Question

我正在尝试使用此处https://github.com/DrSleep/tensorflow-deeplab-resnet

提供的deeplab resnet模型

我能够使用我的数据集成功训练模型。

结果也不错。但是此仓库没有使用模型进行动态预测的信息

所以我尝试加载模型图并进行预测

在我以前加载的代码下面

img = <img path>
sess = tf.Session()
mod_dir = actual_path(model_dir)
model_file = os.path.join(mod_dir, "snapshots_fine_tune_new_2")
model_file = os.path.join(model_file, "model.ckpt-1400")
loader = tf.train.import_meta_graph(model_file+".meta")
loader.restore(sess, model_file)
placeholder = tf.placeholder(name='data', dtype=tf.float32,
                             shape=[None, None,
                                    3])

# Predictions.
raw_output = sess.graph.get_tensor_by_name('fc1_voc12:0')
raw_output = tf.image.resize_bilinear(raw_output, tf.shape(placeholder)[1:3, 
])

# CRF.
inv_image = tf.py_func(inv_preprocess, [placeholder], tf.uint8)
raw_output = tf.py_func(dense_crf, [tf.nn.softmax(raw_output), inv_image], 
tf.float32)

raw_output = tf.argmax(raw_output, dimension=3)
pred = tf.expand_dims(raw_output, dim=3)  # Create a 4-d tensor.

img = tf.expand_dims(read_image(img), dim=0)

preds = sess.run(pred,feed_dict= 
                         {sess.graph.get_tensor_by_name("data:0"):img})

但是在执行时我遇到了这个错误

KeyError：“名称'data：0'表示不存在的张量。图中的操作'data'不存在。”

在型号代码下方

class DeepLabResNetModel(Network):
def setup(self, is_training):
    '''Network definition.

    Args:
      is_training: whether to update the running mean and variance of the batch normalisation layer.
                   If the batch size is small, it is better to keep the running mean and variance of 
                   the-pretrained model frozen.
    '''
    (self.feed('data')
         .conv(7, 7, 64, 2, 2, biased=False, relu=False, name='conv1')
         .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn_conv1')
         .max_pool(3, 3, 2, 2, name='pool1')
         .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2a_branch1')
         .batch_normalization(is_training=is_training, activation_fn=None, name='bn2a_branch1'))

    (self.feed('pool1')
         .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2a_branch2a')
         .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2a_branch2a')
         .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2a_branch2b')
         .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2a_branch2b')
         .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2a_branch2c')
         .batch_normalization(is_training=is_training, activation_fn=None, name='bn2a_branch2c'))

    (self.feed('bn2a_branch1', 
               'bn2a_branch2c')
         .add(name='res2a')
         .relu(name='res2a_relu')
         .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2b_branch2a')
         .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2b_branch2a')
         .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2b_branch2b')
         .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2b_branch2b')
         .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2b_branch2c')
         .batch_normalization(is_training=is_training, activation_fn=None, name='bn2b_branch2c'))

    (self.feed('res2a_relu', 
               'bn2b_branch2c')
         .add(name='res2b')
         .relu(name='res2b_relu')
         .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2c_branch2a')
         .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2c_branch2a')
         .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2c_branch2b')
         .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2c_branch2b')
         .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2c_branch2c')
         .batch_normalization(is_training=is_training, activation_fn=None, name='bn2c_branch2c'))

...

有人可以帮助我动态使用输入吗？

如何将feed_dict用于Deeplap Resnet模型？

0 个答案: