import tensorflow as tf
from tensorflow.python.platform import flags
import numpy as np
from utils import conv_block, get_weight, attention, conv_cond_concat, init_conv_weight, init_attention_weight, init_res_weight, smart_res_block, smart_res_block_optim, init_convt_weight
from utils import init_fc_weight, smart_conv_block, smart_fc_block, smart_atten_block, groupsort, smart_convt_block, swish
flags.DEFINE_bool('swish_act', False, 'use the swish activation for dsprites')
FLAGS = flags.FLAGS
class MnistNet(object):
def __init__(self, num_channels=1, num_filters=64):
self.channels = num_channels
self.dim_hidden = num_filters
self.datasource = FLAGS.datasource
if FLAGS.cclass:
self.label_size = 10
else:
self.label_size = 0
def construct_weights(self, scope=''):
weights = {}
dtype = tf.float32
conv_initializer = tf.contrib.layers.xavier_initializer_conv2d(dtype=dtype)
fc_initializer = tf.contrib.layers.xavier_initializer(dtype=dtype)
classes = 1
with tf.variable_scope(scope):
init_conv_weight(weights, 'c1_pre', 3, 1, 64)
init_conv_weight(weights, 'c1', 4, 64, self.dim_hidden, classes=classes)
init_conv_weight(weights, 'c2', 4, self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_conv_weight(weights, 'c3', 4, 2*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_fc_weight(weights, 'fc_dense', 4*4*4*self.dim_hidden, 2*self.dim_hidden, spec_norm=True)
init_fc_weight(weights, 'fc5', 2*self.dim_hidden, 1, spec_norm=False)
if FLAGS.cclass:
self.label_size = 10
else:
self.label_size = 0
return weights
def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, **kwargs):
channels = self.channels
weights = weights.copy()
inp = tf.reshape(inp, (tf.shape(inp)[0], 28, 28, 1))
if FLAGS.swish_act:
act = swish
else:
act = tf.nn.leaky_relu
if stop_grad:
for k, v in weights.items():
if type(v) == dict:
v = v.copy()
weights[k] = v
for k_sub, v_sub in v.items():
v[k_sub] = tf.stop_gradient(v_sub)
else:
weights[k] = tf.stop_gradient(v)
if FLAGS.cclass:
label_d = tf.reshape(label, shape=(tf.shape(label)[0], 1, 1, self.label_size))
inp = conv_cond_concat(inp, label_d)
h1 = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False, activation=act)
h2 = smart_conv_block(h1, weights, reuse, 'c1', use_stride=True, downsample=True, label=label, extra_bias=False, activation=act)
h3 = smart_conv_block(h2, weights, reuse, 'c2', use_stride=True, downsample=True, label=label, extra_bias=False, activation=act)
h4 = smart_conv_block(h3, weights, reuse, 'c3', use_stride=True, downsample=True, label=label, use_scale=False, extra_bias=False, activation=act)
h5 = tf.reshape(h4, [-1, np.prod([int(dim) for dim in h4.get_shape()[1:]])])
h6 = act(smart_fc_block(h5, weights, reuse, 'fc_dense'))
hidden6 = smart_fc_block(h6, weights, reuse, 'fc5')
return hidden6
class DspritesNet(object):
def __init__(self, num_channels=1, num_filters=64, cond_size=False, cond_shape=False, cond_pos=False,
cond_rot=False, label_size=1):
self.channels = num_channels
self.dim_hidden = num_filters
self.img_size = 64
self.label_size = label_size
if FLAGS.cclass:
self.label_size = 3
try:
if FLAGS.dshape_only:
self.label_size = 3
if FLAGS.dpos_only:
self.label_size = 2
if FLAGS.dsize_only:
self.label_size = 1
if FLAGS.drot_only:
self.label_size = 2
except:
pass
if cond_size:
self.label_size = 1
if cond_shape:
self.label_size = 3
if cond_pos:
self.label_size = 2
if cond_rot:
self.label_size = 2
self.cond_size = cond_size
self.cond_shape = cond_shape
self.cond_pos = cond_pos
def construct_weights(self, scope=''):
weights = {}
dtype = tf.float32
conv_initializer = tf.contrib.layers.xavier_initializer_conv2d(dtype=dtype)
fc_initializer = tf.contrib.layers.xavier_initializer(dtype=dtype)
k = 5
classes = self.label_size
with tf.variable_scope(scope):
init_conv_weight(weights, 'c1_pre', 3, 1, 32)
