{"action":"created","issue":{"url":"https://api.github.com/repos/tensorpack/tensorpack/issues/977","repository_url":"https://api.github.com/repos/tensorpack/tensorpack","labels_url":"https://api.github.com/repos/tensorpack/tensorpack/issues/977/labels{/name}","comments_url":"https://api.github.com/repos/tensorpack/tensorpack/issues/977/comments","events_url":"https://api.github.com/repos/tensorpack/tensorpack/issues/977/events","html_url":"https://github.com/tensorpack/tensorpack/issues/977","id":378401227,"node_id":"MDU6SXNzdWUzNzg0MDEyMjc=","number":977,"title":"BufferError: memoryview: underlying buffer is not C-contiguous","user":{"login":"notecola","id":4906009,"node_id":"MDQ6VXNlcjQ5MDYwMDk=","avatar_url":"https://avatars1.githubusercontent.com/u/4906009?v=4","gravatar_id":"","url":"https://api.github.com/users/notecola","html_url":"https://github.com/notecola","followers_url":"https://api.github.com/users/notecola/followers","following_url":"https://api.github.com/users/notecola/following{/other_user}","gists_url":"https://api.github.com/users/notecola/gists{/gist_id}","starred_url":"https://api.github.com/users/notecola/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/notecola/subscriptions","organizations_url":"https://api.github.com/users/notecola/orgs","repos_url":"https://api.github.com/users/notecola/repos","events_url":"https://api.github.com/users/notecola/events{/privacy}","received_events_url":"https://api.github.com/users/notecola/received_events","type":"User","site_admin":false},"labels":[{"id":965546549,"node_id":"MDU6TGFiZWw5NjU1NDY1NDk=","url":"https://api.github.com/repos/tensorpack/tensorpack/labels/upstream%20bug","name":"upstream bug","color":"ea6764","default":false}],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":5,"created_at":"2018-11-07T17:53:30Z","updated_at":"2018-12-04T22:03:55Z","closed_at":"2018-11-09T06:34:30Z","author_association":"NONE","body":"If you're asking about an unexpected problem you met, use this template.\r\n__PLEASE DO NOT DELETE THIS TEMPLATE, FILL IT__:\r\n\r\n### 1. What you did:\r\n\r\n(1) **If you're using examples, what's the command you run:**\r\n\r\npython3 enet-pat.py --vgg19 vgg19.npz --data train2017.zip --gpu=0\r\n\r\n(2) **If you're using examples, have you made any changes to the examples? Paste them here:**\r\n\r\n(3) **If not using examples, tell us what you did here:**\r\n\r\nNote that we may not be able to investigate it if there is no reproducible code.\r\nIt's always better to paste what you did instead of describing them.\r\n\r\n### 2. What you observed:\r\n\r\nprocess stuck at epoch 1. There are BufferError messages in the log.\r\nCould that be a numpy issue?\r\n\r\n```\r\n(1) \r\nroot@tower:~/a/tensorpack/examples/SuperResolution# python3 enet-pat.py --vgg19 vgg19.npz --data train2017.zip --gpu=0\r\n[1107 18:28:22 @logger.py:108] WRN Log directory train_log/enet-pat exists! Use 'd' to delete it. \r\n[1107 18:28:22 @logger.py:111] WRN If you're resuming from a previous run, you can choose to keep it.\r\nPress any other key to exit. \r\nSelect Action: k (keep) / d (delete) / q (quit):d\r\n[1107 18:28:23 @logger.py:73] Argv: enet-pat.py --vgg19 vgg19.npz --data train2017.zip --gpu=0\r\n[1107 18:28:28 @parallel.py:293] [PrefetchDataZMQ] Will fork a dataflow more than one times. This assumes the datapoints are i.i.d.\r\n[1107 18:28:28 @input_source.py:220] Setting up the queue 'QueueInput/input_queue' for CPU prefetching ...\r\n[1107 18:28:28 @registry.py:121] gen/conv1 input: [None, 32, 32, 3]\r\n[1107 18:28:28 @registry.py:129] gen/conv1 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_0/conv0 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_0/conv0 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_0/conv1 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_0/conv1 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_1/conv0 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_1/conv0 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_1/conv1 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_1/conv1 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_2/conv0 