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add cool gpt3 examples
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mvonsteinkirch
parent
0e8871e15e
commit
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16 changed files with 205 additions and 0 deletions
622
EBMs/models.py
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622
EBMs/models.py
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import tensorflow as tf
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from tensorflow.python.platform import flags
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import numpy as np
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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
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from utils import init_fc_weight, smart_conv_block, smart_fc_block, smart_atten_block, groupsort, smart_convt_block, swish
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flags.DEFINE_bool('swish_act', False, 'use the swish activation for dsprites')
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FLAGS = flags.FLAGS
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class MnistNet(object):
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def __init__(self, num_channels=1, num_filters=64):
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self.channels = num_channels
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self.dim_hidden = num_filters
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self.datasource = FLAGS.datasource
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if FLAGS.cclass:
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self.label_size = 10
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else:
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self.label_size = 0
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def construct_weights(self, scope=''):
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weights = {}
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dtype = tf.float32
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conv_initializer = tf.contrib.layers.xavier_initializer_conv2d(dtype=dtype)
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fc_initializer = tf.contrib.layers.xavier_initializer(dtype=dtype)
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classes = 1
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with tf.variable_scope(scope):
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init_conv_weight(weights, 'c1_pre', 3, 1, 64)
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init_conv_weight(weights, 'c1', 4, 64, self.dim_hidden, classes=classes)
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init_conv_weight(weights, 'c2', 4, self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_conv_weight(weights, 'c3', 4, 2*self.dim_hidden, 4*self.dim_hidden, classes=classes)
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init_fc_weight(weights, 'fc_dense', 4*4*4*self.dim_hidden, 2*self.dim_hidden, spec_norm=True)
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init_fc_weight(weights, 'fc5', 2*self.dim_hidden, 1, spec_norm=False)
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if FLAGS.cclass:
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self.label_size = 10
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else:
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self.label_size = 0
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return weights
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def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, **kwargs):
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channels = self.channels
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weights = weights.copy()
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inp = tf.reshape(inp, (tf.shape(inp)[0], 28, 28, 1))
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if FLAGS.swish_act:
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act = swish
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else:
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act = tf.nn.leaky_relu
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if stop_grad:
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for k, v in weights.items():
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if type(v) == dict:
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v = v.copy()
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weights[k] = v
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for k_sub, v_sub in v.items():
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v[k_sub] = tf.stop_gradient(v_sub)
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else:
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weights[k] = tf.stop_gradient(v)
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if FLAGS.cclass:
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label_d = tf.reshape(label, shape=(tf.shape(label)[0], 1, 1, self.label_size))
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inp = conv_cond_concat(inp, label_d)
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h1 = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False, activation=act)
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h2 = smart_conv_block(h1, weights, reuse, 'c1', use_stride=True, downsample=True, label=label, extra_bias=False, activation=act)
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h3 = smart_conv_block(h2, weights, reuse, 'c2', use_stride=True, downsample=True, label=label, extra_bias=False, activation=act)
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h4 = smart_conv_block(h3, weights, reuse, 'c3', use_stride=True, downsample=True, label=label, use_scale=False, extra_bias=False, activation=act)
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h5 = tf.reshape(h4, [-1, np.prod([int(dim) for dim in h4.get_shape()[1:]])])
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h6 = act(smart_fc_block(h5, weights, reuse, 'fc_dense'))
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hidden6 = smart_fc_block(h6, weights, reuse, 'fc5')
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return hidden6
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class DspritesNet(object):
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def __init__(self, num_channels=1, num_filters=64, cond_size=False, cond_shape=False, cond_pos=False,
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cond_rot=False, label_size=1):
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self.channels = num_channels
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self.dim_hidden = num_filters
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self.img_size = 64
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self.label_size = label_size
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if FLAGS.cclass:
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self.label_size = 3
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try:
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if FLAGS.dshape_only:
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self.label_size = 3
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if FLAGS.dpos_only:
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self.label_size = 2
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if FLAGS.dsize_only:
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self.label_size = 1
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if FLAGS.drot_only:
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self.label_size = 2
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except:
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pass
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if cond_size:
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self.label_size = 1
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if cond_shape:
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self.label_size = 3
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if cond_pos:
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self.label_size = 2
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if cond_rot:
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self.label_size = 2
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self.cond_size = cond_size
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self.cond_shape = cond_shape
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self.cond_pos = cond_pos
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def construct_weights(self, scope=''):
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weights = {}
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dtype = tf.float32
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conv_initializer = tf.contrib.layers.xavier_initializer_conv2d(dtype=dtype)
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fc_initializer = tf.contrib.layers.xavier_initializer(dtype=dtype)
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k = 5
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classes = self.label_size
