cuda

02_production.ipynb

@@ -313,7 +313,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "key = 'XXX'"
+    "key = os.environ.get('AZURE_SEARCH_KEY', 'XXX')"
    ]
   },
   {
@@ -324,11 +324,7 @@
     "\n",
     "    export AZURE_SEARCH_KEY=your_key_here\n",
     "\n",
-    "and then restart Jupyter Notebook, type this in a cell and execute it:\n",
-    "\n",
-    "```python\n",
-    "key = os.environ['AZURE_SEARCH_KEY']\n",
-    "```\n",
+    "and then restart Jupyter Notebook, and use the above line without editing it.\n",
     "\n",
     "Once you've set `key`, you can use `search_images_bing`. This function is provided by the small `utils` class included with the notebooks online. If you're not sure where a function is defined, you can just type it in your notebook to find out:"
    ]

06_multicat.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -175,7 +175,7 @@
        "4  000016.jpg       bicycle      True"
       ]
      },
-     "execution_count": null,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -210,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -230,7 +230,7 @@
        "Name: fname, Length: 5011, dtype: object"
       ]
      },
-     "execution_count": null,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -253,7 +253,7 @@
        "Name: 0, dtype: object"
       ]
      },
-     "execution_count": null,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -274,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -294,7 +294,7 @@
        "Name: fname, Length: 5011, dtype: object"
       ]
      },
-     "execution_count": null,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -361,7 +361,7 @@
        "1  2  4"
       ]
      },
-     "execution_count": null,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -425,7 +425,7 @@
        "1  2  4  6"
       ]
      },
-     "execution_count": null,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -498,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -514,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -530,7 +530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -539,7 +539,7 @@
        "(4009, 1002)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -550,22 +550,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(fname             000224.jpg\n",
-       " labels      tvmonitor bottle\n",
-       " is_valid                True\n",
-       " Name: 113, dtype: object, fname             000224.jpg\n",
-       " labels      tvmonitor bottle\n",
-       " is_valid                True\n",
-       " Name: 113, dtype: object)"
+       "(fname       008663.jpg\n",
+       " labels      car person\n",
+       " is_valid         False\n",
+       " Name: 4346, dtype: object,\n",
+       " fname       008663.jpg\n",
+       " labels      car person\n",
+       " is_valid         False\n",
+       " Name: 4346, dtype: object)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -584,16 +585,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'000224.jpg'"
+       "'008663.jpg'"
       ]
      },
-     "execution_count": null,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -604,16 +605,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('009879.jpg', 'car person')"
+       "('005620.jpg', 'aeroplane')"
       ]
      },
-     "execution_count": null,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -633,16 +634,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('006350.jpg', 'aeroplane')"
+       "('002549.jpg', 'tvmonitor')"
       ]
      },
-     "execution_count": null,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -671,17 +672,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(Path('/home/sgugger/.fastai/data/pascal_2007/train/008663.jpg'),\n",
-       " ['car', 'person'])"
+       "(Path('/home/jhoward/.fastai/data/pascal_2007/train/002844.jpg'), ['train'])"
       ]
      },
-     "execution_count": null,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -703,17 +703,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(PILImage mode=RGB size=500x374,\n",
-       " TensorMultiCategory([0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))"
+       "(PILImage mode=RGB size=500x375,\n",
+       " TensorMultiCategory([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]))"
       ]
      },
-     "execution_count": null,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -748,16 +748,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(#1) ['chair']"
+       "(#1) ['dog']"
       ]
      },
-     "execution_count": null,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -778,7 +778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -788,7 +788,7 @@
        " TensorMultiCategory([0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))"
       ]
      },
-     "execution_count": null,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -819,7 +819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -840,12 +840,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 648x216 with 3 Axes>"
       ]
@@ -890,7 +890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -906,7 +906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -915,13 +915,13 @@
        "torch.Size([64, 20])"
       ]
      },
-     "execution_count": null,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x,y = dls.train.one_batch()\n",
