diff --git a/_README.ipynb b/_README.ipynb
index 75867ff..14add59 100644
--- a/_README.ipynb
+++ b/_README.ipynb
@@ -17,6 +17,48 @@
     "    - ```_README.md```*-----说明文档*\n",
     "    - ```app_spec.yml```*-----定义项目的输入输出，为部署服务*\n",
     "    - ```coding_here.ipynb```*-----输入并运行代码*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def handle(conf):\n",
+    "    \"\"\"\n",
+    "    该方法是部署之后，其他人调用你的服务时候的处理方法。\n",
+    "    请按规范填写参数结构，这样我们就能替你自动生成配置文件，方便其他人的调用。\n",
+    "    范例：\n",
+    "    params['key'] = value # value_type: str # description: some description\n",
+    "    value_type 可以选择：img, video, audio, str, int, float, [int], [str], [float]\n",
+    "    参数请放到params字典中，我们会自动解析该变量。\n",
+    "    \"\"\"\n",
+    "\n",
+    "    param1 = conf['param1']  # value_type: str # description: some description\n",
+    "    # add your code\n",
+    "    return {'ret1': 'cat'}\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/work'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pwd"
    ]
   },
   {
@@ -121,7 +163,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -135,17 +177,17 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-   },
+   "version": "3.7.5"
+  },
   "pycharm": {
    "stem_cell": {
     "cell_type": "raw",
-    "source": [],
     "metadata": {
      "collapsed": false
-    }
-   } 
- }
+    },
+    "source": []
+   }
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2
diff --git a/app_spec.yml b/app_spec.yml
new file mode 100644
index 0000000..3635ae6
--- /dev/null
+++ b/app_spec.yml
@@ -0,0 +1,10 @@
+input:
+  Photo:
+    name: Photo
+    value_type: img
+    description: 请传入128*128的图片
+output:
+  Output:
+    name: Output
+    value_type: img
+    description: ''
diff --git a/etc/.ipynb_checkpoints/dev-indices-checkpoint.txt b/etc/.ipynb_checkpoints/dev-indices-checkpoint.txt
new file mode 100644
index 0000000..db36ce3
--- /dev/null
+++ b/etc/.ipynb_checkpoints/dev-indices-checkpoint.txt
@@ -0,0 +1,10 @@
+Indices (0-indexed) of the 100 images held out from training.
+[  113   509   242   280   533   638   644   698   751   832 10989 16008
+ 13473  6659 20401 24841 26378  8103 11730  8363 16512  6736 27666 30287
+  6685 30696 16591  8424 26689 21078 27971  7202  6615 36150  9681 13137
+  1598  9726  4825  2864  1346 21784  4159 13270 19239  9844 16056  2822
+ 15792 19837  5198 19980 30042 36491 15648 20315  3604  8020  1108 18235
+ 16373 25717 32200 10547  6786 31384 33999 25763 20226  9447  4573  5938
+  1837 25121 17611 32751 28158 29381 13090 32210 17027 30171 12001 16240
+ 22205 11808 20113 10682 33338 24015 15154 10449 11373  8736 26320  4095
+ 13855 23504  2004 33307]
diff --git a/etc/.ipynb_checkpoints/outpainting-checkpoint.png b/etc/.ipynb_checkpoints/outpainting-checkpoint.png
new file mode 100644
index 0000000..10c71e5
Binary files /dev/null and b/etc/.ipynb_checkpoints/outpainting-checkpoint.png differ
diff --git a/etc/.ipynb_checkpoints/recursive-checkpoint.png b/etc/.ipynb_checkpoints/recursive-checkpoint.png
new file mode 100644
index 0000000..2e7c82c
Binary files /dev/null and b/etc/.ipynb_checkpoints/recursive-checkpoint.png differ
diff --git a/etc/.ipynb_checkpoints/results-checkpoint.png b/etc/.ipynb_checkpoints/results-checkpoint.png
new file mode 100644
index 0000000..f1cc5f0
Binary files /dev/null and b/etc/.ipynb_checkpoints/results-checkpoint.png differ
diff --git a/handler.py b/handler.py
new file mode 100644
index 0000000..6639374
--- /dev/null
+++ b/handler.py
@@ -0,0 +1,45 @@
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
+tf.reset_default_graph()
+import numpy as np
+from PIL import Image
+import model
+import util
+import os
+import sys
+
+model_PATH='/home/jovyan/work/src/output/models/model2000.ckpt'
+
+def load_demo_image(in_PATH):
+    img = np.array(Image.open(in_PATH).convert('RGB'))[np.newaxis] / 255.0
+    img_p = util.preprocess_images_outpainting(img)
+    return img_p
+
+def inference(model_PATH, img_p):
+    G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')
+    G_sample = model.generator(G_Z)
+    
+    saver = tf.train.Saver()
+    with tf.Session() as sess:
+        saver.restore(sess, model_PATH)
+        output, = sess.run([G_sample], feed_dict={G_Z: img_p})
+        img_norm = (output[0] * 255.0).astype(np.uint8)
+        img = Image.fromarray(img_norm, 'RGB')
+        #util.save_image(output[0], out_PATH)
+    return img
+
+def handle(conf):
+    """
+    该方法是部署之后，其他人调用你的服务时候的处理方法。
+    请按规范填写参数结构，这样我们就能替你自动生成配置文件，方便其他人的调用。
+    范例：
+    params['key'] = value # value_type: str # description: some description
+    value_type 可以选择：img, video, audio, str, int, float, [int], [str], [float]
+    参数请放到params字典中，我们会自动解析该变量。
+    """
+    base64_str = conf['Photo']
+    image = load_demo_image(base64_str, image_size, device)
+    res = inference(model, image)
+    # add your code
+    return {'Output': res}
+    
\ No newline at end of file
diff --git a/images/.ipynb_checkpoints/city_128-checkpoint.png b/images/.ipynb_checkpoints/city_128-checkpoint.png
new file mode 100644
index 0000000..a91190e
Binary files /dev/null and b/images/.ipynb_checkpoints/city_128-checkpoint.png differ
diff --git a/images/test.png b/images/test.png
new file mode 100644
index 0000000..441db6b
Binary files /dev/null and b/images/test.png differ
diff --git a/job_logs/job-gpu-62b5d2d8c06b81cd38279610.log b/job_logs/job-gpu-62b5d2d8c06b81cd38279610.log
new file mode 100644
index 0000000..d72375e
--- /dev/null
+++ b/job_logs/job-gpu-62b5d2d8c06b81cd38279610.log
@@ -0,0 +1,10 @@
+2022-06-24T15:06:06.543501249Z SYSTEM: Preparing env...
+2022-06-24T15:06:07.124450272Z SYSTEM: Running...
+2022-06-24T15:06:07.897430232Z 2022-06-24 23:06:07.895520: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-24T15:06:09.566498086Z Imported model (for Places365, 128x128 images)
+2022-06-24T15:06:12.24384624Z Traceback (most recent call last):
+2022-06-24T15:06:12.243895488Z   File "src/train.py", line 15, in <module>
+2022-06-24T15:06:12.263438511Z     tf.reset_default_graph()
+2022-06-24T15:06:12.263468552Z AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
+2022-06-24T15:06:12.683968769Z SYSTEM: Finishing...
+2022-06-24T15:06:12.897833055Z SYSTEM: Error Exists!
diff --git a/job_logs/job-gpu-62b5d344b5c4eec184cc05b0.log b/job_logs/job-gpu-62b5d344b5c4eec184cc05b0.log
new file mode 100644
index 0000000..1aae42c
--- /dev/null
+++ b/job_logs/job-gpu-62b5d344b5c4eec184cc05b0.log
@@ -0,0 +1,10 @@
+2022-06-24T15:07:54.304456417Z SYSTEM: Preparing env...
+2022-06-24T15:07:54.807933369Z SYSTEM: Running...
+2022-06-24T15:07:55.517547472Z 2022-06-24 23:07:55.513238: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-24T15:07:57.101646267Z Imported model (for Places365, 128x128 images)
+2022-06-24T15:07:59.609309108Z Traceback (most recent call last):
+2022-06-24T15:07:59.609359562Z   File "src/train.py", line 64, in <module>
+2022-06-24T15:07:59.614462932Z     G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')
+2022-06-24T15:07:59.614484248Z AttributeError: module 'tensorflow' has no attribute 'placeholder'
+2022-06-24T15:08:00.047025825Z SYSTEM: Finishing...
+2022-06-24T15:08:00.254692017Z SYSTEM: Error Exists!
diff --git a/job_logs/job-gpu-62b5d39fd4e7f8c811b53eb1.log b/job_logs/job-gpu-62b5d39fd4e7f8c811b53eb1.log
new file mode 100644
index 0000000..a5bbc26
--- /dev/null
+++ b/job_logs/job-gpu-62b5d39fd4e7f8c811b53eb1.log
@@ -0,0 +1,12 @@
+2022-06-24T15:09:25.204980934Z SYSTEM: Preparing env...
+2022-06-24T15:09:25.719685215Z SYSTEM: Running...
+2022-06-24T15:09:26.429518202Z 2022-06-24 23:09:26.428748: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-24T15:09:28.084842936Z Imported model (for Places365, 128x128 images)
+2022-06-24T15:09:30.663258107Z Traceback (most recent call last):
+2022-06-24T15:09:30.663303708Z   File "src/train.py", line 64, in <module>
+2022-06-24T15:09:30.663549466Z     G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')
+2022-06-24T15:09:30.664301035Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py", line 3097, in placeholder
+2022-06-24T15:09:30.687425674Z     raise RuntimeError("tf.placeholder() is not compatible with "
+2022-06-24T15:09:30.687454335Z RuntimeError: tf.placeholder() is not compatible with eager execution.
+2022-06-24T15:09:31.089543597Z SYSTEM: Finishing...
+2022-06-24T15:09:31.296093256Z SYSTEM: Error Exists!
diff --git a/job_logs/job-gpu-62b5d4052a85ae797c345e17.log b/job_logs/job-gpu-62b5d4052a85ae797c345e17.log
new file mode 100644
index 0000000..eac12d0
--- /dev/null
+++ b/job_logs/job-gpu-62b5d4052a85ae797c345e17.log
@@ -0,0 +1,10 @@
+2022-06-24T15:11:08.102681479Z SYSTEM: Preparing env...
+2022-06-24T15:11:08.698452928Z SYSTEM: Running...
+2022-06-24T15:11:09.445497917Z 2022-06-24 23:11:09.437930: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-24T15:11:11.078881179Z Imported model (for Places365, 128x128 images)
+2022-06-24T15:11:13.662043015Z Traceback (most recent call last):
+2022-06-24T15:11:13.662088455Z   File "src/train.py", line 64, in <module>
+2022-06-24T15:11:13.667490013Z     reset_graph()
+2022-06-24T15:11:13.667534099Z NameError: name 'reset_graph' is not defined
+2022-06-24T15:11:14.148404869Z SYSTEM: Finishing...
+2022-06-24T15:11:14.346444169Z SYSTEM: Error Exists!
diff --git a/job_logs/job-gpu-62b5d43bc06b81cd38279613.log b/job_logs/job-gpu-62b5d43bc06b81cd38279613.log
new file mode 100644
index 0000000..6ec79da
--- /dev/null
+++ b/job_logs/job-gpu-62b5d43bc06b81cd38279613.log
@@ -0,0 +1,12 @@
+2022-06-24T15:12:01.617089532Z SYSTEM: Preparing env...
+2022-06-24T15:12:02.203109161Z SYSTEM: Running...
+2022-06-24T15:12:03.004629571Z 2022-06-24 23:12:03.003884: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-24T15:12:04.639278285Z Imported model (for Places365, 128x128 images)
+2022-06-24T15:12:07.282805014Z Traceback (most recent call last):
+2022-06-24T15:12:07.282856749Z   File "src/train.py", line 71, in <module>
+2022-06-24T15:12:07.283554986Z     data = np.load('places/places_128.npz')
+2022-06-24T15:12:07.283591663Z   File "/usr/local/lib/python3.7/dist-packages/numpy/lib/npyio.py", line 428, in load
+2022-06-24T15:12:07.299470153Z     fid = open(os_fspath(file), "rb")
+2022-06-24T15:12:07.299489677Z FileNotFoundError: [Errno 2] No such file or directory: 'places/places_128.npz'
+2022-06-24T15:12:07.850426625Z SYSTEM: Finishing...
+2022-06-24T15:12:08.075164736Z SYSTEM: Error Exists!
diff --git a/job_logs/job-gpu-62b94f70c584fdf74ee42dd3.log b/job_logs/job-gpu-62b94f70c584fdf74ee42dd3.log
new file mode 100644
index 0000000..d7e9e9a
--- /dev/null
+++ b/job_logs/job-gpu-62b94f70c584fdf74ee42dd3.log
@@ -0,0 +1,9 @@
+2022-06-27T06:34:35.306224872Z SYSTEM: Preparing env...
+2022-06-27T06:34:35.980785521Z SYSTEM: Running...
+2022-06-27T06:34:36.741505339Z 2022-06-27 14:34:36.738257: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:34:38.457388991Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
+2022-06-27T06:34:38.457442694Z Instructions for updating:
+2022-06-27T06:34:38.457449025Z non-resource variables are not supported in the long term
+2022-06-27T06:34:38.468014128Z Imported model (for Places365, 128x128 images)
+2022-06-27T06:34:43.13214278Z Usage: python test.py [model_PATH] [in_PATH] [out_PATH]
+2022-06-27T06:34:43.648430148Z SYSTEM: Finishing...
diff --git a/job_logs/job-gpu-62b94f9cb17f87f3a6d7445b.log b/job_logs/job-gpu-62b94f9cb17f87f3a6d7445b.log
new file mode 100644
index 0000000..45ca465
--- /dev/null
+++ b/job_logs/job-gpu-62b94f9cb17f87f3a6d7445b.log
@@ -0,0 +1,14 @@
+2022-06-27T06:35:15.051483054Z SYSTEM: Preparing env...
+2022-06-27T06:35:15.65442522Z SYSTEM: Running...
+2022-06-27T06:35:16.362517265Z 2022-06-27 14:35:16.361256: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:35:18.024936822Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
+2022-06-27T06:35:18.024986723Z Instructions for updating:
+2022-06-27T06:35:18.024991459Z non-resource variables are not supported in the long term
+2022-06-27T06:35:18.029401275Z Imported model (for Places365, 128x128 images)
+2022-06-27T06:35:20.683442548Z Traceback (most recent call last):
+2022-06-27T06:35:20.683486794Z   File "src/test.py", line 28, in <module>
+2022-06-27T06:35:20.68361951Z     G_sample = model.generator(G_Z)
+2022-06-27T06:35:20.683632773Z   File "/home/jovyan/work/src/model.py", line 12, in generator
+2022-06-27T06:35:20.69064768Z     with tf.variable_scope('G', reuse=tf.AUTO_REUSE):
+2022-06-27T06:35:20.690694567Z AttributeError: module 'tensorflow' has no attribute 'variable_scope'
+2022-06-27T06:35:21.152841351Z SYSTEM: Finishing...
diff --git a/job_logs/job-gpu-62b94fd2f752e3e25d1d3e30.log b/job_logs/job-gpu-62b94fd2f752e3e25d1d3e30.log
new file mode 100644
index 0000000..d5132d7
--- /dev/null
+++ b/job_logs/job-gpu-62b94fd2f752e3e25d1d3e30.log
@@ -0,0 +1,128 @@
+2022-06-27T06:36:09.110278599Z SYSTEM: Preparing env...
