))
return self._ext_wts[var_name]
else:
log.error('Not found variable {} in external weights'.format(
var_name))
raise ValueError('Variable not found')
def build_fast_weights(self):
"""Build fast weights for task B."""
transfer_config = self.config.transfer_config
num_classes_b = self.num_classes_b
h_size = self._h_size
suffix = '' if self.num_classes_b == 5 else '_{}'.format(num_classes_b)
if transfer_config.fast_model == 'lr':
w_class_b = weight_variable([h_size, num_classes_b],
dtype=self.dtype,
init_method='constant',
init_param={'val': 0.0},
wd=transfer_config.finetune_wd,
name='w_class_b' + suffix)
b_class_b = weight_variable([num_classes_b],
dtype=self.dtype,
init_method='constant',
init_param={'val': -1.0},
name='b_class_b' + suffix)
fast_weights = [w_class_b, b_class_b]
elif transfer_config.fast_model == 'resmlp':
mlp_size = transfer_config.fast_mlp_hidden
w_class_b2 = weight_variable([h_size, mlp_size],
dtype=self.dtype,
init_method='constant',
init_param={'val': 0.0},
wd=transfer_config.finetune_wd,
name='w_class_b2' + suffix)
b_class_b2 = weight_variable([mlp_size],
dtype=self.dtype,
init_method='constant',
init_param={'val': 0.0},
name='b_class_b2' + suffix)
w_class_b3 = weight_variable([h_size, num_classes_b],
dtype=self.dtype,
init_method='constant',
init_param={'val': 0.0},
wd=transfer_config.finetune_wd,
name='w_class_b3' + suffix)
w_class_b = weight_variable([mlp_size, num_classes_b],
dtype=self.dtype,
init_method='constant',
init_param={'val': 0.0},
wd=transfer_config.finetune_wd,
name='w_class_b' + suffix)
b_class_b = weight_variable([num_classes_b],
dtype=self.dtype,
init_method='constant',
init_param={'val': -1.0},
name='b_class_b' + suffix)
fast_weights = [
w_class_b3, w_class_b2, b_class_b2, w_class_b, b_class_b
]
else:
assert False
return fast_weights
def __call__(self, fast_weights, reuse=None, **kwargs):
"""Applies static attractor.
Args:
fast_weights: A tuple of two elements.
- w_b: [D, K]. Weights of the logistic regression.
- b_b: [K]. Bias of the logistic regression.
reuse: Bool. Whether to reuse variables.
"""
with tf.variable_scope("transfer_loss", reuse=reuse):
w_class_b_reg = self.combine_wb(fast_weights[0], fast_weights[1])
dtype = fast_weights[0].dtype
h_size_reg = int(w_class_b_reg.shape[0])
attr = weight_variable([h_size_reg],
dtype=dtype,
init_method='constant',
init_param={'val': 0.0},
wd=self.config.wd,
name='attr')
attr_ = tf.expand_dims(attr, 1)
if self.config.learn_gamma:
log_gamma = weight_variable(
[h_size_reg],
init_method='constant',
dtype=dtype,
init_param={'val': np.log(self.config.init_gamma)},
wd=self.config.wd,
name='log_gamma')
else:
log_gamma = tf.ones([h_size_reg], dtype=dtype) * np.log(
self.config.init_gamma)
log_gamma_ = tf.expand_dims(log_gamma, 1)
gamma_ = tf.exp(log_gamma_)
self.gamma = gamma_
dist = tf.reduce_sum(tf.square(w_class_b_reg - attr_) * gamma_, [0])
transfer_loss = tf.reduce_mean(dist)
return transfer_loss
def __call__(self, fast_weights, reuse=None, **kwargs):
"""Applies static attractor.
Args:
fast_weights: A tuple of the following elements:
w_1: [D, K]. Logistic regression weights.
w_2: [D, H]. First layer weights.
b_2: [H]. First layer biases.
w_3: [H, K]. Second layer weights.
b_3: [K]. Second layer bias.
reuse: Bool. Whether to reuse variables.
