inpainting_gmcnn
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shepnerd
data_file when training own images
Does anyone know the format of the data file with all of the paths when training your own images? I can't figure it out. I tried a .flist file tat had each image on a newline but that didn't work. Commas didn't either. Is it some special file format?
Using the absolute paths of the target images, e.g., /data/proj/a.png or D:\\proj\\a.png. And yes, one line for one file path in the data file. The file extension name does not matter as long as it is readable text for IO functions.
Thank you! that worked but then I ran into the next issue when trying to train and it's not clear what cauase is. The error stack trace is:
(inpainting_gmcnn) G:\pythonAI\inpainting_gmcnn\tensorflow>python train.py --dataset celeba --data_file G:\pythonAI\training_images\source_images\t2.index --pretrain_network 1 ------------ Options ------------- ae_loss_alpha: 1.2 batch_size: 24 checkpoints_dir: ./checkpoints d_cnum: 64 data_file: G:\pythonAI\training_images\source_images\t2.index dataset: celeba dataset_path: G:\pythonAI\training_images\source_images\t2.index date_str: 20200825-103721 g_cnum: 32 gan_loss_alpha: 0.001 gpu_ids: ['0', '1'] img_shapes: [256, 256, 3] l1_loss_alpha: 1.4 load_model_dir: lr: 1e-05 margins: [0, 0] mask_shapes: [256, 256] mask_type: rect max_delta_shapes: [32, 32] max_iters: 1000 model_folder: ./checkpoints\20200825-103721_GMCNN_celeba_b24_s256x256_gc32_dc64_randmask-rect_pretrain model_name: GMCNN model_prefix: snap mrf_alpha: 0.05 pretrain_l1_alpha: 1.2 pretrain_network: True random_mask: True random_seed: False train_spe: 1000 vgg19_path: vgg19_weights/imagenet-vgg-verydeep-19.mat viz_steps: 5 wgan_gp_lambda: 10 -------------- End ---------------- [256, 256, 3] G:\pythonAI\training_images\source_images\t2.index 24 ['G:\pythonAI\training_images\source_images\001.png', 'G:\pythonAI\training_images\source_images\002.png'] WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\data\data.py:15: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\data\data.py:17: slice_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(tuple(tensor_list)).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\lib\site-packages\tensorflow_core\python\training\input.py:373: range_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.range(limit).shuffle(limit).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\lib\site-packages\tensorflow_core\python\training\input.py:319: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\lib\site-packages\tensorflow_core\python\training\input.py:189: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensors(tensor).repeat(num_epochs).
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\lib\site-packages\tensorflow_core\python\training\input.py:198: QueueRunner.init (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the tf.data module.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\lib\site-packages\tensorflow_core\python\training\input.py:198: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the tf.data module.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\data\data.py:19: The name tf.read_file is deprecated. Please use tf.io.read_file instead.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\data\data.py:22: The name tf.image.resize_image_with_crop_or_pad is deprecated. Please use tf.image.resize_with_crop_or_pad instead.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\data\data.py:24: batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.batch(batch_size) (or padded_batch(...) if dynamic_pad=True).
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\net\ops.py:119: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\net\ops.py:155: 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, there are two
options available in V2.
- tf.py_function 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_functions can use accelerators such as GPUs as well as
being differentiable using a gradient tape.
- tf.numpy_function maintains the semantics of the deprecated tf.py_func
(it is not differentiable, and manipulates numpy arrays). It drops the
stateful argument making all functions stateful.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\net\network.py:35: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras.layers.Conv2D instead.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\lib\site-packages\tensorflow_core\python\layers\convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use layer.__call__ method instead.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\net\network.py:49: The name tf.image.resize_bilinear is deprecated. Please use tf.compat.v1.image.resize_bilinear instead.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\net\network.py:66: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.
Pretrain the whole net with only reconstruction loss. WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\net\network.py:224: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\net\network.py:225: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\net\network.py:137: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. Instructions for updating: Use keras.layers.flatten instead. WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\net\network.py:156: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. Instructions for updating: Use keras.layers.Dense instead. Set L1_LOSS_ALPHA to 1.400000 Set GAN_LOSS_ALPHA to 0.001000 Set AE_LOSS_ALPHA to 1.200000 WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\net\network.py:286: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\tensorflow\net\network.py:287: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.
WARNING:tensorflow:From train.py:20: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.
WARNING:tensorflow:From train.py:24: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.
WARNING:tensorflow:From G:\pythonAI\inpainting_gmcnn\lib\site-packages\tensorflow_core\python\ops\math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where WARNING:tensorflow:From train.py:30: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.
WARNING:tensorflow:From train.py:32: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.
WARNING:tensorflow:From train.py:34: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
2020-08-25 10:37:24.089038: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 WARNING:tensorflow:From train.py:35: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.
WARNING:tensorflow:From train.py:50: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.
WARNING:tensorflow:From train.py:53: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the tf.data module.