+2022-06-27T06:36:09.755697526Z SYSTEM: Running...
+2022-06-27T06:36:10.582438824Z 2022-06-27 14:36:10.578599: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:36:12.27114199Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
+2022-06-27T06:36:12.27118886Z Instructions for updating:
+2022-06-27T06:36:12.271196741Z non-resource variables are not supported in the long term
+2022-06-27T06:36:12.27588787Z Imported model (for Places365, 128x128 images)
+2022-06-27T06:36:15.127571182Z WARNING:tensorflow:From /home/jovyan/work/src/model.py:20: conv2d (from tensorflow.python.keras.legacy_tf_layers.convolutional) is deprecated and will be removed in a future version.
+2022-06-27T06:36:15.127625625Z Instructions for updating:
+2022-06-27T06:36:15.127635424Z Use `tf.keras.layers.Conv2D` instead.
+2022-06-27T06:36:15.127641681Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.
+2022-06-27T06:36:15.127654313Z Instructions for updating:
+2022-06-27T06:36:15.127659052Z Please use `layer.__call__` method instead.
+2022-06-27T06:36:15.256535403Z WARNING:tensorflow:From /home/jovyan/work/src/model.py:79: conv2d_transpose (from tensorflow.python.keras.legacy_tf_layers.convolutional) is deprecated and will be removed in a future version.
+2022-06-27T06:36:15.256581182Z Instructions for updating:
+2022-06-27T06:36:15.256588253Z Use `tf.keras.layers.Conv2DTranspose` instead.
+2022-06-27T06:36:15.327579181Z 2022-06-27 14:36:15.326484: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
+2022-06-27T06:36:15.413601745Z 2022-06-27 14:36:15.410378: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:36:15.413646851Z 2022-06-27 14:36:15.411277: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
+2022-06-27T06:36:15.413654172Z pciBusID: 0000:00:08.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
+2022-06-27T06:36:15.413657624Z coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 7.75GiB deviceMemoryBandwidth: 681.88GiB/s
+2022-06-27T06:36:15.413660889Z 2022-06-27 14:36:15.411345: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:36:15.424689012Z 2022-06-27 14:36:15.423389: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T06:36:15.432079412Z 2022-06-27 14:36:15.431364: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
+2022-06-27T06:36:15.496414752Z 2022-06-27 14:36:15.493736: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
+2022-06-27T06:36:15.593235058Z 2022-06-27 14:36:15.591720: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
+2022-06-27T06:36:15.599321627Z 2022-06-27 14:36:15.596773: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
+2022-06-27T06:36:15.805424915Z 2022-06-27 14:36:15.801512: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T06:36:15.805479188Z 2022-06-27 14:36:15.801981: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:36:15.805486999Z 2022-06-27 14:36:15.803112: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:36:15.805492776Z 2022-06-27 14:36:15.804002: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
+2022-06-27T06:36:15.805497988Z 2022-06-27 14:36:15.804847: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+2022-06-27T06:36:15.805503237Z To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2022-06-27T06:36:15.81417475Z 2022-06-27 14:36:15.813899: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2499990000 Hz
+2022-06-27T06:36:15.814946288Z 2022-06-27 14:36:15.814691: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5547d70 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
+2022-06-27T06:36:15.814965987Z 2022-06-27 14:36:15.814721: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
+2022-06-27T06:36:15.978500134Z 2022-06-27 14:36:15.972620: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:36:15.978540247Z 2022-06-27 14:36:15.973668: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4916460 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
+2022-06-27T06:36:15.978547933Z 2022-06-27 14:36:15.973703: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
+2022-06-27T06:36:15.978553148Z 2022-06-27 14:36:15.974073: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:36:15.978558435Z 2022-06-27 14:36:15.974960: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
+2022-06-27T06:36:15.978579396Z pciBusID: 0000:00:08.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
+2022-06-27T06:36:15.978583413Z coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 7.75GiB deviceMemoryBandwidth: 681.88GiB/s
+2022-06-27T06:36:15.978586337Z 2022-06-27 14:36:15.974996: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:36:15.978589107Z 2022-06-27 14:36:15.975027: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T06:36:15.97859187Z 2022-06-27 14:36:15.975052: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
+2022-06-27T06:36:15.978594744Z 2022-06-27 14:36:15.975075: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
+2022-06-27T06:36:15.978597775Z 2022-06-27 14:36:15.975098: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
+2022-06-27T06:36:15.978600631Z 2022-06-27 14:36:15.975121: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
+2022-06-27T06:36:15.978604493Z 2022-06-27 14:36:15.975144: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T06:36:15.978607328Z 2022-06-27 14:36:15.975351: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:36:15.978610359Z 2022-06-27 14:36:15.976383: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:36:15.978613279Z 2022-06-27 14:36:15.977323: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
+2022-06-27T06:36:15.978616007Z 2022-06-27 14:36:15.977373: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:36:16.61051414Z 2022-06-27 14:36:16.609849: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
+2022-06-27T06:36:16.610556469Z 2022-06-27 14:36:16.609912: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 
+2022-06-27T06:36:16.61056136Z 2022-06-27 14:36:16.609925: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N 
+2022-06-27T06:36:16.621638578Z 2022-06-27 14:36:16.618766: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:36:16.621703314Z 2022-06-27 14:36:16.619824: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:36:16.621711487Z 2022-06-27 14:36:16.620774: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7116 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:08.0, compute capability: 6.0)
+2022-06-27T06:36:16.659863984Z 2022-06-27 14:36:16.659585: W tensorflow/core/util/tensor_slice_reader.cc:95] Could not open /home/jovyan/work/src/output/models: Failed precondition: /home/jovyan/work/src/output/models; Is a directory: perhaps your file is in a different file format and you need to use a different restore operator?
+2022-06-27T06:36:16.661458563Z 2022-06-27 14:36:16.661189: W tensorflow/core/util/tensor_slice_reader.cc:95] Could not open /home/jovyan/work/src/output/models: Failed precondition: /home/jovyan/work/src/output/models; Is a directory: perhaps your file is in a different file format and you need to use a different restore operator?
+2022-06-27T06:36:16.66147741Z 2022-06-27 14:36:16.661256: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at save_restore_tensor.cc:182 : Data loss: Unable to open table file /home/jovyan/work/src/output/models: Failed precondition: /home/jovyan/work/src/output/models; Is a directory: perhaps your file is in a different file format and you need to use a different restore operator?
+2022-06-27T06:36:16.858577056Z Traceback (most recent call last):
+2022-06-27T06:36:16.858614223Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1365, in _do_call
+2022-06-27T06:36:16.860723274Z     return fn(*args)
+2022-06-27T06:36:16.860770309Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1350, in _run_fn
+2022-06-27T06:36:16.860778361Z     target_list, run_metadata)
+2022-06-27T06:36:16.860783251Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1443, in _call_tf_sessionrun
+2022-06-27T06:36:16.860788557Z     run_metadata)
+2022-06-27T06:36:16.860793314Z tensorflow.python.framework.errors_impl.DataLossError: 2 root error(s) found.
+2022-06-27T06:36:16.860798308Z   (0) Data loss: Unable to open table file /home/jovyan/work/src/output/models: Failed precondition: /home/jovyan/work/src/output/models; Is a directory: perhaps your file is in a different file format and you need to use a different restore operator?
+2022-06-27T06:36:16.860804505Z 	 [[{{node save/RestoreV2}}]]
+2022-06-27T06:36:16.860809493Z   (1) Data loss: Unable to open table file /home/jovyan/work/src/output/models: Failed precondition: /home/jovyan/work/src/output/models; Is a directory: perhaps your file is in a different file format and you need to use a different restore operator?
+2022-06-27T06:36:16.860815269Z 	 [[{{node save/RestoreV2}}]]
+2022-06-27T06:36:16.860819647Z 	 [[save/RestoreV2/_17]]
+2022-06-27T06:36:16.860824283Z 0 successful operations.
+2022-06-27T06:36:16.860829198Z 0 derived errors ignored.
+2022-06-27T06:36:16.860833772Z 
+2022-06-27T06:36:16.860838066Z During handling of the above exception, another exception occurred:
+2022-06-27T06:36:16.86084295Z 
+2022-06-27T06:36:16.860847246Z Traceback (most recent call last):
+2022-06-27T06:36:16.860865911Z   File "src/test.py", line 33, in <module>
+2022-06-27T06:36:16.867479466Z     saver.restore(sess, model_PATH)
+2022-06-27T06:36:16.867497042Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/training/saver.py", line 1299, in restore
+2022-06-27T06:36:16.867501318Z     {self.saver_def.filename_tensor_name: save_path})
+2022-06-27T06:36:16.867504328Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 958, in run
+2022-06-27T06:36:16.867507425Z     run_metadata_ptr)
+2022-06-27T06:36:16.867510248Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1181, in _run
+2022-06-27T06:36:16.867513326Z     feed_dict_tensor, options, run_metadata)
+2022-06-27T06:36:16.867516126Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1359, in _do_run
+2022-06-27T06:36:16.867519148Z     run_metadata)
+2022-06-27T06:36:16.867521656Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1384, in _do_call
+2022-06-27T06:36:16.867524457Z     raise type(e)(node_def, op, message)
+2022-06-27T06:36:16.867527053Z tensorflow.python.framework.errors_impl.DataLossError: 2 root error(s) found.
+2022-06-27T06:36:16.867529858Z   (0) Data loss: Unable to open table file /home/jovyan/work/src/output/models: Failed precondition: /home/jovyan/work/src/output/models; Is a directory: perhaps your file is in a different file format and you need to use a different restore operator?
+2022-06-27T06:36:16.867532909Z 	 [[node save/RestoreV2 (defined at src/test.py:30) ]]
+2022-06-27T06:36:16.867535905Z   (1) Data loss: Unable to open table file /home/jovyan/work/src/output/models: Failed precondition: /home/jovyan/work/src/output/models; Is a directory: perhaps your file is in a different file format and you need to use a different restore operator?
+2022-06-27T06:36:16.86753885Z 	 [[node save/RestoreV2 (defined at src/test.py:30) ]]
+2022-06-27T06:36:16.867541735Z 	 [[save/RestoreV2/_17]]
+2022-06-27T06:36:16.86754431Z 0 successful operations.
+2022-06-27T06:36:16.867547008Z 0 derived errors ignored.
+2022-06-27T06:36:16.86754958Z 
+2022-06-27T06:36:16.8675522Z Original stack trace for 'save/RestoreV2':
+2022-06-27T06:36:16.867554898Z   File "src/test.py", line 30, in <module>
+2022-06-27T06:36:16.86755794Z     saver = tf.train.Saver()
+2022-06-27T06:36:16.867560564Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/training/saver.py", line 836, in __init__
+2022-06-27T06:36:16.867563425Z     self.build()
+2022-06-27T06:36:16.867565938Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/training/saver.py", line 848, in build
+2022-06-27T06:36:16.867580705Z     self._build(self._filename, build_save=True, build_restore=True)
+2022-06-27T06:36:16.867583844Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/training/saver.py", line 886, in _build
+2022-06-27T06:36:16.867586717Z     build_restore=build_restore)
+2022-06-27T06:36:16.86758929Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/training/saver.py", line 516, in _build_internal
+2022-06-27T06:36:16.867592137Z     restore_sequentially, reshape)
+2022-06-27T06:36:16.867594694Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/training/saver.py", line 336, in _AddRestoreOps
+2022-06-27T06:36:16.867597486Z     restore_sequentially)
+2022-06-27T06:36:16.867600011Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/training/saver.py", line 583, in bulk_restore
+2022-06-27T06:36:16.867602824Z     return io_ops.restore_v2(filename_tensor, names, slices, dtypes)
+2022-06-27T06:36:16.867605422Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/ops/gen_io_ops.py", line 1524, in restore_v2
+2022-06-27T06:36:16.867608998Z     name=name)
+2022-06-27T06:36:16.867611624Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py", line 744, in _apply_op_helper
+2022-06-27T06:36:16.867614426Z     attrs=attr_protos, op_def=op_def)
+2022-06-27T06:36:16.867616967Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3485, in _create_op_internal
+2022-06-27T06:36:16.8676198Z     op_def=op_def)
+2022-06-27T06:36:16.867622461Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
+2022-06-27T06:36:16.867625257Z     self._traceback = tf_stack.extract_stack()
+2022-06-27T06:36:16.86762792Z 
+2022-06-27T06:36:17.563235506Z SYSTEM: Finishing...
diff --git a/job_logs/job-gpu-62b95050f752e3e25d1d3e32.log b/job_logs/job-gpu-62b95050f752e3e25d1d3e32.log
new file mode 100644
index 0000000..de85ff8
--- /dev/null
+++ b/job_logs/job-gpu-62b95050f752e3e25d1d3e32.log
@@ -0,0 +1,67 @@
+2022-06-27T06:38:15.394534065Z SYSTEM: Preparing env...
+2022-06-27T06:38:15.936080766Z SYSTEM: Running...
+2022-06-27T06:38:16.723513366Z 2022-06-27 14:38:16.723151: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:38:18.316735193Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
+2022-06-27T06:38:18.316789541Z Instructions for updating:
+2022-06-27T06:38:18.316798297Z non-resource variables are not supported in the long term
+2022-06-27T06:38:18.323267476Z Imported model (for Places365, 128x128 images)
+2022-06-27T06:38:21.064529748Z WARNING:tensorflow:From /home/jovyan/work/src/model.py:20: conv2d (from tensorflow.python.keras.legacy_tf_layers.convolutional) is deprecated and will be removed in a future version.
+2022-06-27T06:38:21.064570688Z Instructions for updating:
+2022-06-27T06:38:21.064600199Z Use `tf.keras.layers.Conv2D` instead.
+2022-06-27T06:38:21.076688641Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.
+2022-06-27T06:38:21.076716602Z Instructions for updating:
+2022-06-27T06:38:21.076724171Z Please use `layer.__call__` method instead.
+2022-06-27T06:38:21.221472244Z WARNING:tensorflow:From /home/jovyan/work/src/model.py:79: conv2d_transpose (from tensorflow.python.keras.legacy_tf_layers.convolutional) is deprecated and will be removed in a future version.
