avalonstrel / gatedconvolution Goto Github PK

Thank you for your work.！
Could you tell me what the irrmask file in inpaint.yml looks like? How should I generate it?

Firstly, thanks for the sharing the code

I have trained for a while, reach around 10+ epoch and the result seems not good.
I am not sure if I did any wrong on the configuration or others.
It seems the result is very weird and can't converge in the long run.

It would be very nice if any hints or suggestion.
Thanks in advanced.

when i run the python3 train.py the error occurs . it is:
[2019-01-15 07:49:19 @init.py:79] Set root logger. Unset logger with neuralgym.unset_logger().
[2019-01-15 07:49:19 @init.py:80] Saving logging to file: neuralgym_logs/20190115074918983527.
[2019-01-15 07:49:19 @config.py:92] ---------------------------------- APP CONFIG ----------------------------------
[2019-01-15 07:49:19 @config.py:119] WGAN_GP_LAMBDA: 10
[2019-01-15 07:49:19 @config.py:119] RANDOM_CROP: False
[2019-01-15 07:49:19 @config.py:119] FEATURE_LOSS: False
[2019-01-15 07:49:19 @config.py:119] VAL: True
[2019-01-15 07:49:19 @config.py:119] L1_LOSS: True
[2019-01-15 07:49:19 @config.py:119] MASKFROMFILE: False
[2019-01-15 07:49:19 @config.py:119] LOG_DIR: places2_256
[2019-01-15 07:49:19 @config.py:119] MASKDATASET: irrmask
[2019-01-15 07:49:19 @config.py:119] SPATIAL_DISCOUNTING_GAMMA: 0.9
[2019-01-15 07:49:19 @config.py:119] GAN_WITH_GUIDE: False
[2019-01-15 07:49:19 @config.py:119] DATASET: places2
[2019-01-15 07:49:19 @config.py:119] FEATURE_LOSS_ALPHA: 0.01
[2019-01-15 07:49:19 @config.py:119] DISCOUNTED_MASK: True
[2019-01-15 07:49:19 @config.py:119] HORIZONTAL_MARGIN: 0
[2019-01-15 07:49:19 @config.py:119] GAN: sn_pgan
[2019-01-15 07:49:19 @config.py:119] MODEL_RESTORE:
[2019-01-15 07:49:19 @config.py:119] GRAMS_LOSS_ALPHA: 50
[2019-01-15 07:49:19 @config.py:119] TRAIN_SPE: 10000
[2019-01-15 07:49:19 @config.py:119] AE_LOSS_ALPHA: 1.2
[2019-01-15 07:49:19 @config.py:119] WIDTH: 128
[2019-01-15 07:49:19 @config.py:119] VIZ_MAX_OUT: 10
[2019-01-15 07:49:19 @config.py:119] GRADIENT_CLIP: False
[2019-01-15 07:49:19 @config.py:119] MAXBRUSHWIDTH: 10
[2019-01-15 07:49:19 @config.py:119] GPU_ID: 3
[2019-01-15 07:49:19 @config.py:119] LOAD_VGG_MODEL: False
[2019-01-15 07:49:19 @config.py:119] MAXLENGTH: 40
[2019-01-15 07:49:19 @config.py:119] NUM_GPUS: 1
[2019-01-15 07:49:19 @config.py:119] TV_LOSS_ALPHA: 0.0
[2019-01-15 07:49:19 @config.py:119] PADDING: SAME
[2019-01-15 07:49:19 @config.py:119] MAX_ITERS: 1000000
[2019-01-15 07:49:19 @config.py:119] MAX_DELTA_WIDTH: 32
[2019-01-15 07:49:19 @config.py:119] GLOBAL_DCGAN_LOSS_ALPHA: 1.0
[2019-01-15 07:49:19 @config.py:119] COARSE_L1_ALPHA: 1.2
[2019-01-15 07:49:19 @config.py:119] VAL_PSTEPS: 1000
[2019-01-15 07:49:19 @config.py:119] GAN_LOSS_ALPHA: 0.001
[2019-01-15 07:49:19 @config.py:111] DATA_FLIST:
[2019-01-15 07:49:19 @config.py:119] celebahq: ['data/celeba_hq/train_shuffled.flist', 'data/celeba_hq/validation_static_view.flist']
[2019-01-15 07:49:19 @config.py:119] horse_mask: ['/unsullied/sharefs/linhangyu/Inpainting/Data/VOCData/voc_horse_bbox_train_flist.txt', '/unsullied/sharefs/linhangyu/Inpainting/Data/VOCData/voc_horse_bbox_val_flist.txt']
[2019-01-15 07:49:19 @config.py:119] horse: ['/unsullied/sharefs/linhangyu/Inpainting/Data/VOCData/voc_horse_train_flist.txt', '/unsullied/sharefs/linhangyu/Inpainting/Data/VOCData/voc_horse_val_flist.txt']
[2019-01-15 07:49:19 @config.py:119] celeba: ['data/celeba/train_shuffled.flist', 'data/celeba/validation_static_view.flist']
[2019-01-15 07:49:19 @config.py:119] places2: ['/data/data_256/place_train.list', '/data/data_256/place_val.flist']
[2019-01-15 07:49:19 @config.py:119] imagenet: ['data/imagenet/train_shuffled.flist', 'data/imagenet/validation_static_view.flist']
[2019-01-15 07:49:19 @config.py:119] irrmask: ['../Data/MaskData/irrmask_flist.txt', '../Data/MaskData/irrmask_flist.txt']
[2019-01-15 07:49:19 @config.py:119] RANDOM_SEED: False