2020-08-25 10:37:27.684580: W tensorflow/core/kernels/queue_base.cc:277] _0_feed/input_producer/input_producer: Skipping cancelled enqueue attempt with queue not closed
2020-08-25 10:37:27.687946: W tensorflow/core/kernels/queue_base.cc:277] _1_feed/batch/fifo_queue: Skipping cancelled enqueue attempt with queue not closed
2020-08-25 10:37:27.691128: W tensorflow/core/kernels/queue_base.cc:277] _1_feed/batch/fifo_queue: Skipping cancelled enqueue attempt with queue not closed
2020-08-25 10:37:27.705672: W tensorflow/core/kernels/queue_base.cc:277] _1_feed/batch/fifo_queue: Skipping cancelled enqueue attempt with queue not closed
2020-08-25 10:37:27.719887: W tensorflow/core/kernels/queue_base.cc:277] _1_feed/batch/fifo_queue: Skipping cancelled enqueue attempt with queue not closed
Traceback (most recent call last):
File "G:\pythonAI\inpainting_gmcnn\lib\site-packages\tensorflow_core\python\client\session.py", line 1365, in _do_call
return fn(*args)
File "G:\pythonAI\inpainting_gmcnn\lib\site-packages\tensorflow_core\python\client\session.py", line 1350, in _run_fn
target_list, run_metadata)
File "G:\pythonAI\inpainting_gmcnn\lib\site-packages\tensorflow_core\python\client\session.py", line 1443, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Need minval < maxval, got 0 >= 0
[[{{node random_uniform_1}}]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train.py", line 61, in
Original stack trace for 'random_uniform_1':
File "train.py", line 18, in
(inpainting_gmcnn) G:\pythonAI\inpainting_gmcnn\tensorflow>
is it because I don't have enough images? This is just a test run with 181 images
Above was with tensorflow implementation. I also tried the pytorch implementation in windows but it causes this error on training:
(inpainting_gmcnn) G:\pythonAI\inpainting_gmcnn\pytorch>python train.py --dataset celeba --data_file G:\pythonAI\training_images\source_images\train_images.index ------------ Options ------------- D_max_iters: 5 batch_size: 16 checkpoint_dir: ./checkpoints d_cnum: 64 data_file: G:\pythonAI\training_images\source_images\train_images.index dataset: celeba dataset_path: G:\pythonAI\training_images\source_images\train_images.index date_str: 20200825-120636 epochs: 40 g_cnum: 32 gpu_ids: ['0'] img_shapes: [256, 256, 3] lambda_adv: 0.001 lambda_ae: 1.2 lambda_gp: 10 lambda_mrf: 0.05 lambda_rec: 1.4 load_model_dir: lr: 1e-05 margins: [0, 0] mask_shapes: [128, 128] mask_type: rect max_delta_shapes: [32, 32] model_folder: ./checkpoints\20200825-120636_GMCNN_celeba_b16_s256x256_gc32_dc64_randmask-rect model_name: GMCNN padding: SAME phase: train pretrain_network: False random_crop: True random_mask: True random_seed: False spectral_norm: True train_spe: 1000 vgg19_path: vgg19_weights/imagenet-vgg-verydeep-19.mat viz_steps: 5 -------------- End ---------------- loading data.. data loaded.. configuring model.. initialize network with normal initialize network with normal ---------- Networks initialized ------------- GMCNN( (EB1): ModuleList( (0): Conv2d(4, 32, kernel_size=(7, 7), stride=(1, 1)) (1): Conv2d(32, 64, kernel_size=(7, 7), stride=(2, 2)) (2): Conv2d(64, 64, kernel_size=(7, 7), stride=(1, 1)) (3): Conv2d(64, 128, kernel_size=(7, 7), stride=(2, 2)) (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (5): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(2, 2)) (7): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(4, 4)) (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(8, 8)) (9): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(16, 16)) (10): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (11): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (12): PureUpsampling() ) (EB2): ModuleList( (0): Conv2d(4, 32, kernel_size=(5, 5), stride=(1, 1)) (1): Conv2d(32, 64, kernel_size=(5, 5), stride=(2, 2)) (2): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1)) (3): Conv2d(64, 128, kernel_size=(5, 5), stride=(2, 2)) (4): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (5): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (6): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(2, 2)) (7): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(4, 4)) (8): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(8, 8)) (9): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(16, 16)) (10): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (11): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (12): PureUpsampling() (13): Conv2d(128, 64, kernel_size=(5, 5), stride=(1, 1)) (14): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1)) (15): PureUpsampling() ) (EB3): ModuleList( (0): Conv2d(4, 