def FeatureExtractor(self, X, reuse = False): input_X = utils.NormalizeImage(X) with tf.variable_scope('feature_extractor_conv1',reuse = reuse): h_conv1 = layers.conv2d(input_X, self.ef_dim, 3, stride=1, activation_fn=tf.nn.relu, weights_initializer=self.initializer) h_conv1 = layers.conv2d(h_conv1, self.ef_dim, 3, stride=1, activation_fn=tf.nn.relu, weights_initializer=self.initializer) h_conv1 = layers.max_pool2d(h_conv1, [2, 2], 2, padding='SAME') with tf.variable_scope('feature_extractor_conv2',reuse = reuse): h_conv2 = layers.conv2d(h_conv1, self.ef_dim * 2, 3, stride=1, activation_fn=tf.nn.relu, weights_initializer=self.initializer) h_conv2 = layers.conv2d(h_conv2, self.ef_dim * 2, 3, stride=1, activation_fn=tf.nn.relu, weights_initializer=self.initializer) h_conv2 = layers.max_pool2d(h_conv2, [2, 2], 2, padding='SAME') with tf.variable_scope('feature_extractor_conv3',reuse = reuse): h_conv3 = layers.conv2d(h_conv2, self.ef_dim * 4, 3, stride=1, activation_fn=tf.nn.relu, weights_initializer=self.initializer) h_conv3 = layers.conv2d(h_conv3, self.ef_dim * 4, 3, stride=1, activation_fn=tf.nn.relu, weights_initializer=self.initializer) h_conv3 = layers.max_pool2d(h_conv3, [2, 2], 2, padding='SAME') with tf.variable_scope('feature_extractor_fc1', reuse = reuse): fc_input = layers.flatten(h_conv3) fc_1 = layers.fully_connected(inputs=fc_input, num_outputs=self.latent_dim, activation_fn=None, weights_initializer=self.initializer) features = fc_1 return features
def create_operators(self): size = 224 img_mean = [0.485, 0.456, 0.406] img_std = [0.229, 0.224, 0.225] img_scale = 1.0 / 255.0 decode_op = utils.DecodeImage() resize_op = utils.ResizeImage(resize_short=256) crop_op = utils.CropImage(size=(size, size)) normalize_op = utils.NormalizeImage(scale=img_scale, mean=img_mean, std=img_std) totensor_op = utils.ToTensor() return [decode_op, resize_op, crop_op, normalize_op, totensor_op]
def create_operators(interpolation=1): size = 224 img_mean = [0.485, 0.456, 0.406] img_std = [0.229, 0.224, 0.225] img_scale = 1.0 / 255.0 resize_op = utils.ResizeImage(resize_short=256, interpolation=interpolation) crop_op = utils.CropImage(size=(size, size)) normalize_op = utils.NormalizeImage(scale=img_scale, mean=img_mean, std=img_std) totensor_op = utils.ToTensor() return [resize_op, crop_op, normalize_op, totensor_op]