[2019-01-15 07:49:19 @config.py:119] MAX_DELTA_HEIGHT: 32
[2019-01-15 07:49:19 @config.py:119] BATCH_SIZE: 16
[2019-01-15 07:49:19 @config.py:119] GRAMS_LOSS: False
[2019-01-15 07:49:19 @config.py:119] PRETRAIN_COARSE_NETWORK: False
[2019-01-15 07:49:19 @config.py:119] L1_LOSS_ALPHA: 1.2
[2019-01-15 07:49:19 @config.py:119] VERTICAL_MARGIN: 0
[2019-01-15 07:49:19 @config.py:119] HEIGHT: 128
[2019-01-15 07:49:19 @config.py:119] AE_LOSS: True
[2019-01-15 07:49:19 @config.py:119] TV_LOSS: False
[2019-01-15 07:49:19 @config.py:119] GAN_WITH_MASK: True
[2019-01-15 07:49:19 @config.py:119] IMG_SHAPES: [256, 256, 3]
[2019-01-15 07:49:19 @config.py:119] GRADIENT_CLIP_VALUE: 0.1
[2019-01-15 07:49:19 @config.py:119] STATIC_VIEW_SIZE: 30
[2019-01-15 07:49:19 @config.py:119] MAXVERTEX: 5
[2019-01-15 07:49:19 @config.py:119] VGG_MODEL_FILE: data/model_zoo/vgg16.npz
[2019-01-15 07:49:19 @config.py:119] GLOBAL_WGAN_LOSS_ALPHA: 1.0
[2019-01-15 07:49:19 @config.py:119] GRADS_SUMMARY: False
[2019-01-15 07:49:19 @config.py:119] MAXANGLE: 4.0
[2019-01-15 07:49:19 @config.py:94] --------------------------------------------------------------------------------
[2019-01-15 07:49:19 @gpus.py:20] Set env: CUDA_VISIBLE_DEVICES=[3].
[2019-01-15 07:49:20 @dataset.py:26] --------------------------------- Dataset Info ---------------------------------
[2019-01-15 07:49:20 @dataset.py:36] nthreads: 8
[2019-01-15 07:49:20 @dataset.py:36] file_length: 1599999
[2019-01-15 07:49:20 @dataset.py:36] enqueue_size: 32
[2019-01-15 07:49:20 @dataset.py:36] filetype: image
[2019-01-15 07:49:20 @dataset.py:36] random_crop: False
[2019-01-15 07:49:20 @dataset.py:36] fn_preprocess: None
[2019-01-15 07:49:20 @dataset.py:36] return_fnames: False
[2019-01-15 07:49:20 @dataset.py:36] queue_size: 256
[2019-01-15 07:49:20 @dataset.py:36] dtypes: [tf.float32]
[2019-01-15 07:49:20 @dataset.py:36] random: False
[2019-01-15 07:49:20 @dataset.py:36] index: 0
[2019-01-15 07:49:20 @dataset.py:36] shapes: [[256, 256, 3]]
[2019-01-15 07:49:20 @dataset.py:36] batch_phs: [<tf.Tensor 'Placeholder:0' shape=(?, 256, 256, 3) dtype=float32>]
[2019-01-15 07:49:20 @dataset.py:37] --------------------------------------------------------------------------------
[2019-01-15 07:49:24 @inpaint_model_gc.py:167] Set batch_predicted to x2.
Traceback (most recent call last):
File "train.py", line 79, in
images, masks, guides, config=config)
File "/code/GatedConvolution-master/inpaint_model_gc.py", line 219, in build_graph_with_losses
pos_neg = self.build_sn_pgan_discriminator(batch_pos_neg, training=training, reuse=reuse)
File "/code/GatedConvolution-master/inpaint_model_gc.py", line 134, in build_sn_pgan_discriminator
x = gen_snconv(x, cnum, 5, 2, name='conv1', training=training)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "/code/GatedConvolution-master/inpaint_ops.py", line 105, in gen_snconv
strides=[1, stride, stride, 1], dilations=[1, rate, rate, 1], padding=padding, name=name)
TypeError: conv2d() got an unexpected keyword argument 'dilations'

the tensorflow is 1.4.0

Hi, I find you didn't use activation in your discriminator.

Thanks in advance.

hope the author can share the pretrained model, thus we can run a local demo quickly, thank you

Dear author, I am very interested in the user - guide image in painting part of your work. However, I found that if I train directly, there is no image of EDG. How should I train such a model? What part of the code should I change.
Another question, is your HED a pre-training model? Can you tell me where to modify the code that has been converted to TensorFlow in your code
Looking forward to your reply.

Dear author, in inpaint.yml, I am confused about two parameters
TRAIN_SPE: 10000
MAX_ITERS: 1000000
When I set these two parameters：
TRAIN_SPE: 3000
MAX_ITERS: 1000
Training stopped after four iterations, and the result was also very bad.
What do these two parameters mean and how to determine the number of training iterations?
Thank you for your patience.