32, kernel_size=(3, 3), stride=(1, 1)) (1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2)) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)) (3): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2)) (4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 2)) (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(4, 4)) (8): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(8, 8)) (9): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(16, 16)) (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (11): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (12): PureUpsampling() (13): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1)) (14): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)) (15): PureUpsampling() (16): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1)) (17): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1)) ) (decoding_layers): ModuleList( (0): Conv2d(224, 16, kernel_size=(3, 3), stride=(1, 1)) (1): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1)) ) (pads): ModuleList( (0): ReflectionPad2d((0, 0, 0, 0)) (1): ReflectionPad2d((1, 1, 1, 1)) (2): ReflectionPad2d((2, 2, 2, 2)) (3): ReflectionPad2d((3, 3, 3, 3)) (4): ReflectionPad2d((4, 4, 4, 4)) (5): ReflectionPad2d((5, 5, 5, 5)) (6): ReflectionPad2d((6, 6, 6, 6)) (7): ReflectionPad2d((7, 7, 7, 7)) (8): ReflectionPad2d((8, 8, 8, 8)) (9): ReflectionPad2d((9, 9, 9, 9)) (10): ReflectionPad2d((10, 10, 10, 10)) (11): ReflectionPad2d((11, 11, 11, 11)) (12): ReflectionPad2d((12, 12, 12, 12)) (13): ReflectionPad2d((13, 13, 13, 13)) (14): ReflectionPad2d((14, 14, 14, 14)) (15): ReflectionPad2d((15, 15, 15, 15)) (16): ReflectionPad2d((16, 16, 16, 16)) (17): ReflectionPad2d((17, 17, 17, 17)) (18): ReflectionPad2d((18, 18, 18, 18)) (19): ReflectionPad2d((19, 19, 19, 19)) (20): ReflectionPad2d((20, 20, 20, 20)) (21): ReflectionPad2d((21, 21, 21, 21)) (22): ReflectionPad2d((22, 22, 22, 22)) (23): ReflectionPad2d((23, 23, 23, 23)) (24): ReflectionPad2d((24, 24, 24, 24)) (25): ReflectionPad2d((25, 25, 25, 25)) (26): ReflectionPad2d((26, 26, 26, 26)) (27): ReflectionPad2d((27, 27, 27, 27)) (28): ReflectionPad2d((28, 28, 28, 28)) (29): ReflectionPad2d((29, 29, 29, 29)) (30): ReflectionPad2d((30, 30, 30, 30)) (31): ReflectionPad2d((31, 31, 31, 31)) (32): ReflectionPad2d((32, 32, 32, 32)) (33): ReflectionPad2d((33, 33, 33, 33)) (34): ReflectionPad2d((34, 34, 34, 34)) (35): ReflectionPad2d((35, 35, 35, 35)) (36): ReflectionPad2d((36, 36, 36, 36)) (37): ReflectionPad2d((37, 37, 37, 37)) (38): ReflectionPad2d((38, 38, 38, 38)) (39): ReflectionPad2d((39, 39, 39, 39)) (40): ReflectionPad2d((40, 40, 40, 40)) (41): ReflectionPad2d((41, 41, 41, 41)) (42): ReflectionPad2d((42, 42, 42, 42)) (43): ReflectionPad2d((43, 43, 43, 43)) (44): ReflectionPad2d((44, 44, 44, 44)) (45): ReflectionPad2d((45, 45, 45, 45)) (46): ReflectionPad2d((46, 46, 46, 46)) (47): ReflectionPad2d((47, 47, 47, 47)) (48): ReflectionPad2d((48, 48, 48, 48)) ) ) [Network GM] Total number of parameters : 12.562 M
model setting up.. training initializing.. ------------ Options ------------- D_max_iters: 5 batch_size: 16 checkpoint_dir: ./checkpoints d_cnum: 64 data_file: G:\pythonAI\training_images\source_images\train_images.index dataset: celeba dataset_path: G:\pythonAI\training_images\source_images\train_images.index date_str: 20200825-120640 epochs: 40 g_cnum: 32 gpu_ids: ['0'] img_shapes: [256, 256, 3] lambda_adv: 0.001 lambda_ae: 1.2 lambda_gp: 10 lambda_mrf: 0.05 lambda_rec: 1.4 load_model_dir: lr: 1e-05 margins: [0, 0] mask_shapes: [128, 128] mask_type: rect max_delta_shapes: [32, 32] model_folder: ./checkpoints\20200825-120640_GMCNN_celeba_b16_s256x256_gc32_dc64_randmask-rect model_name: GMCNN padding: SAME phase: train pretrain_network: False random_crop: True random_mask: True random_seed: False spectral_norm: True train_spe: 1000 vgg19_path: vgg19_weights/imagenet-vgg-verydeep-19.mat viz_steps: 5 -------------- End ---------------- loading data.. data loaded.. configuring model.. initialize network with normal initialize network with normal ---------- Networks initialized ------------- GMCNN( (EB1): ModuleList( (0): Conv2d(4, 32, kernel_size=(7, 7), stride=(1, 1)) (1): Conv2d(32, 64, kernel_size=(7, 7), stride=(2, 2)) (2): Conv2d(64, 64, kernel_size=(7, 7), stride=(1, 1)) (3): Conv2d(64, 128, kernel_size=(7, 7), stride=(2, 2)) (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (5): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(2, 2)) (7): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(4, 