+2022-06-27T06:38:21.221498205Z Instructions for updating:
+2022-06-27T06:38:21.221506439Z Use `tf.keras.layers.Conv2DTranspose` instead.
+2022-06-27T06:38:21.312483203Z 2022-06-27 14:38:21.311776: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
+2022-06-27T06:38:21.356502814Z 2022-06-27 14:38:21.355592: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:38:21.361476016Z 2022-06-27 14:38:21.356582: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
+2022-06-27T06:38:21.36149573Z pciBusID: 0000:00:08.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
+2022-06-27T06:38:21.36151434Z coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 7.75GiB deviceMemoryBandwidth: 681.88GiB/s
+2022-06-27T06:38:21.361522652Z 2022-06-27 14:38:21.356626: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:38:21.36152877Z 2022-06-27 14:38:21.359432: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T06:38:21.373639957Z 2022-06-27 14:38:21.362305: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
+2022-06-27T06:38:21.373676623Z 2022-06-27 14:38:21.362747: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
+2022-06-27T06:38:21.373685101Z 2022-06-27 14:38:21.365718: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
+2022-06-27T06:38:21.373690705Z 2022-06-27 14:38:21.367188: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
+2022-06-27T06:38:21.387364092Z 2022-06-27 14:38:21.374139: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T06:38:21.387383972Z 2022-06-27 14:38:21.374593: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:38:21.387390216Z 2022-06-27 14:38:21.376159: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:38:21.387406699Z 2022-06-27 14:38:21.377495: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
+2022-06-27T06:38:21.387412376Z 2022-06-27 14:38:21.378494: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+2022-06-27T06:38:21.387418019Z To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2022-06-27T06:38:21.431497702Z 2022-06-27 14:38:21.431103: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2499990000 Hz
+2022-06-27T06:38:21.432398121Z 2022-06-27 14:38:21.432015: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5e57cd0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
+2022-06-27T06:38:21.432415269Z 2022-06-27 14:38:21.432102: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
+2022-06-27T06:38:21.657683854Z 2022-06-27 14:38:21.654246: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:38:21.657726491Z 2022-06-27 14:38:21.655356: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4efe4c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
+2022-06-27T06:38:21.657733833Z 2022-06-27 14:38:21.655398: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
+2022-06-27T06:38:21.657739026Z 2022-06-27 14:38:21.655783: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:38:21.657757214Z 2022-06-27 14:38:21.656944: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
+2022-06-27T06:38:21.657780321Z pciBusID: 0000:00:08.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
+2022-06-27T06:38:21.6577862Z coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 7.75GiB deviceMemoryBandwidth: 681.88GiB/s
+2022-06-27T06:38:21.657790652Z 2022-06-27 14:38:21.656994: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:38:21.65779509Z 2022-06-27 14:38:21.657030: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T06:38:21.65779984Z 2022-06-27 14:38:21.657057: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
+2022-06-27T06:38:21.657804356Z 2022-06-27 14:38:21.657082: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
+2022-06-27T06:38:21.657808837Z 2022-06-27 14:38:21.657104: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
+2022-06-27T06:38:21.657813355Z 2022-06-27 14:38:21.657126: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
+2022-06-27T06:38:21.65781946Z 2022-06-27 14:38:21.657149: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T06:38:21.657823716Z 2022-06-27 14:38:21.657381: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:38:21.660267085Z 2022-06-27 14:38:21.660041: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:38:21.668181623Z 2022-06-27 14:38:21.661956: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
+2022-06-27T06:38:21.668216811Z 2022-06-27 14:38:21.662074: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:38:22.353724159Z 2022-06-27 14:38:22.351212: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
+2022-06-27T06:38:22.35376374Z 2022-06-27 14:38:22.351279: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 
+2022-06-27T06:38:22.353768071Z 2022-06-27 14:38:22.351336: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N 
+2022-06-27T06:38:22.364752412Z 2022-06-27 14:38:22.361278: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:38:22.364805738Z 2022-06-27 14:38:22.362655: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:38:22.364814235Z 2022-06-27 14:38:22.363649: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7116 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:08.0, compute capability: 6.0)
+2022-06-27T06:38:22.375506339Z Traceback (most recent call last):
+2022-06-27T06:38:22.375556853Z   File "src/test.py", line 33, in <module>
+2022-06-27T06:38:22.386370827Z     saver.restore(sess, model_PATH)
+2022-06-27T06:38:22.386415106Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/training/saver.py", line 1291, in restore
+2022-06-27T06:38:22.386423007Z     checkpoint_prefix)
+2022-06-27T06:38:22.386428006Z ValueError: The passed save_path is not a valid checkpoint: /home/jovyan/work/src/output/models/
+2022-06-27T06:38:23.059942491Z SYSTEM: Finishing...
diff --git a/job_logs/job-gpu-62b95091a393bd89f5bbaa0e.log b/job_logs/job-gpu-62b95091a393bd89f5bbaa0e.log
new file mode 100644
index 0000000..505cdb4
--- /dev/null
+++ b/job_logs/job-gpu-62b95091a393bd89f5bbaa0e.log
@@ -0,0 +1,71 @@
+2022-06-27T06:39:32.227411156Z SYSTEM: Preparing env...
+2022-06-27T06:39:32.755526021Z SYSTEM: Running...
+2022-06-27T06:39:33.491524252Z 2022-06-27 14:39:33.491184: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:39:35.12676689Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
+2022-06-27T06:39:35.126797199Z Instructions for updating:
+2022-06-27T06:39:35.12680371Z non-resource variables are not supported in the long term
+2022-06-27T06:39:35.131545901Z Imported model (for Places365, 128x128 images)
+2022-06-27T06:39:37.832567591Z WARNING:tensorflow:From /home/jovyan/work/src/model.py:20: conv2d (from tensorflow.python.keras.legacy_tf_layers.convolutional) is deprecated and will be removed in a future version.
+2022-06-27T06:39:37.832620614Z Instructions for updating:
+2022-06-27T06:39:37.832628947Z Use `tf.keras.layers.Conv2D` instead.
+2022-06-27T06:39:37.838525717Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.
+2022-06-27T06:39:37.838587273Z Instructions for updating:
+2022-06-27T06:39:37.838594925Z Please use `layer.__call__` method instead.
+2022-06-27T06:39:37.969525841Z WARNING:tensorflow:From /home/jovyan/work/src/model.py:79: conv2d_transpose (from tensorflow.python.keras.legacy_tf_layers.convolutional) is deprecated and will be removed in a future version.
+2022-06-27T06:39:37.969573824Z Instructions for updating:
+2022-06-27T06:39:37.969580998Z Use `tf.keras.layers.Conv2DTranspose` instead.
+2022-06-27T06:39:38.049508225Z 2022-06-27 14:39:38.048495: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
+2022-06-27T06:39:38.143562415Z 2022-06-27 14:39:38.133245: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:39:38.143609396Z 2022-06-27 14:39:38.134162: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
+2022-06-27T06:39:38.143614886Z pciBusID: 0000:00:08.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
+2022-06-27T06:39:38.143619664Z coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 7.75GiB deviceMemoryBandwidth: 681.88GiB/s
+2022-06-27T06:39:38.143624592Z 2022-06-27 14:39:38.134214: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:39:38.143629424Z 2022-06-27 14:39:38.136831: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T06:39:38.14363438Z 2022-06-27 14:39:38.139753: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
+2022-06-27T06:39:38.143656537Z 2022-06-27 14:39:38.140190: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
+2022-06-27T06:39:38.15524677Z 2022-06-27 14:39:38.143579: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
+2022-06-27T06:39:38.15526647Z 2022-06-27 14:39:38.145303: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
+2022-06-27T06:39:38.155272765Z 2022-06-27 14:39:38.151591: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T06:39:38.155278041Z 2022-06-27 14:39:38.151929: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:39:38.155313673Z 2022-06-27 14:39:38.153038: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:39:38.155320285Z 2022-06-27 14:39:38.154006: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
+2022-06-27T06:39:38.155324938Z 2022-06-27 14:39:38.154836: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+2022-06-27T06:39:38.15533058Z To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2022-06-27T06:39:38.169655627Z 2022-06-27 14:39:38.169268: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2499990000 Hz
+2022-06-27T06:39:38.170861573Z 2022-06-27 14:39:38.170220: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5c680c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
+2022-06-27T06:39:38.170882428Z 2022-06-27 14:39:38.170264: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
+2022-06-27T06:39:38.351589663Z 2022-06-27 14:39:38.351012: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:39:38.374522059Z 2022-06-27 14:39:38.352464: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2b14430 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
+2022-06-27T06:39:38.37457054Z 2022-06-27 14:39:38.352532: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
+2022-06-27T06:39:38.37457901Z 2022-06-27 14:39:38.353104: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:39:38.374585377Z 2022-06-27 14:39:38.353982: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
+2022-06-27T06:39:38.374607731Z pciBusID: 0000:00:08.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
+2022-06-27T06:39:38.374614597Z coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 7.75GiB deviceMemoryBandwidth: 681.88GiB/s
+2022-06-27T06:39:38.374619062Z 2022-06-27 14:39:38.354019: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:39:38.37462404Z 2022-06-27 14:39:38.354052: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T06:39:38.374628981Z 2022-06-27 14:39:38.354080: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
+2022-06-27T06:39:38.374634219Z 2022-06-27 14:39:38.354105: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
+2022-06-27T06:39:38.374639131Z 2022-06-27 14:39:38.354129: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
+2022-06-27T06:39:38.374644441Z 2022-06-27 14:39:38.354152: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
+2022-06-27T06:39:38.374649481Z 2022-06-27 14:39:38.354189: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T06:39:38.374657131Z 2022-06-27 14:39:38.354423: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:39:38.374663194Z 2022-06-27 14:39:38.355564: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:39:38.374668509Z 2022-06-27 14:39:38.356746: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
+2022-06-27T06:39:38.374673859Z 2022-06-27 14:39:38.356826: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:39:39.03151241Z 2022-06-27 14:39:39.030685: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
+2022-06-27T06:39:39.031555785Z 2022-06-27 14:39:39.030753: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 
+2022-06-27T06:39:39.031562668Z 2022-06-27 14:39:39.030766: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N 
+2022-06-27T06:39:39.041913611Z 2022-06-27 14:39:39.039050: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:39:39.041959612Z 2022-06-27 14:39:39.040117: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:39:39.041968069Z 2022-06-27 14:39:39.040997: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7116 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:08.0, compute capability: 6.0)
+2022-06-27T06:39:39.350472788Z 2022-06-27 14:39:39.345683: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T06:39:42.628125954Z 2022-06-27 14:39:42.623595: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T06:39:44.061667112Z Traceback (most recent call last):
+2022-06-27T06:39:44.061719351Z   File "src/test.py", line 35, in <module>
+2022-06-27T06:39:44.062379741Z     util.save_image(output[0], out_PATH)
+2022-06-27T06:39:44.062396766Z   File "/home/jovyan/work/src/util.py", line 89, in save_image
+2022-06-27T06:39:44.062672574Z     img.save(name, format='PNG')
+2022-06-27T06:39:44.062683885Z   File "/home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/PIL/Image.py", line 2161, in save
+2022-06-27T06:39:44.071457371Z     fp = builtins.open(filename, "w+b")
+2022-06-27T06:39:44.071493873Z IsADirectoryError: [Errno 21] Is a directory: '/home/jovyan/work/results/'
+2022-06-27T06:39:45.129121047Z SYSTEM: Finishing...
diff --git a/job_logs/job-gpu-62b950d6f9c7fbd55d4e9e0f.log b/job_logs/job-gpu-62b950d6f9c7fbd55d4e9e0f.log
new file mode 100644
index 0000000..701ba9c
--- /dev/null
+++ b/job_logs/job-gpu-62b950d6f9c7fbd55d4e9e0f.log
@@ -0,0 +1,63 @@
+2022-06-27T06:40:28.664029384Z SYSTEM: Preparing env...
+2022-06-27T06:40:29.161948578Z SYSTEM: Running...
+2022-06-27T06:40:29.90655155Z 2022-06-27 14:40:29.905643: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:40:31.552840979Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
+2022-06-27T06:40:31.552884694Z Instructions for updating:
+2022-06-27T06:40:31.552891704Z non-resource variables are not supported in the long term
+2022-06-27T06:40:31.557248833Z Imported model (for Places365, 128x128 images)
+2022-06-27T06:40:34.255714665Z WARNING:tensorflow:From /home/jovyan/work/src/model.py:20: conv2d (from tensorflow.python.keras.legacy_tf_layers.convolutional) is deprecated and will be removed in a future version.
+2022-06-27T06:40:34.255762327Z Instructions for updating:
+2022-06-27T06:40:34.255770327Z Use `tf.keras.layers.Conv2D` instead.
+2022-06-27T06:40:34.255776823Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.
+2022-06-27T06:40:34.255788806Z Instructions for updating:
+2022-06-27T06:40:34.255793807Z Please use `layer.__call__` method instead.
+2022-06-27T06:40:34.385566834Z WARNING:tensorflow:From /home/jovyan/work/src/model.py:79: conv2d_transpose (from tensorflow.python.keras.legacy_tf_layers.convolutional) is deprecated and will be removed in a future version.
+2022-06-27T06:40:34.385615911Z Instructions for updating:
+2022-06-27T06:40:34.385623287Z Use `tf.keras.layers.Conv2DTranspose` instead.
