New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
How to load the pretrained model? #3
Comments
Hey, def model(self, x, training):
"""
Defines the complete graph model for the Tiramisu based on the provided
parameters.
Args:
x: Tensor, input image to segment.
training: Bool Tesnor, indicating whether training or not.
Returns:
x: Tensor, raw unscaled logits of predicted segmentation.
"""
concats = []
with tf.variable_scope('encoder'):
x = tf.layers.conv2d(x,
filters=48,
kernel_size=[3, 3],
strides=[1, 1],
padding='SAME',
dilation_rate=[1, 1],
activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
name='first_conv3x3')
print("First Convolution Out: ", x.get_shape())
for block_nb in range(0, self.nb_blocks):
dense = self.dense_block(x, training, block_nb, 'down_dense_block_' + str(block_nb))
if block_nb != self.nb_blocks - 1:
x = tf.concat([x, dense], axis=3, name='down_concat_' + str(block_nb))
concats.append(x)
x = self.transition_down(x, training, x.get_shape()[-1], 'trans_down_' + str(block_nb))
print("Downsample Out:", x.get_shape())
x = dense
print("Bottleneck Block: ", dense.get_shape())
.....Decoder continues downwards Then when you want to restore the encoder only: encoder_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='encoder')
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, ckpt_name, var_list=encoder_vars) |
Thank you very much. I also want to know how can we download the specifical pretrained model? Can you provide the download link of specifical pretrained DenseNet parameters? Thanks again. |
Currently I do not have a pretrained model file, you'll probably have to do the training first yourself. |
OK, Thank you! |
Hi, I am interested about how to load the pretrained model parameters of encoder part. Can you give me some suggestion? Thanks!
The text was updated successfully, but these errors were encountered: