def resnet101(inputs, num_classes=None, is_training=True, global_pool=False, output_stride=None, spatial_squeeze=False, reuse=tf.AUTO_REUSE, scope='resnet_v2_101'): """ResNet-101 model of [1]. See resnet_v2() for arg and return description.""" blocks = [ resnet_v2.resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), resnet_v2.resnet_v2_block('block2', base_depth=128, num_units=4, stride=2), resnet_v2.resnet_v2_block('block3', base_depth=256, num_units=23, stride=2), ] return resnet_v2.resnet_v2(inputs, blocks, num_classes, is_training=is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, reuse=reuse, scope=scope)
def resnet_v2_50(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, reuse=None, scope='resnet_v2_50'): """ ResNet-50 model of [1]. See resnet_v2() for arg and return description. """ blocks = [ resnet_v2.resnet_v2_block('block1', base_depth=64, num_units=3, stride=1), resnet_v2.resnet_v2_block('block2', base_depth=128, num_units=4, stride=2), resnet_v2.resnet_v2_block('block3', base_depth=256, num_units=6, stride=2), resnet_v2.resnet_v2_block('block4', base_depth=512, num_units=3, stride=2), ] return resnet_v2.resnet_v2( inputs, blocks, num_classes, is_training=is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, reuse=reuse, scope=scope)
def resnet_from_blocks(inputs, blocks, num_classes=None, is_training=None, global_pool=False, output_stride=None, reuse=None, scope=None): return resnet_v2.resnet_v2(inputs, blocks, num_classes, is_training, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
def _resnet_small(self, inputs, num_classes=None, global_pool=True, output_stride=None, include_root_block=True, reuse=None, scope='resnet_v2_small'): """A shallow and thin ResNet v2 for faster tests.""" block = resnet_v2.resnet_v2_block blocks = [ block('block1', base_depth=1, num_units=3, stride=2), block('block2', base_depth=2, num_units=3, stride=2), block('block3', base_depth=4, num_units=3, stride=2), block('block4', base_depth=8, num_units=2, stride=1), ] return resnet_v2.resnet_v2(inputs, blocks, num_classes, global_pool, output_stride, include_root_block, reuse, scope)
def _resnet_small(self, inputs, num_classes=None, global_pool=True, output_stride=None, include_root_block=True, reuse=None, scope='resnet_v2_small'): """A shallow and thin ResNet v2 for faster tests.""" bottleneck = resnet_v2.bottleneck blocks = [ resnet_utils.Block('block1', bottleneck, [(4, 1, 1)] * 2 + [(4, 1, 2)]), resnet_utils.Block('block2', bottleneck, [(8, 2, 1)] * 2 + [(8, 2, 2)]), resnet_utils.Block('block3', bottleneck, [(16, 4, 1)] * 2 + [(16, 4, 2)]), resnet_utils.Block('block4', bottleneck, [(32, 8, 1)] * 2) ] return resnet_v2.resnet_v2(inputs, blocks, num_classes, global_pool, output_stride, include_root_block, reuse, scope)
def resnet_v2_50(inputs, num_classes=None, is_training=True, global_pool=False, output_stride=None, reuse=None, scope='resnet_v2_50'): """ResNet-50 model of [1]. See resnet_v2() for arg and return description.""" resnet_v2_block = resnet_v2.resnet_v2_block blocks = [ resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), resnet_v2_block('block2', base_depth=128, num_units=4, stride=2), resnet_v2_block('block3', base_depth=256, num_units=6, stride=2), resnet_v2_block('block4', base_depth=512, num_units=3, stride=1), ] # custom root block, because original root block will use maxpooling with valid padding. with tf.variable_scope(scope) as sc: with slim.arg_scope([slim.conv2d], activation_fn=None, normalizer_fn=None): net = resnet_utils.conv2d_same(inputs, 64, 7, stride=2, scope='conv1') # stride 2 net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1', padding='SAME') # stride 2 return resnet_v2.resnet_v2(net, blocks, num_classes, is_training=is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=False, reuse=reuse, scope=scope)
def feature_extractor_resnet(images, dim=256, weight_decay=0.0001, batch_norm_decay=0.999, batch_renorm_decay=0.99, batch_renorm_rmax=3., batch_renorm_dmax=5., is_training=True, use_conv3d=True): from tensorflow.contrib.slim.python.slim.nets import resnet_v2 if use_conv3d: orig_shape = tf.shape(images) # [N,T,H,W,C] -> [N*T,H,W,C] images = tf.reshape(images, tf.concat([[-1], orig_shape[2:]], 0)) resnet_arg_scope = resnet_v2.resnet_arg_scope( weight_decay=weight_decay, batch_norm_decay=batch_norm_decay) # batch size is small so we use batch renormalization batch_norm_key = filter(lambda x: 'batch_norm' in x, resnet_arg_scope.keys())[0] resnet_arg_scope[batch_norm_key].update({ 'renorm': True, 'renorm_decay': batch_renorm_decay, 