4)) (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(8, 8)) (9): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(16, 16)) (10): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (11): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (12): PureUpsampling() ) (EB2): ModuleList( (0): Conv2d(4, 32, kernel_size=(5, 5), stride=(1, 1)) (1): Conv2d(32, 64, kernel_size=(5, 5), stride=(2, 2)) (2): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1)) (3): Conv2d(64, 128, kernel_size=(5, 5), stride=(2, 2)) (4): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (5): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (6): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(2, 2)) (7): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(4, 4)) (8): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(8, 8)) (9): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(16, 16)) (10): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (11): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (12): PureUpsampling() (13): Conv2d(128, 64, kernel_size=(5, 5), stride=(1, 1)) (14): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1)) (15): PureUpsampling() ) (EB3): ModuleList( (0): Conv2d(4, 32, kernel_size=(3, 3), stride=(1, 1)) (1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2)) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)) (3): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2)) (4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 2)) (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(4, 4)) (8): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(8, 8)) (9): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(16, 16)) (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (11): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (12): PureUpsampling() (13): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1)) (14): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)) (15): PureUpsampling() (16): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1)) (17): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1)) ) (decoding_layers): ModuleList( (0): Conv2d(224, 16, kernel_size=(3, 3), stride=(1, 1)) (1): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1)) ) (pads): ModuleList( (0): ReflectionPad2d((0, 0, 0, 0)) (1): ReflectionPad2d((1, 1, 1, 1)) (2): ReflectionPad2d((2, 2, 2, 2)) (3): ReflectionPad2d((3, 3, 3, 3)) (4): ReflectionPad2d((4, 4, 4, 4)) (5): ReflectionPad2d((5, 5, 5, 5)) (6): ReflectionPad2d((6, 6, 6, 6)) (7): ReflectionPad2d((7, 7, 7, 7)) (8): ReflectionPad2d((8, 8, 8, 8)) (9): ReflectionPad2d((9, 9, 9, 9)) (10): ReflectionPad2d((10, 10, 10, 10)) (11): ReflectionPad2d((11, 11, 11, 11)) (12): ReflectionPad2d((12, 12, 12, 12)) (13): ReflectionPad2d((13, 13, 13, 13)) (14): ReflectionPad2d((14, 14, 14, 14)) (15): ReflectionPad2d((15, 15, 15, 15)) (16): ReflectionPad2d((16, 16, 16, 16)) (17): ReflectionPad2d((17, 17, 17, 17)) (18): ReflectionPad2d((18, 18, 18, 18)) (19): ReflectionPad2d((19, 19, 19, 19)) (20): ReflectionPad2d((20, 20, 20, 20)) (21): ReflectionPad2d((21, 21, 21, 21)) (22): ReflectionPad2d((22, 22, 22, 22)) (23): ReflectionPad2d((23, 23, 23, 23)) (24): ReflectionPad2d((24, 24, 24, 24)) (25): ReflectionPad2d((25, 25, 25, 25)) (26): ReflectionPad2d((26, 26, 26, 26)) (27): ReflectionPad2d((27, 27, 27, 27)) (28): ReflectionPad2d((28, 28, 28, 28)) (29): ReflectionPad2d((29, 29, 29, 29)) (30): ReflectionPad2d((30, 30, 30, 30)) (31): ReflectionPad2d((31, 31, 31, 31)) (32): ReflectionPad2d((32, 32, 32, 32)) (33): ReflectionPad2d((33, 33, 33, 33)) (34): ReflectionPad2d((34, 34, 34, 34)) (35): ReflectionPad2d((35, 35, 35, 35)) (36): ReflectionPad2d((36, 36, 36, 36)) (37): ReflectionPad2d((37, 37, 37, 37)) (38): ReflectionPad2d((38, 38, 38, 38)) (39): ReflectionPad2d((39, 39, 39, 39)) (40): ReflectionPad2d((40, 40, 40, 40)) (41): ReflectionPad2d((41, 41, 41, 41)) (42): ReflectionPad2d((42, 42, 42, 42)) (43): ReflectionPad2d((43, 43, 43, 43)) (44): ReflectionPad2d((44, 44, 44, 44)) (45): ReflectionPad2d((45, 45, 45, 45)) (46): ReflectionPad2d((46, 46, 46, 46)) (47): ReflectionPad2d((47, 47, 47, 47)) (48): ReflectionPad2d((48, 48, 48, 48)) ) ) [Network GM] Total number of parameters : 12.562 M
model setting up..
training initializing..
Traceback (most recent call last):
File "
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
Hello @shepnerd and @qwerdbeta, Please, I need your help. I have been trying to run the pytorch version using only the image samples in "imgs". However, I have been stuck for many hours with the following error.