+2022-06-27T06:40:34.46652205Z 2022-06-27 14:40:34.463569: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
+2022-06-27T06:40:34.587041885Z 2022-06-27 14:40:34.558264: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:40:34.587091586Z 2022-06-27 14:40:34.559496: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
+2022-06-27T06:40:34.587099226Z pciBusID: 0000:00:08.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
+2022-06-27T06:40:34.587104431Z coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 7.75GiB deviceMemoryBandwidth: 681.88GiB/s
+2022-06-27T06:40:34.587110049Z 2022-06-27 14:40:34.559547: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:40:34.587115546Z 2022-06-27 14:40:34.563201: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T06:40:34.587121023Z 2022-06-27 14:40:34.566815: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
+2022-06-27T06:40:34.587142859Z 2022-06-27 14:40:34.567434: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
+2022-06-27T06:40:34.587149143Z 2022-06-27 14:40:34.571508: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
+2022-06-27T06:40:34.587154254Z 2022-06-27 14:40:34.573557: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
+2022-06-27T06:40:34.587159273Z 2022-06-27 14:40:34.581702: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T06:40:34.58717697Z 2022-06-27 14:40:34.582161: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:40:34.587184192Z 2022-06-27 14:40:34.583629: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:40:34.587191825Z 2022-06-27 14:40:34.584731: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
+2022-06-27T06:40:34.587196874Z 2022-06-27 14:40:34.585721: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+2022-06-27T06:40:34.587202666Z To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2022-06-27T06:40:34.605273814Z 2022-06-27 14:40:34.604881: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2499990000 Hz
+2022-06-27T06:40:34.612440387Z 2022-06-27 14:40:34.611455: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x43ef4a0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
+2022-06-27T06:40:34.612469486Z 2022-06-27 14:40:34.611536: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
+2022-06-27T06:40:34.818065383Z 2022-06-27 14:40:34.816854: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:40:34.818226758Z 2022-06-27 14:40:34.817945: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1133430 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
+2022-06-27T06:40:34.818242687Z 2022-06-27 14:40:34.818059: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
+2022-06-27T06:40:34.819098669Z 2022-06-27 14:40:34.818920: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:40:34.826000267Z 2022-06-27 14:40:34.820425: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
+2022-06-27T06:40:34.826040277Z pciBusID: 0000:00:08.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
+2022-06-27T06:40:34.826047919Z coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 7.75GiB deviceMemoryBandwidth: 681.88GiB/s
+2022-06-27T06:40:34.82605344Z 2022-06-27 14:40:34.820483: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:40:34.82605896Z 2022-06-27 14:40:34.820538: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T06:40:34.826064758Z 2022-06-27 14:40:34.820589: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
+2022-06-27T06:40:34.826069913Z 2022-06-27 14:40:34.820637: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
+2022-06-27T06:40:34.826075151Z 2022-06-27 14:40:34.820683: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
+2022-06-27T06:40:34.826080279Z 2022-06-27 14:40:34.820757: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
+2022-06-27T06:40:34.826085367Z 2022-06-27 14:40:34.820807: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T06:40:34.826090711Z 2022-06-27 14:40:34.821073: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:40:34.8260964Z 2022-06-27 14:40:34.822732: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:40:34.826101603Z 2022-06-27 14:40:34.824064: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
+2022-06-27T06:40:34.826106851Z 2022-06-27 14:40:34.824140: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T06:40:35.48051732Z 2022-06-27 14:40:35.478735: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
+2022-06-27T06:40:35.480572834Z 2022-06-27 14:40:35.478800: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 
+2022-06-27T06:40:35.480580695Z 2022-06-27 14:40:35.478813: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N 
+2022-06-27T06:40:35.491016804Z 2022-06-27 14:40:35.488592: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:40:35.492925518Z 2022-06-27 14:40:35.491127: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T06:40:35.492963862Z 2022-06-27 14:40:35.492080: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7116 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:08.0, compute capability: 6.0)
+2022-06-27T06:40:35.830526984Z 2022-06-27 14:40:35.825663: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T06:40:37.078582913Z 2022-06-27 14:40:37.071379: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T06:40:39.337113298Z SYSTEM: Finishing...
diff --git a/job_logs/job-gpu-62b96ec3f901e7972521f6bc.log b/job_logs/job-gpu-62b96ec3f901e7972521f6bc.log
new file mode 100644
index 0000000..d102a1b
--- /dev/null
+++ b/job_logs/job-gpu-62b96ec3f901e7972521f6bc.log
@@ -0,0 +1,63 @@
+2022-06-27T08:48:09.24239905Z SYSTEM: Preparing env...
+2022-06-27T08:48:09.786941439Z SYSTEM: Running...
+2022-06-27T08:48:10.51858763Z 2022-06-27 16:48:10.517635: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T08:48:12.178056383Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
+2022-06-27T08:48:12.178109572Z Instructions for updating:
+2022-06-27T08:48:12.178117458Z non-resource variables are not supported in the long term
+2022-06-27T08:48:12.183152171Z Imported model (for Places365, 128x128 images)
+2022-06-27T08:48:14.874564815Z WARNING:tensorflow:From /home/jovyan/work/src/model.py:20: conv2d (from tensorflow.python.keras.legacy_tf_layers.convolutional) is deprecated and will be removed in a future version.
+2022-06-27T08:48:14.87460536Z Instructions for updating:
+2022-06-27T08:48:14.874612991Z Use `tf.keras.layers.Conv2D` instead.
+2022-06-27T08:48:14.879516836Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.
+2022-06-27T08:48:14.879552898Z Instructions for updating:
+2022-06-27T08:48:14.879556966Z Please use `layer.__call__` method instead.
+2022-06-27T08:48:14.998783159Z WARNING:tensorflow:From /home/jovyan/work/src/model.py:79: conv2d_transpose (from tensorflow.python.keras.legacy_tf_layers.convolutional) is deprecated and will be removed in a future version.
+2022-06-27T08:48:14.99882407Z Instructions for updating:
+2022-06-27T08:48:14.998827896Z Use `tf.keras.layers.Conv2DTranspose` instead.
+2022-06-27T08:48:15.082132374Z 2022-06-27 16:48:15.075591: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
+2022-06-27T08:48:15.167539823Z 2022-06-27 16:48:15.154770: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:48:15.167590578Z 2022-06-27 16:48:15.156152: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
+2022-06-27T08:48:15.16759558Z pciBusID: 0000:00:08.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
+2022-06-27T08:48:15.167598782Z coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 7.75GiB deviceMemoryBandwidth: 681.88GiB/s
+2022-06-27T08:48:15.167602013Z 2022-06-27 16:48:15.156213: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T08:48:15.167605072Z 2022-06-27 16:48:15.160218: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T08:48:15.167608115Z 2022-06-27 16:48:15.163802: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
+2022-06-27T08:48:15.167628968Z 2022-06-27 16:48:15.164231: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
+2022-06-27T08:48:15.172464872Z 2022-06-27 16:48:15.167436: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
+2022-06-27T08:48:15.17248651Z 2022-06-27 16:48:15.168987: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
+2022-06-27T08:48:15.179400732Z 2022-06-27 16:48:15.175372: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T08:48:15.179421972Z 2022-06-27 16:48:15.175712: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:48:15.179428301Z 2022-06-27 16:48:15.176844: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:48:15.17943329Z 2022-06-27 16:48:15.177689: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
+2022-06-27T08:48:15.179438827Z 2022-06-27 16:48:15.178507: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+2022-06-27T08:48:15.179443601Z To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2022-06-27T08:48:15.189791017Z 2022-06-27 16:48:15.189490: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2499990000 Hz
+2022-06-27T08:48:15.190826889Z 2022-06-27 16:48:15.190353: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5d4d3b0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
+2022-06-27T08:48:15.190840436Z 2022-06-27 16:48:15.190405: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
+2022-06-27T08:48:15.354103229Z 2022-06-27 16:48:15.352890: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:48:15.360495813Z 2022-06-27 16:48:15.353962: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2a91430 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
+2022-06-27T08:48:15.36051428Z 2022-06-27 16:48:15.354039: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
+2022-06-27T08:48:15.360518449Z 2022-06-27 16:48:15.354703: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:48:15.360521962Z 2022-06-27 16:48:15.355674: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
+2022-06-27T08:48:15.360540021Z pciBusID: 0000:00:08.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
+2022-06-27T08:48:15.360543183Z coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 7.75GiB deviceMemoryBandwidth: 681.88GiB/s
+2022-06-27T08:48:15.360546053Z 2022-06-27 16:48:15.355713: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T08:48:15.360548859Z 2022-06-27 16:48:15.355757: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T08:48:15.360551628Z 2022-06-27 16:48:15.355789: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
+2022-06-27T08:48:15.360554374Z 2022-06-27 16:48:15.355815: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
+2022-06-27T08:48:15.360557367Z 2022-06-27 16:48:15.355840: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
+2022-06-27T08:48:15.360560106Z 2022-06-27 16:48:15.355863: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
+2022-06-27T08:48:15.360562889Z 2022-06-27 16:48:15.355889: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T08:48:15.360566266Z 2022-06-27 16:48:15.356094: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:48:15.360569337Z 2022-06-27 16:48:15.357167: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:48:15.360572245Z 2022-06-27 16:48:15.358197: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
+2022-06-27T08:48:15.360574967Z 2022-06-27 16:48:15.358256: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T08:48:16.025531234Z 2022-06-27 16:48:16.024309: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
+2022-06-27T08:48:16.02556822Z 2022-06-27 16:48:16.024381: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 
+2022-06-27T08:48:16.02557269Z 2022-06-27 16:48:16.024394: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N 
+2022-06-27T08:48:16.036018785Z 2022-06-27 16:48:16.033186: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:48:16.036045308Z 2022-06-27 16:48:16.034241: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:48:16.036052087Z 2022-06-27 16:48:16.035136: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7116 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:08.0, compute capability: 6.0)
+2022-06-27T08:48:16.324801304Z 2022-06-27 16:48:16.323386: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T08:48:17.556743331Z 2022-06-27 16:48:17.551710: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T08:48:19.783798978Z SYSTEM: Finishing...
diff --git a/job_logs/job-gpu-62b9701b8029151df74612ce.log b/job_logs/job-gpu-62b9701b8029151df74612ce.log
new file mode 100644
index 0000000..8c40844
--- /dev/null
+++ b/job_logs/job-gpu-62b9701b8029151df74612ce.log
@@ -0,0 +1,15 @@
+2022-06-27T08:53:53.960313457Z SYSTEM: Preparing env...
+2022-06-27T08:53:54.530950342Z SYSTEM: Running...
+2022-06-27T08:53:55.275717821Z 2022-06-27 16:53:55.274909: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T08:53:56.910131992Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
+2022-06-27T08:53:56.910179395Z Instructions for updating:
+2022-06-27T08:53:56.91018644Z non-resource variables are not supported in the long term
+2022-06-27T08:53:56.915074142Z Imported model (for Places365, 128x128 images)
+2022-06-27T08:53:59.436145133Z Traceback (most recent call last):
+2022-06-27T08:53:59.436188131Z   File "src/test.py", line 26, in <module>
+2022-06-27T08:53:59.436409259Z     img_p = util.preprocess_images_outpainting(img)
+2022-06-27T08:53:59.43642622Z   File "/home/jovyan/work/src/util.py", line 58, in preprocess_images_outpainting
+2022-06-27T08:53:59.436715361Z     imgs_p = np.concatenate((imgs, mask), axis=3)
+2022-06-27T08:53:59.436729791Z   File "<__array_function__ internals>", line 6, in concatenate
+2022-06-27T08:53:59.443505289Z ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 250 and the array at index 1 has size 128
+2022-06-27T08:53:59.902211373Z SYSTEM: Finishing...
diff --git a/job_logs/job-gpu-62b971036452cd65a61ca625.log b/job_logs/job-gpu-62b971036452cd65a61ca625.log
new file mode 100644
index 0000000..4504859
--- /dev/null
+++ b/job_logs/job-gpu-62b971036452cd65a61ca625.log
@@ -0,0 +1,63 @@
+2022-06-27T08:57:46.196243927Z SYSTEM: Preparing env...
+2022-06-27T08:57:46.729899376Z SYSTEM: Running...
+2022-06-27T08:57:47.457595515Z 2022-06-27 16:57:47.456138: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T08:57:49.146821587Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
+2022-06-27T08:57:49.146871573Z Instructions for updating:
+2022-06-27T08:57:49.146880336Z non-resource variables are not supported in the long term
+2022-06-27T08:57:49.151937708Z Imported model (for Places365, 128x128 images)
+2022-06-27T08:57:51.925523771Z WARNING:tensorflow:From /home/jovyan/work/src/model.py:20: conv2d (from tensorflow.python.keras.legacy_tf_layers.convolutional) is deprecated and will be removed in a future version.
+2022-06-27T08:57:51.925574935Z Instructions for updating:
+2022-06-27T08:57:51.925582982Z Use `tf.keras.layers.Conv2D` instead.
+2022-06-27T08:57:51.938638561Z WARNING:tensorflow:From /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.
+2022-06-27T08:57:51.938691742Z Instructions for updating:
+2022-06-27T08:57:51.938700174Z Please use `layer.__call__` method instead.
+2022-06-27T08:57:52.053507181Z WARNING:tensorflow:From /home/jovyan/work/src/model.py:79: conv2d_transpose (from tensorflow.python.keras.legacy_tf_layers.convolutional) is deprecated and will be removed in a future version.
+2022-06-27T08:57:52.053542901Z Instructions for updating:
+2022-06-27T08:57:52.053549331Z Use `tf.keras.layers.Conv2DTranspose` instead.
+2022-06-27T08:57:52.137499385Z 2022-06-27 16:57:52.133265: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
+2022-06-27T08:57:52.241583163Z 2022-06-27 16:57:52.233707: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:57:52.241641201Z 2022-06-27 16:57:52.234628: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
+2022-06-27T08:57:52.241649804Z pciBusID: 0000:00:08.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
+2022-06-27T08:57:52.241654633Z coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 7.75GiB deviceMemoryBandwidth: 681.88GiB/s
+2022-06-27T08:57:52.241659849Z 2022-06-27 16:57:52.234668: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T08:57:52.241664684Z 2022-06-27 16:57:52.237458: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T08:57:52.241669496Z 2022-06-27 16:57:52.240254: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
+2022-06-27T08:57:52.241690618Z 2022-06-27 16:57:52.240676: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
+2022-06-27T08:57:52.250526608Z 2022-06-27 16:57:52.243841: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
+2022-06-27T08:57:52.250549004Z 2022-06-27 16:57:52.245384: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
+2022-06-27T08:57:52.255036662Z 2022-06-27 16:57:52.251546: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T08:57:52.255059262Z 2022-06-27 16:57:52.251873: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:57:52.255067412Z 2022-06-27 16:57:52.252957: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:57:52.255073068Z 2022-06-27 16:57:52.253796: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
+2022-06-27T08:57:52.25507835Z 2022-06-27 16:57:52.254586: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+2022-06-27T08:57:52.255083681Z To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2022-06-27T08:57:52.271420544Z 2022-06-27 16:57:52.270498: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2499990000 Hz
+2022-06-27T08:57:52.271743118Z 2022-06-27 16:57:52.271455: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4747740 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
+2022-06-27T08:57:52.271767039Z 2022-06-27 16:57:52.271509: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
+2022-06-27T08:57:52.444513161Z 2022-06-27 16:57:52.438561: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:57:52.444560328Z 2022-06-27 16:57:52.439606: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x44724a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
+2022-06-27T08:57:52.444568583Z 2022-06-27 16:57:52.439644: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
+2022-06-27T08:57:52.444573851Z 2022-06-27 16:57:52.440010: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:57:52.444579411Z 2022-06-27 16:57:52.440946: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
+2022-06-27T08:57:52.444601594Z pciBusID: 0000:00:08.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
+2022-06-27T08:57:52.444607895Z coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 7.75GiB deviceMemoryBandwidth: 681.88GiB/s
+2022-06-27T08:57:52.44461265Z 2022-06-27 16:57:52.440984: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T08:57:52.444617514Z 2022-06-27 16:57:52.441015: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T08:57:52.444622224Z 2022-06-27 16:57:52.441041: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
+2022-06-27T08:57:52.444627069Z 2022-06-27 16:57:52.441064: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
+2022-06-27T08:57:52.444631482Z 2022-06-27 16:57:52.441085: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
+2022-06-27T08:57:52.44463649Z 2022-06-27 16:57:52.441106: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
+2022-06-27T08:57:52.444642981Z 2022-06-27 16:57:52.441130: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T08:57:52.444648137Z 2022-06-27 16:57:52.441319: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:57:52.444653345Z 2022-06-27 16:57:52.442307: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:57:52.444658464Z 2022-06-27 16:57:52.443119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
+2022-06-27T08:57:52.444662832Z 2022-06-27 16:57:52.443165: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
+2022-06-27T08:57:53.113557514Z 2022-06-27 16:57:53.112857: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
+2022-06-27T08:57:53.113618014Z 2022-06-27 16:57:53.112923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 
+2022-06-27T08:57:53.113626372Z 2022-06-27 16:57:53.112936: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N 
+2022-06-27T08:57:53.125264805Z 2022-06-27 16:57:53.122189: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:57:53.125308939Z 2022-06-27 16:57:53.123241: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-06-27T08:57:53.125317622Z 2022-06-27 16:57:53.124239: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7116 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:08.0, compute capability: 6.0)
+2022-06-27T08:57:53.434540742Z 2022-06-27 16:57:53.425442: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
+2022-06-27T08:57:54.72057696Z 2022-06-27 16:57:54.713817: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
+2022-06-27T08:57:57.027310115Z SYSTEM: Finishing...
