Genearting unmasked faces from faces #25
Comments
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Hi! I didn't fully understand your question. Could you please be more specific？ |
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Thanks Peng!! I am research scholar and using your model in regenerating faces on my own dataset with masked faces (created on celebA and celebAHQ). I have used your pre-trained model given on git for the face reconstruction using test.py.
All the issues are urgent as my paper submission is due and my lot of time is going in troubleshoot. I am planning to make it base paper and to be used as transfer learning model. TIA |
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done with tensorflow error now in another notebook. please guide about training . |
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Sorry, I just saw your issues. If you want to train a new model, you need to input the masked image and the corresponding binary mask in the training. In our training code, the masked image and the corresponding binary mask are automatically generated. So you just need to collect the ground truth dataset and leave the masking procedure to the code. You can set the Best wishes, |
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Thanks USTC-JialunPeng/Diverse-Structure-Inpainting
So I need to send ground truth and corresponding binary mask image in
training the new model.
Thanks for reply.
Regards
Chandni Agarwal
…On Tue, Jul 19, 2022 at 6:42 PM USTC-JialunPeng ***@***.***> wrote:
Sorry, I just saw your issues.
If you want to train a new model, you need to input the masked image and
the corresponding binary mask in the training. In our training code, the
masked image and the corresponding binary mask are automatically generated.
So you just need to collect the ground truth dataset and leave the masking
procedure to the code. You can set the random_mask argument to True to
use random masks, otherwise the default center masks will be used.
Best wishes,
Jialun
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Right. You just need to collect your ground truth dataset and make a path list. Please refer to our training instructions for more details. |
|
Dear Sir
Facing issue with train_structure generator and train texture generator
code.
sharing .txt file containing error while running individual codes. Not able
to train data. It is urgent,
PFA file.
Kindly reply asap.
Regards
On Tue, Jul 19, 2022 at 10:26 PM Chandni Agarwal ***@***.***>
wrote:
Thanks!!
Following same instructions. One more question : Should I put binary mask
and ground truth in same folder.?
giving images pl tell which one to load?
ground truth
+ only mask map or mask covered with overlay map.
Regards
On Tue, Jul 19, 2022 at 9:44 PM USTC-JialunPeng ***@***.***>
wrote:
> Right. You just need to collect your ground truth dataset and make a path
> list. Please refer to our training instructions
> <https://github.com/USTC-JialunPeng/Diverse-Structure-Inpainting#training>
> for more details.
>
> —
> Reply to this email directly, view it on GitHub
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/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
------------ Options -------------
batch_size: 8
checkpoints_dir: ./gdata/vqvae-inpainting
commitment_cost: 0.25
dataset: celeba
decay: 0.99
dropout_s: 0.1
ema_decay: 0.9997
embedding_dim: 64
image_size: 256
learning_rate: 0.18
load_size: 266
margins: 0
mask_size: 128
max_delta: 0
max_steps: 1000000
nr_attention_s: 4
nr_channel_cond_s: 32
nr_channel_s: 128
nr_channel_vq: 128
nr_gpu: 2
nr_head_s: 8
nr_res_block_vq: 2
nr_res_channel_cond_s: 32
nr_res_channel_s: 128
nr_res_channel_vq: 64
nr_resnet_out_s: 20
nr_resnet_s: 20
num_embeddings: 512
random_mask: True
resnet_nonlinearity: concat_elu
train_flist: ./gdata/train.flist
train_spe: 10000
val_steps: 10000
valid_flist: ./gdata/val.flist
vqvae_network_dir: /content/gdrive/MyDrive/Diverse-Structure-Inpainting/gdata/vqvae-inpainting/20220719-184610_CelebAHQ_celebahq
-------------- End ----------------
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/control_flow_ops.py:423: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /content/gdrive/MyDrive/Diverse-Structure-Inpainting/net/nn.py:128: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, use
tf.py_function, which takes a python function which manipulates tf eager
tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
an ndarray (just call tensor.numpy()) but having access to eager tensors
means `tf.py_function`s can use accelerators such as GPUs as well as
being differentiable using a gradient tape.