"""
with tf.variable_scope("transfer_loss", reuse=reuse):
w_1 = fast_weights[0]
w_2 = fast_weights[1]
b_2 = fast_weights[2]
w_3 = fast_weights[3]
b_3 = fast_weights[4]
dtype = w_1.dtype
Hplus1 = int(w_3.shape[0]) + 1 # H+1
Dplus1 = int(w_2.shape[0]) + 1 # D+1
# Logistic regression weights + biases.
w_class_b_reg = self.combine_wb(w_3, b_3) # [H+1, K]
# First layer weights + biases.
w_class_b_reg2 = self.combine_wb(w_2, b_2) # [D+1, H]
# Second layer weights.
b_1 = tf.zeros([int(w_1.shape[1])], dtype=dtype)
w_class_b_reg3 = self.combine_wb(w_1, b_1) # [D+1, K]
attr = weight_variable([Hplus1],
init_method='truncated_normal',
dtype=dtype,
init_param={'stddev': 0.01},
wd=self.config.wd,
name='attr')
attr2 = weight_variable([Dplus1],
init_method='truncated_normal',
dtype=dtype,
init_param={'stddev': 0.01},
wd=self.config.wd,
name='attr2')
attr3 = weight_variable([Dplus1],
init_method='truncated_normal',
dtype=dtype,
init_param={'stddev': 0.01},
wd=self.config.wd,
name='attr3')
attr_ = tf.expand_dims(attr, 1) # [H+1, 1]
attr2_ = tf.expand_dims(attr2, 1) # [D+1, 1]
attr3_ = tf.expand_dims(attr3, 1) # [D+1, 1]
init_log_gamma = np.log(self.config.init_gamma)
if self.config.learn_gamma:
log_gamma = weight_variable([Hplus1],
init_method='constant',
dtype=dtype,
init_param={'val': init_log_gamma},
wd=self.config.wd,
name='log_gamma')
log_gamma2 = weight_variable(
[Dplus1],
init_method='constant',
dtype=dtype,
init_param={'val': init_log_gamma},
wd=self.config.wd,
name='log_gamma2')
log_gamma3 = weight_variable(
[Dplus1],
init_method='constant',
dtype=dtype,
init_param={'val': init_log_gamma},
wd=self.config.wd,
name='log_gamma3')
else:
log_gamma = tf.ones([Hplus1], dtype=dtype) * init_log_gamma
log_gamma2 = tf.ones([Dplus1], dtype=dtype) * init_log_gamma
log_gamma3 = tf.ones([Dplus1], dtype=dtype) * init_log_gamma
gamma_ = tf.exp(tf.expand_dims(log_gamma, 1)) # [H+1, 1]
gamma2_ = tf.exp(tf.expand_dims(log_gamma2, 1)) # [D+1, 1]
gamma3_ = tf.exp(tf.expand_dims(log_gamma3, 1)) # [D+1, 1]
loss = tf.reduce_mean(
tf.reduce_sum(tf.square(w_class_b_reg - attr_) * gamma_, [0]))
loss += tf.reduce_mean(
tf.reduce_sum(tf.square(w_class_b_reg2 - attr2_) * gamma2_, [0]))
loss += tf.reduce_mean(
tf.reduce_sum(tf.square(w_class_b_reg3 - attr3_) * gamma3_, [0]))
return loss
def __call__(self, fast_weights, is_training=True, reuse=None, **kwargs):
"""Applies attention attractor for MLP with residual connection.
Args:
fast_weights: A tuple of the following elements:
w_1: [D, K]. Logistic regression weights.
w_2: [D, H]. First layer weights.
b_2: [H]. First layer biases.
w_3: [H, K]. Second layer weights.
b_3: [K]. Second layer bias.
reuse: Bool. Whether to reuse variables.
kwargs: Contains the following fields:
- y_b: Labels of the support examples.
- h_b: Features of the support examples.
- w_class_a: Base class weights.
- mask: Bool flag whether we need to mask the base class weights.
- y_sel: Binary flag on the base class weights.
"""
y_b = kwargs['y_b']
h_b = kwargs['h_b']
w_class_a = kwargs['w_class_a']
mask = kwargs['mask']
y_sel = kwargs['y_sel']
with tf.variable_scope("transfer_loss", reuse=reuse):
w_1 = fast_weights[0]
w_2 = fast_weights[1]
b_2 = fast_weights[2]
w_3 = fast_weights[3]
b_3 = fast_weights[4]
dtype = w_1.dtype
Hplus1 = int(w_3.shape[0]) + 1 # H+1
Dplus1 = int(w_2.shape[0]) + 1 # D+1
K = int(w_1.shape[1])
D = int(w_1.shape[0])
M = self.config.mlp_hidden
# Logistic regression weights + biases.
w_class_b_reg = self.combine_wb(w_3, b_3) # [H+1, K]
# First layer weights + biases.
w_class_b_reg2 = self.combine_wb(w_2, b_2) # [D+1, H]