init_conv_weight(weights, 'c1', 4, 32, self.dim_hidden, classes=classes)
init_conv_weight(weights, 'c2', 4, self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_conv_weight(weights, 'c3', 4, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_conv_weight(weights, 'c4', 4, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_fc_weight(weights, 'fc_dense', 2*4*4*self.dim_hidden, 2*self.dim_hidden, spec_norm=True)
init_fc_weight(weights, 'fc5', 2*self.dim_hidden, 1, spec_norm=False)
return weights
def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, stop_at_grad=False, stop_batch=False, return_logit=False):
channels = self.channels
batch_size = tf.shape(inp)[0]
inp = tf.reshape(inp, (batch_size, 64, 64, 1))
if FLAGS.swish_act:
act = swish
else:
act = tf.nn.leaky_relu
if not FLAGS.cclass:
label = None
weights = weights.copy()
if stop_grad:
for k, v in weights.items():
if type(v) == dict:
v = v.copy()
weights[k] = v
for k_sub, v_sub in v.items():
v[k_sub] = tf.stop_gradient(v_sub)
else:
weights[k] = tf.stop_gradient(v)
h1 = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False, activation=act)
h2 = smart_conv_block(h1, weights, reuse, 'c1', use_stride=True, downsample=True, label=label, extra_bias=True, activation=act)
h3 = smart_conv_block(h2, weights, reuse, 'c2', use_stride=True, downsample=True, label=label, extra_bias=True, activation=act)
h4 = smart_conv_block(h3, weights, reuse, 'c3', use_stride=True, downsample=True, label=label, use_scale=True, extra_bias=True, activation=act)
h5 = smart_conv_block(h4, weights, reuse, 'c4', use_stride=True, downsample=True, label=label, extra_bias=True, activation=act)
hidden6 = tf.reshape(h5, (tf.shape(h5)[0], -1))
hidden7 = act(smart_fc_block(hidden6, weights, reuse, 'fc_dense'))
energy = smart_fc_block(hidden7, weights, reuse, 'fc5')
if return_logit:
return hidden7
else:
return energy
class ResNet32(object):
def __init__(self, num_channels=3, num_filters=128):
self.channels = num_channels
self.dim_hidden = num_filters
self.groupsort = groupsort()
def construct_weights(self, scope=''):
weights = {}
dtype = tf.float32
if FLAGS.cclass:
classes = 10
else:
classes = 1
with tf.variable_scope(scope):
# First block
init_conv_weight(weights, 'c1_pre', 3, self.channels, self.dim_hidden)
init_res_weight(weights, 'res_optim', 3, self.dim_hidden, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_1', 3, self.dim_hidden, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_2', 3, self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_3', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_4', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_5', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_fc_weight(weights, 'fc_dense', 4*4*2*self.dim_hidden, 4*self.dim_hidden)
init_fc_weight(weights, 'fc5', 2*self.dim_hidden , 1, spec_norm=False)
init_attention_weight(weights, 'atten', 2*self.dim_hidden, self.dim_hidden / 2, trainable_gamma=True)
return weights
def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, stop_at_grad=False, stop_batch=False):
weights = weights.copy()
batch = tf.shape(inp)[0]
act = tf.nn.leaky_relu
if not FLAGS.cclass:
label = None
if stop_grad:
for k, v in weights.items():
if type(v) == dict:
v = v.copy()
weights[k] = v
for k_sub, v_sub in v.items():
v[k_sub] = tf.stop_gradient(v_sub)
else:
weights[k] = tf.stop_gradient(v)
# Make sure gradients are modified a bit
inp = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False)
hidden1 = smart_res_block(inp, weights, reuse, 'res_optim', adaptive=False, label=label, act=act)
hidden2 = smart_res_block(hidden1, weights, reuse, 'res_1', stop_batch=stop_batch, downsample=False, adaptive=False, label=label, act=act)