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_2/conv0 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_2/conv1 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_2/conv1 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_3/conv0 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_3/conv0 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_3/conv1 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_3/conv1 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_4/conv0 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_4/conv0 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_4/conv1 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_4/conv1 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_5/conv0 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_5/conv0 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_5/conv1 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_5/conv1 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_6/conv0 input: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:129] gen/block_6/conv0 output: [None, 32, 32, 64]\r\n[1107 18:28:28 @registry.py:121] gen/block_6/conv1 input: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:129] gen/block_6/conv1 output: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:121] gen/block_7/conv0 input: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:129] gen/block_7/conv0 output: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:121] gen/block_7/conv1 input: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:129] gen/block_7/conv1 output: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:121] gen/block_8/conv0 input: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:129] gen/block_8/conv0 output: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:121] gen/block_8/conv1 input: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:129] gen/block_8/conv1 output: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:121] gen/block_9/conv0 input: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:129] gen/block_9/conv0 output: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:121] gen/block_9/conv1 input: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:129] gen/block_9/conv1 output: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:121] gen/conv_post_1 input: [None, 64, 64, 64]\r\n[1107 18:28:29 @registry.py:129] gen/conv_post_1 output: [None, 64, 64, 64]\r\n[1107 18:28:29 @registry.py:121] gen/conv_post_2 input: [None, 128, 128, 64]\r\n[1107 18:28:29 @registry.py:129] gen/conv_post_2 output: [None, 128, 128, 64]\r\n[1107 18:28:29 @registry.py:121] gen/conv_post_3 input: [None, 128, 128, 64]\r\n[1107 18:28:29 @registry.py:129] gen/conv_post_3 output: [None, 128, 128, 64]\r\n[1107 18:28:29 @registry.py:121] gen/conv_post_4 input: [None, 128, 128, 64]\r\n[1107 18:28:29 @registry.py:129] gen/conv_post_4 output: [None, 128, 128, 3]\r\n[1107 18:28:29 @registry.py:121] discrim/conv0 input: [None, 128, 128, 3]\r\n[1107 18:28:29 @registry.py:129] discrim/conv0 output: [None, 128, 128, 32]\r\n[1107 18:28:29 @registry.py:121] discrim/conv0b input: [None, 128, 128, 32]\r\n[1107 18:28:29 @registry.py:129] discrim/conv0b output: [None, 64, 64, 32]\r\n[1107 18:28:29 @registry.py:121] discrim/conv1 input: [None, 64, 64, 32]\r\n[1107 18:28:29 @registry.py:129] discrim/conv1 output: [None, 64, 64, 64]\r\n[1107 18:28:29 @registry.py:121] discrim/conv1b input: [None, 64, 64, 64]\r\n[1107 18:28:29 @registry.py:129] discrim/conv1b output: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:121] discrim/conv2 input: [None, 32, 32, 64]\r\n[1107 18:28:29 @registry.py:129] discrim/conv2 output: [None, 32, 32, 128]\r\n[1107 18:28:29 @registry.py:121] discrim/conv2b input: [None, 32, 32, 128]\r\n[1107 18:28:29 @registry.py:129] discrim/conv2b output: [None, 16, 16, 128]\r\n[1107 18:28:29 @registry.py:121] discrim/conv3 input: [None, 16, 16, 128]\r\n[1107 18:28:29 @registry.py:129] discrim/conv3 output: [None, 16, 16, 256]\r\n[1107 18:28:29 @registry.py:121] discrim/conv3b input: [None, 16, 16, 256]\r\n[1107 18:28:29 @registry.py:129] discrim/conv3b output: [None, 8, 8, 256]\r\n[1107 18:28:29 @registry.py:121] discrim/conv4 input: [None, 8, 8, 256]\r\n[1107 18:28:29 @registry.py:129] discrim/conv4 output: [None, 8, 8, 512]\r\n[1107 18:28:29 @registry.py:121] discrim/conv4b input: [None, 8, 8, 512]\r\n[1107 18:28:29 @registry.py:129] discrim/conv4b output: [None, 4, 4, 512]\r\n[1107 18:28:29 @registry.py:121] discrim/fc0 input: [None, 4, 4, 512]\r\n[1107 18:28:29 @registry.py:129] discrim/fc0 output: [None, 1024]\r\n[1107 18:28:29 @registry.py:121] discrim/fc1 input: [None, 1024]\r\n[1107 18:28:29 @registry.py:129] discrim/fc1 output: [None, 1]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv1_1 input: [32, 128, 128, 3]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv1_1 output: [32, 128, 128, 64]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv1_2 input: [32, 128, 128, 64]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv1_2 output: [32, 128, 128, 64]\r\n[1107 18:28:29 @registry.py:121] VGG19/pool1 input: [32, 128, 128, 64]\r\n[1107 18:28:29 @registry.py:129] VGG19/pool1 output: [32, 64, 64, 64]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv2_1 input: [32, 64, 64, 64]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv2_1 output: [32, 64, 64, 128]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv2_2 input: [32, 64, 64, 128]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv2_2 output: [32, 64, 64, 128]\r\n[1107 18:28:29 @registry.py:121] VGG19/pool2 input: [32, 64, 64, 128]\r\n[1107 18:28:29 @registry.py:129] VGG19/pool2 output: [32, 32, 32, 128]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv3_1 input: [32, 32, 32, 128]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv3_1 output: [32, 32, 32, 256]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv3_2 input: [32, 32, 32, 256]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv3_2 output: [32, 32, 32, 256]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv3_3 input: [32, 32, 32, 256]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv3_3 output: [32, 32, 32, 256]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv3_4 input: [32, 32, 32, 256]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv3_4 output: [32, 32, 32, 256]\r\n[1107 18:28:29 @registry.py:121] VGG19/pool3 input: [32, 32, 32, 256]\r\n[1107 18:28:29 @registry.py:129] VGG19/pool3 output: [32, 16, 16, 256]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv4_1 input: [32, 16, 16, 256]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv4_1 output: [32, 16, 16, 512]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv4_2 input: [32, 16, 16, 512]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv4_2 output: [32, 16, 16, 512]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv4_3 input: [32, 16, 16, 512]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv4_3 output: [32, 16, 16, 512]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv4_4 input: [32, 16, 16, 512]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv4_4 output: [32, 16, 16, 512]\r\n[1107 18:28:29 @registry.py:121] VGG19/pool4 input: [32, 16, 16, 512]\r\n[1107 18:28:29 @registry.py:129] VGG19/pool4 output: [32, 8, 8, 512]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv5_1 input: [32, 8, 8, 512]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv5_1 output: [32, 8, 8, 512]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv5_2 input: [32, 8, 8, 512]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv5_2 output: [32, 8, 8, 512]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv5_3 input: [32, 8, 8, 512]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv5_3 output: [32, 8, 8, 512]\r\n[1107 18:28:29 @registry.py:121] VGG19/conv5_4 input: [32, 8, 8, 512]\r\n[1107 18:28:29 @registry.py:129] VGG19/conv5_4 output: [32, 8, 8, 512]\r\n[1107 18:28:29 @registry.py:121] VGG19/pool5 input: [32, 8, 8, 512]\r\n[1107 18:28:29 @registry.py:129] VGG19/pool5 output: [32, 4, 4, 512]\r\n[1107 18:28:29 @enet-pat.py:147] Create perceptual loss for layer perceptual_loss/truediv:0 with shape (32, 32, 32, 128)\r\n[1107 18:28:29 @enet-pat.py:149] Create perceptual loss for layer perceptual_loss/truediv_1:0 with shape (32, 4, 4, 512)\r\n[1107 18:28:29 @enet-pat.py:158] Create texture loss for layer texture_loss/truediv:0 with shape (32, 128, 128, 64)\r\n[1107 18:28:29 @enet-pat.py:158] Create texture loss for layer texture_loss/truediv_1:0 with shape (32, 64, 64, 128)\r\n[1107 18:28:29 @enet-pat.py:158] Create texture loss for layer texture_loss/truediv_2:0 with shape (32, 32, 32, 256)\r\n[1107 18:28:31 @model_utils.py:64] Trainable Variables: \r\nname                   shape                 dim\r\n---------------------  ----------------  -------\r\ngen/conv1/W:0          [3, 3, 3, 64]        1728\r\ngen/conv1/b:0          [64]                   64\r\ngen/block_0/conv0/W:0  [3, 3, 64, 64]      36864\r\ngen/block_0/conv0/b:0  [64]                   64\r\ngen/block_0/conv1/W:0  [3, 3, 64, 64]      36864\r\ngen/block_0/conv1/b:0  [64]                   64\r\ngen/block_1/conv0/W:0  [3, 3, 64, 64]      36864\r\ngen/block_1/conv0/b:0  [64]                   64\r\ngen/block_1/conv1/W:0  [3, 3, 64, 64]      36864\r\ngen/block_1/conv1/b:0  [64]                   64\r\ngen/block_2/conv0/W:0  [3, 3, 64, 64]      36864\r\ngen/block_2/conv0/b:0  [64]                   64\r\ngen/block_2/conv1/W:0  [3, 3, 64, 64]      36864\r\ngen/block_2/conv1/b:0  [64]                   64\r\ngen/block_3/conv0/W:0  [3, 3, 64, 64]      36864\r\ngen/block_3/conv0/b:0  [64]                   64\r\ngen/block_3/conv1/W:0  [3, 3, 64, 64]      36864\r\ngen/block_3/conv1/b:0  [64]                   64\r\ngen/block_4/conv0/W:0  [3, 3, 64, 64]      36864\r\ngen/block_4/conv0/b:0  [64]                   64\r\ngen/block_4/conv1/W:0  [3, 3, 64, 64]      36864\r\ngen/block_4/conv1/b:0  [64]                   64\r\ngen/block_5/conv0/W:0  [3, 3, 64, 64]      36864\r\ngen/block_5/conv0/b:0  [64]                   64\r\ngen/block_5/conv1/W:0  [3, 3, 64, 64]      36864\r\ngen/block_5/conv1/b:0  [64]                   64\r\ngen/block_6/conv0/W:0  [3, 3, 64, 64]      36864\r\ngen/block_6/conv0/b:0  [64]                   64\r\ngen/block_6/conv1/W:0  [3, 3, 64, 64]      36864\r\ngen/block_6/conv1/b:0  [64]                   64\r\ngen/block_7/conv0/W:0  [3, 3, 64, 64]      36864\r\ngen/block_7/conv0/b:0  [64]                   64\r\ngen/block_7/conv1/W:0  [3, 3, 64, 64]      36864\r\ngen/block_7/conv1/b:0  [64]                   64\r\ngen/block_8/conv0/W:0  [3, 3, 64, 64]      36864\r\ngen/block_8/conv0/b:0  [64]                   64\r\ngen/block_8/conv1/W:0  [3, 3, 64, 64]      36864\r\ngen/block_8/conv1/b:0  [64]                   64\r\ngen/block_9/conv0/W:0  [3, 3, 64, 64]      36864\r\ngen/block_9/conv0/b:0  [64]                   64\r\ngen/block_9/conv1/W:0  [3, 3, 64, 64]      36864\r\ngen/block_9/conv1/b:0  [64]                   64\r\ngen/conv_post_1/W:0    [3, 3, 64, 64]      36864\r\ngen/conv_post_1/b:0    [64]                   64\r\ngen/conv_post_2/W:0    [3, 3, 64, 64]      36864\r\ngen/conv_post_2/b:0    [64]                   64\r\ngen/conv_post_3/W:0    [3, 3, 64, 64]      36864\r\ngen/conv_post_3/b:0    [64]                   64\r\ngen/conv_post_4/W:0    [3, 3, 64, 3]        1728\r\ngen/conv_post_4/b:0    [3]                     3\r\ndiscrim/conv0/W:0      [3, 3, 3, 32]         864\r\ndiscrim/conv0/b:0      [32]                   32\r\ndiscrim/conv0b/W:0     [3, 3, 32, 32]       9216\r\ndiscrim/conv0b/b:0     [32]                   32\r\ndiscrim/conv1/W:0      [3, 3, 32, 64]      18432\r\ndiscrim/conv1/b:0      [64]                   64\r\ndiscrim/conv1b/W:0     [3, 3, 64, 64]      36864\r\ndiscrim/conv1b/b:0     [64]                   64\r\ndiscrim/conv2/W:0      [3, 3, 64, 128]     73728\r\ndiscrim/conv2/b:0      [128]                 128\r\ndiscrim/conv2b/W:0     [3, 3, 128, 128]   147456\r\ndiscrim/conv2b/b:0     [128]                 128\r\ndiscrim/conv3/W:0      [3, 3, 128, 256]   294912\r\ndiscrim/conv3/b:0      [256]                 256\r\ndiscrim/conv3b/W:0     [3, 3, 256, 256]   589824\r\ndiscrim/conv3b/b:0     [256]                 256\r\ndiscrim/conv4/W:0      [3, 3, 256, 512]  1179648\r\ndiscrim/conv4/b:0      [512]                 512\r\ndiscrim/conv4b/W:0     [3, 3, 512, 512]  2359296\r\ndiscrim/conv4b/b:0     [512]                 512\r\ndiscrim/fc0/W:0        [8192, 1024]      8388608\r\ndiscrim/fc0/b:0        [1024]               1024\r\ndiscrim/fc1/W:0        [1024, 1]            1024\r\ndiscrim/fc1/b:0        [1]                     1\r\nVGG19/conv1_1/W:0      [3, 3, 3, 64]        1728\r\nVGG19/conv1_1/b:0      [64]                   64\r\nVGG19/conv1_2/W:0      [3, 3, 64, 64]      36864\r\nVGG19/conv1_2/b:0      [64]                   64\r\nVGG19/conv2_1/W:0      [3, 3, 64, 128]     73728\r\nVGG19/conv2_1/b:0      [128]                 128\r\nVGG19/conv2_2/W:0      [3, 3, 128, 128]   147456\r\nVGG19/conv2_2/b:0      [128]                 128\r\nVGG19/conv3_1/W:0      [3, 3, 128, 256]   294912\r\nVGG19/conv3_1/b:0      [256]                 256\r\nVGG19/conv3_2/W:0      [3, 3, 256, 256]   589824\r\nVGG19/conv3_2/b:0      [256]                 256\r\nVGG19/conv3_3/W:0      [3, 3, 256, 256]   589824\r\nVGG19/conv3_3/b:0      [256]                 256\r\nVGG19/conv3_4/W:0      [3, 3, 256, 256]   589824\r\nVGG19/conv3_4/b:0      [256]                 256\r\nVGG19/conv4_1/W:0      [3, 3, 256, 512]  1179648\r\nVGG19/conv4_1/b:0      [512]                 512\r\nVGG19/conv4_2/W:0      [3, 3, 512, 512]  2359296\r\nVGG19/conv4_2/b:0      [512]                 512\r\nVGG19/conv4_3/W:0      [3, 3, 512, 512]  2359296\r\nVGG19/conv4_3/b:0      [512]                 512\r\nVGG19/conv4_4/W:0      [3, 3, 512, 512]  2359296\r\nVGG19/conv4_4/b:0      [512]                 512\r\nVGG19/conv5_1/W:0      [3, 3, 512, 512]  2359296\r\nVGG19/conv5_1/b:0      [512]                 512\r\nVGG19/conv5_2/W:0      [3, 3, 512, 512]  2359296\r\nVGG19/conv5_2/b:0      [512]                 512\r\nVGG19/conv5_3/W:0      [3, 3, 512, 512]  2359296\r\nVGG19/conv5_3/b:0      [512]                 512\r\nVGG19/conv5_4/W:0      [3, 3, 512, 512]  2359296\r\nVGG19/conv5_4/b:0      [512]                 512\r\nTotal #vars=106, #params=33980132, size=129.62MB\r\n[1107 18:28:31 @base.py:209] Setup callbacks graph ...\r\n[1107 18:28:31 @summary.py:38] Maintain moving average summary of 12 tensors in collection MOVING_SUMMARY_OPS.\r\n[1107 18:28:31 @summary.py:75] Summarizing collection 'summaries' of size 16.\r\n[1107 18:28:32 @base.py:227] Creating the session ...\r\n2018-11-07 18:28:32.289980: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\r\n2018-11-07 18:28:32.396358: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2018-11-07 18:28:32.396683: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1411] Found device 0 with properties: \r\nname: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582\r\npciBusID: 0000:01:00.0\r\ntotalMemory: 10.91GiB freeMemory: 9.74GiB\r\n2018-11-07 18:28:32.396695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1490] Adding visible gpu devices: 0\r\n2018-11-07 18:28:32.578973: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] Device interconnect StreamExecutor with strength 1 edge matrix:\r\n2018-11-07 18:28:32.579005: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977]      0 \r\n2018-11-07 18:28:32.579010: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0:   N \r\n2018-11-07 18:28:32.579156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1103] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11057 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)\r\nProcess _Worker-1:\r\nProcess _Worker-2:\r\nProcess _Worker-3:\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\n  File \"/usr/lib/python3.5/multiprocessing/process.py\", line 249, in _bootstrap\r\n    self.run()\r\n  File \"/usr/lib/python3.5/multiprocessing/process.py\", line 249, in _bootstrap\r\n    self.run()\r\nTraceback (most recent call last):\r\n  File \"/usr/local/lib/python3.5/dist-packages/tensorpack/dataflow/parallel.py\", line 270, in run\r\n    socket.send(dumps(dp), copy=False)\r\n  File \"/usr/local/lib/python3.5/dist-packages/tensorpack/dataflow/parallel.py\", line 270, in run\r\n    socket.send(dumps(dp), copy=False)\r\n  File \"/usr/local/lib/python3.5/dist-packages/tensorpack/utils/serialize.py\", line 19, in dumps_msgpack\r\n    return msgpack.dumps(obj, use_bin_type=True)\r\n  File \"/usr/local/lib/python3.5/dist-packages/tensorpack/utils/serialize.py\", line 