I've tried to build a rather special network in TensorFlow and I kinda got it to work. Sadly, I stumbled upon an error that I am not able to fix or even find the right place to start with. As far as I can tell the network is built successfully until the loss function is defined. The error message then says something about uncompatible shapes:
ValueError: Shapes (1, 17, 17, 44) and (1, 16, 16, 44) are not compatible
The thing is the error does not say at which tensor or line of code the problem occurs. I've printed all shapes that I could come up with and I can't even find something of the shape (1, 17, 17, 44).
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_nn_ops
@ops.RegisterGradient("MaxPoolWithArgmax")
def _MaxPoolWithArgmaxGrad(op, grad, some_other_arg):
return gen_nn_ops._max_pool_grad(op.inputs[0],
op.outputs[0],
grad,
op.get_attr("ksize"),
op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format='NHWC')
class FCN_RGBD:
def __init__(self, checkpoint_dir='./checkpoints/'):
self.build(1)
# "allow_soft_placement = True" makes TensorFlow automatically choose an existing and supported GPU device
self.config = tf.ConfigProto(allow_soft_placement = True)
self.session = tf.Session(config = self.config)
self.session.run(tf.global_variables_initializer())
def weight_variable(self, shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(self, shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv_layer(self, x, W_shape, b_shape, strides, name, padding):
W = self.weight_variable(W_shape)
b = self.bias_variable([b_shape])
return tf.nn.relu(tf.nn.conv2d(x, W, strides=strides, padding=padding) + b)
def conv_skip_layer(self, x, W_shape, b_shape, name, padding):
W = self.weight_variable(W_shape)
b = self.bias_variable([b_shape])
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding=padding) + b
def deconv_layer(self, x, out_shape, W_shape, b_shape, strides, name, padding):
W = self.weight_variable(W_shape)
b = self.bias_variable([b_shape])
return tf.nn.conv2d_transpose(x, W, output_shape=out_shape, strides=strides, padding=padding) + b
def pool_layer3x3(self, x):
with tf.device('/gpu:0'):
return tf.nn.max_pool_with_argmax(x, ksize=[1, 3, 3, 1], strides=[1, 3, 3, 1], padding='SAME')
def pool_layer2x2(self, x):
with tf.device('/gpu:0'):
return tf.nn.max_pool_with_argmax(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
def build(self, batchsize):
print('Building the FCN...')
with tf.device('/gpu:0'):
self.x = tf.placeholder(tf.float32, shape=(batchsize, 250, 250, 1))
self.y = tf.placeholder(tf.int64, shape=(batchsize, 250, 250, 1))
self.rate = tf.placeholder(tf.float32, shape=[])
conv1 = self.conv_layer(self.x, [5, 5, 1, 64], 64, [1, 2, 2, 1], 'conv1', 'SAME')
pool1, pool_1_argmax = self.pool_layer3x3(conv1)
conv1_skip = self.conv_skip_layer(pool1, [1, 1, 64, 44], 44, 'conv1_skip', 'VALID')
conv2 = self.conv_layer(pool1, [3, 3, 64, 128], 128, [1, 1, 1, 1], 'conv2', 'VALID')
pool2, pool_2_argmax = self.pool_layer2x2(conv2)
conv2_skip = self.conv_skip_layer(pool2, [1, 1, 128, 44], 44, 'conv2_skip', 'VALID')
conv3 = self.conv_layer(pool2, [5, 5, 128, 256], 256, [1, 1, 1, 1], 'conv3', 'VALID')
conv4 = self.conv_layer(conv3, [3, 3, 256, 44], 44, [1, 1, 1, 1], 'conv4', 'SAME')
deconv1 = self.deconv_layer(conv4, tf.stack([batchsize, 16, 16, 44]), [3, 3, 44, 44], 44, [1, 1, 1, 1], 'deconv1', 'SAME')
conv2_skip = tf.image.resize_image_with_crop_or_pad(conv2_skip, 16, 16)
sum1 = conv2_skip + deconv1
dropout1 = tf.nn.dropout(sum1, keep_prob=0.5)
deconv2 = self.deconv_layer(dropout1, tf.stack([batchsize, 34, 34, 44]), [4, 4, 44, 44], 44, [1, 2, 2, 1], 'deconv2', 'SAME')
conv1_skip = tf.image.resize_image_with_crop_or_pad(conv1_skip, 34, 34)
sum2 = conv1_skip + deconv2
dropout2 = tf.nn.dropout(sum2, keep_prob=0.5)
deconv_final = self.deconv_layer(dropout2, tf.stack([batchsize, 250, 250, 44]), [19, 19, 44, 44], 44, [1, 7, 7, 1], 'deconv_final', 'VALID')
annotation_pred = tf.argmax(deconv_final, dimension=3, name='prediction')
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.squeeze(self.y, squeeze_dims=[3]), logits=deconv_final)
self.loss = tf.reduce_mean(cross_entropy, name='cross_entropy_mean')
self.train_step = tf.train.AdamOptimizer(self.rate).minimize(self.loss)
self.prediction = tf.argmax(tf.reshape(tf.nn.softmax(logits), tf.shape(deconv_final)), dimension=3)
And this is the error message:
Traceback (most recent call last):
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 560, in merge_with
new_dims.append(dim.merge_with(other[i]))
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 135, in merge_with
self.assert_is_compatible_with(other)
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 108, in assert_is_compatible_with
% (self, other))
ValueError: Dimensions 17 and 16 are not compatible
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "main.py", line 5, in <module>
fcn_rgbd = FCN_RGBD()
File "C:\Users\user\netcase\Workspace\Depth_BPC_v1\FCN_RGBD.py", line 23, in __init__
self.build(1)
File "C:\Users\user\netcase\Workspace\Depth_BPC_v1\FCN_RGBD.py", line 162, in build
self.train_step = tf.train.AdamOptimizer(self.rate).minimize(self.loss)
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\training\optimizer.py", line 315, in minimize
grad_loss=grad_loss)
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\training\optimizer.py", line 386, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 580, in gradients
in_grad.set_shape(t_in.get_shape())
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\framework\ops.py", line 413, in set_shape
self._shape = self._shape.merge_with(shape)
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 564, in merge_with
(self, other))
ValueError: Shapes (1, 17, 17, 44) and (1, 16, 16, 44) are not compatible
I am very sorry for this vaguely described problem, but I really don’t have a notion where to start.