python train.py --dataset celebahq_256x256 --data_file /home/gaofei/newResearch/Inpainting_new/inpainting_gmcnn-master/pytorch/imgs/
------------ Options -------------
D_max_iters: 5
batch_size: 16
checkpoint_dir: ./checkpoints
d_cnum: 64
data_file: /home/gaofei/newResearch/Inpainting_new/inpainting_gmcnn-master/pytorch/imgs/
dataset: celebahq_256x256
dataset_path: /home/gaofei/newResearch/Inpainting_new/inpainting_gmcnn-master/pytorch/imgs/
date_str: 20201013-145021
epochs: 40
g_cnum: 32
gpu_ids: ['0']
img_shapes: [256, 256, 3]
lambda_adv: 0.001
lambda_ae: 1.2
lambda_gp: 10
lambda_mrf: 0.05
lambda_rec: 1.4
load_model_dir:
lr: 1e-05
margins: [0, 0]
mask_shapes: [128, 128]
mask_type: rect
max_delta_shapes: [32, 32]
model_folder: ./checkpoints/20201013-145021_GMCNN_celebahq_256x256_b16_s256x256_gc32_dc64_randmask-rect
model_name: GMCNN
padding: SAME
phase: train
pretrain_network: False
random_crop: True
random_mask: True
random_seed: False
spectral_norm: True
train_spe: 1000
vgg19_path: vgg19_weights/imagenet-vgg-verydeep-19.mat
viz_steps: 5
-------------- End ----------------
loading data..
Traceback (most recent call last):
File "train.py", line 15, in <module>
ToTensor()
File "/home/gaofei/newResearch/Inpainting_new/inpainting_gmcnn-master/pytorch/data/data.py", line 20, in __init__
self.filenames = open(info_list, 'rt').read().splitlines()
IsADirectoryError: [Errno 21] Is a directory: '/home/gaofei/newResearch/Inpainting_new/inpainting_gmcnn-master/pytorch/imgs/'
Please, how can I fix this error? My aim is to first make the codes run, and then train again with my own dataset. Any comments or suggestions would be highly appreciated.
Best regards, Patrice
Hello @shepnerd and @qwerdbeta, Please, I need your help. I have been trying to run the pytorch version using only the image samples in "imgs". However, I have been stuck for many hours with the following error.
python train.py --dataset celebahq_256x256 --data_file /home/gaofei/newResearch/Inpainting_new/inpainting_gmcnn-master/pytorch/imgs/ ------------ Options ------------- D_max_iters: 5 batch_size: 16 checkpoint_dir: ./checkpoints d_cnum: 64 data_file: /home/gaofei/newResearch/Inpainting_new/inpainting_gmcnn-master/pytorch/imgs/ dataset: celebahq_256x256 dataset_path: /home/gaofei/newResearch/Inpainting_new/inpainting_gmcnn-master/pytorch/imgs/ date_str: 20201013-145021 epochs: 40 g_cnum: 32 gpu_ids: ['0'] img_shapes: [256, 256, 3] lambda_adv: 0.001 lambda_ae: 1.2 lambda_gp: 10 lambda_mrf: 0.05 lambda_rec: 1.4 load_model_dir: lr: 1e-05 margins: [0, 0] mask_shapes: [128, 128] mask_type: rect max_delta_shapes: [32, 32] model_folder: ./checkpoints/20201013-145021_GMCNN_celebahq_256x256_b16_s256x256_gc32_dc64_randmask-rect model_name: GMCNN padding: SAME phase: train pretrain_network: False random_crop: True random_mask: True random_seed: False spectral_norm: True train_spe: 1000 vgg19_path: vgg19_weights/imagenet-vgg-verydeep-19.mat viz_steps: 5 -------------- End ---------------- loading data.. Traceback (most recent call last): File "train.py", line 15, in <module> ToTensor() File "/home/gaofei/newResearch/Inpainting_new/inpainting_gmcnn-master/pytorch/data/data.py", line 20, in __init__ self.filenames = open(info_list, 'rt').read().splitlines() IsADirectoryError: [Errno 21] Is a directory: '/home/gaofei/newResearch/Inpainting_new/inpainting_gmcnn-master/pytorch/imgs/'Please, how can I fix this error? My aim is to first make the codes run, and then train again with my own dataset. Any comments or suggestions would be highly appreciated.