19, in dumps_msgpack\r\n    return msgpack.dumps(obj, use_bin_type=True)\r\n  File \"/home/master/.local/lib/python3.5/site-packages/msgpack_numpy.py\", line 196, in packb\r\n    return Packer(**kwargs).pack(o)\r\n  File \"/home/master/.local/lib/python3.5/site-packages/msgpack_numpy.py\", line 196, in packb\r\n    return Packer(**kwargs).pack(o)\r\n  File \"msgpack/_packer.pyx\", line 284, in msgpack._packer.Packer.pack\r\n  File \"msgpack/_packer.pyx\", line 284, in msgpack._packer.Packer.pack\r\n  File \"msgpack/_packer.pyx\", line 290, in msgpack._packer.Packer.pack\r\n  File \"msgpack/_packer.pyx\", line 287, in msgpack._packer.Packer.pack\r\n  File \"msgpack/_packer.pyx\", line 290, in msgpack._packer.Packer.pack\r\n  File \"/usr/lib/python3.5/multiprocessing/process.py\", line 249, in _bootstrap\r\n    self.run()\r\n  File \"msgpack/_packer.pyx\", line 263, in msgpack._packer.Packer._pack\r\n  File \"msgpack/_packer.pyx\", line 287, in msgpack._packer.Packer.pack\r\n  File \"msgpack/_packer.pyx\", line 234, in msgpack._packer.Packer._pack\r\n  File \"/usr/local/lib/python3.5/dist-packages/tensorpack/dataflow/parallel.py\", line 270, in run\r\n    socket.send(dumps(dp), copy=False)\r\n  File \"msgpack/_packer.pyx\", line 266, in msgpack._packer.Packer._pack\r\n  File \"msgpack/_packer.pyx\", line 263, in msgpack._packer.Packer._pack\r\n  File \"/usr/local/lib/python3.5/dist-packages/tensorpack/utils/serialize.py\", line 19, in dumps_msgpack\r\n    return msgpack.dumps(obj, use_bin_type=True)\r\n  File \"msgpack/_packer.pyx\", line 234, in msgpack._packer.Packer._pack\r\n  File \"/home/master/.local/lib/python3.5/site-packages/msgpack_numpy.py\", line 196, in packb\r\n    return Packer(**kwargs).pack(o)\r\n  File \"msgpack/_packer.pyx\", line 266, in msgpack._packer.Packer._pack\r\n  File \"msgpack/_packer.pyx\", line 284, in msgpack._packer.Packer.pack\r\nBufferError: memoryview: underlying buffer is not C-contiguous\r\n  File \"msgpack/_packer.pyx\", line 290, in msgpack._packer.Packer.pack\r\n  File \"msgpack/_packer.pyx\", line 287, in msgpack._packer.Packer.pack\r\nBufferError: memoryview: underlying buffer is not C-contiguous\r\n  File \"msgpack/_packer.pyx\", line 263, in msgpack._packer.Packer._pack\r\n  File \"msgpack/_packer.pyx\", line 234, in msgpack._packer.Packer._pack\r\n  File \"msgpack/_packer.pyx\", line 266, in msgpack._packer.Packer._pack\r\nBufferError: memoryview: underlying buffer is not C-contiguous\r\n2018-11-07 18:28:32.703881: W tensorflow/core/framework/allocator.cc:113] Allocation of 33554432 exceeds 10% of system memory.\r\n2018-11-07 18:28:32.719443: W tensorflow/core/framework/allocator.cc:113] Allocation of 33554432 exceeds 10% of system memory.\r\n2018-11-07 18:28:32.755082: W tensorflow/core/framework/allocator.cc:113] Allocation of 33554432 exceeds 10% of system memory.\r\n2018-11-07 18:28:32.765388: W tensorflow/core/framework/allocator.cc:113] Allocation of 33554432 exceeds 10% of system memory.\r\n2018-11-07 18:28:32.798465: W tensorflow/core/framework/allocator.cc:113] Allocation of 33554432 exceeds 10% of system memory.\r\n[1107 18:28:33 @base.py:233] Initializing the session ...\r\n[1107 18:28:33 @sessinit.py:207] Variables to restore from dict: VGG19/conv4_3/W:0, VGG19/conv3_1/W:0, VGG19/conv5_4/b:0, VGG19/conv3_2/b:0, VGG19/conv4_3/b:0, VGG19/conv1_2/b:0, VGG19/conv3_2/W:0, VGG19/conv5_2/W:0, VGG19/conv5_1/W:0, VGG19/conv1_2/W:0, VGG19/conv4_4/b:0, VGG19/conv4_1/W:0, VGG19/conv5_3/W:0, VGG19/conv5_1/b:0, VGG19/conv5_4/W:0, VGG19/conv4_4/W:0, VGG19/conv4_2/b:0, VGG19/conv2_1/b:0, VGG19/conv4_1/b:0, VGG19/conv3_3/W:0, VGG19/conv2_2/b:0, VGG19/conv4_2/W:0, VGG19/conv1_1/b:0, VGG19/conv2_1/W:0, VGG19/conv3_4/W:0, VGG19/conv3_3/b:0, VGG19/conv5_2/b:0, VGG19/conv1_1/W:0, VGG19/conv2_2/W:0, VGG19/conv3_4/b:0, VGG19/conv5_3/b:0, VGG19/conv3_1/b:0\r\n[1107 18:28:33 @sessinit.py:90] WRN The following variables are in the graph, but not found in the dict: