mirror of
https://github.com/sbrl/research-rainfallradar
synced 2024-11-16 22:53:00 +00:00
153 lines
480 KiB
Text
153 lines
480 KiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "54f827d5-35f8-4b9d-b657-7c361907eb66",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2023-02-23 16:17:12.682973: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
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"2023-02-23 16:17:12.682987: 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"
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]
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}
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],
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"source": [
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"import tensorflow as tf\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"%matplotlib widget"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "f61829a6-9d21-4286-aec9-73352cb57dff",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2023-02-23 16:17:14.892338: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
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"2023-02-23 16:17:14.892537: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
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"2023-02-23 16:17:14.892589: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory\n",
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"2023-02-23 16:17:14.892638: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory\n",
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"2023-02-23 16:17:14.892685: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory\n",
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"2023-02-23 16:17:14.892732: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory\n",
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"2023-02-23 16:17:14.892780: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory\n",
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"2023-02-23 16:17:14.892826: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory\n",
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"2023-02-23 16:17:14.892872: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory\n",
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"2023-02-23 16:17:14.892879: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
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"Skipping registering GPU devices...\n",
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"2023-02-23 16:17:14.893690: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
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]
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}
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],
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"source": [
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"tensor_in = tf.random.uniform([16,16,3])\n",
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"\n",
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"interpolation_modes = ([\"area\", \"bicubic\", \"bilinear\", \"gaussian\", \"lanczos3\", \"lanczos5\", \"mitchellcubic\", \"nearest\"])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "80947a57-0162-4ec7-9239-e129d7a54ac7",
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"metadata": {},
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"outputs": [],
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"source": [
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"def do_upsample_test(tensor_in, interpolation):\n",
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"\tmodel = tf.keras.Sequential([\n",
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"\t\ttf.keras.layers.UpSampling2D(size=4, interpolation=interpolation)\n",