Best regards, Patrice
u need creat a file.txt or any others file,then input the datasetpath to the file's every lines for ex: c:/xxx/1.png c:/xxx/2.png
Above was with tensorflow implementation. I also tried the pytorch implementation in windows but it causes this error on training:
(inpainting_gmcnn) G:\pythonAI\inpainting_gmcnn\pytorch>python train.py --dataset celeba --data_file G:\pythonAI\training_images\source_images\train_images.index
------------ Options ------------- D_max_iters: 5 batch_size: 16 checkpoint_dir: ./checkpoints d_cnum: 64 data_file: G:\pythonAI\training_images\source_images\train_images.index dataset: celeba dataset_path: G:\pythonAI\training_images\source_images\train_images.index date_str: 20200825-120636 epochs: 40 g_cnum: 32 gpu_ids: ['0'] img_shapes: [256, 256, 3] lambda_adv: 0.001 lambda_ae: 1.2 lambda_gp: 10 lambda_mrf: 0.05 lambda_rec: 1.4 load_model_dir: lr: 1e-05 margins: [0, 0] mask_shapes: [128, 128] mask_type: rect max_delta_shapes: [32, 32] model_folder: ./checkpoints\20200825-120636_GMCNN_celeba_b16_s256x256_gc32_dc64_randmask-rect model_name: GMCNN padding: SAME phase: train pretrain_network: False random_crop: True random_mask: True random_seed: False spectral_norm: True train_spe: 1000 vgg19_path: vgg19_weights/imagenet-vgg-verydeep-19.mat viz_steps: 5 -------------- End ---------------- loading data.. data loaded.. configuring model.. initialize network with normal initialize network with normal ---------- Networks initialized ------------- GMCNN( (EB1): ModuleList( (0): Conv2d(4, 32, kernel_size=(7, 7), stride=(1, 1)) (1): Conv2d(32, 64, kernel_size=(7, 7), stride=(2, 2)) (2): Conv2d(64, 64, kernel_size=(7, 7), stride=(1, 1)) (3): Conv2d(64, 128, kernel_size=(7, 7), stride=(2, 2)) (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (5): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(2, 2)) (7): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(4, 4)) (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(8, 8)) (9): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(16, 16)) (10): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (11): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (12): PureUpsampling() ) (EB2): ModuleList( (0): Conv2d(4, 32, kernel_size=(5, 5), stride=(1, 1)) (1): Conv2d(32, 64, kernel_size=(5, 5), stride=(2, 2)) (2): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1)) (3): Conv2d(64, 128, kernel_size=(5, 5), stride=(2, 2)) (4): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (5): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (6): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(2, 2)) (7): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(4, 4)) (8): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(8, 8)) (9): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(16, 16)) (10): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (11): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (12): PureUpsampling() (13): Conv2d(128, 64, kernel_size=(5, 5), stride=(1, 1)) (14): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1)) (15): PureUpsampling() ) (EB3): ModuleList( (0): Conv2d(4, 32, kernel_size=(3, 3), stride=(1, 1)) (1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2)) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)) (3): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2)) (4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 2)) (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(4, 4)) (8): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(8, 8)) (9): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(16, 16)) (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (11): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (12): PureUpsampling() (13): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1)) (14): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)) (15): PureUpsampling() (16): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1)) (17): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1)) ) (decoding_layers): ModuleList( (0): Conv2d(224, 16, kernel_size=(3, 3), stride=(1, 1)) (1): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1)) ) (pads): ModuleList( (0): ReflectionPad2d((0, 0, 0, 0)) (1): ReflectionPad2d((1, 1, 1, 1)) (2): ReflectionPad2d((2, 2, 2, 2)) (3): ReflectionPad2d((3, 3, 3, 3)) (4): ReflectionPad2d((4, 4, 4, 4)) (5): ReflectionPad2d((5, 5, 5, 5)) (6): ReflectionPad2d((6, 6, 6, 6)) (7): ReflectionPad2d((7, 7, 7, 7)) (8): ReflectionPad2d((8, 8, 8, 8)) (9): ReflectionPad2d((9, 9, 9, 9)) (10): ReflectionPad2d((10, 10, 10, 10)) (11): ReflectionPad2d((11, 11, 11, 11)) (12): ReflectionPad2d((12, 12, 12, 12)) (13): ReflectionPad2d((13, 13, 13, 13)) (14): ReflectionPad2d((14, 14, 14, 14)) (15): ReflectionPad2d((15, 15, 15, 15)) (16): ReflectionPad2d((16, 16, 16, 16)) (17): ReflectionPad2d((17, 17, 17, 17)) (18): ReflectionPad2d((18, 18, 18, 18)) (19): ReflectionPad2d((19, 19, 19, 19)) (20): ReflectionPad2d((20, 20, 20, 20)) (21): ReflectionPad2d((21, 21, 21, 21)) (22): ReflectionPad2d((22, 22, 22, 22)) (23): ReflectionPad2d((23, 23, 23, 23)) (24): ReflectionPad2d((24, 24, 24, 24)) (25): ReflectionPad2d((25, 25, 25, 25)) (26): ReflectionPad2d((26, 26, 26, 26)) (27): ReflectionPad2d((27, 27, 27, 27)) (28): ReflectionPad2d((28, 28, 28, 28)) (29): ReflectionPad2d((29, 29, 29, 29)) (30): ReflectionPad2d((30, 30, 30, 30)) (31): ReflectionPad2d((31, 31, 31, 31)) (32): ReflectionPad2d((32, 32, 32, 32)) (33): ReflectionPad2d((33, 33, 33, 33)) (34): ReflectionPad2d((34, 34, 34, 34)) (35): ReflectionPad2d((35, 35, 35, 35)) (36): ReflectionPad2d((36, 36, 36, 36)) (37): ReflectionPad2d((37, 37, 37, 37)) (38): ReflectionPad2d((38, 38, 38, 38)) (39): ReflectionPad2d((39, 39, 39, 39)) (40): ReflectionPad2d((40, 40, 40, 40)) (41): ReflectionPad2d((41, 41, 41, 41)) (42): ReflectionPad2d((42, 42, 42, 42)) (43): ReflectionPad2d((43, 43, 43, 43)) (44): ReflectionPad2d((44, 44, 44, 44)) (45): ReflectionPad2d((45, 45, 45, 45)) (46): ReflectionPad2d((46, 46, 46, 46)) (47): ReflectionPad2d((47, 47, 47, 47)) (48): ReflectionPad2d((48, 48, 48, 48)) ) ) [Network GM] Total number of parameters : 12.562 M
model setting up..