# Second layer weights.
b_1 = tf.zeros([int(w_1.shape[1])], dtype=dtype)
w_class_b_reg3 = self.combine_wb(w_1, b_1) # [D+1, K]
h_size_reg = int(w_class_b_reg.shape[0]) # H+1
h_size_reg2 = int(w_class_b_reg2.shape[0]) # D+1
h_size = int(w_class_a.shape[0]) # D
tau_init = self.config.attn_attr_tau_init
tau_q = weight_variable([],
init_method='constant',
dtype=dtype,
init_param={'val': tau_init},
name='tau_qq')
tau_q2 = weight_variable([],
init_method='constant',
dtype=dtype,
init_param={'val': tau_init},
name='tau_qq2')
Ko = int(w_class_a.shape[1]) # Kold
h_attend_bias = weight_variable([Hplus1],
dtype=dtype,
init_method='truncated_normal',
init_param={'std': 1e-2},
wd=self.config.wd,
name='h_attend_bias')
h_attend_bias2 = weight_variable([Dplus1],
dtype=dtype,
init_method='truncated_normal',
init_param={'std': 1e-2},
wd=self.config.wd,
name='h_attend_bias2')
h_attend_bias3 = weight_variable([Dplus1],
dtype=dtype,
init_method='truncated_normal',
init_param={'std': 1e-2},
wd=self.config.wd,
name='h_attend_bias3')
assert self.config.mlp_hidden != 0
w_kb = weight_variable([D, M],
init_method='truncated_normal',
dtype=dtype,
init_param={'stddev': self.config.mlp_init},
wd=self.config.wd,
name='w_kb')
b_kb = weight_variable([M],
init_method='constant',
dtype=dtype,
init_param={'val': 0.0},
wd=self.config.wd,
name='b_kb')
w_kb21 = weight_variable(
[M, Hplus1],
init_method='truncated_normal',
dtype=dtype,
init_param={'stddev': self.config.mlp_init},
wd=self.config.wd,
name='w_kb21')
b_kb21 = weight_variable([Hplus1],
init_method='constant',
dtype=dtype,
init_param={'val': 0.0},
wd=self.config.wd,
name='b_kb21')
w_kb22 = weight_variable(
[M, 2 * Dplus1],
init_method='truncated_normal',
dtype=dtype,
init_param={'stddev': self.config.mlp_init},
wd=self.config.wd,
name='w_kb22')
b_kb22 = weight_variable([2 * Dplus1],
init_method='constant',
dtype=dtype,
init_param={'val': 0.0},
wd=self.config.wd,
name='b_kb22')
w_class_a_mask = tf.cond(mask,
self._get_mask_fn(w_class_a, y_sel, Ko),
lambda: w_class_a)
# [Ko, D+1] -> [Ko, M]
kbz = tf.tanh(tf.matmul(tf.transpose(w_class_a_mask), w_kb) + b_kb)
# [Ko, M] -> [Ko, H+1]
k_b = tf.matmul(kbz, w_kb21) + b_kb21
# [Ko, M] -> [Ko, 2(D+1)]
k_b2 = tf.matmul(kbz, w_kb22) + b_kb22
k_b = tf.transpose(k_b) # [H+1, Ko]
k_b2 = tf.transpose(k_b2) # [2(D+2), Ko]
k_b_mask = tf.cond(mask, self._get_mask_fn(k_b, y_sel, Ko),
lambda: k_b)
k_b2_mask = tf.cond(mask, self._get_mask_fn(k_b2, y_sel, Ko),
lambda: k_b2)
if self.config.old_and_new:
y_b = y_b - Ko
protos = self._compute_protos(K, h_b, y_b) # [K, D]
if is_training:
protos = tf.nn.dropout(protos, keep_prob=0.9)
protos_norm = self._normalize(protos, axis=1) # [K, D]
episode_mean = tf.reduce_mean(h_b, [0], keepdims=True) # [1, D]
episode_norm = self._normalize(episode_mean, axis=1) # [1, D]
w_class_a_norm = self._normalize(w_class_a_mask, axis=0) # [D, Ko]
h_dot_w = tf.matmul(protos_norm, w_class_a_norm) # [K, Ko]
e_dot_w = tf.matmul(episode_norm, w_class_a_norm) # [1, Ko]
h_dot_w *= tau_q # [K, Ko]
e_dot_w *= tau_q2 # [1, Ko]