hidden3 = smart_res_block(hidden2, weights, reuse, 'res_2', stop_batch=stop_batch, label=label, act=act)
if FLAGS.use_attention:
hidden4 = smart_atten_block(hidden3, weights, reuse, 'atten', stop_at_grad=stop_at_grad, label=label)
else:
hidden4 = smart_res_block(hidden3, weights, reuse, 'res_3', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, act=act)
hidden5 = smart_res_block(hidden4, weights, reuse, 'res_4', stop_batch=stop_batch, adaptive=False, label=label, act=act)
compact = hidden6 = smart_res_block(hidden5, weights, reuse, 'res_5', adaptive=False, downsample=False, stop_batch=stop_batch, label=label)
hidden6 = tf.nn.relu(hidden6)
hidden5 = tf.reduce_sum(hidden6, [1, 2])
hidden6 = smart_fc_block(hidden5, weights, reuse, 'fc5')
energy = hidden6
return energy
class ResNet32Large(object):
def __init__(self, num_channels=3, num_filters=128, train=False):
self.channels = num_channels
self.dim_hidden = num_filters
self.dropout = train
self.train = train
def construct_weights(self, scope=''):
weights = {}
dtype = tf.float32
if FLAGS.cclass:
classes = 10
else:
classes = 1
with tf.variable_scope(scope):
# First block
init_conv_weight(weights, 'c1_pre', 3, self.channels, self.dim_hidden)
init_res_weight(weights, 'res_optim', 3, self.dim_hidden, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_1', 3, self.dim_hidden, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_2', 3, self.dim_hidden, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_3', 3, self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_4', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_5', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_6', 3, 2*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_7', 3, 4*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_8', 3, 4*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_fc_weight(weights, 'fc5', 4*self.dim_hidden , 1, spec_norm=False)
init_attention_weight(weights, 'atten', 2*self.dim_hidden, self.dim_hidden, trainable_gamma=True)
return weights
def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, stop_at_grad=False, stop_batch=False):
weights = weights.copy()
batch = tf.shape(inp)[0]
if not FLAGS.cclass:
label = None
if stop_grad:
for k, v in weights.items():
if type(v) == dict:
v = v.copy()
weights[k] = v
for k_sub, v_sub in v.items():
v[k_sub] = tf.stop_gradient(v_sub)
else:
weights[k] = tf.stop_gradient(v)
# Make sure gradients are modified a bit
inp = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False)
dropout = self.dropout
train = self.train
hidden1 = smart_res_block(inp, weights, reuse, 'res_optim', adaptive=False, label=label, dropout=dropout, train=train)
hidden2 = smart_res_block(hidden1, weights, reuse, 'res_1', stop_batch=stop_batch, downsample=False, adaptive=False, label=label, dropout=dropout, train=train)
hidden3 = smart_res_block(hidden2, weights, reuse, 'res_2', stop_batch=stop_batch, downsample=False, adaptive=False, label=label, dropout=dropout, train=train)
hidden4 = smart_res_block(hidden3, weights, reuse, 'res_3', stop_batch=stop_batch, label=label, dropout=dropout, train=train)
if FLAGS.use_attention:
hidden5 = smart_atten_block(hidden4, weights, reuse, 'atten', stop_at_grad=stop_at_grad)
else:
hidden5 = smart_res_block(hidden4, weights, reuse, 'res_4', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, dropout=dropout, train=train)
hidden6 = smart_res_block(hidden5, weights, reuse, 'res_5', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, dropout=dropout, train=train)
hidden7 = smart_res_block(hidden6, weights, reuse, 'res_6', stop_batch=stop_batch, label=label, dropout=dropout, train=train)
hidden8 = smart_res_block(hidden7, weights, reuse, 'res_7', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, dropout=dropout, train=train)
compact = hidden9 = smart_res_block(hidden8, weights, reuse, 'res_8', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, dropout=dropout, train=train)
if FLAGS.cclass:
hidden6 = tf.nn.leaky_relu(hidden9)
else:
hidden6 = tf.nn.relu(hidden9)
hidden5 = tf.reduce_sum(hidden6, [1, 2])
hidden6 = smart_fc_block(hidden5, weights, reuse, 'fc5')
energy = hidden6
return energy
class ResNet32Wider(object):
def __init__(self, num_channels=3, num_filters=128, train=False):
self.channels = num_channels
self.dim_hidden = num_filters
self.dropout = train
self.train = train
def construct_weights(self, scope=''):
weights = {}
dtype = tf.float32
if FLAGS.cclass and FLAGS.dataset == "cifar10":
classes = 10
elif FLAGS.cclass and FLAGS.dataset == "imagenet":
classes = 1000
else:
classes = 1
with tf.variable_scope(scope):
# First block
init_conv_weight(weights, 'c1_pre', 3, self.channels, 128)
init_res_weight(weights, 'res_optim', 3, 128, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_1', 3, self.dim_hidden, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_2', 3, self.dim_hidden, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_3', 3, self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_4', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_5', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_6', 3, 2*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_7', 3, 4*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_8', 3, 4*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_fc_weight(weights, 'fc5', 4*self.dim_hidden , 1, spec_norm=False)
init_attention_weight(weights, 'atten', self.dim_hidden, self.dim_hidden / 2, trainable_gamma=True)
return weights
def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, stop_at_grad=False, stop_batch=False):
weights = weights.copy()
batch = tf.shape(inp)[0]
if not FLAGS.cclass:
label = None
if stop_grad:
for k, v in weights.items():
if type(v) == dict:
v = v.copy()
weights[k] = v
for k_sub, v_sub in v.items():
v[k_sub] = tf.stop_gradient(v_sub)
else:
weights[k] = tf.stop_gradient(v)
if FLAGS.swish_act:
act = swish
else:
act = tf.nn.leaky_relu
# Make sure gradients are modified a bit
inp = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False, activation=act)
dropout = self.dropout
train = self.train
hidden1 = smart_res_block(inp, weights, reuse, 'res_optim', adaptive=True, label=label, dropout=dropout, train=train)
if FLAGS.use_attention:
hidden2 = smart_atten_block(hidden1, weights, reuse, 'atten', train=train, dropout=dropout, stop_at_grad=stop_at_grad)
else:
hidden2 = smart_res_block(hidden1, weights, reuse, 'res_1', stop_batch=stop_batch, downsample=False, adaptive=False, label=label, dropout=dropout, train=train, act=act)
hidden3 = smart_res_block(hidden2, weights, reuse, 'res_2', stop_batch=stop_batch, downsample=False, adaptive=False, label=label, dropout=dropout, train=train, act=act)
hidden4 = smart_res_block(hidden3, weights, reuse, 'res_3', stop_batch=stop_batch, label=label, dropout=dropout, train=train, act=act)
hidden5 = smart_res_block(hidden4, weights, reuse, 'res_4', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, dropout=dropout, train=train, act=act)
hidden6 = smart_res_block(hidden5, weights, reuse, 'res_5', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, dropout=dropout, train=train, act=act)
hidden7 = smart_res_block(hidden6, weights, reuse, 'res_6', stop_batch=stop_batch, label=label, dropout=dropout, train=train, act=act)
hidden8 = smart_res_block(hidden7, weights, reuse, 'res_7', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, dropout=dropout, train=train, act=act)
hidden9 = smart_res_block(hidden8, weights, reuse, 'res_8', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, dropout=dropout, train=train, act=act)
if FLAGS.swish_act:
hidden6 = act(hidden9)
