ajbrock / neural-photo-editor Goto Github PK
View Code? Open in Web Editor NEWA simple interface for editing natural photos with generative neural networks.
License: MIT License
A simple interface for editing natural photos with generative neural networks.
License: MIT License
IAN_simple.npz is and IANv1.npz are empty.
When I try to run NPE.py, it fails in reading the npz files.
I just want to run NPE.py file, but it cant load weights
I got
Traceback (most recent call last):
File "NPE.py", line 57, in <module>
config_module = imp.load_source('config',config_path)
File "IAN_simple.py", line 12, in <module>
from lasagne.layers import batch_norm as BN
ImportError: cannot import name batch_norm
when trying to run this with stable lasagne.
That appears to be fixed after running
sudo pip2 install --upgrade https://github.com/Theano/Theano/archive/master.zip
sudo pip2 install --upgrade https://github.com/Lasagne/Lasagne/archive/master.zip
But now I get
Compiling Theano Functions
Traceback (most recent call last):
File "NPE.py", line 75, in <module>
Xh = lasagne.layers.get_output(model['l_out'],{model['l_latents']:ZZ},deterministic=True)
File "/usr/lib/python2.7/site-packages/lasagne/layers/helper.py", line 191, in get_output
all_outputs[layer] = layer.get_output_for(layer_inputs, **kwargs)
File "/usr/lib/python2.7/site-packages/lasagne/layers/conv.py", line 330, in get_output_for
conved = self.convolve(input, **kwargs)
File "/home/tehdog/data/tmp/nobackup/pkg/Neural-Photo-Editor/layers.py", line 277, in convolve
img = gpu_contiguous(input)
File "/usr/lib/python2.7/site-packages/theano/gof/op.py", line 602, in __call__
node = self.make_node(*inputs, **kwargs)
File "/usr/lib/python2.7/site-packages/theano/sandbox/cuda/basic_ops.py", line 3963, in make_node
input = as_cuda_ndarray_variable(input)
File "/usr/lib/python2.7/site-packages/theano/sandbox/cuda/basic_ops.py", line 46, in as_cuda_ndarray_variable
return gpu_from_host(tensor_x)
File "/usr/lib/python2.7/site-packages/theano/gof/op.py", line 602, in __call__
node = self.make_node(*inputs, **kwargs)
File "/usr/lib/python2.7/site-packages/theano/sandbox/cuda/basic_ops.py", line 139, in make_node
dtype=x.dtype)()])
File "/usr/lib/python2.7/site-packages/theano/sandbox/cuda/type.py", line 95, in __init__
(self.__class__.__name__, dtype, name))
TypeError: CudaNdarrayType only supports dtype float32 for now. Tried using dtype float64 for variable None
Can you elaborate which exact versions of the libraries you are using?
After changing NPE.py to model = IAN(config_path = 'IAN_simple.py', dnn = False)
, I get:
$ python NPE.py
Loading weights
Traceback (most recent call last):
File "NPE.py", line 53, in <module>
model = IAN(config_path = 'IAN_simple.py', dnn = False)
File "/Users/skurilyak/Documents/dev/testing/Neural-Photo-Editor/API.py", line 30, in __init__
GANcheckpoints.load_weights(self.weights_fname,params)
File "/Users/skurilyak/Documents/dev/testing/Neural-Photo-Editor/GANcheckpoints.py", line 39, in load_weights
param_dict = np.load(fname)
File "/usr/local/lib/python2.7/site-packages/numpy/lib/npyio.py", line 416, in load
"Failed to interpret file %s as a pickle" % repr(file))
IOError: Failed to interpret file 'IAN_simple.npz' as a pickle
Any ideas?
I came across your implementation of batch re-normalization in the BatchReNormDNNLayer class, and I think there might be an error that might be affecting the model's performance.