diff --git a/poster/.ipynb_checkpoints/msabini-gili__image-outpainting-poster-checkpoint.pdf b/poster/.ipynb_checkpoints/msabini-gili__image-outpainting-poster-checkpoint.pdf
new file mode 100755
index 0000000..0d1eb5e
Binary files /dev/null and b/poster/.ipynb_checkpoints/msabini-gili__image-outpainting-poster-checkpoint.pdf differ
diff --git a/project_requirements.txt b/project_requirements.txt
new file mode 100644
index 0000000..0a645d9
--- /dev/null
+++ b/project_requirements.txt
@@ -0,0 +1,140 @@
+murmurhash==1.0.6
+jmespath==0.10.0
+configparser==5.2.0
+imbalanced-learn==0.6.2
+Augmentor==0.2.8
+scikit-image==0.15.0
+tensorboard-data-server==0.6.1
+requests-oauthlib==1.3.1
+argon2-cffi-bindings==21.2.0
+thinc==7.4.1
+charset-normalizer==2.0.12
+nltk==3.5
+transformers==4.1.1
+astunparse==1.6.3
+PyWavelets==1.2.0
+semantic-version==2.8.5
+sentencepiece==0.1.91
+PyAudio==0.2.11
+greenlet==1.1.2
+ruamel.yaml.clib==0.2.6
+pyasn1==0.4.8
+attrs==19.3.0
+retrying==1.3.3
+torchtext==0.6.0
+func-timeout==4.3.5
+gym==0.15.7
+importlib-metadata==4.11.1
+pyasn1-modules==0.2.8
+cmake==3.21.1
+word2vec==0.11.1
+packaging==21.3
+preshed==3.0.6
+regex==2022.1.18
+tensorflow-privacy==0.5.2
+rsa==4.8
+easydict==1.9
+spacy==2.3.2
+tensorboardX==2.0
+defusedxml==0.7.1
+s3transfer==0.3.3
+networkx==2.6.3
+catalogue==1.0.0
+openpyxl==2.6.4
+dm-tree==0.1.6
+imageio==2.8.0
+metakernel==0.28.2
+opt-einsum==3.3.0
+cachetools==3.1.1
+smart-open==5.2.1
+multipledispatch==0.6.0
+argon2-cffi==21.3.0
+tinycss2==1.1.1
+graphviz==0.14
+minio==5.0.10
+boto3==1.16.25
+unification==0.2.2
+oauthlib==3.2.0
+google-auth==2.6.0
+gensim==3.8.3
+tensorboard-plugin-wit==1.8.1
+pytorch-pretrained-bert==0.6.2
+plotly==4.8.1
+joblib==1.1.0
+pydot==1.4.1
+sacremoses==0.0.47
+calysto==1.0.6
+mpmath==1.2.1
+toolz==0.11.2
+en-core-web-sm==https://files.momodel.cn/en_core_web_sm-2.3.0.tar.gz
+rouge==1.0.0
+plac==1.1.3
+importlib-resources==5.4.0
+et-xmlfile==1.1.0
+typing-extensions==4.1.1
+certipy==0.1.3
+platformdirs==2.5.1
+debugpy==1.5.1
+kanren==0.2.3
+cryptography==36.0.1
+python-json-logger==2.0.2
+ruamel.yaml==0.17.21
+pyglet==1.5.0
+cymem==2.0.6
+Shapely==1.7.0
+click==8.0.4
+jdcal==1.4.1
+jupyter-telemetry==0.1.0
+tensorflow-federated==0.17.0
+tensorflow-estimator==2.3.0
+cloudpickle==1.2.2
+jupyterlab-server==0.2.0
+nest-asyncio==1.5.4
+mindspore==https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.0.0/MindSpore/cpu/ubuntu_x86/mindspore-1.0.0-cp37-cp37m-linux_x86_64.whl
+google-pasta==0.2.0
+async-generator==1.10
+yellowbrick==1.1
+tf-slim==1.1.0
+xlrd==1.2.0
+numpyencoder==0.3.0
+copulas==0.3.3
+torch==1.4.0+cpu
+typeguard==2.13.3
+pyOpenSSL==22.0.0
+matplotlib-inline==0.1.3
+tqdm==4.46.1
+torchvision==0.5.0+cpu
+wrapt==1.13.3
+google-auth-oauthlib==0.4.6
+pycparser==2.21
+filelock==3.6.0
+botocore==1.19.25
+XlsxWriter==1.4.3
+dlib==19.22.0
+portpicker==1.3.9
+pytorch-transformers==1.2.0
+srsly==1.0.5
+distlib==0.3.4
+Cython==0.29.20
+install==1.3.5
+cssselect2==0.4.1
+CairoSVG==2.5.2
+pygame==2.0.1
+ipdb==0.13.2
+zipp==3.7.0
+tensorflow-model-optimization==0.4.1
+wasabi==0.9.0
+svgwrite==1.4.1
+baytune==0.3.12
+jieba==0.42.1
+minepy==1.2.4
+cffi==1.15.0
+blis==0.4.1
+paddlepaddle==2.0.1
+cairocffi==1.3.0
+tensorflow-addons==0.11.2
+sympy==1.6.2
+pyrsistent==0.18.1
+imgaug==0.4.0
+asttokens==2.0.5
+tokenizers==0.9.4
diff --git a/results/.ipynb_checkpoints/README-checkpoint.md b/results/.ipynb_checkpoints/README-checkpoint.md
new file mode 100644
index 0000000..81902f1
--- /dev/null
+++ b/results/.ipynb_checkpoints/README-checkpoint.md
@@ -0,0 +1,2 @@
+Please store your training checkpoints or results here
+请在此处存储 checkpoints 和结果文件
\ No newline at end of file
diff --git a/results/.ipynb_checkpoints/city_output-checkpoint.png b/results/.ipynb_checkpoints/city_output-checkpoint.png
new file mode 100644
index 0000000..ef334ed
Binary files /dev/null and b/results/.ipynb_checkpoints/city_output-checkpoint.png differ
diff --git a/results/.ipynb_checkpoints/test_output-checkpoint.png b/results/.ipynb_checkpoints/test_output-checkpoint.png
new file mode 100644
index 0000000..3aad43a
Binary files /dev/null and b/results/.ipynb_checkpoints/test_output-checkpoint.png differ
diff --git a/results/city_output.png b/results/city_output.png
new file mode 100644
index 0000000..4c58e77
Binary files /dev/null and b/results/city_output.png differ
diff --git a/results/test_output.png b/results/test_output.png
new file mode 100644
index 0000000..3aad43a
Binary files /dev/null and b/results/test_output.png differ
diff --git a/src/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/src/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..4f73a2b
--- /dev/null
+++ b/src/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "db0b0a11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-06-27 17:55:39.372674: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory\n",
+      "2022-06-27 17:55:39.372709: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "non-resource variables are not supported in the long term\n",
+      "Imported model (for Places365, 128x128 images)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior()\n",
+    "tf.reset_default_graph()\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "import model\n",
+    "import util\n",
+    "import os\n",
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "377355df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_PATH='/home/jovyan/work/src/output/models/model2000.ckpt'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fd4ec8ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_demo_image(in_PATH):\n",
+    "    img = np.array(Image.open(in_PATH).convert('RGB'))[np.newaxis] / 255.0\n",
+    "    img_p = util.preprocess_images_outpainting(img)\n",
+    "    return img_p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cc76d61f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def inference(model_PATH, img_p):\n",
+    "    G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')\n",
+    "    G_sample = model.generator(G_Z)\n",
+    "    \n",
+    "    saver = tf.train.Saver()\n",
+    "    with tf.Session() as sess:\n",
+    "        saver.restore(sess, model_PATH)\n",
+    "        output, = sess.run([G_sample], feed_dict={G_Z: img_p})\n",
+    "        img_norm = (output[0] * 255.0).astype(np.uint8)\n",
+    "        img = Image.fromarray(img_norm, 'RGB')\n",
+    "        #util.save_image(output[0], out_PATH)\n",
+    "    return img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d93edf59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def handle(conf):\n",
+    "    \"\"\"\n",
+    "    该方法是部署之后，其他人调用你的服务时候的处理方法。\n",
+    "    请按规范填写参数结构，这样我们就能替你自动生成配置文件，方便其他人的调用。\n",
+    "    范例：\n",
+    "    params['key'] = value # value_type: str # description: some description\n",
+    "    value_type 可以选择：img, video, audio, str, int, float, [int], [str], [float]\n",
+    "    参数请放到params字典中，我们会自动解析该变量。\n",
+    "    \"\"\"\n",
+    "    base64_str = conf['Photo']\n",
+    "    image = load_demo_image(base64_str, image_size, device)\n",
+    "    res = inference(model, image)\n",
+    "    # add your code\n",
+    "    return {'Output': res}\n",
+    "    "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/.ipynb_checkpoints/model-checkpoint.py b/src/.ipynb_checkpoints/model-checkpoint.py
new file mode 100644
index 0000000..77b1317
--- /dev/null
+++ b/src/.ipynb_checkpoints/model-checkpoint.py
@@ -0,0 +1,158 @@
+# proj:    image-outpainting
+# file:    model.py
+# authors: Mark Sabini, Gili Rusak
+# desc:    Model for outpainting on 128x128 images with only
+#          a global discriminator.
+# -------------------------------------------------------------
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
+
+print('Imported model (for Places365, 128x128 images)')
+
+def generator(z):
+    with tf.variable_scope('G', reuse=tf.AUTO_REUSE):
+        conv1 = tf.layers.conv2d(
+            inputs=z,
+            filters=64,
+            kernel_size=[5, 5],
+            strides=(1, 1),
+            padding="same",
+            activation=tf.nn.relu)
+
+        conv2 = tf.layers.conv2d(
+            inputs=conv1,
+            filters=128,
+            kernel_size=[3, 3],
+            strides=(2, 2),
+            padding="same",
+            activation=tf.nn.relu)
+
+        conv3 = tf.layers.conv2d(
+            inputs=conv2,
+            filters=256,
+            kernel_size=[3, 3],
+            strides=(1, 1),
+            padding="same",
+            activation=tf.nn.relu)
+
+        conv4 = tf.layers.conv2d(
+            inputs=conv3,
+            filters=256,
+            kernel_size=[3, 3],
+            strides=(1, 1),
+            dilation_rate=(2, 2),
+            padding="same",
+            activation=tf.nn.relu)
+
+        conv5 = tf.layers.conv2d(
+            inputs=conv4,
+            filters=256,
+            kernel_size=[3, 3],
+            strides=(1, 1),
+            dilation_rate=(4, 4),
+            padding="same",
+            activation=tf.nn.relu)
+
+        conv5_p = tf.layers.conv2d(
+            inputs=conv5,
+            filters=256,
+            kernel_size=[3, 3],
+            strides=(1, 1),
+            dilation_rate=(8, 8),
+            padding="same",
+            activation=tf.nn.relu)
+
+        conv6 = tf.layers.conv2d(
+            inputs=conv5_p,
+            filters=256,
+            kernel_size=[3, 3],
+            strides=(1, 1),
+            padding="same",
+            activation=tf.nn.relu)
+
+        deconv7 = tf.layers.conv2d_transpose(
+            inputs=conv6,
+            filters=128,
+            kernel_size=[4, 4],
+            strides=(2, 2),
+            padding="same",
+            activation=tf.nn.relu)
+
+        conv8 = tf.layers.conv2d(
+            inputs=deconv7,
+            filters=64,
+            kernel_size=[3, 3],
+            strides=(1, 1),
+            padding="same",
+            activation=tf.nn.relu)
+
+        out = tf.layers.conv2d(
+            inputs=conv8,
+            filters=3,
+            kernel_size=[3, 3],
+            strides=(1, 1),
+            padding="same",
+            activation=tf.sigmoid)
+
+    return out
+
+def global_discriminator(x):
+    with tf.variable_scope('DG', reuse=tf.AUTO_REUSE):
+        conv1 = tf.layers.conv2d(
+            inputs=x,
+            filters=32,
+            kernel_size=[5, 5],
+            strides=(2, 2),
+            padding="same",
+            activation=tf.nn.relu)
+
+        conv2 = tf.layers.conv2d(
+            inputs=conv1,
+            filters=64,
+            kernel_size=[5, 5],
+            strides=(2, 2),
+            padding="same",
+            activation=tf.nn.relu)
+
+        conv3 = tf.layers.conv2d(
+            inputs=conv2,
+            filters=64,
+            kernel_size=[5, 5],
+            strides=(2, 2),
+            padding="same",
+            activation=tf.nn.relu)
+
+        conv4 = tf.layers.conv2d(
+            inputs=conv3,
+            filters=64,
+            kernel_size=[5, 5],
+            strides=(2, 2),
+            padding="same",
+            activation=tf.nn.relu)
+
+        conv5 = tf.layers.conv2d(
+            inputs=conv4,
+            filters=64,
+            kernel_size=[5, 5],
+            strides=(2, 2),
+            padding="same",
+            activation=tf.nn.relu)
+
+        conv5_flat = tf.layers.flatten(
+            inputs=conv5)
+
+        dense6 = tf.layers.dense(
+            inputs=conv5_flat,
+            units=512,
+            activation=tf.nn.relu)
+
+    return dense6
+
+def concatenator(global_x):
+    with tf.variable_scope('C', reuse=tf.AUTO_REUSE):
+        dense1 = tf.layers.dense(
+            inputs=global_x,
+            units=1,
+            activation=tf.sigmoid)
+
+    return dense1
diff --git a/src/.ipynb_checkpoints/run-checkpoint.sh b/src/.ipynb_checkpoints/run-checkpoint.sh
new file mode 100644
index 0000000..906dc92
--- /dev/null
+++ b/src/.ipynb_checkpoints/run-checkpoint.sh
@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+
+# Runs train.py and saves the console output to output/out
+stdbuf -i0 -o0 -e0 python -u train.py | tee output/out
diff --git a/src/.ipynb_checkpoints/run_ld-checkpoint.sh b/src/.ipynb_checkpoints/run_ld-checkpoint.sh
new file mode 100644
index 0000000..533429a
--- /dev/null
+++ b/src/.ipynb_checkpoints/run_ld-checkpoint.sh
@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+
+# Runs train_ld.py and saves the console output to output/out
+stdbuf -i0 -o0 -e0 python -u train_ld.py | tee output/out
diff --git a/src/.ipynb_checkpoints/test-checkpoint.py b/src/.ipynb_checkpoints/test-checkpoint.py
new file mode 100644
index 0000000..7767aae
--- /dev/null
+++ b/src/.ipynb_checkpoints/test-checkpoint.py
@@ -0,0 +1,36 @@
+# proj:    image-outpainting
+# file:    test.py
+# authors: Mark Sabini, Gili Rusak
+# desc:    Script for simulating the training pipeline. Masks out
+#          the sides of an image, feeds it through the network, and
+#          compares the network output to the original image.