training initializing.. ------------ Options ------------- D_max_iters: 5 batch_size: 16 checkpoint_dir: ./checkpoints d_cnum: 64 data_file: G:\pythonAI\training_images\source_images\train_images.index dataset: celeba dataset_path: G:\pythonAI\training_images\source_images\train_images.index date_str: 20200825-120640 epochs: 40 g_cnum: 32 gpu_ids: ['0'] img_shapes: [256, 256, 3] lambda_adv: 0.001 lambda_ae: 1.2 lambda_gp: 10 lambda_mrf: 0.05 lambda_rec: 1.4 load_model_dir: lr: 1e-05 margins: [0, 0] mask_shapes: [128, 128] mask_type: rect max_delta_shapes: [32, 32] model_folder: ./checkpoints\20200825-120640_GMCNN_celeba_b16_s256x256_gc32_dc64_randmask-rect model_name: GMCNN padding: SAME phase: train pretrain_network: False random_crop: True random_mask: True random_seed: False spectral_norm: True train_spe: 1000 vgg19_path: vgg19_weights/imagenet-vgg-verydeep-19.mat viz_steps: 5 -------------- End ---------------- loading data.. data loaded.. configuring model.. initialize network with normal initialize network with normal ---------- Networks initialized ------------- GMCNN( (EB1): ModuleList( (0): Conv2d(4, 32, kernel_size=(7, 7), stride=(1, 1)) (1): Conv2d(32, 64, kernel_size=(7, 7), stride=(2, 2)) (2): Conv2d(64, 64, kernel_size=(7, 7), stride=(1, 1)) (3): Conv2d(64, 128, kernel_size=(7, 7), stride=(2, 2)) (4): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (5): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (6): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(2, 2)) (7): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(4, 4)) (8): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(8, 8)) (9): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), dilation=(16, 16)) (10): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (11): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1)) (12): PureUpsampling() ) (EB2): ModuleList( (0): Conv2d(4, 32, kernel_size=(5, 5), stride=(1, 1)) (1): Conv2d(32, 64, kernel_size=(5, 5), stride=(2, 2)) (2): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1)) (3): Conv2d(64, 128, kernel_size=(5, 5), stride=(2, 2)) (4): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (5): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (6): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(2, 2)) (7): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(4, 4)) (8): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(8, 8)) (9): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), dilation=(16, 16)) (10): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (11): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1)) (12): PureUpsampling() (13): Conv2d(128, 64, kernel_size=(5, 5), stride=(1, 1)) (14): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1)) (15): PureUpsampling() ) (EB3): ModuleList( (0): Conv2d(4, 32, kernel_size=(3, 3), stride=(1, 1)) (1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2)) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)) (3): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2)) (4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 2)) (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(4, 4)) (8): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(8, 8)) (9): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), dilation=(16, 16)) (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (11): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (12): PureUpsampling() (13): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1)) (14): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)) (15): PureUpsampling() (16): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1)) (17): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1)) ) (decoding_layers): ModuleList( (0): Conv2d(224, 16, kernel_size=(3, 3), stride=(1, 1)) (1): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1)) ) (pads): ModuleList( (0): ReflectionPad2d((0, 0, 0, 0)) (1): ReflectionPad2d((1, 1, 1, 1)) (2): ReflectionPad2d((2, 2, 2, 2)) (3): ReflectionPad2d((3, 3, 3, 3)) (4): ReflectionPad2d((4, 4, 4, 4)) (5): ReflectionPad2d((5, 5, 5, 5)) (6): ReflectionPad2d((6, 6, 6, 6)) (7): ReflectionPad2d((7, 7, 7, 7)) (8): ReflectionPad2d((8, 8, 8, 8)) (9): ReflectionPad2d((9, 9, 9, 9)) (10): ReflectionPad2d((10, 10, 10, 10)) (11): ReflectionPad2d((11, 11, 11, 11)) (12): ReflectionPad2d((12, 12, 12, 12)) (13): ReflectionPad2d((13, 13, 13, 13)) (14): ReflectionPad2d((14, 14, 14, 14)) (15): ReflectionPad2d((15, 15, 15, 15)) (16): ReflectionPad2d((16, 16, 16, 16)) (17): ReflectionPad2d((17, 17, 17, 17)) (18): ReflectionPad2d((18, 18, 18, 18)) (19): ReflectionPad2d((19, 19, 19, 19)) (20): ReflectionPad2d((20, 20, 20, 20)) (21): ReflectionPad2d((21, 21, 21, 21)) (22): ReflectionPad2d((22, 22, 22, 