My understanding of batch re-norm is that it applies the standard BN normalization first, then applies the r/d correction, and then finally applies the gamma/beta scaling and bias. Something along the lines of this:
normed_x = (x - batch_mean) / batch_std # standard BN
normed_x = normed_x * r + d # The batch renorm correction
normed_x = normed_x * gamma + beta # final scale and bias
However, this line is applying the r/d correction after the scaling and centering with gamma and beta.
https://github.com/ajbrock/Neural-Photo-Editor/blob/master/layers.py#L128
It probably works anyway, based on the good results you seem to have gotten. I just thought I'd bring it to your attention.
Traceback (most recent call last):
File "train_IAN_simple.py", line 112, in
import CAcheckpoints
ImportError: No module named CAcheckpoints
"Neural-Photo-Editor-master/GANcheckpoints.py", line 7, in
from path import Path
ImportError: No module named path
Traceback (most recent call last):
File ".\NPE.py", line 18, in
model = IAN(config_path = 'IAN_simple.py', dnn = False)
File "C:\Users\user\Desktop\Neural-Photo-Editor-master\API.py", line 30, in init
GANcheckpoints.load_weights(self.weights_fname,params)
File "C:\Users\user\Desktop\Neural-Photo-Editor-master\GANcheckpoints.py", line 39, in load_weights
param_dict = np.load(fname)
File "C:\Python27\lib\site-packages\numpy\lib\npyio.py", line 429, in load
"Failed to interpret file %s as a pickle" % repr(file))
IOError: Failed to interpret file 'IAN_simple.npz' as a pickle
Any ideas?
I presume @vdumoulin's folder sharing was turned off somehow (or reached a limit). I'd suggest making a placeholder project release on GitHub and putting the files there—it's hosted on S3 too but no limits and public access is expected.
Hi, I finally managed to install cuda and dev versions of lasagne and theano. Now when I try to launch NPE I get this:
gray@gray-linux:~/Neural-Photo-Editor$ python NPE.py
Using gpu device 0: GeForce GTX 970 (CNMeM is disabled, cuDNN 5105)
Loading weights
Traceback (most recent call last):
File "NPE.py", line 53, in
model = IAN(config_path = 'IAN_simple.py', dnn = True)
File "/home/gray/Neural-Photo-Editor/API.py", line 30, in init
GANcheckpoints.load_weights(self.weights_fname,params)
File "/home/gray/Neural-Photo-Editor/GANcheckpoints.py", line 39, in load_weights
param_dict = np.load(fname)
File "/home/gray/miniconda2/lib/python2.7/site-packages/numpy/lib/npyio.py", line 416, in load
"Failed to interpret file %s as a pickle" % repr(file))
IOError: Failed to interpret file 'IAN_simple.npz' as a pickle
Am I doing something wrong? As far as I can understand from search results, np.load is used for binary .npz files, but all I can see in IAN_Simple.npz are three strings of text:
version https://git-lfs.github.com/spec/v1
oid sha256:82e5fd3ff68b2c9095935c9db269e086e2dd27704b629853e1f03473e7059bd7
size 205207893
UPD: Whoops, my bad. For some reason git clone didn't download raw .npz files. I downloaded them manually and now everything works
Hi,
I am trying to reproduce the code on a V100 instance and I ran into the following issues when I ran python NPE.py
Do you have any recommendations on how we can reproduce your experimental setup in the form of a Dockerfile?