+# -------------------------------------------------------------
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
+import numpy as np
+from PIL import Image
+import model
+import util
+import os
+import sys
+
+model_PATH='/home/jovyan/work/src/output/models/model2000.ckpt'
+in_PATH='/home/jovyan/work/images/test.png'
+out_PATH='/home/jovyan/work/results/test_output.png'
+
+tf.reset_default_graph()
+
+IMAGE_SZ = 128
+
+img = np.array(Image.open(in_PATH).convert('RGB'))[np.newaxis] / 255.0
+img_p = util.preprocess_images_outpainting(img)
+
+G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')
+G_sample = model.generator(G_Z)
+
+saver = tf.train.Saver()
+
+with tf.Session() as sess:
+    saver.restore(sess, model_PATH)
+    output, = sess.run([G_sample], feed_dict={G_Z: img_p})
+    util.save_image(output[0], out_PATH)
diff --git a/src/.ipynb_checkpoints/train-checkpoint.py b/src/.ipynb_checkpoints/train-checkpoint.py
new file mode 100644
index 0000000..c1ff616
--- /dev/null
+++ b/src/.ipynb_checkpoints/train-checkpoint.py
@@ -0,0 +1,202 @@
+# proj:    image-outpainting
+# file:    train.py
+# authors: Mark Sabini, Gili Rusak
+# desc:    Train the model specified in model.py, which only
+#          uses a global discriminator.
+# -------------------------------------------------------------
+import tensorflow.compat.v1 as tf
+import numpy as np
+from PIL import Image
+import model
+import util
+import os
+import sys
+from tensorflow.python.framework import ops
+tf.disable_eager_execution()
+ops.reset_default_graph()
+
+# Places365 Training Hyperparameters
+BATCH_SZ = 16
+VERBOSE = False
+EPSILON = 1e-9
+IMAGE_SZ = 128
+OUT_DIR = 'output'
+MODEL_DIR = os.path.join(OUT_DIR, 'models')
+INFO_PATH = os.path.join(OUT_DIR, 'run.txt')
+N_TEST = 10
+N_ITERS = 227500
+N_ITERS_P1 = 40950 # How many iterations to train in phase 1
+N_ITERS_P2 = 4550 # How many iterations to train in phase 2
+INTV_PRINT = 200 # How often to print
+INTV_SAVE = 1000 # How often to save the model
+ALPHA = 0.0004
+
+'''
+# City Training Hyperparameters
+BATCH_SZ = 1
+VERBOSE = False
+EPSILON = 1e-9
+IMAGE_SZ = 128
+OUT_DIR = 'output'
+MODEL_DIR = os.path.join(OUT_DIR, 'models')
+INFO_PATH = os.path.join(OUT_DIR, 'run.txt')
+N_TEST = 1
+N_ITERS = 5000
+N_ITERS_P1 = 1000 # How many iterations to train in phase 1
+N_ITERS_P2 = 400 # How many iterations to train in phase 2
+INTV_PRINT = 50 # How often to print
+INTV_SAVE = 10000 # How often to save the model
+ALPHA = 0.0004
+'''
+
+# Check that we don't clobber a pre-existing run
+if len(sys.argv) < 2 and os.path.isdir(OUT_DIR) and len(os.listdir(OUT_DIR)) > 2:
+    print('Warning, OUT_DIR already exists. Aborting.')
+    exit()
+
+# Load in a model if specified as the second argument.
+start_iter = 0
+model_filename = None
+if len(sys.argv) >= 2:
+    start_iter = int(sys.argv[1])
+    model_filename = os.path.join(MODEL_DIR, 'model%d.ckpt' % start_iter)
+
+
+
+# Generator code
+G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')
+DG_X = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 3], name='DG_X')
+
+# Load Places365 data
+data = np.load('places/places_128.npz')
+imgs = data['imgs_train'] # Originally from http://data.csail.mit.edu/places/places365/val_256.tar
+imgs_p = util.preprocess_images_outpainting(imgs)
+
+test_imgs = data['imgs_test']
+test_imgs_p = util.preprocess_images_outpainting(test_imgs)
+
+test_img = test_imgs[:N_TEST]
+test_img_p = test_imgs_p[:N_TEST]
+
+train_img = imgs[4, np.newaxis]
+train_img_p = imgs_p[4, np.newaxis]
+
+'''
+# Load city image data
+imgs = util.load_city_image()
+imgs_p = util.preprocess_images_outpainting(imgs)
+
+test_imgs = util.load_city_image()
+test_imgs_p = util.preprocess_images_outpainting(test_imgs)
+
+test_img = test_imgs
+test_img_p = test_imgs_p
+
+train_img = imgs
+train_img_p = imgs_p
+'''
+
+# Write training and testing sample ground truths as reference
+util.save_image(train_img[0], os.path.join(OUT_DIR, 'train_img.png'))
+for i_test in range(N_TEST):
+    util.save_image(test_imgs[i_test], os.path.join(OUT_DIR, 'test_img_%d.png' % i_test))
+
+G_sample = model.generator(G_Z)
+vars_G = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='G')
+
+C_real = model.concatenator(model.global_discriminator(DG_X))
+C_fake = model.concatenator(model.global_discriminator(G_sample))
+vars_DG = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='DG')
+vars_C = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='C')
+
+C_loss = -tf.reduce_mean(tf.log(tf.maximum(C_real, EPSILON)) + tf.log(tf.maximum(1. - C_fake, EPSILON)))
+G_MSE_loss = tf.losses.mean_squared_error(G_sample, DG_X, weights=tf.expand_dims(G_Z[:,:,:,3], -1)) # TODO: MULTIPLY with mask. Actually see if we want to remove this.
+G_loss = G_MSE_loss - ALPHA * tf.reduce_mean(tf.log(tf.maximum(C_fake, EPSILON)))
+
+C_solver = tf.train.AdamOptimizer().minimize(C_loss, var_list=(vars_DG + vars_C))
+G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=vars_G)
+G_MSE_solver = tf.train.AdamOptimizer().minimize(G_MSE_loss, var_list=vars_G)
+
+train_MSE_loss = []
+dev_MSE_loss = []
+
+last_output_PATH = [None] * N_TEST
+
+assert N_ITERS > N_ITERS_P1 + N_ITERS_P2
+
+# Saver to save the session
+saver = tf.train.Saver()
+
+with tf.Session() as sess:
+    if model_filename is None:
+        sess.run(tf.global_variables_initializer())
+    else:
+        saver.restore(sess, model_filename)
+    for i in range(start_iter, N_ITERS + 1):
+        batch, batch_p = util.sample_random_minibatch(imgs, imgs_p, BATCH_SZ)
+        G_sample_ = None
+        C_loss_curr, G_loss_curr, G_MSE_loss_curr = None, None, None
+        if i < N_ITERS_P1: # Stage 1 - Train Generator Only
+            if i == 0:
+                print('------------------> Beginning Phase 1...')
+            _, G_MSE_loss_curr, G_sample_ = sess.run([G_MSE_solver, G_MSE_loss, G_sample], feed_dict={DG_X: batch, G_Z: batch_p})
+        elif i < N_ITERS_P1 + N_ITERS_P2: # Stage 2 - Train Discriminator Only
+            if i == N_ITERS_P1:
+                print('------------------> Beginning Phase 2...')
+            _, C_loss_curr, C_real_, C_fake_ = sess.run([C_solver, C_loss, C_real, C_fake], feed_dict={DG_X: batch, G_Z: batch_p})
+            if VERBOSE:
+                print((i, C_loss_curr, np.min(C_real_), np.max(C_real_), np.min(C_fake_), np.max(C_fake_)))
+        else: # Stage 3 - Train both Generator and Discriminator
+            if i == N_ITERS_P1 + N_ITERS_P2:
+                print('------------------> Beginning Phase 3...')
+            _, C_loss_curr, C_real_, C_fake_ = sess.run([C_solver, C_loss, C_real, C_fake], feed_dict={DG_X: batch, G_Z: batch_p})
+            if VERBOSE:
+                print((i, C_loss_curr, 'D', np.min(C_real_), np.max(C_real_), np.min(C_fake_), np.max(C_fake_)))
+            _, G_loss_curr, G_MSE_loss_curr, G_sample_, C_fake_ = sess.run([G_solver, G_loss, G_MSE_loss, G_sample, C_fake], feed_dict={DG_X: batch, G_Z: batch_p})
+            if VERBOSE:
+                print((i, G_loss_curr, 'G', np.min(C_fake_), np.max(C_fake_)))
+
+        # Periodically test the generator on held-out images
+        if i % INTV_PRINT == 0:
+            G_MSE_loss_curr_dev = None
+            if G_sample_ is not None:
+                # Print out the dev image
+                output, G_MSE_loss_curr_dev = sess.run([G_sample, G_MSE_loss], feed_dict={DG_X: test_img, G_Z: test_img_p})
+                for i_test in range(N_TEST):
+                    util.save_image(output[i_test], os.path.join(OUT_DIR, 'dev_%d_%d.png' % (i_test, i)))
+                    last_output_PATH[i_test] = os.path.join(OUT_DIR, 'dev_%d_%d.png' % (i_test, i))
+                # Also save the train image
+                output, = sess.run([G_sample], feed_dict={DG_X: train_img, G_Z: train_img_p})
+                util.save_image(output[0], os.path.join(OUT_DIR, 'train%d.png' % i))
+            print('Iteration [%d/%d]:' % (i, N_ITERS))
+            if G_MSE_loss_curr is not None:
+                print('\tG_MSE_loss (train) = %f' % G_MSE_loss_curr)
+            if G_MSE_loss_curr_dev is not None:
+                print('\tG_MSE_loss (dev) = %f' % G_MSE_loss_curr_dev)
+            if G_loss_curr is not None:
+                print('\tG_loss = %f' % G_loss_curr)
+            if C_loss_curr is not None:
+                print('\tC_loss = %f' % C_loss_curr)
+
+        # Keep track of losses for logging
+        if G_MSE_loss_curr is not None:
+            train_MSE_loss.append([i, G_MSE_loss_curr])
+        if G_MSE_loss_curr_dev is not None:
+            dev_MSE_loss.append([i, G_MSE_loss_curr_dev])
+
+        # Save the model every so often
+        if i % INTV_SAVE == 0:
+            save_path = saver.save(sess, os.path.join(MODEL_DIR, 'model%d.ckpt' % i))
+            print('Model saved in path: %s' % save_path)
+
+        # Save the loss every so often
+        if i % INTV_SAVE == 0:
+            np.savez(os.path.join(OUT_DIR, 'loss.npz'), train_MSE_loss=np.array(train_MSE_loss), dev_MSE_loss=np.array(dev_MSE_loss))
+
+# Save the loss
+np.savez(os.path.join(OUT_DIR, 'loss.npz'), train_MSE_loss=np.array(train_MSE_loss), dev_MSE_loss=np.array(dev_MSE_loss))
+# Save the final blended output, and make a graph of the loss.
+util.plot_loss(os.path.join(OUT_DIR, 'loss.npz'), 'MSE Loss During Training', os.path.join(OUT_DIR, 'loss_plot.png'))
+for i_test in range(N_TEST):
+    util.postprocess_images_outpainting(os.path.join(OUT_DIR, 'test_img_%d.png' % i_test), last_output_PATH[i_test], os.path.join(OUT_DIR, 'out_paste_%d.png' % i_test), blend=False)
+    util.postprocess_images_outpainting(os.path.join(OUT_DIR, 'test_img_%d.png' % i_test), last_output_PATH[i_test], os.path.join(OUT_DIR, 'out_blend_%d.png' % i_test), blend=True)
diff --git a/src/.ipynb_checkpoints/train_ld-checkpoint.py b/src/.ipynb_checkpoints/train_ld-checkpoint.py
new file mode 100644
index 0000000..7a1b4cf
--- /dev/null
+++ b/src/.ipynb_checkpoints/train_ld-checkpoint.py
@@ -0,0 +1,200 @@
+# proj:    image-outpainting
+# file:    train_ld.py
+# authors: Mark Sabini, Gili Rusak
+# desc:    Train the model specified in model_ld.py, which
+#          uses both global and local discriminators.
+# -------------------------------------------------------------
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import model_ld as model
+import util
+import os
+import sys
+
+tf.reset_default_graph()
+
+# Places365 Training Hyperparameters
+BATCH_SZ = 16
+VERBOSE = False
+EPSILON = 1e-9
+IMAGE_SZ = 128
+OUT_DIR = 'output'
+MODEL_DIR = os.path.join(OUT_DIR, 'models')
+INFO_PATH = os.path.join(OUT_DIR, 'run.txt')
+N_TEST = 10
+N_ITERS = 64000
+N_ITERS_P1 = 20000 # How many iterations to train in phase 1
+N_ITERS_P2 = 4000 # How many iterations to train in phase 2
+INTV_PRINT = 200 # How often to print
+INTV_SAVE = 1000 # How often to save the model
+ALPHA = 0.0004
+
+'''
+# City Training Hyperparameters
+BATCH_SZ = 1
+VERBOSE = False
+EPSILON = 1e-9
+IMAGE_SZ = 128
+OUT_DIR = 'output'
+MODEL_DIR = os.path.join(OUT_DIR, 'models')
+INFO_PATH = os.path.join(OUT_DIR, 'run.txt')
+N_TEST = 1
+N_ITERS = 5000
+N_ITERS_P1 = 1000 # How many iterations to train in phase 1
+N_ITERS_P2 = 400 # How many iterations to train in phase 2
+INTV_PRINT = 50 # How often to print
+INTV_SAVE = 10000 # How often to save the model
+ALPHA = 0.0004
+'''
+
+# Check that we don't clobber a pre-existing run
+if len(sys.argv) < 2 and os.path.isdir(OUT_DIR) and len(os.listdir(OUT_DIR)) > 2:
+    print('Warning, OUT_DIR already exists. Aborting.')