discrim/conv0/W:0, discrim/conv0/b:0, discrim/conv0b/W:0, discrim/conv0b/b:0, discrim/conv1/W:0, discrim/conv1/b:0, discrim/conv1b/W:0, discrim/conv1b/b:0, discrim/conv2/W:0, discrim/conv2/b:0, discrim/conv2b/W:0, discrim/conv2b/b:0, discrim/conv3/W:0, discrim/conv3/b:0, discrim/conv3b/W:0, discrim/conv3b/b:0, discrim/conv4/W:0, discrim/conv4/b:0, discrim/conv4b/W:0, discrim/conv4b/b:0, discrim/fc0/W:0, discrim/fc0/b:0, discrim/fc1/W:0, discrim/fc1/b:0, gen/block_0/conv0/W:0, gen/block_0/conv0/b:0, gen/block_0/conv1/W:0, gen/block_0/conv1/b:0, gen/block_1/conv0/W:0, gen/block_1/conv0/b:0, gen/block_1/conv1/W:0, gen/block_1/conv1/b:0, gen/block_2/conv0/W:0, gen/block_2/conv0/b:0, gen/block_2/conv1/W:0, gen/block_2/conv1/b:0, gen/block_3/conv0/W:0, gen/block_3/conv0/b:0, gen/block_3/conv1/W:0, gen/block_3/conv1/b:0, gen/block_4/conv0/W:0, gen/block_4/conv0/b:0, gen/block_4/conv1/W:0, gen/block_4/conv1/b:0, gen/block_5/conv0/W:0, gen/block_5/conv0/b:0, gen/block_5/conv1/W:0, gen/block_5/conv1/b:0, gen/block_6/conv0/W:0, gen/block_6/conv0/b:0, gen/block_6/conv1/W:0, gen/block_6/conv1/b:0, gen/block_7/conv0/W:0, gen/block_7/conv0/b:0, gen/block_7/conv1/W:0, gen/block_7/conv1/b:0, gen/block_8/conv0/W:0, gen/block_8/conv0/b:0, gen/block_8/conv1/W:0, gen/block_8/conv1/b:0, gen/block_9/conv0/W:0, gen/block_9/conv0/b:0, gen/block_9/conv1/W:0, gen/block_9/conv1/b:0, gen/conv1/W:0, gen/conv1/b:0, gen/conv_post_1/W:0, gen/conv_post_1/b:0, gen/conv_post_2/W:0, gen/conv_post_2/b:0, gen/conv_post_3/W:0, gen/conv_post_3/b:0, gen/conv_post_4/W:0, gen/conv_post_4/b:0, global_step:0, learning_rate:0, optimize/beta1_power:0, optimize/beta2_power:0\r\n[1107 18:28:33 @sessinit.py:90] WRN The following variables are in the dict, but not found in the graph: VGG19/fc6/W:0, VGG19/fc6/b:0, VGG19/fc7/W:0, VGG19/fc7/b:0, VGG19/fc8/W:0, VGG19/fc8/b:0\r\n[1107 18:28:33 @sessinit.py:220] Restoring from dict ...\r\n[1107 18:28:34 @base.py:240] Graph Finalized.\r\n[1107 18:28:34 @concurrency.py:37] Starting EnqueueThread QueueInput/input_queue ...\r\n[1107 18:28:34 @base.py:272] Start Epoch 1 ...\r\n  0%|                                                                                  |0/1848[00:00<?,?it/s]^C^C^[[BKilled\r\n\r\n```\r\n\r\nIt's always better to paste what you observed instead of describing them.\r\n\r\nA part of logs is sometimes enough, but it's always better to paste as much as possible.\r\n\r\nYou can run a command with `CMD 2>&1 | tee logs.txt` to save all stdout & stderr logs to one file.\r\n\r\n(2) **Other observations, if any:**\r\n\r\nThis is pip3 freeze:\r\n```\r\nroot@tower:~/a/tensorpack/examples/SuperResolution# pip3 freeze\r\nabsl-py==0.6.1\r\napturl==0.5.2\r\nasn1crypto==0.24.0\r\nastor==0.7.1\r\nattrs==18.2.0\r\nbackcall==0.1.0\r\nbeautifulsoup4==4.4.1\r\nbleach==3.0.2\r\nblinker==1.3\r\nBrlapi==0.6.4\r\ncertifi==2018.10.15\r\ncffi==1.11.5\r\nchardet==3.0.4\r\ncheckbox-support==0.22\r\nchrome-gnome-shell==0.0.0\r\ncolorama==0.4.0\r\ncommand-not-found==0.3\r\ncryptography==1.2.3\r\ncycler==0.10.0\r\ndecorator==4.3.0\r\ndefer==1.0.6\r\ndefusedxml==0.5.0\r\nentrypoints==0.2.3\r\nfeedparser==5.1.3\r\ngast==0.2.0\r\ngrpcio==1.16.0\r\nguacamole==0.9.2\r\nh5py==2.8.0\r\nhtml5lib==0.999\r\nhttplib2==0.9.1\r\nidna==2.7\r\nipykernel==5.1.0\r\nipython==7.1.1\r\nipython-genutils==0.2.0\r\nipywidgets==7.4.2\r\njedi==0.13.1\r\nJinja2==2.10\r\njsonschema==2.6.0\r\njupyter==1.0.0\r\njupyter-client==5.2.3\r\njupyter-console==6.0.0\r\njupyter-core==4.4.0\r\nKeras-Applications==1.0.6\r\nKeras-Preprocessing==1.0.5\r\nkiwisolver==1.0.1\r\nlanguage-selector==0.1\r\nLasagne==0.1\r\nlightdm-gtk-greeter-settings==1.2.1\r\nlouis==2.6.4\r\nlxml==3.5.0\r\nMako==1.0.3\r\nMarkdown==3.0.1\r\nMarkupSafe==1.1.0\r\nmatplotlib==3.0.1\r\nmistune==0.8.4\r\nmsgpack==0.5.6\r\nmsgpack-numpy==0.4.4.1\r\nnbconvert==5.4.0\r\nnbformat==4.4.0\r\nnotebook==5.7.0\r\nnumpy==1.15.4\r\noauthlib==1.0.3\r\nonboard==1.2.0\r\nopencv-python==3.4.3.18\r\npadme==1.1.1\r\npanda==0.3.1\r\npandas==0.23.4\r\npandocfilters==1.4.2\r\nparso==0.3.1\r\npexpect==4.6.0\r\npickleshare==0.7.5\r\nPillow==5.3.0\r\nplainbox==0.25\r\nprometheus-client==0.4.2\r\nprompt-toolkit==2.0.7\r\nprotobuf==3.6.1\r\nptyprocess==0.6.0\r\npyasn1==0.4.4\r\npyasn1-modules==0.2.2\r\npycparser==2.19\r\npycups==1.9.73\r\npycurl==7.43.0\r\nPygments==2.2.0\r\npygobject==3.20.0\r\nPyJWT==1.3.0\r\npyparsing==2.3.0\r\npython-apt==1.1.0b1+ubuntu0.16.4.1\r\npython-dateutil==2.7.5\r\npython-debian==0.1.27\r\npython-prctl==1.7\r\npython-systemd==231\r\npytz==2018.7\r\npyxdg==0.25\r\npyzmq==17.1.2\r\nqtconsole==4.4.2\r\nreportlab==3.3.0\r\nrequests==2.20.0\r\nscikit-learn==0.20.0\r\nscipy==1.1.0\r\nscreen-resolution-extra==0.0.0\r\nSend2Trash==1.5.0\r\nsessioninstaller==0.0.0\r\nsix==1.11.0\r\nsystem-service==0.3\r\ntabulate==0.8.2\r\ntensorboard==1.11.0\r\ntensorflow-gpu==1.11.0\r\ntensorpack==0.9.0\r\ntermcolor==1.1.0\r\nterminado==0.8.1\r\ntestpath==0.4.2\r\nTheano==0.8.2\r\ntornado==5.1.1\r\ntqdm==4.28.1\r\ntraitlets==4.3.2\r\nubuntu-drivers-common==0.0.0\r\nufw==0.35\r\nunattended-upgrades==0.1\r\nunity-scope-calculator==0.1\r\nunity-scope-chromiumbookmarks==0.1\r\nunity-scope-colourlovers==0.1\r\nunity-scope-devhelp==0.1\r\nunity-scope-firefoxbookmarks==0.1\r\nunity-scope-gdrive==0.7\r\nunity-scope-manpages==0.1\r\nunity-scope-openclipart==0.1\r\nunity-scope-texdoc==0.1\r\nunity-scope-tomboy==0.1\r\nunity-scope-virtualbox==0.1\r\nunity-scope-yelp==0.1\r\nunity-scope-zotero==0.1\r\nurllib3==1.24.1\r\nusb-creator==0.3.0\r\nvirtualenv==15.0.1\r\nw3lib==1.19.0\r\nwcwidth==0.1.7\r\nwebencodings==0.5.1\r\nWerkzeug==0.14.1\r\nwidgetsnbextension==3.4.2\r\nxdiagnose==3.8.4.1\r\nxkit==0.0.0\r\nXlsxWriter==0.7.3\r\n\r\n```\r\n\r\nFor example, CPU/GPU utilization, output images, tensorboard curves, if relevant to your issue.\r\n\r\n### 3. What you expected, if not obvious.\r\n\r\n### 4. Your environment:\r\n\r\n  + Python version:\r\n\r\nPython 3.5.2\r\n\r\n  + TF version: `python -c 'import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)'`.\r\n\r\nv1.11.0-0-gc19e29306c 1.11.0\r\n\r\n  + Tensorpack version: `python -c 'import tensorpack; print(tensorpack.__version__)'`.\r\n\r\n0.9.0\r\n\r\n      You can install Tensorpack master by `pip install -U git+https://github.com/ppwwyyxx/tensorpack.git`\r\n      and see if your issue is already solved.\r\n\r\n  + Hardware information, e.g. number of GPUs used.\r\n1 GPU NV 1080\r\nCUDA Version 9.0.176\r\nCuDNN:\r\n#define CUDNN_MAJOR 7\r\n#define CUDNN_MINOR 3\r\n#define CUDNN_PATCHLEVEL 1\r\n\r\n\r\nAbout efficiency issues, PLEASE first read http://tensorpack.readthedocs.io/en/latest/tutorial/performance-tuning.html\r\n"},"comment":{"url":"https://api.github.com/repos/tensorpack/tensorpack/issues/comments/444276382","html_url":"https://github.com/tensorpack/tensorpack/issues/977#issuecomment-444276382","issue_url":"https://api.github.com/repos/tensorpack/tensorpack/issues/977","id":444276382,"node_id":"MDEyOklzc3VlQ29tbWVudDQ0NDI3NjM4Mg==","user":{"login":"ywpkwon","id":18429284,"node_id":"MDQ6VXNlcjE4NDI5Mjg0","avatar_url":"https://avatars1.githubusercontent.com/u/18429284?v=4","gravatar_id":"","url":"https://api.github.com/users/ywpkwon","html_url":"https://github.com/ywpkwon","followers_url":"https://api.github.com/users/ywpkwon/followers","following_url":"https://api.github.com/users/ywpkwon/following{/other_user}","gists_url":"https://api.github.com/users/ywpkwon/gists{/gist_id}","starred_url":"https://api.github.com/users/ywpkwon/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ywpkwon/subscriptions","organizations_url":"https://api.github.com/users/ywpkwon/orgs","repos_url":"https://api.github.com/users/ywpkwon/repos","events_url":"https://api.github.com/users/ywpkwon/events{/privacy}","received_events_url":"https://api.github.com/users/ywpkwon/received_events","type":"User","site_admin":false},"created_at":"2018-12-04T22:03:55Z","updated_at":"2018-12-04T22:03:55Z","author_association":"NONE","body":"Thanks a lot.  \r\n\r\nActually, I editted my question (sorry, I didn't know if your response would be this quick..). Now it doesn't have the nested ZMQ (btw, good to know, thanks.). It seems to relate `RandomCrop`.\r\n\r\n**However, I tried to make a new clean `virtualenv` and pip-installed packages from the working machine's pip status, and now it works!** As you said, although pip looks the same, some packages seemed to have problems.  Thank you!"}}
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