/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/matplotlib/__init__.py:1067: UserWarning: Duplicate key in file "/home/ubuntu/.config/matplotlib/matplotlibrc", line #2
(fname, cnt))
/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/matplotlib/__init__.py:1067: UserWarning: Duplicate key in file "/home/ubuntu/.config/matplotlib/matplotlibrc", line #3
(fname, cnt))
Loading weights
Compiling Theano Functions
ERROR (theano.gof.opt): Optimization failure due to: LocalOptGroup(local_abstractconv_gemm,local_abstractconv_gradweight_gemm,local_abstractconv_gradinputs_gemm,local_abstractconv3d_gemm,local_abstractconv3d_gradweight_gemm,local_abstractconv3d_gradinputs_gemm,local_conv2d_cpu,local_conv2d_gradweight_cpu,local_conv2d_gradinputs_cpu)
ERROR (theano.gof.opt): node: AbstractConv2d{convdim=2, border_mode=(2, 2), subsample=(2, 2), filter_flip=False, imshp=(None, 3, 64, 64), kshp=(128, 3, 5, 5), filter_dilation=(1, 1), num_groups=1, unshared=False}(X, enc_conv1.W)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 2074, in process_node
remove=remove)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/toolbox.py", line 569, in replace_all_validate_remove
chk = fgraph.replace_all_validate(replacements, reason)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/toolbox.py", line 518, in replace_all_validate
fgraph.replace(r, new_r, reason=reason, verbose=False)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/fg.py", line 486, in replace
". The type of the replacement must be the same.", old, new)
BadOptimization: BadOptimization Error
Variable: id 139714617198864 CorrMM{((2, 2), (2, 2)), (2, 2), (1, 1), 1 False}.0
Op CorrMM{((2, 2), (2, 2)), (2, 2), (1, 1), 1 False}(Elemwise{Cast{float64}}.0, enc_conv1.W)
Value Type: <type 'NoneType'>
Old Value: None
New Value: None
Reason: LocalOptGroup(local_abstractconv_gemm,local_abstractconv_gradweight_gemm,local_abstractconv_gradinputs_gemm,local_abstractconv3d_gemm,local_abstractconv3d_gradweight_gemm,local_abstractconv3d_gradinputs_gemm,local_conv2d_cpu,local_conv2d_gradweight_cpu,local_conv2d_gradinputs_cpu). The type of the replacement must be the same.
Old Graph:
AbstractConv2d{convdim=2, border_mode=(2, 2), subsample=(2, 2), filter_flip=False, imshp=(None, 3, 64, 64), kshp=(128, 3, 5, 5), filter_dilation=(1, 1), num_groups=1, unshared=False} [id A] <TensorType(float32, 4D)> ''
|X [id B] <TensorType(float32, 4D)>
|enc_conv1.W [id C] <TensorType(float64, 4D)>
New Graph:
CorrMM{((2, 2), (2, 2)), (2, 2), (1, 1), 1 False} [id D] <TensorType(float64, 4D)> ''
|Elemwise{Cast{float64}} [id E] <TensorType(float64, 4D)> ''
| |X [id B] <TensorType(float32, 4D)>
|enc_conv1.W [id C] <TensorType(float64, 4D)>
Hint: relax the tolerance by setting tensor.cmp_sloppy=1
or even tensor.cmp_sloppy=2 for less-strict comparison
ERROR (theano.gof.opt): Optimization failure due to: LocalOptGroup(local_abstractconv_gemm,local_abstractconv_gradweight_gemm,local_abstractconv_gradinputs_gemm,local_abstractconv3d_gemm,local_abstractconv3d_gradweight_gemm,local_abstractconv3d_gradinputs_gemm,local_conv2d_cpu,local_conv2d_gradweight_cpu,local_conv2d_gradinputs_cpu)
ERROR (theano.gof.opt): node: AbstractConv2d{convdim=2, border_mode=(2, 2), subsample=(2, 2), filter_flip=False, imshp=(None, 3, 64, 64), kshp=(128, 3, 5, 5), filter_dilation=(1, 1), num_groups=1, unshared=False}(X, enc_conv1.W)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 2074, in process_node
remove=remove)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/toolbox.py", line 569, in replace_all_validate_remove
chk = fgraph.replace_all_validate(replacements, reason)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/toolbox.py", line 518, in replace_all_validate
fgraph.replace(r, new_r, reason=reason, verbose=False)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/fg.py", line 486, in replace
". The type of the replacement must be the same.", old, new)
BadOptimization: BadOptimization Error
Variable: id 139714617838416 CorrMM{((2, 2), (2, 2)), (2, 2), (1, 1), 1 False}.0
Op CorrMM{((2, 2), (2, 2)), (2, 2), (1, 1), 1 False}(Elemwise{Cast{float64}}.0, enc_conv1.W)
Value Type: <type 'NoneType'>
Old Value: None
New Value: None
Reason: LocalOptGroup(local_abstractconv_gemm,local_abstractconv_gradweight_gemm,local_abstractconv_gradinputs_gemm,local_abstractconv3d_gemm,local_abstractconv3d_gradweight_gemm,local_abstractconv3d_gradinputs_gemm,local_conv2d_cpu,local_conv2d_gradweight_cpu,local_conv2d_gradinputs_cpu). The type of the replacement must be the same.