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with tf.variable_scope(scope):
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init_conv_weight(weights, 'c1_pre', 3, 1, 32)
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init_conv_weight(weights, 'c1', 4, 32, self.dim_hidden, classes=classes)
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init_conv_weight(weights, 'c2', 4, self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_conv_weight(weights, 'c3', 4, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_conv_weight(weights, 'c4', 4, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_fc_weight(weights, 'fc_dense', 2*4*4*self.dim_hidden, 2*self.dim_hidden, spec_norm=True)
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init_fc_weight(weights, 'fc5', 2*self.dim_hidden, 1, spec_norm=False)
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return weights
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def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, stop_at_grad=False, stop_batch=False, return_logit=False):
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channels = self.channels
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batch_size = tf.shape(inp)[0]
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inp = tf.reshape(inp, (batch_size, 64, 64, 1))
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if FLAGS.swish_act:
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act = swish
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else:
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act = tf.nn.leaky_relu
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if not FLAGS.cclass:
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label = None
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weights = weights.copy()
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if stop_grad:
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for k, v in weights.items():
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if type(v) == dict:
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v = v.copy()
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weights[k] = v
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for k_sub, v_sub in v.items():
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v[k_sub] = tf.stop_gradient(v_sub)
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else:
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weights[k] = tf.stop_gradient(v)
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h1 = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False, activation=act)
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h2 = smart_conv_block(h1, weights, reuse, 'c1', use_stride=True, downsample=True, label=label, extra_bias=True, activation=act)
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h3 = smart_conv_block(h2, weights, reuse, 'c2', use_stride=True, downsample=True, label=label, extra_bias=True, activation=act)
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h4 = smart_conv_block(h3, weights, reuse, 'c3', use_stride=True, downsample=True, label=label, use_scale=True, extra_bias=True, activation=act)
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h5 = smart_conv_block(h4, weights, reuse, 'c4', use_stride=True, downsample=True, label=label, extra_bias=True, activation=act)
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hidden6 = tf.reshape(h5, (tf.shape(h5)[0], -1))
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hidden7 = act(smart_fc_block(hidden6, weights, reuse, 'fc_dense'))
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energy = smart_fc_block(hidden7, weights, reuse, 'fc5')
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if return_logit:
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return hidden7
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else:
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return energy
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class ResNet32(object):
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def __init__(self, num_channels=3, num_filters=128):
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self.channels = num_channels
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self.dim_hidden = num_filters
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self.groupsort = groupsort()
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def construct_weights(self, scope=''):
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weights = {}
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dtype = tf.float32
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if FLAGS.cclass:
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classes = 10
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else:
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classes = 1
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with tf.variable_scope(scope):
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# First block
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init_conv_weight(weights, 'c1_pre', 3, self.channels, self.dim_hidden)
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init_res_weight(weights, 'res_optim', 3, self.dim_hidden, self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_1', 3, self.dim_hidden, self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_2', 3, self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_3', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_4', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_5', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_fc_weight(weights, 'fc_dense', 4*4*2*self.dim_hidden, 4*self.dim_hidden)
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init_fc_weight(weights, 'fc5', 2*self.dim_hidden , 1, spec_norm=False)
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init_attention_weight(weights, 'atten', 2*self.dim_hidden, self.dim_hidden / 2, trainable_gamma=True)
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return weights
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def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, stop_at_grad=False, stop_batch=False):
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weights = weights.copy()
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batch = tf.shape(inp)[0]
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act = tf.nn.leaky_relu
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if not FLAGS.cclass:
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label = None
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if stop_grad:
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for k, v in weights.items():
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if type(v) == dict:
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v = v.copy()
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weights[k] = v
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for k_sub, v_sub in v.items():
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v[k_sub] = tf.stop_gradient(v_sub)
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else:
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weights[k] = tf.stop_gradient(v)
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# Make sure gradients are modified a bit
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inp = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False)
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hidden1 = smart_res_block(inp, weights, reuse, 'res_optim', adaptive=False, label=label, act=act)
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hidden2 = smart_res_block(hidden1, weights, reuse, 'res_1', stop_batch=stop_batch, downsample=False, adaptive=False, label=label, act=act)
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hidden3 = smart_res_block(hidden2, weights, reuse, 'res_2', stop_batch=stop_batch, label=label, act=act)
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if FLAGS.use_attention:
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hidden4 = smart_atten_block(hidden3, weights, reuse, 'atten', stop_at_grad=stop_at_grad, label=label)
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else:
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hidden4 = smart_res_block(hidden3, weights, reuse, 'res_3', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, act=act)
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hidden5 = smart_res_block(hidden4, weights, reuse, 'res_4', stop_batch=stop_batch, adaptive=False, label=label, act=act)
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compact = hidden6 = smart_res_block(hidden5, weights, reuse, 'res_5', adaptive=False, downsample=False, stop_batch=stop_batch, label=label)
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hidden6 = tf.nn.relu(hidden6)
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hidden5 = tf.reduce_sum(hidden6, [1, 2])
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hidden6 = smart_fc_block(hidden5, weights, reuse, 'fc5')
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energy = hidden6
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return energy
