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