Old Graph:
AbstractConv2d{convdim=2, border_mode=(2, 2), subsample=(2, 2), filter_flip=False, imshp=(None, 3, 64, 64), kshp=(128, 3, 5, 5), filter_dilation=(1, 1), num_groups=1, unshared=False} [id A] <TensorType(float32, 4D)> ''
|X [id B] <TensorType(float32, 4D)>
|enc_conv1.W [id C] <TensorType(float64, 4D)>
New Graph:
CorrMM{((2, 2), (2, 2)), (2, 2), (1, 1), 1 False} [id D] <TensorType(float64, 4D)> ''
|Elemwise{Cast{float64}} [id E] <TensorType(float64, 4D)> ''
| |X [id B] <TensorType(float32, 4D)>
|enc_conv1.W [id C] <TensorType(float64, 4D)>
Hint: relax the tolerance by setting tensor.cmp_sloppy=1
or even tensor.cmp_sloppy=2 for less-strict comparison
ERROR (theano.gof.opt): Optimization failure due to: LocalOptGroup(local_abstractconv_gemm,local_abstractconv_gradweight_gemm,local_abstractconv_gradinputs_gemm,local_abstractconv3d_gemm,local_abstractconv3d_gradweight_gemm,local_abstractconv3d_gradinputs_gemm,local_conv2d_cpu,local_conv2d_gradweight_cpu,local_conv2d_gradinputs_cpu)
ERROR (theano.gof.opt): node: AbstractConv2d{convdim=2, border_mode=(2, 2), subsample=(2, 2), filter_flip=False, imshp=(None, 3, 64, 64), kshp=(128, 3, 5, 5), filter_dilation=(1, 1), num_groups=1, unshared=False}(X, enc_conv1.W)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 2074, in process_node
remove=remove)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/toolbox.py", line 569, in replace_all_validate_remove
chk = fgraph.replace_all_validate(replacements, reason)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/toolbox.py", line 518, in replace_all_validate
fgraph.replace(r, new_r, reason=reason, verbose=False)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/fg.py", line 486, in replace
". The type of the replacement must be the same.", old, new)
BadOptimization: BadOptimization Error
Variable: id 139714617837136 CorrMM{((2, 2), (2, 2)), (2, 2), (1, 1), 1 False}.0
Op CorrMM{((2, 2), (2, 2)), (2, 2), (1, 1), 1 False}(Elemwise{Cast{float64}}.0, enc_conv1.W)
Value Type: <type 'NoneType'>
Old Value: None
New Value: None
Reason: LocalOptGroup(local_abstractconv_gemm,local_abstractconv_gradweight_gemm,local_abstractconv_gradinputs_gemm,local_abstractconv3d_gemm,local_abstractconv3d_gradweight_gemm,local_abstractconv3d_gradinputs_gemm,local_conv2d_cpu,local_conv2d_gradweight_cpu,local_conv2d_gradinputs_cpu). The type of the replacement must be the same.