22)) (23): ReflectionPad2d((23, 23, 23, 23)) (24): ReflectionPad2d((24, 24, 24, 24)) (25): ReflectionPad2d((25, 25, 25, 25)) (26): ReflectionPad2d((26, 26, 26, 26)) (27): ReflectionPad2d((27, 27, 27, 27)) (28): ReflectionPad2d((28, 28, 28, 28)) (29): ReflectionPad2d((29, 29, 29, 29)) (30): ReflectionPad2d((30, 30, 30, 30)) (31): ReflectionPad2d((31, 31, 31, 31)) (32): ReflectionPad2d((32, 32, 32, 32)) (33): ReflectionPad2d((33, 33, 33, 33)) (34): ReflectionPad2d((34, 34, 34, 34)) (35): ReflectionPad2d((35, 35, 35, 35)) (36): ReflectionPad2d((36, 36, 36, 36)) (37): ReflectionPad2d((37, 37, 37, 37)) (38): ReflectionPad2d((38, 38, 38, 38)) (39): ReflectionPad2d((39, 39, 39, 39)) (40): ReflectionPad2d((40, 40, 40, 40)) (41): ReflectionPad2d((41, 41, 41, 41)) (42): ReflectionPad2d((42, 42, 42, 42)) (43): ReflectionPad2d((43, 43, 43, 43)) (44): ReflectionPad2d((44, 44, 44, 44)) (45): ReflectionPad2d((45, 45, 45, 45)) (46): ReflectionPad2d((46, 46, 46, 46)) (47): ReflectionPad2d((47, 47, 47, 47)) (48): ReflectionPad2d((48, 48, 48, 48)) ) ) [Network GM] Total number of parameters : 12.562 M model setting up.. training initializing.. Traceback (most recent call last): File "", line 1, in Traceback (most recent call last): File "train.py", line 34, in for i, data in enumerate(dataloader): File "G:\pythonAI\Miniconda3\Lib\multiprocessing\spawn.py", line 105, in spawn_main File "G:\pythonAI\Miniconda3\lib\site-packages\torch\utils\data\dataloader.py", line 819, in iter exitcode = _main(fd) File "G:\pythonAI\Miniconda3\Lib\multiprocessing\spawn.py", line 114, in _main return _DataLoaderIter(self) prepare(preparation_data) File "G:\pythonAI\Miniconda3\lib\site-packages\torch\utils\data\dataloader.py", line 560, in init File "G:\pythonAI\Miniconda3\Lib\multiprocessing\spawn.py", line 225, in prepare w.start() File "G:\pythonAI\Miniconda3\Lib\multiprocessing\process.py", line 112, in start _fixup_main_from_path(data['init_main_from_path']) self._popen = self._Popen(self) File "G:\pythonAI\Miniconda3\Lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path File "G:\pythonAI\Miniconda3\Lib\multiprocessing\context.py", line 223, in _Popen run_name="mp_main") return _default_context.get_context().Process._Popen(process_obj) File "G:\pythonAI\Miniconda3\Lib\runpy.py", line 263, in run_path File "G:\pythonAI\Miniconda3\Lib\multiprocessing\context.py", line 322, in _Popen pkg_name=pkg_name, script_name=fname) File "G:\pythonAI\Miniconda3\Lib\runpy.py", line 96, in _run_module_code return Popen(process_obj) mod_name, mod_spec, pkg_name, script_name) File "G:\pythonAI\Miniconda3\Lib\multiprocessing\popen_spawn_win32.py", line 89, in init File "G:\pythonAI\Miniconda3\Lib\runpy.py", line 85, in _run_code reduction.dump(process_obj, to_child) exec(code, run_globals) File "G:\pythonAI\Miniconda3\Lib\multiprocessing\reduction.py", line 60, in dump File "G:\pythonAI\inpainting_gmcnn\pytorch\train.py", line 34, in ForkingPickler(file, protocol).dump(obj) for i, data in enumerate(dataloader): BrokenPipeError: [Errno 32] Broken pipe File "G:\pythonAI\Miniconda3\lib\site-packages\torch\utils\data\dataloader.py", line 819, in iter return _DataLoaderIter(self) File "G:\pythonAI\Miniconda3\lib\site-packages\torch\utils\data\dataloader.py", line 560, in init w.start() File "G:\pythonAI\Miniconda3\Lib\multiprocessing\process.py", line 112, in start self._popen = self._Popen(self) File "G:\pythonAI\Miniconda3\Lib\multiprocessing\context.py", line 223, in _Popen return _default_context.get_context().Process._Popen(process_obj) File "G:\pythonAI\Miniconda3\Lib\multiprocessing\context.py", line 322, in _Popen return Popen(process_obj) File "G:\pythonAI\Miniconda3\Lib\multiprocessing\popen_spawn_win32.py", line 46, in init prep_data = spawn.get_preparation_data(process_obj._name) File "G:\pythonAI\Miniconda3\Lib\multiprocessing\spawn.py", line 143, in get_preparation_data _check_not_importing_main() File "G:\pythonAI\Miniconda3\Lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main is not going to be frozen to produce an executable.''') RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable.
I got the same error. How did you get the pytorch implementation from working? Thanks
Dear I do not understand how to use my personalized mask as input. In particular for the training stage it is only used the image without mask and the masks is random generated? If yes this means that I need to use only correct images withouth error for the input? For the test if I would like to use my masks for the network it would be in a separate file or I need to set to white the three color channels on the input image? If the image has to be an input in the test_option which is the right parameter to be used to give it?