+    exit()
+
+# Load in a model if specified as the second argument.
+start_iter = 0
+model_filename = None
+if len(sys.argv) >= 2:
+    start_iter = int(sys.argv[1])
+    model_filename = os.path.join(MODEL_DIR, 'model%d.ckpt' % start_iter)
+
+# Generator code
+G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')
+DG_X = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 3], name='DG_X')
+
+# Load Places365 data
+data = np.load('places/places_128.npz')
+imgs = data['imgs_train'] # Originally from http://data.csail.mit.edu/places/places365/val_256.tar
+imgs_p = util.preprocess_images_outpainting(imgs)
+
+test_imgs = data['imgs_test']
+test_imgs_p = util.preprocess_images_outpainting(test_imgs)
+
+test_img = test_imgs[:N_TEST]
+test_img_p = test_imgs_p[:N_TEST]
+
+train_img = imgs[4, np.newaxis]
+train_img_p = imgs_p[4, np.newaxis]
+
+'''
+# Load city image data
+imgs = util.load_city_image()
+imgs_p = util.preprocess_images_outpainting(imgs)
+
+test_imgs = util.load_city_image()
+test_imgs_p = util.preprocess_images_outpainting(test_imgs)
+
+test_img = test_imgs
+test_img_p = test_imgs_p
+
+train_img = imgs
+train_img_p = imgs_p
+'''
+
+# Write training and testing sample ground truths as reference
+util.save_image(train_img[0], os.path.join(OUT_DIR, 'train_img.png'))
+for i_test in range(N_TEST):
+    util.save_image(test_imgs[i_test], os.path.join(OUT_DIR, 'test_img_%d.png' % i_test))
+
+G_sample = model.generator(G_Z)
+vars_G = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='G')
+
+C_real = model.concatenator(model.global_discriminator(DG_X), model.local_discriminator(DG_X[:, :, :IMAGE_SZ // 2, :]), model.local_discriminator(tf.reverse(DG_X[:, :, -IMAGE_SZ // 2:, :], axis=[2])))
+C_fake = model.concatenator(model.global_discriminator(G_sample), model.local_discriminator(G_sample[:, :, :IMAGE_SZ // 2, :]), model.local_discriminator(tf.reverse(G_sample[:, :, -IMAGE_SZ // 2:, :], axis=[2])))
+vars_DG = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='DG')
+vars_DL = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='DL')
+vars_C = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='C')
+
+C_loss = -tf.reduce_mean(tf.log(tf.maximum(C_real, EPSILON)) + tf.log(tf.maximum(1. - C_fake, EPSILON)))
+G_MSE_loss = tf.losses.mean_squared_error(G_sample, DG_X, weights=tf.expand_dims(G_Z[:,:,:,3], -1)) # TODO: MULTIPLY with mask. Actually see if we want to remove this.
+G_loss = G_MSE_loss - ALPHA * tf.reduce_mean(tf.log(tf.maximum(C_fake, EPSILON)))
+
+C_solver = tf.train.AdamOptimizer().minimize(C_loss, var_list=(vars_DG + vars_DL + vars_C))
+G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=vars_G)
+G_MSE_solver = tf.train.AdamOptimizer().minimize(G_MSE_loss, var_list=vars_G)
+
+train_MSE_loss = []
+dev_MSE_loss = []
+
+last_output_PATH = [None] * N_TEST
+
+assert N_ITERS > N_ITERS_P1 + N_ITERS_P2
+
+# Saver to save the session
+saver = tf.train.Saver()
+
+with tf.Session() as sess:
+    if model_filename is None:
+        sess.run(tf.global_variables_initializer())
+    else:
+        saver.restore(sess, model_filename)
+    for i in range(start_iter, N_ITERS + 1):
+        batch, batch_p = util.sample_random_minibatch(imgs, imgs_p, BATCH_SZ)
+        G_sample_ = None
+        C_loss_curr, G_loss_curr, G_MSE_loss_curr = None, None, None
+        if i < N_ITERS_P1: # Stage 1 - Train Generator Only
+            if i == 0:
+                print('------------------> Beginning Phase 1...')
+            _, G_MSE_loss_curr, G_sample_ = sess.run([G_MSE_solver, G_MSE_loss, G_sample], feed_dict={DG_X: batch, G_Z: batch_p})
+        elif i < N_ITERS_P1 + N_ITERS_P2: # Stage 2 - Train Discriminator Only
+            if i == N_ITERS_P1:
+                print('------------------> Beginning Phase 2...')
+            _, C_loss_curr, C_real_, C_fake_ = sess.run([C_solver, C_loss, C_real, C_fake], feed_dict={DG_X: batch, G_Z: batch_p})
+            if VERBOSE:
+                print((i, C_loss_curr, np.min(C_real_), np.max(C_real_), np.min(C_fake_), np.max(C_fake_)))
+        else: # Stage 3 - Train both Generator and Discriminator
+            if i == N_ITERS_P1 + N_ITERS_P2:
+                print('------------------> Beginning Phase 3...')
+            _, C_loss_curr, C_real_, C_fake_ = sess.run([C_solver, C_loss, C_real, C_fake], feed_dict={DG_X: batch, G_Z: batch_p})
+            if VERBOSE:
+                print((i, C_loss_curr, 'D', np.min(C_real_), np.max(C_real_), np.min(C_fake_), np.max(C_fake_)))
+            _, G_loss_curr, G_MSE_loss_curr, G_sample_, C_fake_ = sess.run([G_solver, G_loss, G_MSE_loss, G_sample, C_fake], feed_dict={DG_X: batch, G_Z: batch_p})
+            if VERBOSE:
+                print((i, G_loss_curr, 'G', np.min(C_fake_), np.max(C_fake_)))
+
+        # Periodically test the generator on held-out images
+        if i % INTV_PRINT == 0:
+            G_MSE_loss_curr_dev = None
+            if G_sample_ is not None:
+                # Print out the dev image
+                output, G_MSE_loss_curr_dev = sess.run([G_sample, G_MSE_loss], feed_dict={DG_X: test_img, G_Z: test_img_p})
+                for i_test in range(N_TEST):
+                    util.save_image(output[i_test], os.path.join(OUT_DIR, 'dev_%d_%d.png' % (i_test, i)))
+                    last_output_PATH[i_test] = os.path.join(OUT_DIR, 'dev_%d_%d.png' % (i_test, i))
+                # Also save the train image
+                output, = sess.run([G_sample], feed_dict={DG_X: train_img, G_Z: train_img_p})
+                util.save_image(output[0], os.path.join(OUT_DIR, 'train%d.png' % i))
+            print('Iteration [%d/%d]:' % (i, N_ITERS))
+            if G_MSE_loss_curr is not None:
+                print('\tG_MSE_loss (train) = %f' % G_MSE_loss_curr)
+            if G_MSE_loss_curr_dev is not None:
+                print('\tG_MSE_loss (dev) = %f' % G_MSE_loss_curr_dev)
+            if G_loss_curr is not None:
+                print('\tG_loss = %f' % G_loss_curr)
+            if C_loss_curr is not None:
+                print('\tC_loss = %f' % C_loss_curr)
+
+        # Keep track of losses for logging
+        if G_MSE_loss_curr is not None:
+            train_MSE_loss.append([i, G_MSE_loss_curr])
+        if G_MSE_loss_curr_dev is not None:
+            dev_MSE_loss.append([i, G_MSE_loss_curr_dev])
+
+        # Save the model every so often
+        if i % INTV_SAVE == 0 and i > 0:
+            save_path = saver.save(sess, os.path.join(MODEL_DIR, 'model%d.ckpt' % i))
+            print('Model saved in path: %s' % save_path)
+
+        # Save the loss every so often
+        if i % INTV_SAVE == 0:
+            np.savez(os.path.join(OUT_DIR, 'loss.npz'), train_MSE_loss=np.array(train_MSE_loss), dev_MSE_loss=np.array(dev_MSE_loss))
+
+# Save the loss
+np.savez(os.path.join(OUT_DIR, 'loss.npz'), train_MSE_loss=np.array(train_MSE_loss), dev_MSE_loss=np.array(dev_MSE_loss))
+# Save the final blended output, and make a graph of the loss.
+util.plot_loss(os.path.join(OUT_DIR, 'loss.npz'), 'MSE Loss During Training', os.path.join(OUT_DIR, 'loss_plot.png'))
+for i_test in range(N_TEST):
+    util.postprocess_images_outpainting(os.path.join(OUT_DIR, 'test_img_%d.png' % i_test), last_output_PATH[i_test], os.path.join(OUT_DIR, 'out_paste_%d.png' % i_test), blend=False)
+    util.postprocess_images_outpainting(os.path.join(OUT_DIR, 'test_img_%d.png' % i_test), last_output_PATH[i_test], os.path.join(OUT_DIR, 'out_blend_%d.png' % i_test), blend=True)
diff --git a/src/.ipynb_checkpoints/util-checkpoint.py b/src/.ipynb_checkpoints/util-checkpoint.py
new file mode 100644
index 0000000..aea9dfa
--- /dev/null
+++ b/src/.ipynb_checkpoints/util-checkpoint.py
@@ -0,0 +1,274 @@
+# proj:    image-outpainting
+# file:    util.py
+# authors: Mark Sabini, Gili Rusak
+# desc:    Various utility functions for all sorts of things.
+# -------------------------------------------------------------
+import numpy as np
+from PIL import Image
+import scipy.misc
+import matplotlib.pyplot as plt
+import cv2
+import os
+import re
+import imageio
+
+IMAGE_SZ = 128 # Should be a power of 2
+
+# Loads the city image.
+# Returns: normalized numpy array of size (1, IMAGE_SZ, IMAGE_SZ, 3)
+def load_city_image():
+    im = Image.open('images/city_128.png').convert('RGB')
+    width, height = im.size
+    left = (width - IMAGE_SZ) / 2
+    top = (height - IMAGE_SZ) / 2
+    im = im.crop((left, top, left + IMAGE_SZ, top + IMAGE_SZ))
+    pix = np.array(im)
+    assert pix.shape == (IMAGE_SZ, IMAGE_SZ, 3)
+    return pix[np.newaxis] / 255.0 # Need to normalize images to [0, 1]
+
+# Loads multiple images from a directory.
+# Returns: normalized numpy array of size (m, IMAGE_SZ, IMAGE_SZ, 3)
+def load_images(in_PATH, verbose=False):
+    imgs = []
+    for filename in sorted(os.listdir(in_PATH)):
+        if verbose:
+            print('Processing %s' % filename)
+        full_filename = os.path.join(os.path.abspath(in_PATH), filename)
+        img = Image.open(full_filename).convert('RGB')
+        pix = np.array(img)
+        pix_norm = pix / 255.0
+        imgs.append(pix_norm)
+    return np.array(imgs)
+
+# Reads in all the images in a directory and saves them to an .npy file.
+def compile_images(in_PATH, out_PATH):
+    imgs = load_images(in_PATH, verbose=True)
+    np.save(out_PATH, imgs)
+
+# Masks and preprocesses an (m, IMAGE_SZ, IMAGE_SZ, 3) batch of images for image outpainting.
+# Returns: numpy array of size (m, IMAGE_SZ, IMAGE_SZ, 4)
+def preprocess_images_outpainting(imgs, crop=True):
+    m = imgs.shape[0]
+    imgs = np.array(imgs, copy=True)
+    pix_avg = np.mean(imgs, axis=(1, 2, 3))
+    if crop:
+        imgs[:, :, :int(2 * IMAGE_SZ / 8), :] = imgs[:, :, int(-2 * IMAGE_SZ / 8):, :] = pix_avg[:, np.newaxis, np.newaxis, np.newaxis]
+    mask = np.zeros((m, IMAGE_SZ, IMAGE_SZ, 1))
+    mask[:, :, :int(2 * IMAGE_SZ / 8), :] = mask[:, :, int(-2 * IMAGE_SZ / 8):, :] = 1.0
+    imgs_p = np.concatenate((imgs, mask), axis=3)
+    return imgs_p
+
+# Expands and preprocesses a single (h, w, 3) image for image outpainting.
+# Returns: numpy array of size (h, w + 2 * dw, 4)
+def preprocess_images_gen(img):
+    img = np.array(img, copy=True)
+    pix_avg = np.mean(img)
+    dw = int(2 * IMAGE_SZ / 8) # Amount that will be outpainted on each side
+    img_expand = np.ones((img.shape[0], img.shape[1] + 2 * dw, img.shape[2])) * pix_avg
+    img_expand[:, dw:-dw, :] = img
+    mask = np.zeros((img_expand.shape[0], img_expand.shape[1], 1))
+    mask[:, :int(2 * IMAGE_SZ / 8), :] = mask[:, int(-2 * IMAGE_SZ / 8):, :] = 1.0
+    img_p = np.concatenate((img_expand, mask), axis=2)
+    return img_p[np.newaxis]
+
+# Renormalizes an image to [0, 255].
+def norm_image(img_r):
+    img_norm = (img_r * 255.0).astype(np.uint8)
+    return img_norm
+
+# Visualize an image.
+def vis_image(img_r, mode='RGB'):
+    img_norm = norm_image(img_r)
+    img = Image.fromarray(img_norm, mode)
+    img.show()
+
+# Save an image as a .png file.
+def save_image(img_r, name, mode='RGB'):
+    img_norm = norm_image(img_r)
+    img = Image.fromarray(img_norm, mode)
+    img.save(name, format='PNG')
+
+# Sample a random minibatch from data.
+# Returns: Two numpy arrays, representing examples and their corresponding
+#          preprocessed arrays.
+def sample_random_minibatch(data, data_p, m):
+    indices = np.random.randint(0, data.shape[0], m)
+    return data[indices], data_p[indices]
+
+# Plots the loss and saves the plot.
+def plot_loss(loss_filename, title, out_filename):
+    loss = np.load(loss_filename)
+    assert 'train_MSE_loss' in loss and 'dev_MSE_loss' in loss
+    train_MSE_loss = loss['train_MSE_loss']
+    dev_MSE_loss = loss['dev_MSE_loss'] # TODO: Deal with dev_MSE_loss not changing during Phase 2
+    label_train, = plt.plot(train_MSE_loss[:, 0], train_MSE_loss[:, 1], label='Training MSE loss')
+    label_dev, = plt.plot(dev_MSE_loss[:, 0], dev_MSE_loss[:, 1], label='Dev MSE loss')
+    plt.legend(handles=[label_train, label_dev])
+    plt.xlabel('Iteration')
+    plt.ylabel('MSE Loss')
+    plt.title(title)
+    plt.savefig(out_filename)
+    plt.clf()
+
+# Plots the loss and saves the plot, but fancier.