WARNING:tensorflow:From train_structure_generator.py:225: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.
See `tf.nn.softmax_cross_entropy_with_logits_v2`.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
WARNING:tensorflow:From train_structure_generator.py:267: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.random.categorical instead.
2022-07-21 17:17:20.484784: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2022-07-21 17:17:20.487942: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2000175000 Hz
2022-07-21 17:17:20.488233: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x3111c80 executing computations on platform Host. Devices:
2022-07-21 17:17:20.488270: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
VQVAE network restored ...
2022-07-21 17:18:20.145768: E tensorflow/core/common_runtime/executor.cc:624] Executor failed to create kernel. Invalid argument: Current libxsmm and customized CPU implementations do not yet support dilation rates larger than 1.
[[{{node gradients_1/structure_condition_3/wnconv2d_22/Conv2D_grad/Conv2DBackpropInput}}]]
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py", line 1334, in _do_call
return fn(*args)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py", line 1319, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Current libxsmm and customized CPU implementations do not yet support dilation rates larger than 1.
[[{{node gradients_1/structure_condition_3/wnconv2d_22/Conv2D_grad/Conv2DBackpropInput}}]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train_structure_generator.py", line 322, in <module>
result = sess.run([train_op, bits_per_dim], {tf_lr:lr})
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py", line 929, in run
run_metadata_ptr)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py", line 1152, in _run
feed_dict_tensor, options, run_metadata)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py", line 1328, in _do_run
run_metadata)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py", line 1348, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Current libxsmm and customized CPU implementations do not yet support dilation rates larger than 1.
[[node gradients_1/structure_condition_3/wnconv2d_22/Conv2D_grad/Conv2DBackpropInput (defined at train_structure_generator.py:229) ]]
Caused by op 'gradients_1/structure_condition_3/wnconv2d_22/Conv2D_grad/Conv2DBackpropInput', defined at:
File "train_structure_generator.py", line 229, in <module>
tower_grads.append(optimizer.compute_gradients(loss, var_list=structure_generator_params))
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/optimizer.py", line 512, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 664, in gradients
unconnected_gradients)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 965, in _GradientsHelper
lambda: grad_fn(op, *out_grads))
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 420, in _MaybeCompile
return grad_fn() # Exit early
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 965, in <lambda>
lambda: grad_fn(op, *out_grads))
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/nn_grad.py", line 532, in _Conv2DGrad
data_format=data_format),
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 1307, in conv2d_backprop_input
name=name)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
op_def=op_def)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py", line 1801, in __init__
self._traceback = tf_stack.extract_stack()
...which was originally created as op 'structure_condition_3/wnconv2d_22/Conv2D', defined at:
File "train_structure_generator.py", line 223, in <module>
cond_masked = structure_condition(masked, mask, ema=None, **structure_condition_opt)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/template.py", line 360, in __call__
return self._call_func(args, kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/template.py", line 306, in _call_func
result = self._func(*args, **kwargs)
File "/content/gdrive/MyDrive/Diverse-Structure-Inpainting/net/structure_generator.py", line 45, in structure_condition_spec
x = nn.gated_resnet(x, num_res_filters=8*nr_res_channel, conv=nn.wnconv2d, rate=16)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 182, in func_with_args
return func(*args, **current_args)
File "/content/gdrive/MyDrive/Diverse-Structure-Inpainting/net/nn.py", line 561, in gated_resnet
c1 = conv(nonlinearity(x), num_res_filters, rate=rate)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 182, in func_with_args
return func(*args, **current_args)
File "/content/gdrive/MyDrive/Diverse-Structure-Inpainting/net/nn.py", line 364, in wnconv2d
x = tf.nn.bias_add(tf.nn.conv2d(x, W, [1] + stride + [1], pad, dilations=[1,rate,rate,1]), b)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 1026, in conv2d
data_format=data_format, dilations=dilations, name=name)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
op_def=op_def)
InvalidArgumentError (see above for traceback): Current libxsmm and customized CPU implementations do not yet support dilation rates larger than 1.
[[node gradients_1/structure_condition_3/wnconv2d_22/Conv2D_grad/Conv2DBackpropInput (defined at train_structure_generator.py:229) ]]
|
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Are you training the model on GPU? BTW, which TensorFlow version are you using？ |
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is it possible instead of generating a mask , I give a binary image of the
mask for training from the file list.
Kindly reply.
Regards
Chandni Agarwal
…On Tue, Jul 19, 2022 at 6:42 PM USTC-JialunPeng ***@***.***> wrote:
Sorry, I just saw your issues.
If you want to train a new model, you need to input the masked image and
the corresponding binary mask in the training. In our training code, the
masked image and the corresponding binary mask are automatically generated.
So you just need to collect the ground truth dataset and leave the masking
procedure to the code. You can set the random_mask argument to True to
use random masks, otherwise the default center masks will be used.
Best wishes,
Jialun
—
Reply to this email directly, view it on GitHub
<#25 (comment)>,
or unsubscribe
<https://github.com/notifications/unsubscribe-auth/ACJSE45WZUCGGJHPGHLPEVTVU2ST5ANCNFSM53YL6XAA>
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com>
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Of course you can use your own masks instead of generated masks. But it may be challenging for you to modify the corresponding code. You can take a try and I wish you good luck. |
|
Can I use your regular mask code instead as I can give it a try later . As
the faces are not correctly created so trying with training with new
dataset.
checked nn.py too for own mask.
Thanks for quick reply.
…On Sun, Jul 24, 2022 at 2:07 PM USTC-JialunPeng ***@***.***> wrote:
Of course you can use your own masks instead of generated masks. But it
may be challenging for you to modify the corresponding code. You can take a
try and I wish you good luck.
—
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<#25 (comment)>,
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2022-07-24 12:53:22.183438: I
tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer
filled. 2022-07-24 12:53:24.173695: I
tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully
opened dynamic library libcudnn.so.7 100 iterations, time: 427s, train NLL:
nan. 200 iterations, time: 332s, train NLL: nan. 300 iterations, time:
333s, train NLL: nan.
Not able to remove this error.
On Sun, Jul 24, 2022 at 6:09 PM Chandni Agarwal ***@***.***>
wrote:
… Can I use your regular mask code instead as I can give it a try later . As
the faces are not correctly created so trying with training with new
dataset.
checked nn.py too for own mask.
Thanks for quick reply.
On Sun, Jul 24, 2022 at 2:07 PM USTC-JialunPeng ***@***.***>
wrote:
> Of course you can use your own masks instead of generated masks. But it
> may be challenging for you to modify the corresponding code. You can take a
> try and I wish you good luck.
>
> —
> Reply to this email directly, view it on GitHub
> <#25 (comment)>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/ACJSE47VFIDOGGU3A6NNNKTVVT6FTANCNFSM53YL6XAA>
> .
> You are receiving this because you authored the thread.Message ID:
> ***@***.***
> .com>
>
|
|
For train_structure_generator
2022-07-24 12:53:22.183438: I
tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer
filled. 2022-07-24 12:53:24.173695: I
tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully
opened dynamic library libcudnn.so.7 100 iterations, time: 427s, train NLL:
nan. 200 iterations, time: 332s, train NLL: nan. 300 iterations, time:
333s, train NLL: nan.
How to solve
On Sun, Jul 24, 2022 at 6:42 PM Chandni Agarwal ***@***.***>
wrote:
… 2022-07-24 12:53:22.183438: I
tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer
filled. 2022-07-24 12:53:24.173695: I
tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully
opened dynamic library libcudnn.so.7 100 iterations, time: 427s, train NLL:
nan. 200 iterations, time: 332s, train NLL: nan. 300 iterations, time:
333s, train NLL: nan.
Not able to remove this error.
On Sun, Jul 24, 2022 at 6:09 PM Chandni Agarwal ***@***.***>
wrote:
> Can I use your regular mask code instead as I can give it a try later .
> As the faces are not correctly created so trying with training with new
> dataset.