Old Graph:
AbstractConv2d{convdim=2, border_mode=(2, 2), subsample=(2, 2), filter_flip=False, imshp=(None, 3, 64, 64), kshp=(128, 3, 5, 5), filter_dilation=(1, 1), num_groups=1, unshared=False} [id A] <TensorType(float32, 4D)> ''
|X [id B] <TensorType(float32, 4D)>
|enc_conv1.W [id C] <TensorType(float64, 4D)>
New Graph:
CorrMM{((2, 2), (2, 2)), (2, 2), (1, 1), 1 False} [id D] <TensorType(float64, 4D)> ''
|Elemwise{Cast{float64}} [id E] <TensorType(float64, 4D)> ''
| |X [id B] <TensorType(float32, 4D)>
|enc_conv1.W [id C] <TensorType(float64, 4D)>
Hint: relax the tolerance by setting tensor.cmp_sloppy=1
or even tensor.cmp_sloppy=2 for less-strict comparison
ERROR (theano.gof.opt): Optimization failure due to: LocalOptGroup(local_abstractconv_gemm,local_abstractconv_gradweight_gemm,local_abstractconv_gradinputs_gemm,local_abstractconv3d_gemm,local_abstractconv3d_gradweight_gemm,local_abstractconv3d_gradinputs_gemm,local_conv2d_cpu,local_conv2d_gradweight_cpu,local_conv2d_gradinputs_cpu)
ERROR (theano.gof.opt): node: AbstractConv2d{convdim=2, border_mode=(2, 2), subsample=(2, 2), filter_flip=False, imshp=(None, 3, 64, 64), kshp=(128, 3, 5, 5), filter_dilation=(1, 1), num_groups=1, unshared=False}(X, enc_conv1.W)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 2074, in process_node
remove=remove)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/toolbox.py", line 569, in replace_all_validate_remove
chk = fgraph.replace_all_validate(replacements, reason)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/toolbox.py", line 518, in replace_all_validate
fgraph.replace(r, new_r, reason=reason, verbose=False)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/fg.py", line 486, in replace
". The type of the replacement must be the same.", old, new)
BadOptimization: BadOptimization Error
Variable: id 139714617737296 CorrMM{((2, 2), (2, 2)), (2, 2), (1, 1), 1 False}.0
Op CorrMM{((2, 2), (2, 2)), (2, 2), (1, 1), 1 False}(Elemwise{Cast{float64}}.0, enc_conv1.W)
Value Type: <type 'NoneType'>
Old Value: None
New Value: None
Reason: LocalOptGroup(local_abstractconv_gemm,local_abstractconv_gradweight_gemm,local_abstractconv_gradinputs_gemm,local_abstractconv3d_gemm,local_abstractconv3d_gradweight_gemm,local_abstractconv3d_gradinputs_gemm,local_conv2d_cpu,local_conv2d_gradweight_cpu,local_conv2d_gradinputs_cpu). The type of the replacement must be the same.
Old Graph:
AbstractConv2d{convdim=2, border_mode=(2, 2), subsample=(2, 2), filter_flip=False, imshp=(None, 3, 64, 64), kshp=(128, 3, 5, 5), filter_dilation=(1, 1), num_groups=1, unshared=False} [id A] <TensorType(float32, 4D)> ''
|X [id B] <TensorType(float32, 4D)>
|enc_conv1.W [id C] <TensorType(float64, 4D)>
New Graph:
CorrMM{((2, 2), (2, 2)), (2, 2), (1, 1), 1 False} [id D] <TensorType(float64, 4D)> ''
|Elemwise{Cast{float64}} [id E] <TensorType(float64, 4D)> ''
| |X [id B] <TensorType(float32, 4D)>
|enc_conv1.W [id C] <TensorType(float64, 4D)>
Hint: relax the tolerance by setting tensor.cmp_sloppy=1
or even tensor.cmp_sloppy=2 for less-strict comparison
ERROR (theano.gof.opt): Optimization failure due to: local_abstractconv_check
ERROR (theano.gof.opt): node: AbstractConv2d{convdim=2, border_mode=(2, 2), subsample=(2, 2), filter_flip=False, imshp=(None, 3, 64, 64), kshp=(128, 3, 5, 5), filter_dilation=(1, 1), num_groups=1, unshared=False}(X, enc_conv1.W)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 2034, in process_node
replacements = lopt.transform(node)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/tensor/nnet/opt.py", line 500, in local_abstractconv_check
node.op.__class__.__name__)
LocalMetaOptimizerSkipAssertionError: AbstractConv2d Theano optimization failed: there is no implementation available supporting the requested options. Did you exclude both "conv_dnn" and "conv_gemm" from the optimizer? If on GPU, is cuDNN available and does the GPU support it? If on CPU, do you have a BLAS library installed Theano can link against? On the CPU we do not support float16.