proto_attend = tf.nn.softmax(h_dot_w) # [K, Ko]
episode_attend = tf.nn.softmax(e_dot_w) # [1, Ko]
k_b3 = k_b2[h_size_reg2:, :] # [D+1, Ko]
k_b2 = k_b2[:h_size_reg2, :] # [D+1, Ko]
h_attend = tf.matmul(proto_attend, k_b,
transpose_b=True) # [K, H+1]
h_attend2 = tf.matmul(episode_attend, k_b2,
transpose_b=True) # [1, D+1]
h_attend3 = tf.matmul(proto_attend, k_b3,
transpose_b=True) # [K, D+1]
attr_ = tf.transpose(h_attend +
h_attend_bias) # [K, H+1] -> [H+1, K]
attr2_ = tf.transpose(h_attend2 +
h_attend_bias2) # [1, D+1] -> [D+1, 1]
attr3_ = tf.transpose(h_attend3 +
h_attend_bias3) # [K, D+1] -> [D+1, K]
with tf.variable_scope("new_loss", reuse=reuse):
init_log_gamma = np.log(self.config.init_gamma)
if self.config.learn_gamma:
log_gamma = weight_variable([Hplus1],
init_method='constant',
dtype=dtype,
init_param={'val': init_log_gamma},
wd=self.config.wd,
name='log_gamma')
log_gamma2 = weight_variable(
[Dplus1],
init_method='constant',
dtype=dtype,
init_param={'val': init_log_gamma},
wd=self.config.wd,
name='log_gamma2')
log_gamma3 = weight_variable(
[Dplus1],
init_method='constant',
dtype=dtype,
init_param={'val': init_log_gamma},
wd=self.config.wd,
name='log_gamma3')
else:
log_gamma = tf.ones([Hplus1], dtype=dtype) * init_log_gamma
log_gamma2 = tf.ones([Dplus1], dtype=dtype) * init_log_gamma
log_gamma3 = tf.ones([Dplus1], dtype=dtype) * init_log_gamma
gamma_ = tf.exp(tf.expand_dims(log_gamma, 1)) # [H+1, 1]
gamma2_ = tf.exp(tf.expand_dims(log_gamma2, 1)) # [D+1, 1]
gamma3_ = tf.exp(tf.expand_dims(log_gamma3, 1)) # [D+1, 1]
loss = tf.reduce_mean(
tf.reduce_sum(tf.square(w_class_b_reg - attr_) * gamma_, [0]))
loss += tf.reduce_mean(
tf.reduce_sum(tf.square(w_class_b_reg2 - attr2_) * gamma2_, [0]))
loss += tf.reduce_mean(
tf.reduce_sum(tf.square(w_class_b_reg3 - attr3_) * gamma3_, [0]))
return loss
def __init__(self,
config,
x,
y,
num_classes,
is_training=True,
dtype=tf.float32):
"""Constructor.
Args:
config:
x:
y:
num_classes:
"""
h, _ = cnn(x,
config.filter_size,
strides=config.strides,
pool_fn=[tf.nn.max_pool] * len(config.pool_fn),
pool_size=config.pool_size,
pool_strides=config.pool_strides,
act_fn=[tf.nn.relu for aa in config.conv_act_fn],
add_bias=True,
init_std=config.conv_init_std,
init_method=config.conv_init_method,
wd=config.wd,
dtype=dtype,
batch_norm=True,
is_training=is_training,
ext_wts=None)
h_shape = h.get_shape()
h_size = 1
for ss in h_shape[1:]:
h_size *= int(ss)
h = tf.reshape(h, [-1, h_size])
w_class = weight_variable([h_size, num_classes],
init_method='truncated_normal',
dtype=tf.float32,
init_param={'stddev': 0.01},
name='w_class')
b_class = weight_variable([num_classes],
init_method='constant',
init_param={'val': 0.0},
name='b_class')
self._feature = h
logits = tf.matmul(h, w_class) + b_class
xent = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=y)
xent = tf.reduce_mean(xent, name='xent')
cost = xent
cost += self._decay()
self._cost = cost
self._inputs = x
self._labels = y
global_step = tf.get_variable('global_step',
shape=[],
dtype=tf.int64,
trainable=False)