+    "x,y = to_cpu(dls.train.one_batch())\n",
     "activs = learn.model(x)\n",
     "activs.shape"
    ]
@@ -935,17 +935,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor([ 2.0258, -1.3543,  1.4640,  1.7754, -1.2820, -5.8053,  3.6130,  0.7193, -4.3683, -2.5001, -2.8373, -1.8037,  2.0122,  0.6189,  1.9729,  0.8999, -2.6769, -0.3829,  1.2212,  1.6073],\n",
-       "       device='cuda:0', grad_fn=<SelectBackward>)"
+       "tensor([ 0.1987, -2.7846, -1.1943,  0.0250, -2.2920,  3.9218,  0.3482,  0.9501,  0.5162,  0.6932, -0.1302, -2.0550,  2.5748,  1.1635,  2.9216, -4.5089,  1.1606, -2.3782, -2.3923,  0.2469],\n",
+       "       grad_fn=<SelectBackward>)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -970,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1013,16 +1013,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor(1.0082, device='cuda:5', grad_fn=<BinaryCrossEntropyWithLogitsBackward>)"
+       "tensor(1.0835, grad_fn=<BinaryCrossEntropyWithLogitsBackward>)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1067,7 +1067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -1076,7 +1076,7 @@
        "('Hello Jeremy.', 'Ahoy! Jeremy.')"
       ]
      },
-     "execution_count": null,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1095,7 +1095,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1104,7 +1104,7 @@
        "('Bonjour Jeremy.', 'Bonjour Sylvain.')"
       ]
      },
-     "execution_count": null,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1123,7 +1123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1142,31 +1142,31 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>0.903610</td>\n",
-       "      <td>0.659728</td>\n",
-       "      <td>0.263068</td>\n",
+       "      <td>0.939651</td>\n",
+       "      <td>0.685462</td>\n",
+       "      <td>0.234263</td>\n",
        "      <td>00:07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>0.724266</td>\n",
-       "      <td>0.346332</td>\n",
-       "      <td>0.525458</td>\n",
-       "      <td>00:07</td>\n",
+       "      <td>0.822047</td>\n",
+       "      <td>0.552421</td>\n",
+       "      <td>0.284761</td>\n",
+       "      <td>00:06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
-       "      <td>0.415597</td>\n",
-       "      <td>0.125662</td>\n",
-       "      <td>0.937590</td>\n",
-       "      <td>00:07</td>\n",
+       "      <td>0.601497</td>\n",
+       "      <td>0.204911</td>\n",
+       "      <td>0.819562</td>\n",
+       "      <td>00:06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3</td>\n",
-       "      <td>0.254987</td>\n",
-       "      <td>0.116880</td>\n",
-       "      <td>0.945418</td>\n",
-       "      <td>00:07</td>\n",
+       "      <td>0.359507</td>\n",
+       "      <td>0.124968</td>\n",
+       "      <td>0.939144</td>\n",
+       "      <td>00:06</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
@@ -1194,23 +1194,23 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>0.123872</td>\n",
-       "      <td>0.132634</td>\n",
-       "      <td>0.940179</td>\n",
+       "      <td>0.136813</td>\n",
+       "      <td>0.117581</td>\n",
+       "      <td>0.945976</td>\n",
        "      <td>00:08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>0.112387</td>\n",
-       "      <td>0.113758</td>\n",
-       "      <td>0.949343</td>\n",
+       "      <td>0.120487</td>\n",
+       "      <td>0.108087</td>\n",
+       "      <td>0.952769</td>\n",
        "      <td>00:08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
-       "      <td>0.092151</td>\n",
-       "      <td>0.104368</td>\n",
-       "      <td>0.951195</td>\n",
+       "      <td>0.098464</td>\n",
+       "      <td>0.103450</td>\n",
+       "      <td>0.950777</td>\n",
        "      <td>00:08</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1238,7 +1238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -1254,10 +1254,10 @@
     {
      "data": {
       "text/plain": [
-       "(#2) [0.10436797887086868,0.93057781457901]"
+       "(#2) [0.10344962775707245,0.9285657405853271]"
       ]
      },
-     "execution_count": null,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1276,7 +1276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1292,10 +1292,10 @@
     {
      "data": {
       "text/plain": [
-       "(#2) [0.10436797887086868,0.9416930675506592]"
+       "(#2) [0.10344962775707245,0.9431872963905334]"
       ]
      },
-     "execution_count": null,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1314,7 +1314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1341,16 +1341,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "TensorMultiCategory(0.9554)"
+       "tensor(0.9569)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1368,12 +1368,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1435,7 +1435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1444,7 +1444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1461,7 +1461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1470,7 +1470,7 @@
        "(#50) [Path('01'),Path('01.obj'),Path('02'),Path('02.obj'),Path('03'),Path('03.obj'),Path('04'),Path('04.obj'),Path('05'),Path('05.obj')...]"