Traceback (most recent call last):
File "NPE.py", line 19, in <module>
model = IAN(config_path = 'IAN_simple.py', dnn = False)
File "/home/ubuntu/Neural-Photo-Editor/API.py", line 51, in __init__
self.Z_hat_fn = theano.function([self.X],self.Z_hat)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/compile/function.py", line 317, in function
output_keys=output_keys)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/compile/pfunc.py", line 486, in pfunc
output_keys=output_keys)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/compile/function_module.py", line 1839, in orig_function
name=name)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/compile/function_module.py", line 1519, in __init__
optimizer_profile = optimizer(fgraph)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 108, in __call__
return self.optimize(fgraph)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 97, in optimize
ret = self.apply(fgraph, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 251, in apply
sub_prof = optimizer.optimize(fgraph)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 97, in optimize
ret = self.apply(fgraph, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 2143, in apply
nb += self.process_node(fgraph, node)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 2039, in process_node
lopt, node)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 1933, in warn_inplace
return NavigatorOptimizer.warn(exc, nav, repl_pairs, local_opt, node)
File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/theano/gof/opt.py", line 1919, in warn
raise exc
theano.gof.opt.LocalMetaOptimizerSkipAssertionError: AbstractConv2d Theano optimization failed: there is no implementation available supporting the requested options. Did you exclude both "conv_dnn" and "conv_gemm" from the optimizer? If on GPU, is cuDNN available and does the GPU support it? If on CPU, do you have a BLAS library installed Theano can link against? On the CPU we do not support float16.
I was trying to make multiple independent changes to an image and I came to the following conclusion:
The masking technique means the output image is based off the original
ground-truth image. After an aesthetically pleasing change is made, such as
growing the length of the hair, a new ground-truth image must be saved and
the latent space recalculated before a different change is made such as
changing the hair color.
By adding the following PR I was able to get these results:
#8
First operation - increase hair length:
Second operation AFTER saving the new ground-truth image - new hair color:
Without saving the new ground-truth image, I was unable to get these results as the algorithm attempted to remove my longer black hair when I attempted to change it to yellow, because it matches the skin color.
This project looks very interesting, but I don't have access to an nVidia card. The readme says "You'll need to uncomment my explicit DNN calls if you wish to not use it.", but if I have a look at the code, there's a lot of references to DNN, so this doesn't look very trivial.
Is it possible to create a custom version (maybe a branch?) that works without having cuDNN installed?
Hello
Thanks for releasing source code. I've got problem while running script on laptop without cuda gpu:
Traceback (most recent call last):
File "NPE.py", line 57, in <module>
config_module = imp.load_source('config',config_path)
File "IAN_simple.py", line 10, in <module>
import lasagne.layers.dnn
File "/usr/local/lib/python2.7/dist-packages/lasagne/layers/dnn.py", line 14, in <module>
"requires GPU support -- see http://lasagne.readthedocs.org/en/"
ImportError: requires GPU support -- see http://lasagne.readthedocs.org/en/latest/user/installation.html#gpu-support
How can I enforce CPU mode?
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