# Learning rate decay.
learn_rate = tf.train.piecewise_constant(
global_step,
list(np.array(config.lr_decay_steps).astype(np.int64)),
[config.learn_rate] + list(config.lr_list))
self._learn_rate = learn_rate
self._train_op = tf.train.AdamOptimizer(learn_rate).minimize(
cost, global_step=global_step)
correct = tf.equal(tf.argmax(logits, axis=1), y)
self._acc = tf.reduce_mean(tf.cast(correct, dtype))
def __init__(self,
config,
x,
y,
x_b,
y_b,
x_b_v,
y_b_v,
num_classes_a,
num_classes_b,
is_training=True,
ext_wts=None,
y_sel=None,
w_class_a=None,
b_class_a=None,
nshot=None):
self._config = config
self._is_training = is_training
self._num_classes_a = num_classes_a
self._num_classes_b = num_classes_b
if config.backbone_class == 'resnet_backbone':
bb_config = config.resnet_config
else:
assert False, 'Not supported'
opt_config = config.optimizer_config
proto_config = config.protonet_config
transfer_config = config.transfer_config
self._backbone = get_model(config.backbone_class, bb_config)
self._inputs = x
self._labels = y
# if opt_config.num_gpu > 1:
# self._labels_all = allgather(self._labels)
# else:
self._labels_all = self._labels
self._inputs_b = x_b
self._labels_b = y_b
self._inputs_b_v = x_b_v
self._labels_b_v = y_b_v
# if opt_config.num_gpu > 1:
# self._labels_b_v_all = allgather(self._labels_b_v)
# else:
self._labels_b_v_all = self._labels_b_v
self._y_sel = y_sel
self._mask = tf.placeholder(tf.bool, [], name='mask')
# global_step = tf.get_variable(
# 'global_step', shape=[], dtype=tf.int64, trainable=False)
global_step = tf.contrib.framework.get_or_create_global_step()
self._global_step = global_step
log.info('LR decay steps {}'.format(opt_config.lr_decay_steps))
log.info('LR list {}'.format(opt_config.lr_list))
learn_rate = tf.train.piecewise_constant(
global_step, list(
np.array(opt_config.lr_decay_steps).astype(np.int64)),
list(opt_config.lr_list))
self._learn_rate = learn_rate
opt = self.get_optimizer(opt_config.optimizer, learn_rate)
# if opt_config.num_gpu > 1:
# opt = hvd.DistributedOptimizer(opt)
with tf.name_scope('TaskA'):
h_a = self.backbone(x, is_training=is_training, ext_wts=ext_wts)
self._h_a = h_a
# Apply BN ops.
bn_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.name_scope('TaskB'):
x_b_all = tf.concat([x_b, x_b_v], axis=0)
if ext_wts is not None:
h_b_all = self.backbone(
x_b_all, is_training=is_training, reuse=True, ext_wts=ext_wts)
else:
h_b_all = self.backbone(x_b_all, is_training=is_training, reuse=True)
with tf.name_scope('TaskA'):
# Calculates hidden activation size.
h_shape = h_a.get_shape()
h_size = 1
for ss in h_shape[1:]:
h_size *= int(ss)
if w_class_a is None:
if ext_wts is not None:
w_class_a = weight_variable(
[h_size, num_classes_a],
init_method='numpy',
dtype=tf.float32,
init_param={'val': np.transpose(ext_wts['w_class_a'])},
wd=config.wd,
name='w_class_a')
b_class_a = weight_variable([],
init_method='numpy',
dtype=tf.float32,
init_param={'val': ext_wts['b_class_a']},
wd=0e0,
name='b_class_a')
else:
w_class_a = weight_variable([h_size, num_classes_a],
init_method='truncated_normal',
dtype=tf.float32,
init_param={'stddev': 0.01},
wd=bb_config.wd,
name='w_class_a')
b_class_a = weight_variable([num_classes_a],
init_method='constant',
init_param={'val': 0.0},
name='b_class_a')
self._w_class_a_orig = w_class_a
self._b_class_a_orig = b_class_a
else:
assert b_class_a is not None
w_class_a_orig = weight_variable([h_size, num_classes_a],
init_method='truncated_normal',
dtype=tf.float32,
init_param={'stddev': 0.01},
wd=bb_config.wd,
name='w_class_a')
b_class_a_orig = weight_variable([num_classes_a],
init_method='constant',
init_param={'val': 0.0},
name='b_class_a')
self._w_class_a_orig = w_class_a_orig
self._b_class_a_orig = b_class_a_orig
self._w_class_a = w_class_a
self._b_class_a = b_class_a
num_classes_a_dyn = tf.cast(tf.shape(b_class_a)[0], tf.int64)
num_classes_a_dyn32 = tf.shape(b_class_a)[0]
if proto_config.cosine_a:
if proto_config.cosine_tau:
if ext_wts is None:
init_val = 10.0
else:
init_val = ext_wts['tau'][0]
tau = weight_variable([],
init_method='constant',
init_param={'val': init_val},
name='tau')
else:
tau = tf.constant(1.0)
w_class_a_norm = self._normalize(w_class_a, 0)
h_a_norm = self._normalize(h_a, 1)
dot = tf.matmul(h_a_norm, w_class_a_norm)
if ext_wts is not None:
dot += b_class_a
logits_a = tau * dot
else:
logits_a = compute_euc(tf.transpose(w_class_a), h_a)
self._prediction_a = logits_a
# if opt_config.num_gpu > 1:
# self._prediction_a_all = allgather(self._prediction_a)
# else:
self._prediction_a_all = self._prediction_a
xent_a = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits_a, labels=y)
cost_a = tf.reduce_mean(xent_a, name='xent')
self._cost_a = cost_a
cost_a += self._decay()
correct_a = tf.equal(tf.argmax(logits_a, axis=1), y)
self._correct_a = correct_a
self._acc_a = tf.reduce_mean(tf.cast(correct_a, cost_a.dtype))
with tf.name_scope('TaskB'):
h_b = h_b_all[:tf.shape(x_b)[0]]
h_b_v = h_b_all[tf.shape(x_b)[0]:]