       ]
      },
-     "execution_count": null,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1488,7 +1488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1497,7 +1497,7 @@
        "(#1000) [Path('01/depth.cal'),Path('01/frame_00003_pose.txt'),Path('01/frame_00003_rgb.jpg'),Path('01/frame_00004_pose.txt'),Path('01/frame_00004_rgb.jpg'),Path('01/frame_00005_pose.txt'),Path('01/frame_00005_rgb.jpg'),Path('01/frame_00006_pose.txt'),Path('01/frame_00006_rgb.jpg'),Path('01/frame_00007_pose.txt')...]"
       ]
      },
-     "execution_count": null,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1515,7 +1515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1524,7 +1524,7 @@
        "Path('13/frame_00349_pose.txt')"
       ]
      },
-     "execution_count": null,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1544,7 +1544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -1553,7 +1553,7 @@
        "(480, 640)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1565,17 +1565,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=160x120 at 0x7F4213FAD350>"
+       "<PIL.Image.Image image mode=RGB size=160x120 at 0x7FE3DE68AC10>"
       ]
      },
-     "execution_count": null,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1593,7 +1593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1614,7 +1614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1623,7 +1623,7 @@
        "tensor([384.6370, 259.4787])"
       ]
      },
-     "execution_count": null,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1645,7 +1645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1675,12 +1675,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAckAAAFUCAYAAABPx8fsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOy9eZBl113n+fmdc+597+XLtaqyVpVKm7V4kXdsQI1tjMHgHsaGwWyNu1k9wzLAdMQw9GaChiCmu5npmYCYnp6I7umYhomYhukB02AMBmxDY2PJi2RbkqUqqaTaqzIrl5dvufec85s/zrn3vSxJJQnLLv7IX0RWVr58ed+9Z/kt39/39zuiquzJnuzJnuzJnuzJM8Xc6BvYkz3Zkz3Zkz35myp7RnJP9mRP9mRP9uQ5ZM9I7sme7Mme7MmePIfsGck92ZM92ZM92ZPnkD0juSd7sid7sid78hyyZyT3ZE/2ZE/2ZE+eQ9z1fnn+3LqGEACDRsUWnl5nnqsb2+zft59f+MWf56d+8mdwrkuMkYMHl9naHNCdK1FVytICYK3FGINz1/24Z4iIMJlMKMvyr/2As7KzDcPJWeYXDnJ14wzHDt/G9vY2RVEQQmBSBcJogy89+iinnz7F9vaIulaqyZDB9oCra+tMxiM2trbp9IWlfSvE2EV1nv2rh7nt9uMgkbmusDi/j/e+9zv4jd/495y/cIUYlfW1i5w9/SQxToh+wvb2gF7Z4ejNB/E+snrwCM6ViDGAQUTAGozm/wMiiqogIvgYEaMQIs5aogZiFGKsiSEwHo+oqprB1gaHbrqd0kYUA1FQVaKCarqeqhBjBCDNOfzcP/wH8pIM/IuUGKNCzM+cviIQAY3KYGfIcDji5KOn2bi6AZrWisYRUcdErYg+UtfKeOKZDK8y3NlhMprQ6S3Q6Tq63YJebxFnC4qywFpLp2uxxuFsiZQGa7sYCjauPsbyykFiGNHrLHP4yFEWl5bo9udxzuX5EkKIxBiJQVHA1wEfIyFEQgicP3+GgwcPEbwnxECMimokakBjJMQI6on5d6ik96CgypmzT3Po0CrjcUWv10PyeKhqGiZAY6Qp61JVRKbzahA0KpNqG1eUiBgg3bNGIYYJIUbGoxH1ZIet7R1uu+tVhGpM8qdNXjcRVNJnAaqSftaQ15OmtRgKPvbRj/HWt92HSEQkrWNjHBCR7KKLSH49ff+Gt73jq77u/vWv/IJaa4A0dh2xqEAUEGzagyiI4mWCNRHRDqKKEgCPxA4Aqmm1pnnIDxk8IunvVdN7JK+ZEAPeK9YViLFUdU1UMCbt8RgjiiLGEjW9z4eAGMNfPvA5vvDwozz2xJPcfPQIb/26r+Oee25jaLs88IkHOXf+NB1GvObu21nedxNbVy+z/+BBfu9P/jMd4/mub30L9dikezcTgsklgaqIF1b2H8TaAiXkudY8j2nNO2fBZN1kDM5anHN0ul1ULMYkXRNckd6jYDCICsbujtGMzE67gAgxQlVVrF25yMblCxitqMKEYVA2L495zSvm6a/exaiCqI5ez2HE4L3HGJP0pbGIDWgArFDHCAKFGAgKLvDev/v+51xz17VatY+EIAiBalyzb3WOK2vrhKCMxjt8+MMf5md++u9T1zVGhNF4RIiKryK2SMrWGNNuHO891tpW4T+feO9f0PteqOyMxizMLxJ2LCtLi6gqc3NzWYkIxgK9ZW5/+Wu5+WWvZjK+wMXzl/jSYyfpLcyzb3U/k/GAjatb7OyMGWwP2NxYY3HFcOXiBdYuP0YIcOzELayuDvnn//O/ZGNri4Or+/nZn/1ZPvCBD7C0tEz0gQvnziDuHKoVjz9+muiVzY1tFhcW2X9wFecczpWgQsCACEYsIh5IzodRJapisEQVokZULaii6kHyl4IPI0qbNrGIMFsf++ylsjeufnZcCRtXhzz11Dk2NjYZbGxTe0+n41CdEGJNCDXVeIu6nrBxdZ3haMh4YOh2LQuLXcq+YWlhjrluwf7Fg1jnKIse1qaxc4XDljbvRcEaS+kKenNzHFhdpd9foNPrYazlTz90kVe++o1sXl3n2PFb0prOQ9Qs5QBgDdaSNEEybQgO8R4ChKCUZYcag4mNoRREIUpSCiFoUh4S0QjWJiMnGinLAmsd4/EW8/PzaIyIkWxoNU+kJGUMgLSGEoAIIg5rC6xxaV9iEImoRIQCocYYA66kcEMuXTrP4YOH8HVoLo8gaJyukOS47V4vIoIQue+++4gBrAMlG1PAqAENIIIg7Zp8obrhpZbSOBSf7zsZR0hTKSaCRIiKACUFxJD9Ek/UkOaLqh0UVYsxNhs7gxoIIeJDJIqg2OxcQTATorEEtYTgefz0Wf7oo3/O6bPnOHboIHffcRtv+fo3YlTbVSUKpXXc98Y38PWvex0hBqwYrEbqaPncQw9z9coanVCx2i/pF30e+NSn6M71+cKFAXOFcqjfIU5AMGA8Ig5DMiA+BsrSgTXUKmkMUIIoIpGOdpOh12RnrDEYBef6lEWJwRLUQFTA46J9UfPRrANf1xAnDDauYKRm5CfUVc2pU2c4dCSyuPxuVDoszBm+9NhjnLj1FqKJXFm7wk3HbmI8GWNNSdRtDA4QCrHJSItJqlSL697L9Y1kHQghbeSq9igl42rM0vwhhsMtfus//L+UZZfJeECn32V7a4S1BSEqWkF0ijEQo2CtZK/CJ+/7BWyGwWDA4uLiCx3X55UzF87y2nuP8cCDD/O6N70SiYoxJj0jyQvyqnQ6XURqRA5z9Ph+Vg7cRl3X1PUao+3A2TMn2d7eYDQcAbC1ucnO9oDBYMhga5tTO1d5yhXYTokizM8v85M/9VOMR2OstSyvLPFz//B/4F/8s3+Or4XRaJML504zHK2ztnWFM0+fxVeRI8cPMd/vs7C8RKfTAeNQiaABNYJVEI1EIhIlRxUVMYCqAS1Q9Smq8R4tSkSVKIrKTBSCpvcTUU3IwY2U/+VXf4m6rhgNBiwsLDGsJqzu289ir5+81F5Jx1kWl/dhjXDb8VtSJOIUk71abMHCwgK9uTnOPvU0b3zTm5hfXAQkeeXtp0lr6fSa527ec/e9r2ZuYRnXmSfptZiiOEmKXxBmVYA4IYakHEUVFUGMYWlxhbIoibFGYgFBMBIJKhgJRGMwIoTokyGSZPfEpO+HDx3l0uXzLCwstghLukeDQVFivoPGiMd2n6kqapQYa2zRxfsJ3U4X7z0Rl/apCxjvcM4RY6Ryls31y9x09CjB+/SsGcXAASFdt7WemHwPKQpGFOsMIURMdIiZRsXpWsnTjxozWpJm4UZItBUuuOT4mLRGEGnvJuSxlUjWmj3E1BC6BPUoEWMsxho++cBn+dBHPs7pc+d53Svu4bWvuoevee29adVZSyRHpSiiisSIeI8xBaUV7jlxjHt+4LsgR5CoQkxrU8QQ6kBhLOIjFpvWrUmLpNLARCxXL55nMrnK8aUuPgbGdLn1tuN88fRFxpMh9x6Z45V33cnALFIwQWNayZGIC4alziLF/AKqihHFxGRInEgyqjY57YjBChQ2oTHdTrddc9YYVCPGOqIGRGx+BkHMdAfORpCqCXms65oYA6dOnSJMhthYESeeELv8xX/+MD/4Iz+JMx1ssZidE2Wh36frSqrK03Vd8IqqRUyNkxKygywmRfCtSH3dtXFdI1lVFSDJWPrIZDzGGAVxbGytY21N4W5BGdDtdrl0cYN+v6Cua4rCEapIUabFkAYgfQ8hPG9E2cBEL6VnefDwYcDgEaIBG/IvrOAQKq+IEaJXjLVICFjrKMvk4Sr76PTHHLvlHqLf5pEHHwYrLBclywf2MRlPGA2HbG1uMh6OWb+0TtEFUw05uXWFGA2uKHnZnffwP/7SrwJKf9FSq+H2O1+BryPnzp7BcA6jFVub61w4d5mlxXn6C31WVw9QdEuKokyOLRYiqA2YhIXkwQONDfQliASqqsL054lhZkByxINKVlYeWq/+JRv2Fy3HD91ELAyuKFjp9fFYjAHnBKMRYw1RlcWlBXq9OY4cPczi4iJl2ZuGdu0mFO68++W7rr/bpM28fu0z50sszi9iBQgB1LWvP+fK1AZCTMrQmAhq2Ldvkdr7FCmqoEaIGExUosnK0iR4PRrNiqsx6QYngY519DsFw1HAmhKN9UwEluBChaR8jUmwbWs8k6MaY6T2ijBJDqtacpyX4HtjMc5S2g51AeAwEgiqGKGFXCXfHaRoksaAiqAhZKcYfvd3P8h73vPt6fqtQ6JJLUQQ69qB0/jS7fcXIyEaaJx3gcpXRFWKosBaw1998kE+87nP8+nPf5Gbjh3hnW9/O298/cvzmkkjIShW4M2vu5evufeVoGBNcqKMemJUQu0JxPx6kigQDCA1sQaLpFQIZgYdEKLqFALOmYhI8keiRoJG6HZ54NMPMdrZplTHa95wJ+oX+cInP0q1dDs72yO6wXPvPd9AFS2F+jaSBxCxRDHMzS9SP6vDIs/4n6jDmZIiQ6qq2iKIkGB5Y5Jb8OwGMhleyWie9xGNjnMXLsFkAxcFP/GMK+XPPvkf+Yn3/TAUPZwr0QhgQSMHDxwCFVzhWFxYZHswoJwvqOsqRcUz+75RE+m5v4xIcjSeEIPifU23o1y4MGZcOT72sb/goYc+ww/94I/y5NNn6PYqQjyIWIePQvRgTMTYiPeKc0IGpAAlxtgayucyljFGiuL6N/9CpYFyJoMRw84884srPPzoKe592a1A8vinnj9EiaCKNYZoklJ2aonRIXRSPtA7tNPlzrvuYmcwIMSa8XhE9J6NzcsEXxGqmqqq2NoYMN4ZcXn9CiGM+NzgEp2yQwBEOhw9dhy33MNY4ZY7buaxR8acuOkEdTCcP3eK8c5FBjtXuHz5PFoJy/tXmFuc48D+/VjnMNESjW1zRFFjNpI+5ylD8pxUUbFJyc04ICqaFpta0Gle5kbJfW/Line truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 576x432 with 9 Axes>"
       ]
@@ -1705,7 +1705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -1714,7 +1714,7 @@
        "(torch.Size([64, 3, 240, 320]), torch.Size([64, 1, 2]))"
       ]
      },
-     "execution_count": null,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1740,16 +1740,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor([[-0.0753,  0.0237]], device='cuda:5')"
+       "tensor([[-0.3545, -0.0016]], device='cuda:5')"
       ]
      },