> checked nn.py too for own mask.
> Thanks for quick reply.
>
>
> On Sun, Jul 24, 2022 at 2:07 PM USTC-JialunPeng ***@***.***>
> wrote:
>
>> Of course you can use your own masks instead of generated masks. But it
>> may be challenging for you to modify the corresponding code. You can take a
>> try and I wish you good luck.
>>
>> —
>> Reply to this email directly, view it on GitHub
>> <#25 (comment)>,
>> or unsubscribe
>> <https://github.com/notifications/unsubscribe-auth/ACJSE47VFIDOGGU3A6NNNKTVVT6FTANCNFSM53YL6XAA>
>> .
>> You are receiving this because you authored the thread.Message ID:
>> <USTC-JialunPeng/Diverse-Structure-Inpainting/issues/25/1193273028@
>> github.com>
>>
>
|
|
It seems that the negative log-likelihood (NLL) diverges at the beginning of training. Could you provide more details about your code modifications? |
|
Can we have meeting on Google meet so that I can share my code with you?
Pl tell suitable time.
Regards
…On Thu, 28 Jul 2022, 12:09 USTC-JialunPeng, ***@***.***> wrote:
It seems that the negative log-likelihood (NLL) diverges at the beginning
of training. Could you provide more details about your code modifications?
—
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Hello sir
Stucked in research and not able figure out problem.
Regards
…On Thu, 28 Jul 2022, 18:18 Chandni Agarwal, ***@***.***> wrote:
Can we have meeting on Google meet so that I can share my code with you?
Pl tell suitable time.
Regards
On Thu, 28 Jul 2022, 12:09 USTC-JialunPeng, ***@***.***>
wrote:
> It seems that the negative log-likelihood (NLL) diverges at the beginning
> of training. Could you provide more details about your code modifications?
>
> —
> Reply to this email directly, view it on GitHub
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I'm afraid it's not convenient for me to attend google meeting. I also don't know why the NLL diverges. Can you briefly describe what changes were made? |
|
Thanks for reply,
could the size 178x218 a reason of failing , just checked the size of image
from dataset
…On Sat, Jul 30, 2022 at 8:16 PM USTC-JialunPeng ***@***.***> wrote:
I'm afraid it's not convenient for me to attend google meeting. I also
don't know why the NLL diverges. Can you briefly describe what changes were
made?
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I think it may be the reason. I have only evaluated our method on 256x256 image size. |
|
ok doing in that way. will revert soon
…On Sat, Jul 30, 2022 at 9:21 PM USTC-JialunPeng ***@***.***> wrote:
I think is may be the reason. I have only evaluated our method on 256x256
image size.
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done train-vqvae using 256x256 ground truth images and still getting nan
for train structure generator.
pl reply
On Sat, Jul 30, 2022 at 10:33 PM Chandni Agarwal ***@***.***>
wrote:
… ok doing in that way. will revert soon
On Sat, Jul 30, 2022 at 9:21 PM USTC-JialunPeng ***@***.***>
wrote:
> I think is may be the reason. I have only evaluated our method on 256x256
> image size.
>
> —
> Reply to this email directly, view it on GitHub
> <#25 (comment)>,
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Thanks!!
Following same instructions. One more question : Should I put binary mask
and ground truth in same folder.?
giving images pl tell which one to load?
ground truth
+ only mask map or mask covered with overlay map.
Regards
…On Tue, Jul 19, 2022 at 9:44 PM USTC-JialunPeng ***@***.***> wrote:
Right. You just need to collect your ground truth dataset and make a path
list. Please refer to our training instructions
<https://github.com/USTC-JialunPeng/Diverse-Structure-Inpainting#training>
for more details.
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We didn't put masks and images in the same folder. One random mask and one random image are loaded to generate a masked image in the training. The generated masked image and the corresponding mask are send into the model while the image is used as ground truth. |
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Hi! I am using the model for generating new faces from masked faces. I have run test.py and modify the code a little for removing face mask.
The model is not performing on CelebA masked faces , kindly guide for training for masked face, do i need to pass masks ?
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