# Add new axes for the `batch` dimension.
h_b_ = tf.expand_dims(h_b, 0)
h_b_v_ = tf.expand_dims(h_b_v, 0)
y_b_ = tf.expand_dims(y_b, 0)
y_b_v_ = tf.expand_dims(y_b_v, 0)
if transfer_config.old_and_new:
protos_b = self._compute_protos(num_classes_b, h_b_,
y_b_ - num_classes_a)
else:
protos_b = self._compute_protos(num_classes_b, h_b_, y_b_)
w_class_a_ = tf.expand_dims(tf.transpose(w_class_a), 0)
if proto_config.protos_phi:
w_p1 = weight_variable([h_size],
init_method='constant',
dtype=tf.float32,
init_param={'val': 1.0},
wd=bb_config.wd,
name='w_p1')
if proto_config.cosine_attention:
w_q = weight_variable([h_size, h_size],
init_method='truncated_normal',
dtype=tf.float32,
init_param={'stddev': 0.1},
wd=bb_config.wd,
name='w_q')
k_b = weight_variable([num_classes_a, h_size],
init_method='truncated_normal',
dtype=tf.float32,
init_param={'stddev': 0.1},
wd=bb_config.wd,
name='k_b')
tau_q = weight_variable([],
init_method='constant',
init_param={'val': 10.0},
name='tau_q')
if transfer_config.old_and_new:
w_class_b = self._compute_protos_attend_fix(
num_classes_b, h_b_, y_b_ - num_classes_a_dyn, w_q, tau_q, k_b,
self._w_class_a_orig)
else:
w_class_b = self._compute_protos_attend_fix(
num_classes_b, h_b_, y_b_, w_q, tau_q, k_b, self._w_class_a_orig)
assert proto_config.protos_phi
w_p2 = weight_variable([h_size],
init_method='constant',
dtype=tf.float32,
init_param={'val': 1.0},
wd=bb_config.wd,
name='w_p2')
self._k_b = tf.expand_dims(w_p2, 1) * self._w_class_a_orig
self._k_b2 = k_b
self.bias = w_class_b
self.new_protos = w_p1 * protos_b
self.new_bias = w_p2 * w_class_b
w_class_b = w_p1 * protos_b + w_p2 * w_class_b
self.protos = protos_b
self.w_class_b_final = w_class_b
else:
w_class_b = protos_b
if proto_config.protos_phi:
w_class_b = w_p1 * w_class_b
self._w_class_b = w_class_b
if transfer_config.old_and_new:
w_class_all = tf.concat([w_class_a_, w_class_b], axis=1)
else:
w_class_all = w_class_b
if proto_config.cosine_softmax_tau:
tau_b = weight_variable([],
init_method='constant',
init_param={'val': 10.0},
name='tau_b')
else:
tau_b = tf.constant(1.0)
if proto_config.similarity == 'euclidean':
logits_b_v = compute_logits(w_class_all, h_b_v_)
elif proto_config.similarity == 'cosine':
logits_b_v = tau_b * compute_logits_cosine(w_class_all, h_b_v_)
else:
raise ValueError('Unknown similarity')
self._logits_b_v = logits_b_v
self._prediction_b = logits_b_v[0]