else:
hidden6 = tf.nn.relu(hidden9)
hidden5 = tf.reduce_sum(hidden6, [1, 2])
hidden6 = smart_fc_block(hidden5, weights, reuse, 'fc5')
energy = hidden6
return energy
class ResNet32Larger(object):
def __init__(self, num_channels=3, num_filters=128):
self.channels = num_channels
self.dim_hidden = num_filters
def construct_weights(self, scope=''):
weights = {}
dtype = tf.float32
if FLAGS.cclass:
classes = 10
else:
classes = 1
with tf.variable_scope(scope):
# First block
init_conv_weight(weights, 'c1_pre', 3, self.channels, self.dim_hidden)
init_res_weight(weights, 'res_optim', 3, self.dim_hidden, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_1', 3, self.dim_hidden, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_2', 3, self.dim_hidden, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_2a', 3, self.dim_hidden, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_2b', 3, self.dim_hidden, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_3', 3, self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_4', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_5', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_5a', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_5b', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_6', 3, 2*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_7', 3, 4*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_8', 3, 4*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_8a', 3, 4*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_8b', 3, 4*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_fc_weight(weights, 'fc_dense', 4*4*2*self.dim_hidden, 4*self.dim_hidden)
init_fc_weight(weights, 'fc5', 4*self.dim_hidden , 1, spec_norm=False)
init_attention_weight(weights, 'atten', 2*self.dim_hidden, self.dim_hidden / 2, trainable_gamma=True)
return weights
def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, stop_at_grad=False, stop_batch=False):
weights = weights.copy()
batch = tf.shape(inp)[0]
if not FLAGS.cclass:
label = None
if stop_grad:
for k, v in weights.items():
if type(v) == dict:
v = v.copy()
weights[k] = v
for k_sub, v_sub in v.items():
v[k_sub] = tf.stop_gradient(v_sub)
else:
weights[k] = tf.stop_gradient(v)
# Make sure gradients are modified a bit
inp = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False)
hidden1 = smart_res_block(inp, weights, reuse, 'res_optim', adaptive=False, label=label)
hidden2 = smart_res_block(hidden1, weights, reuse, 'res_1', stop_batch=stop_batch, downsample=False, adaptive=False, label=label)
hidden3 = smart_res_block(hidden2, weights, reuse, 'res_2', stop_batch=stop_batch, downsample=False, adaptive=False, label=label)
hidden3 = smart_res_block(hidden3, weights, reuse, 'res_2a', stop_batch=stop_batch, downsample=False, adaptive=False, label=label)
hidden3 = smart_res_block(hidden3, weights, reuse, 'res_2b', stop_batch=stop_batch, downsample=False, adaptive=False, label=label)
hidden4 = smart_res_block(hidden3, weights, reuse, 'res_3', stop_batch=stop_batch, label=label)
if FLAGS.use_attention:
hidden5 = smart_atten_block(hidden4, weights, reuse, 'atten', stop_at_grad=stop_at_grad)
else:
hidden5 = smart_res_block(hidden4, weights, reuse, 'res_4', adaptive=False, downsample=False, stop_batch=stop_batch, label=label)
hidden6 = smart_res_block(hidden5, weights, reuse, 'res_5', adaptive=False, downsample=False, stop_batch=stop_batch, label=label)
hidden6 = smart_res_block(hidden6, weights, reuse, 'res_5a', adaptive=False, downsample=False, stop_batch=stop_batch, label=label)
hidden6 = smart_res_block(hidden6, weights, reuse, 'res_5b', adaptive=False, downsample=False, stop_batch=stop_batch, label=label)
hidden7 = smart_res_block(hidden6, weights, reuse, 'res_6', stop_batch=stop_batch, label=label)