+def plot_loss2(loss_filename, title, out_filename):
+    loss = np.load(loss_filename)
+    itrain_MSE_loss, train_MSE_loss = loss['itrain_MSE_loss'], loss['train_MSE_loss']
+    idev_MSE_loss, dev_MSE_loss = loss['idev_MSE_loss'], loss['dev_MSE_loss']
+    iG_loss, G_loss = loss['iG_loss'], loss['G_loss']
+    iD_loss, D_loss = loss['iD_loss'], loss['D_loss']
+    label_train, = plt.plot(itrain_MSE_loss, train_MSE_loss, label='Training MSE loss')
+    label_dev, = plt.plot(idev_MSE_loss, dev_MSE_loss, label='Dev MSE loss')
+    label_G, = plt.plot(iG_loss, G_loss, label='Generator loss')
+    label_D, = plt.plot(iD_loss, D_loss, label='Discriminator loss')
+    plt.legend(handles=[label_train, label_dev, label_G, label_D])
+    plt.xlabel('Iteration')
+    plt.ylabel('Loss')
+    plt.title(title)
+    plt.savefig(out_filename)
+    plt.clf()
+
+# Use seamless cloning to improve the generator's output.
+def postprocess_images_outpainting(img_PATH, img_o_PATH, out_PATH, blend=False): # img, img_0 are (64, 64, 3), mask is (64, 64, 1)
+    src = cv2.imread(img_PATH)[:, int(2 * IMAGE_SZ / 8):-int(2 * IMAGE_SZ / 8), :]
+    dst = cv2.imread(img_o_PATH)
+    if blend:
+        mask = np.ones(src.shape, src.dtype) * 255
+        center = (int(IMAGE_SZ / 2) - 1, int(IMAGE_SZ / 2) - 1)
+        out = cv2.seamlessClone(src, dst, mask, center, cv2.NORMAL_CLONE)
+    else:
+        out = dst.copy()
+        out[:, int(2 * IMAGE_SZ / 8):-int(2 * IMAGE_SZ / 8), :] = src
+    cv2.imwrite(out_PATH, out)
+
+# Use seamless cloning to improve the generator's output.
+def postprocess_images_gen(img, img_o, blend=False):
+    src = img[:, :, ::-1].copy()
+    dst = img_o[:, :, ::-1].copy()
+    if blend:
+        mask = np.ones(src.shape, src.dtype) * 255
+        center = (int(dst.shape[1] / 2) - 1, int(dst.shape[0] / 2) - 1)
+        out = cv2.seamlessClone(src, dst, mask, center, cv2.NORMAL_CLONE)
+    else:
+        out = dst.copy()
+        out[:, int(2 * IMAGE_SZ / 8):-int(2 * IMAGE_SZ / 8), :] = src
+    return out[:, :, ::-1].copy()
+
+# Crop and resize all the images in a directory.
+def resize_images(src_PATH, dst_PATH):
+    for filename in os.listdir(src_PATH):
+        print('Processing %s' % filename)
+        full_filename = os.path.join(os.path.abspath(src_PATH), filename)
+        img_raw = Image.open(full_filename).convert('RGB')
+        w, h = img_raw.size
+        if w <= h:
+            dim = w
+            y_start = int((h - dim) / 2)
+            img_crop = img_raw.crop(box=(0, y_start, dim, y_start + dim))
+        else: # w > h
+            dim = h
+            x_start = int((w - dim) / 2)
+            img_crop = img_raw.crop(box=(x_start, 0, x_start + dim, dim))
+        img_scale = img_crop.resize((IMAGE_SZ, IMAGE_SZ), Image.ANTIALIAS)
+        full_outfilename = os.path.join(os.path.abspath(dst_PATH), filename)
+        img_scale.save(full_outfilename, format='PNG')
+
+# Parse the output of train.py to extract the various losses.
+def parse_log(in_PATH, out_PATH):
+    data = []
+    curr_list = []
+    with open(in_PATH, 'r') as fp:
+        for i, line in enumerate(fp):
+            if i == 0:
+                continue
+            line = line.strip()
+            if line.startswith('----'):
+                continue
+            elif line.startswith('Model'):
+                continue
+            elif line.startswith('Iteration'):
+                if len(curr_list):
+                    data.append(curr_list)
+                    curr_list = []
+                curr_list.append(line)
+            else:
+                curr_list.append(line)
+        if len(curr_list):
+            data.append(curr_list)
+    G_MSE_train, G_MSE_dev, G, C = None, None, None, None
+    G_MSE_train_s, G_MSE_dev_s, G_s, C_s = [], [], [], []
+    G_MSE_train_is, G_MSE_dev_is, G_is, C_is = [], [], [], []
+    def extract_loss(str):
+        return float(re.findall('= ([\d, .]+)', str)[0])
+    for entry in data:
+        i = int(re.findall('\[(\d+)/', entry[0])[0])
+        if len(entry) == 3: # Phase 1
+            G_MSE_train = extract_loss(entry[1])
+            G_MSE_dev = extract_loss(entry[2])
+        elif len(entry) == 2: # Phase 2
+            C = extract_loss(entry[1])
+        elif len(entry) == 5: # Phase 3
+            G_MSE_train = extract_loss(entry[1])
+            G_MSE_dev = extract_loss(entry[2])
+            G = extract_loss(entry[3])
+            C = extract_loss(entry[4])
+        if G_MSE_train is not None:
+            G_MSE_train_s.append(G_MSE_train)
+            G_MSE_train_is.append(i)
+        if G_MSE_dev is not None:
+            G_MSE_dev_s.append(G_MSE_dev)
+            G_MSE_dev_is.append(i)
+        if G is not None:
+            G_s.append(G)
+            G_is.append(i)
+        if C is not None:
+            C_s.append(C)
+            C_is.append(i)
+    G_MSE_train_sm = np.array(G_MSE_train_s)
+    G_MSE_dev_sm = np.array(G_MSE_dev_s)
+    G_sm = np.array(G_s)
+    C_sm = np.array(C_s)
+    G_MSE_train_ism = np.array(G_MSE_train_is)
+    G_MSE_dev_ism = np.array(G_MSE_dev_is)
+    G_ism = np.array(G_is)
+    C_ism = np.array(C_is)
+    np.savez(out_PATH, train_MSE_loss=G_MSE_train_sm, dev_MSE_loss=G_MSE_dev_sm, G_loss=G_sm, D_loss=C_sm,
+             itrain_MSE_loss=G_MSE_train_ism, idev_MSE_loss=G_MSE_dev_ism, iG_loss=G_ism, iD_loss=C_ism)
+
+# Smoothes the MSE loss in the output loss file to make plotting easier.
+def smooth_MSE_loss(loss_file, window_size, outfile):
+    losses = np.load(loss_file)
+    train = losses['train_MSE_loss']
+    dev = losses['dev_MSE_loss']
+    num_train = train.shape[0]
+    new_train_list = []
+    for i in range(0, num_train, window_size):
+        window_avg = np.sum(train[i:i+window_size, 1]) / float(window_size)
+        window_avg_val = np.sum(train[i:i+window_size, 0]) / float(window_size)
+        new_train_list.append([window_avg_val, window_avg])
+    np_train = np.array(new_train_list[:-2])
+    np.savez(outfile, train_MSE_loss=np_train, dev_MSE_loss=dev)
+
+# Create a GIF to enable visualization of generator outputs over the course of training.
+def create_GIF(in_PATH, prefix, out_PATH):
+    indices = range(0, 227401, 200)
+    images = []
+    for index in indices:
+        full_filename = os.path.join(os.path.abspath(in_PATH), prefix + str(index) + '.png')
+        try:
+            images.append(imageio.imread(full_filename))
+        except:
+            continue
+    images = images[:50] + images[50::10] + [images[-1]]
+    imageio.mimwrite(out_PATH, images, loop=1, duration=0.1)
+
+# Compute the RMSE between a ground truth and outpainted image.
+def compute_RMSE(image_gt_PATH, image_o_PATH):
+    im_gt = np.array(Image.open(image_gt_PATH).convert('RGB')).astype(np.float64)
+    im_o = np.array(Image.open(image_o_PATH).convert('RGB')).astype(np.float64)
+    assert im_gt.shape == (128, 128, 3)
+    assert im_o.shape == (128, 128, 3)
+    M = np.ones((128, 128, 3))
+    M[:, 32:96, :] = 0
+    num_pixels = 128 * 64 * 3
+    return np.sqrt(np.sum(((im_gt - im_o) * M) ** 2) / num_pixels)
diff --git a/src/Untitled.ipynb b/src/Untitled.ipynb
new file mode 100644
index 0000000..6966702
--- /dev/null
+++ b/src/Untitled.ipynb
@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ea42a489",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-06-27 17:55:39.372674: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory\n",
+      "2022-06-27 17:55:39.372709: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "non-resource variables are not supported in the long term\n",
+      "Imported model (for Places365, 128x128 images)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior()\n",
+    "tf.reset_default_graph()\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "import model\n",
+    "import util\n",
+    "import os\n",
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "24a28be9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_PATH='/home/jovyan/work/src/output/models/model2000.ckpt'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2f04ed3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_demo_image(in_PATH):\n",
+    "    img = np.array(Image.open(in_PATH).convert('RGB'))[np.newaxis] / 255.0\n",
+    "    img_p = util.preprocess_images_outpainting(img)\n",
+    "    return img_p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4cc717e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def inference(model_PATH, img_p):\n",
+    "    G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')\n",
+    "    G_sample = model.generator(G_Z)\n",
+    "    \n",
+    "    saver = tf.train.Saver()\n",
+    "    with tf.Session() as sess:\n",
+    "        saver.restore(sess, model_PATH)\n",
+    "        output, = sess.run([G_sample], feed_dict={G_Z: img_p})\n",
+    "        img_norm = (output[0] * 255.0).astype(np.uint8)\n",
+    "        img = Image.fromarray(img_norm, 'RGB')\n",
+    "        #util.save_image(output[0], out_PATH)\n",
+    "    return img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ce2fc25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def handle(conf):\n",
+    "    \"\"\"\n",
+    "    该方法是部署之后，其他人调用你的服务时候的处理方法。\n",
+    "    请按规范填写参数结构，这样我们就能替你自动生成配置文件，方便其他人的调用。\n",
+    "    范例：\n",
+    "    params['key'] = value # value_type: str # description: some description\n",
+    "    value_type 可以选择：img, video, audio, str, int, float, [int], [str], [float]\n",
+    "    参数请放到params字典中，我们会自动解析该变量。\n",
+    "    \"\"\"\n",
+    "    base64_str = conf['Photo']\n",
+    "    image = load_demo_image(base64_str, image_size, device)\n",
+    "    res = inference(model, image)\n",
+    "    # add your code\n",
+    "    return {'Output': res}\n",
+    "    "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/model.py b/src/model.py
index 1719a09..77b1317 100644
--- a/src/model.py
+++ b/src/model.py
@@ -4,7 +4,8 @@
 # desc:    Model for outpainting on 128x128 images with only
 #          a global discriminator.
 # -------------------------------------------------------------
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
 
 print('Imported model (for Places365, 128x128 images)')
 
diff --git a/src/output/models/checkpoint b/src/output/models/checkpoint
new file mode 100644
index 0000000..f393a80
--- /dev/null
+++ b/src/output/models/checkpoint
@@ -0,0 +1,2 @@
+model_checkpoint_path: "model0.ckpt"
+all_model_checkpoint_paths: "model0.ckpt"
diff --git a/src/output/models/model0.ckpt.data-00000-of-00001 b/src/output/models/model0.ckpt.data-00000-of-00001
new file mode 100644
index 0000000..9779b1f
Binary files /dev/null and b/src/output/models/model0.ckpt.data-00000-of-00001 differ
diff --git a/src/output/models/model0.ckpt.index b/src/output/models/model0.ckpt.index
new file mode 100644
index 0000000..337ee51
Binary files /dev/null and b/src/output/models/model0.ckpt.index differ
diff --git a/src/output/models/model0.ckpt.meta b/src/output/models/model0.ckpt.meta
new file mode 100644
index 0000000..7771612
Binary files /dev/null and b/src/output/models/model0.ckpt.meta differ
diff --git a/src/output/models/model2000.ckpt.data-00000-of-00001 b/src/output/models/model2000.ckpt.data-00000-of-00001
new file mode 100644
index 0000000..e34d579
Binary files /dev/null and b/src/output/models/model2000.ckpt.data-00000-of-00001 differ
diff --git a/src/output/models/model2000.ckpt.index b/src/output/models/model2000.ckpt.index
new file mode 100644
index 0000000..af7cd69
Binary files /dev/null and b/src/output/models/model2000.ckpt.index differ
diff --git a/src/output/models/model2000.ckpt.meta b/src/output/models/model2000.ckpt.meta
new file mode 100644
index 0000000..f540352
Binary files /dev/null and b/src/output/models/model2000.ckpt.meta differ
diff --git a/src/test.py b/src/test.py
index 6dd2f6b..7767aae 100644
--- a/src/test.py
+++ b/src/test.py
@@ -5,7 +5,8 @@
 #          the sides of an image, feeds it through the network, and
 #          compares the network output to the original image.
 # -------------------------------------------------------------
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
 import numpy as np
 from PIL import Image
 import model
@@ -13,11 +14,9 @@
 import os
 import sys
 
-if len(sys.argv) != 4:
-    print('Usage: python test.py [model_PATH] [in_PATH] [out_PATH]')
-    exit()
-
-_, model_PATH, in_PATH, out_PATH = sys.argv
+model_PATH='/home/jovyan/work/src/output/models/model2000.ckpt'
+in_PATH='/home/jovyan/work/images/test.png'
+out_PATH='/home/jovyan/work/results/test_output.png'
 
 tf.reset_default_graph()
 
diff --git a/src/train.py b/src/train.py
index 608e0ae..c1ff616 100644
--- a/src/train.py
+++ b/src/train.py
@@ -4,15 +4,16 @@
 # desc:    Train the model specified in model.py, which only
 #          uses a global discriminator.
 # -------------------------------------------------------------
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 import numpy as np
 from PIL import Image
 import model
 import util
 import os
 import sys
-
-tf.reset_default_graph()
+from tensorflow.python.framework import ops
+tf.disable_eager_execution()
+ops.reset_default_graph()
 
 # Places365 Training Hyperparameters
 BATCH_SZ = 16
@@ -59,6 +60,8 @@
 if len(sys.argv) >= 2:
     start_iter = int(sys.argv[1])
     model_filename = os.path.join(MODEL_DIR, 'model%d.ckpt' % start_iter)
+
+
 
 # Generator code
 G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')