# if opt_config.num_gpu > 1:
# self._prediction_b_all = allgather(self._prediction_b)
# else:
self._prediction_b_all = self._prediction_b
# Mask out the old classes.
def mask_fn():
bin_mask = tf.expand_dims(
tf.reduce_sum(
tf.one_hot(y_sel, num_classes_a + num_classes_b),
0,
keep_dims=True), 0)
logits_b_v_m = logits_b_v * (1.0 - bin_mask)
logits_b_v_m -= bin_mask * 100.0
return logits_b_v_m
# if transfer_config.old_and_new:
# logits_b_v = tf.cond(self._mask, mask_fn, lambda: logits_b_v)
xent_b_v = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits_b_v, labels=y_b_v_)
cost_b = tf.reduce_mean(xent_b_v, name='xent')
self._cost_b = cost_b
if transfer_config.old_and_new:
total_cost = cost_b
else:
total_cost = (transfer_config.cost_a_ratio * cost_a +
transfer_config.cost_b_ratio * cost_b)
self._total_cost = total_cost
if not transfer_config.meta_only:
# assert False, 'let us go for pretrained model first'
var_list = tf.trainable_variables()
var_list = list(filter(lambda x: 'phi' in x.name, var_list))
layers = self.config.transfer_config.meta_layers
if layers == "all":
pass
elif layers == "4":
keywords = ['TaskB', 'unit_4_']
filter_fn = lambda x: any([kw in x.name for kw in keywords])
var_list = list(filter(filter_fn, var_list))
else:
raise ValueError('Unknown finetune layers {}'.format(layers))
[log.info('Slow weights {}'.format(v.name)) for v in var_list]
else:
var_list = []
if proto_config.cosine_softmax_tau:
var_list += [tau_b]
if proto_config.cosine_attention:
var_list += [w_q, tau_q, k_b, w_p2]
if proto_config.protos_phi:
var_list += [w_p1]
if transfer_config.train_wclass_a:
if proto_config.similarity == 'euclidean':
var_list += [w_class_a, b_class_a]
elif proto_config.similarity == 'cosine':
var_list += [w_class_a]
if is_training:
grads_and_vars = opt.compute_gradients(total_cost, var_list)
with tf.control_dependencies(bn_ops):
[log.info('BN op {}'.format(op.name)) for op in bn_ops]
train_op = opt.apply_gradients(grads_and_vars, global_step=global_step)
grads_and_vars_b = opt.compute_gradients(cost_b, var_list)
with tf.control_dependencies(bn_ops):
train_op_b = opt.apply_gradients(
grads_and_vars_b, global_step=global_step)
with tf.control_dependencies(bn_ops):
train_op_a = opt.minimize(cost_a, global_step=global_step)
self._train_op = train_op
self._train_op_a = train_op_a
self._train_op_b = train_op_b
self._initializer = tf.global_variables_initializer()
self._w_class_a = w_class_a
def __call__(self, fast_weights, is_training=True, reuse=None, **kwargs):
"""Applies attention attractor.
Args:
fast_weights: A tuple of two elements.
- w_b: [D, K]. Weights of the logistic regression.
- b_b: [K]. Bias of the logistic regression.
reuse: Bool. Whether to reuse variables.
kwargs: Contains the following fields:
- y_b: Labels of the support examples.
- h_b: Features of the support examples.
- w_class_a: Base class weights.
- mask: Bool flag whether we need to mask the base class weights.
- y_sel: Binary flag on the base class weights.
"""
y_b = kwargs['y_b']
h_b = kwargs['h_b']
w_class_a = kwargs['w_class_a']
mask = kwargs['mask']
y_sel = kwargs['y_sel']
dtype = h_b.dtype
with tf.variable_scope("transfer_loss", reuse=reuse):
w_class_b_reg = self.combine_wb(fast_weights[0], fast_weights[1])
h_size_reg = int(w_class_b_reg.shape[0])
h_size = int(w_class_a.shape[0])
tau_qq = weight_variable(
[],
dtype=dtype,
init_method='constant',
init_param={'val': self.config.attn_attr_tau_init},
name='tau_qq')
h_attend_bias = weight_variable(
[h_size_reg],
dtype=dtype,
init_method='truncated_normal',
init_param={'stddev': 1e-2},
wd=self.config.wd, # wasn't there before.
name='h_attend_bias')
num_classes_a = int(w_class_a.shape[1])
num_classes_b = int(w_class_b_reg.shape[1])
assert self.config.mlp_hidden != 0
w_kb = weight_variable([h_size, self.config.mlp_hidden],
init_method='truncated_normal',
dtype=dtype,
init_param={'stddev': self.config.mlp_init},
wd=self.config.wd,
name='w_kb')
b_kb = weight_variable([self.config.mlp_hidden],
init_method='constant',
dtype=dtype,
init_param={'val': 0.0},
wd=self.config.wd,
name='b_kb')
w_kb2 = weight_variable(
[self.config.mlp_hidden, h_size_reg],
init_method='truncated_normal',
dtype=dtype,
init_param={'stddev': self.config.mlp_init},
wd=self.config.wd,
name='w_kb2')
b_kb2 = weight_variable([h_size_reg],
init_method='constant',
dtype=dtype,
init_param={'val': 0.0},
wd=self.config.wd,
name='b_kb2')
w_class_a_mask = tf.cond(
mask, self._get_mask_fn(w_class_a, y_sel, num_classes_a),
lambda: w_class_a)
k_b = tf.matmul(
tf.tanh(tf.matmul(tf.transpose(w_class_a_mask), w_kb) + b_kb),
w_kb2) + b_kb2
self._k_b = k_b
k_b = tf.transpose(k_b)
k_b_mask = tf.cond(mask,
self._get_mask_fn(k_b, y_sel, num_classes_a),
lambda: k_b)
if self.config.old_and_new:
attended_h = self._compute_protos_attend(
num_classes_b,
h_b,
y_b - num_classes_a,
tau_qq,
h_attend_bias,
k_b_mask,
w_class_a_mask,
is_training=is_training)
else:
attended_h = self._compute_protos_attend5_fix(
num_classes_b,
h_b,
y_b,
tau_qq,
h_attend_bias,
k_b_mask,
w_class_a_mask,
is_training=is_training)