'renorm_clipping': { 'rmin': 1. / batch_renorm_rmax, 'rmax': batch_renorm_rmax, 'dmax': batch_renorm_dmax } }) with slim.arg_scope(resnet_arg_scope): blocks = [ resnet_v2.resnet_v2_block('block1', base_depth=16, num_units=3, stride=2), resnet_v2.resnet_v2_block('block2', base_depth=32, num_units=4, stride=2), resnet_v2.resnet_v2_block('block3', base_depth=64, num_units=6, stride=2), #256 resnet_v2.resnet_v2_block('block4', base_depth=128, num_units=3, stride=1) #512 ] _, end_points = resnet_v2.resnet_v2(images, blocks, is_training=is_training, include_root_block=False) net = end_points['resnet_v2/block4'] if use_conv3d: # [N*T,H',W',C'] -> [N,T,H',W',C'] net = tf.reshape(net, tf.concat( [orig_shape[:2], tf.shape(net)[1:]], 0)) arg_scope = convert_resnet_arg_scope_to_slim(resnet_arg_scope) arg_scope[slim.conv2d].update({'stride': 1, 'padding': 'SAME'}) arg_scope[slim.conv3d].update({'stride': 1, 'padding': 'SAME'}) arg_scope[slim.batch_norm]['is_training'] = is_training with slim.arg_scope(arg_scope): if use_conv3d: net = slim.conv3d(net, 512, [3, 3, 3]) net = slim.conv3d(net, 256, [1, 1, 1]) net = slim.conv3d(net, 512, [3, 3, 3]) # the last layer without activation function feature_map = slim.conv3d(net, dim, [1, 1, 1], activation_fn=None, normalizer_fn=None) else: # the last layer without activation function feature_map = slim.conv2d(net, dim, [1, 1], activation_fn=None, normalizer_fn=None) return feature_map
def _build_network(self, sess, is_training=True): # select initializers if cfg.TRAIN.TRUNCATED: initializer = tf.truncated_normal_initializer(mean=0.0, stddev=0.01) initializer_bbox = tf.truncated_normal_initializer(mean=0.0, stddev=0.001) else: initializer = tf.random_normal_initializer(mean=0.0, stddev=0.01) initializer_bbox = tf.random_normal_initializer(mean=0.0, stddev=0.001) # choose different blocks for different number of layers if self._num_layers == 50: blocks = [resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), resnet_v2_block('block2', base_depth=128, num_units=4, stride=2), # use stride 1 for the last conv4 layer resnet_v2_block('block3', base_depth=256, num_units=6, stride=1), resnet_v2_block('block4', base_depth=512, num_units=3, stride=1)] elif self._num_layers == 101: blocks = [resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), resnet_v2_block('block2', base_depth=128, num_units=4, stride=2), # use stride 1 for the last conv4 layer resnet_v2_block('block3', base_depth=256, num_units=23, stride=1), resnet_v2_block('block4', base_depth=512, num_units=3, stride=1)] elif self._num_layers == 152: blocks = [resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), resnet_v2_block('block2', base_depth=128, num_units=8, stride=2), # use stride 1 for the last conv4 layer resnet_v2_block('block3', base_depth=256, num_units=36, stride=1), resnet_v2_block('block4', base_depth=512, num_units=3, stride=1)] else: # other numbers are not supported raise NotImplementedError assert (0 <= cfg.RESNET.FIXED_BLOCKS <= 3) # Now the base is always fixed during training with slim.arg_scope(resnet_arg_scope(is_training=False)): net_conv = self._build_base() if cfg.RESNET.FIXED_BLOCKS > 0: with slim.arg_scope(resnet_arg_scope(is_training=False)): net_conv, _ = resnet_v2.resnet_v2(net_conv, blocks[0:cfg.RESNET.FIXED_BLOCKS], global_pool=False, include_root_block=False, scope=self._resnet_scope) if cfg.RESNET.FIXED_BLOCKS < 3: with slim.arg_scope(resnet_arg_scope(is_training=is_training)): net_conv, _ = resnet_v2.resnet_v2(net_conv, blocks[cfg.RESNET.FIXED_BLOCKS:-1], global_pool=False, include_root_block=False, scope=self._resnet_scope) self._act_summaries.append(net_conv) self._layers['head'] = net_conv with tf.variable_scope(self._resnet_scope, self._resnet_scope): # build the anchors for the image self._anchor_component() # region proposal network rois = self._region_proposal(net_conv, is_training, initializer) # region of interest pooling if cfg.POOLING_MODE == 'crop': pool5 = self._crop_pool_layer(net_conv, rois, "pool5") else: raise NotImplementedError with slim.arg_scope(resnet_arg_scope(is_training=is_training)): fc7, _ = resnet_v2.resnet_v2(pool5, blocks[-1:], global_pool=False, include_root_block=False, scope=self._resnet_scope) with tf.variable_scope(self._resnet_scope, self._resnet_scope): # average pooling done by reduce_mean fc7 = tf.reduce_mean(fc7, axis=[1, 2]) # region classification cls_prob, poly_pred = self._region_classification(fc7, is_training, initializer, initializer_bbox) self._score_summaries.update(self._predictions) return rois, cls_prob, poly_pred