-     "execution_count": null,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1788,7 +1788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1804,7 +1804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1822,12 +1822,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1851,7 +1851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -1860,7 +1860,7 @@
        "FlattenedLoss of MSELoss()"
       ]
      },
-     "execution_count": null,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1882,7 +1882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -1898,16 +1898,16 @@
     {
      "data": {
       "text/plain": [
-       "SuggestedLRs(lr_min=0.005754399299621582, lr_steep=0.03981071710586548)"
+       "SuggestedLRs(lr_min=0.00831763744354248, lr_steep=1.3182567499825382e-06)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1931,7 +1931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1949,9 +1949,9 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>0.049488</td>\n",
-       "      <td>0.022839</td>\n",
-       "      <td>00:39</td>\n",
+       "      <td>0.051178</td>\n",
+       "      <td>0.009347</td>\n",
+       "      <td>00:36</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
@@ -1978,21 +1978,21 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>0.008415</td>\n",
-       "      <td>0.005187</td>\n",
-       "      <td>00:54</td>\n",
+       "      <td>0.008558</td>\n",
+       "      <td>0.005210</td>\n",
+       "      <td>00:47</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>0.003400</td>\n",
-       "      <td>0.000343</td>\n",
-       "      <td>00:55</td>\n",
+       "      <td>0.002872</td>\n",
+       "      <td>0.000449</td>\n",
+       "      <td>00:48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
-       "      <td>0.001462</td>\n",
-       "      <td>0.000100</td>\n",
-       "      <td>00:55</td>\n",
+       "      <td>0.001470</td>\n",
+       "      <td>0.000160</td>\n",
+       "      <td>00:48</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
@@ -2019,7 +2019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -2028,7 +2028,7 @@
        "0.01"
       ]
      },
-     "execution_count": null,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2046,7 +2046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -2061,9 +2061,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<Figure size 432x576 with 6 Axes>"
+       "<Figure size 432x576 with 2 Axes>"
       ]
      },
      "metadata": {