self.attended_h = attended_h
self.h_b = h_b
# Cache the value of the attended features.
if self.config.cache_transfer_loss_var:
self._transfer_loss_var = attended_h
tloss_var_plh = tf.placeholder(dtype, [None, h_size_reg],
name='transfer_loss_var_plh')
self._transfer_loss_var_plh = tloss_var_plh
attended_h = tloss_var_plh
with tf.variable_scope("new_loss", reuse=reuse):
if self.config.learn_gamma:
log_gamma = weight_variable(
[h_size_reg],
init_method='constant',
dtype=dtype,
init_param={'val': np.log(self.config.init_gamma)},
wd=self.config.wd,
name='log_gamma')
else:
log_gamma = tf.ones([h_size_reg], dtype=dtype) * np.log(
self.config.init_gamma)
log_gamma_ = tf.expand_dims(log_gamma, 1)
gamma_ = tf.exp(log_gamma_)
self.gamma = gamma_
# [D, K2] and [K2, D]
dist = tf.reduce_sum(
tf.square(w_class_b_reg - tf.transpose(attended_h)) * gamma_, [0])
transfer_loss = tf.reduce_mean(dist)
return transfer_loss
def build_task_a(self, x, y, is_training, ext_wts=None):
print('build_task_a')
"""Build task A branch.
Args:
x: Tensor. [N, H, W, C]. Inputs tensor.
y: Tensor. [N]. Labels tensor.
is_training: Bool. Whether in training mode.
ext_wts: Dict. External weights dictionary.
opt: Optimizer object.
"""
config = self.config
global_step = self.global_step
if config.backbone_class == 'resnet_backbone' or config.backbone_class == 'resnet_backbone_metaCNN':
bb_config = config.resnet_config
else:
assert False, 'Not supported'
proto_config = config.protonet_config
opt_config = config.optimizer_config
num_classes_a = self._num_classes_a
# Classification branch for task A.
h_a, h_meta = self._run_backbone(x,
is_training=is_training,
ext_wts=ext_wts)
self._h_a = h_a
h_shape = h_a.get_shape()
h_size = 1
for ss in h_shape[1:]:
h_size *= int(ss)
self._h_size = h_size
if ext_wts is not None:
w_class_a = weight_variable(
[h_size, num_classes_a],
init_method='numpy',
dtype=self.dtype,
init_param={'val': np.transpose(ext_wts['w_class_a'])},
wd=bb_config.wd,
name='w_class_a')
b_class_a = weight_variable(
[],
init_method='numpy',
dtype=self.dtype,
init_param={'val': ext_wts['b_class_a']},
wd=0e0,
name='b_class_a')
else:
w_class_a = weight_variable([h_size, num_classes_a],
init_method='truncated_normal',
dtype=self.dtype,
init_param={'stddev': 0.01},
wd=bb_config.wd,
name='w_class_a')
b_class_a = weight_variable([num_classes_a],
dtype=self.dtype,
init_method='constant',
init_param={'val': 0.0},
name='b_class_a')
self._w_class_a = w_class_a
self._b_class_a = b_class_a
num_classes_a_dyn = tf.cast(tf.shape(b_class_a)[0], tf.int64)
num_classes_a_dyn32 = tf.shape(b_class_a)[0]
if proto_config.cosine_a:
if proto_config.cosine_tau:
if ext_wts is None:
tau_init_val = 10.0
else:
tau_init_val = ext_wts['tau'][0]
tau = weight_variable([],
dtype=self.dtype,
init_method='constant',
init_param={'val': tau_init_val},
name='tau')
else:
tau = tf.constant(1.0)
w_class_a_norm = self._normalize(w_class_a, axis=0)
h_a_norm = self._normalize(h_a, axis=1)
dot = tf.matmul(h_a_norm, w_class_a_norm)
if ext_wts is not None:
dot += b_class_a
logits_a = tau * dot
else:
# logits_a = tf.matmul(h_a, w_class_a) + b_class_a
logits_a = compute_euc(tf.transpose(w_class_a), h_a)
self._prediction_a = logits_a
self._prediction_a_all = self._prediction_a
y_dense = tf.one_hot(y, num_classes_a)
xent_a = tf.nn.softmax_cross_entropy_with_logits(logits=logits_a,
labels=y_dense)
xent_a = tf.reduce_mean(xent_a, name='xent')
cost_a = xent_a
self._cost_a = cost_a
cost_a += self._decay()
self._prediction_a = logits_a
print('build_task_a done')
return logits_a