hidden8 = smart_res_block(hidden7, weights, reuse, 'res_7', adaptive=False, downsample=False, stop_batch=stop_batch, label=label)
hidden9 = smart_res_block(hidden8, weights, reuse, 'res_8', adaptive=False, downsample=False, stop_batch=stop_batch, label=label)
hidden9 = smart_res_block(hidden9, weights, reuse, 'res_8a', adaptive=False, downsample=False, stop_batch=stop_batch, label=label)
compact = hidden9 = smart_res_block(hidden9, weights, reuse, 'res_8b', adaptive=False, downsample=False, stop_batch=stop_batch, label=label)
if FLAGS.cclass:
hidden6 = tf.nn.leaky_relu(hidden9)
else:
hidden6 = tf.nn.relu(hidden9)
hidden5 = tf.reduce_sum(hidden6, [1, 2])
hidden6 = smart_fc_block(hidden5, weights, reuse, 'fc5')
energy = hidden6
return energy
class ResNet128(object):
"""Construct the convolutional network specified in MAML"""
def __init__(self, num_channels=3, num_filters=64, train=False):
self.channels = num_channels
self.dim_hidden = num_filters
self.dropout = train
self.train = train
def construct_weights(self, scope=''):
weights = {}
dtype = tf.float32
classes = 1000
with tf.variable_scope(scope):
# First block
init_conv_weight(weights, 'c1_pre', 3, self.channels, 64)
init_res_weight(weights, 'res_optim', 3, 64, self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_3', 3, self.dim_hidden, 2*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_5', 3, 2*self.dim_hidden, 4*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_7', 3, 4*self.dim_hidden, 8*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_9', 3, 8*self.dim_hidden, 8*self.dim_hidden, classes=classes)
init_res_weight(weights, 'res_10', 3, 8*self.dim_hidden, 8*self.dim_hidden, classes=classes)
init_fc_weight(weights, 'fc5', 8*self.dim_hidden , 1, spec_norm=False)
init_attention_weight(weights, 'atten', self.dim_hidden, self.dim_hidden / 2., trainable_gamma=True)
return weights
def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, stop_at_grad=False, stop_batch=False):
weights = weights.copy()
batch = tf.shape(inp)[0]
if not FLAGS.cclass:
label = None
if stop_grad:
for k, v in weights.items():
if type(v) == dict:
v = v.copy()
weights[k] = v
for k_sub, v_sub in v.items():
v[k_sub] = tf.stop_gradient(v_sub)
else:
weights[k] = tf.stop_gradient(v)
if FLAGS.swish_act:
act = swish
else:
act = tf.nn.leaky_relu
dropout = self.dropout
train = self.train
# Make sure gradients are modified a bit
inp = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False, activation=act)
hidden1 = smart_res_block(inp, weights, reuse, 'res_optim', label=label, dropout=dropout, train=train, downsample=True, adaptive=False)
if FLAGS.use_attention:
hidden1 = smart_atten_block(hidden1, weights, reuse, 'atten', stop_at_grad=stop_at_grad)
hidden2 = smart_res_block(hidden1, weights, reuse, 'res_3', stop_batch=stop_batch, downsample=True, adaptive=True, label=label, dropout=dropout, train=train, act=act)
hidden3 = smart_res_block(hidden2, weights, reuse, 'res_5', stop_batch=stop_batch, downsample=True, adaptive=True, label=label, dropout=dropout, train=train, act=act)
hidden4 = smart_res_block(hidden3, weights, reuse, 'res_7', stop_batch=stop_batch, label=label, dropout=dropout, train=train, act=act, downsample=True, adaptive=True)
hidden5 = smart_res_block(hidden4, weights, reuse, 'res_9', stop_batch=stop_batch, label=label, dropout=dropout, train=train, act=act, downsample=True, adaptive=False)
hidden6 = smart_res_block(hidden5, weights, reuse, 'res_10', stop_batch=stop_batch, label=label, dropout=dropout, train=train, act=act, downsample=False, adaptive=False)
if FLAGS.swish_act:
hidden6 = act(hidden6)
else:
hidden6 = tf.nn.relu(hidden6)
hidden5 = tf.reduce_sum(hidden6, [1, 2])
hidden6 = smart_fc_block(hidden5, weights, reuse, 'fc5')
energy = hidden6
return energy