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class ResNet32Large(object):
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def __init__(self, num_channels=3, num_filters=128, train=False):
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self.channels = num_channels
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self.dim_hidden = num_filters
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self.dropout = train
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self.train = train
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def construct_weights(self, scope=''):
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weights = {}
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dtype = tf.float32
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if FLAGS.cclass:
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classes = 10
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else:
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classes = 1
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with tf.variable_scope(scope):
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# First block
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init_conv_weight(weights, 'c1_pre', 3, self.channels, self.dim_hidden)
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init_res_weight(weights, 'res_optim', 3, self.dim_hidden, self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_1', 3, self.dim_hidden, self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_2', 3, self.dim_hidden, self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_3', 3, self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_4', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_5', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_6', 3, 2*self.dim_hidden, 4*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_7', 3, 4*self.dim_hidden, 4*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_8', 3, 4*self.dim_hidden, 4*self.dim_hidden, classes=classes)
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init_fc_weight(weights, 'fc5', 4*self.dim_hidden , 1, spec_norm=False)
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init_attention_weight(weights, 'atten', 2*self.dim_hidden, self.dim_hidden, trainable_gamma=True)
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return weights
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def forward(self, inp, weights, reuse=False, scope='', stop_grad=False, label=None, stop_at_grad=False, stop_batch=False):
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weights = weights.copy()
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batch = tf.shape(inp)[0]
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if not FLAGS.cclass:
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label = None
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if stop_grad:
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for k, v in weights.items():
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if type(v) == dict:
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v = v.copy()
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weights[k] = v
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for k_sub, v_sub in v.items():
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v[k_sub] = tf.stop_gradient(v_sub)
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else:
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weights[k] = tf.stop_gradient(v)
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# Make sure gradients are modified a bit
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inp = smart_conv_block(inp, weights, reuse, 'c1_pre', use_stride=False)
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dropout = self.dropout
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train = self.train
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hidden1 = smart_res_block(inp, weights, reuse, 'res_optim', adaptive=False, label=label, dropout=dropout, train=train)
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hidden2 = smart_res_block(hidden1, weights, reuse, 'res_1', stop_batch=stop_batch, downsample=False, adaptive=False, label=label, dropout=dropout, train=train)
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hidden3 = smart_res_block(hidden2, weights, reuse, 'res_2', stop_batch=stop_batch, downsample=False, adaptive=False, label=label, dropout=dropout, train=train)
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hidden4 = smart_res_block(hidden3, weights, reuse, 'res_3', stop_batch=stop_batch, label=label, dropout=dropout, train=train)
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if FLAGS.use_attention:
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hidden5 = smart_atten_block(hidden4, weights, reuse, 'atten', stop_at_grad=stop_at_grad)
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else:
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hidden5 = smart_res_block(hidden4, weights, reuse, 'res_4', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, dropout=dropout, train=train)
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hidden6 = smart_res_block(hidden5, weights, reuse, 'res_5', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, dropout=dropout, train=train)
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hidden7 = smart_res_block(hidden6, weights, reuse, 'res_6', stop_batch=stop_batch, label=label, dropout=dropout, train=train)
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hidden8 = smart_res_block(hidden7, weights, reuse, 'res_7', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, dropout=dropout, train=train)
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compact = hidden9 = smart_res_block(hidden8, weights, reuse, 'res_8', adaptive=False, downsample=False, stop_batch=stop_batch, label=label, dropout=dropout, train=train)
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if FLAGS.cclass:
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hidden6 = tf.nn.leaky_relu(hidden9)
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else:
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hidden6 = tf.nn.relu(hidden9)
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hidden5 = tf.reduce_sum(hidden6, [1, 2])
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hidden6 = smart_fc_block(hidden5, weights, reuse, 'fc5')
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energy = hidden6
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return energy
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class ResNet32Wider(object):
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def __init__(self, num_channels=3, num_filters=128, train=False):
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self.channels = num_channels
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self.dim_hidden = num_filters
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self.dropout = train
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self.train = train
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def construct_weights(self, scope=''):
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weights = {}
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dtype = tf.float32
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if FLAGS.cclass and FLAGS.dataset == "cifar10":
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classes = 10
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elif FLAGS.cclass and FLAGS.dataset == "imagenet":
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classes = 1000
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else:
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classes = 1
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with tf.variable_scope(scope):
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# First block
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init_conv_weight(weights, 'c1_pre', 3, self.channels, 128)
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init_res_weight(weights, 'res_optim', 3, 128, self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_1', 3, self.dim_hidden, self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_2', 3, self.dim_hidden, self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_3', 3, self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_4', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_5', 3, 2*self.dim_hidden, 2*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_6', 3, 2*self.dim_hidden, 4*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_7', 3, 4*self.dim_hidden, 4*self.dim_hidden, classes=classes)
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init_res_weight(weights, 'res_8', 3, 4*self.dim_hidden, 4*self.dim_hidden, classes=classes)
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init_fc_weight(weights, 'fc5', 4*self.dim_hidden , 1, spec_norm=False)
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|
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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
|
||||
Loading…
Add table
Add a link
Reference in a new issue