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"\t])\n",
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"\t\n",
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"\treturn tf.squeeze(model(tf.expand_dims(tensor_in, 0)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "d79df324",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
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"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
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"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
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"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "99877c3e493b4e57a14e3a6391224738",
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"version_major": 2,
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"version_minor": 0
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},
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9ebQ1WV3fj78+e6qqc869z9QjLTSCjEHghwQDhkmDRBQR9Wuc0eCK/DC/SCAxkEQxBic0a+lyOcV8nSLJ0iQOiXGIRjQkREQxxCUig0IjQ9Pdz3TvOaeq9vD5/bHP80hzbxOGpyFw92ut00/fqjqn9qnaZ9d7788kqqo0Go1Go9FoNE4M5mPdgEaj0Wg0Go3GR5cmABuNRqPRaDROGE0ANhqNRqPRaJwwmgBsNBqNRqPROGE0AdhoNBqNRqNxwmgCsNFoNBqNRuOE0QRgo9FoNBqNxgmjCcBGo9FoNBqNE0YTgI1Go9FoNBonjCYAG41Go9FoNE4YTQA2Go1Go9FonDCaAGw0Go1Go9E4YTQB2Gg0Go1Go3HCaAKw0Wg0Go1G44TRBGCj0Wg0Go3GCaMJwEaj0Wg0Go0TRhOAjUaj0Wg0GieMJgAbjUaj0Wg0ThhNADYajUaj0WicMJoAbDQajUaj0ThhNAHYaDQajUajccJoArDRaDQajUbjhNEEYKPRaDQajcYJownARqPRaDQajRNGE4CNRqPRaDQaJ4wmABuNRqPRaDROGE0ANhqNRqPRaJwwmgBsNBqNRqPROGE0AdhoNBqNRqNxwmgCsNFoNBqNRuOE0QRgo9FoNBqNxgmjCcBGo9FoNBqNE0YTgI1Go9FoNBonjCYAG41Go9FoNE4YTQA2Go1Go9FonDCaAGw0Go1Go9E4YTQB2Gg0Go1Go3HCaAKw0Wg0Go1G44TRBGCj0Wg0Go3GCaMJwEaj0Wg0Go0TRhOAjUaj0Wg0GieMJgAbjUaj0Wg0ThhNADYajUaj0WicMJoAbDQajUaj0ThhNAHYaDQajUajccJoArDRaDQajUbjhNEEYKPRaDQajcYJownARqPRaDQajRNGE4CNRqPRaDQaJ4wmABuNRqPRaDROGE0ANhqNRqPRaJwwmgBsNBqNRqPROGE0AdhoNBqNRqNxwmgCsNFoNBqNRuOE0QRgo9FoNBqNxgmjCcBGo9FoNBqNE0YTgI1Go9FoNBonjCYAG41Go9FoNE4YTQA2Go1Go9FonDCaAGw0Go1Go9E4YTQB2Gg0Go1Go3HCaAKw0Wg0Go1G44TRBGCj0Wg0Go3GCaMJwEaj0Wg0Go0TRhOAjUaj0Wg0GieMJgAbjUaj0Wg0ThhNADYaHyd867d+KyLCnXfe+QGPe8pTnsJTnvKUj3k7PhptOUl8JPdfRPjWb/3Wq3//9m//NiLCb//2b1/7hjYawNd8zddw//vf/2PdjMYHwH2sG9BoNK4tP/RDP/SxbsJV/m9qy0nhg7nmj3nMY/if//N/8vCHP/yj0KLGSeSbv/mb+cZv/MaPdTMaH4AmABtXiTEiIjjXusXHM/83PdT/b2rLSeGDueb7+/v8tb/21z4Krfnw2Ww2LBaLj3UzGh8mD3zgAz/WTWj8H2gm4E8w3vKWt/C1X/u1POhBD2KxWHDLLbfwzGc+kz/6oz+623FXTED/+l//a170ohdxyy230HUdb3nLWwD4zd/8TT7rsz6L/f19FosFn/EZn8F//a//9cM6V+Pa8o53vIMv/MIvZH9/n1OnTvGVX/mV3HHHHVf3H2cCnKaJb/u2b+NhD3sYfd9z7tw5nvrUp/LqV78agLe97W2ICD/5kz955Hzvbz78YNvx4bal8YH5cO7/+3OcCfhrvuZrWK1WvOUtb+EZz3gGq9WK+973vrzoRS9imqa7vX+eZ172spfx0Ic+lK7ruP766/nar/3aI/f/Z3/2Z/nsz/5sbr75ZoZh4GEPexgvfvGLWa/Xdzvuyrn/6I/+iM/+7M9mb2+Pz/qsz/rwLtAnEL/0S7/EIx/5SLqu4wEPeADf//3ff9UV4Ao/+IM/yJOe9CRuuOEGlssln/qpn8rLX/5yYox3+6z73//+fM3XfM2Rc7x/fyml8LKXvYyHPOQhDMPA6dOneeQjH8n3f//3Xz3mjjvu4O/8nb/Dfe9736v3/zM+4zP4zd/8zavHHGcC/mDb+pSnPIVHPOIRvPa1r+WJT3wii8WCBzzgAXzXd30XpZQP40o2jqMt9XyC8a53vYtz587xXd/1XVx//fWcP3+en/qpn+LTP/3T+cM//EMe8pCH3O34l7zkJTz+8Y/nR37kRzDGcMMNN/AzP/MzfPVXfzXPetaz+Kmf+im89/zoj/4oT3/60/n1X//1qwPzh3quxrXh2c9+Nl/yJV/C8573PP74j/+Yb/7mb+YNb3gDr3nNa/DeHzk+pcTnfM7n8KpXvYoXvOAFfOZnfiYpJX73d3+X2267jSc84QkflXbcm205SXw41/2DJcbI53/+5/Pc5z6XF73oRfy3//bf+Of//J9z6tQpvuVbvgWoAuFZz3oWr3rVq/imb/omnvCEJ/D2t7+dl770pTzlKU/h93//9xmGAYA3v/nNPOMZz+AFL3gBy+WSN77xjXz3d383v/d7v8dv/dZv3e3c8zzz+Z//+Xz91389L37xi0kpfUTf5eOdX/u1X+MLv/ALedKTnsTP/uzPklLie7/3e7n99tvvdtxb3/pWvvzLv5xP/uRPJoTA61//er7927+dN77xjfz4j//4h3zel7/85Xzrt34r//Sf/lOe9KQnEWPkjW98IxcvXrx6zFd91Vfxute9jm//9m/nwQ9+MBcvXuR1r3sdd9111wf87A+lre95z3v4iq/4Cl70ohfx0pe+lF/4hV/gJS95Cfe5z3346q/+6g/5ezWOQRuf0KSUdJ5nfdCDHqR//+///avbX/nKVyqgT3rSk+52/Hq91rNnz+ozn/nMu23POeujHvUofdzjHvchn6txbXjpS1+qwJFr+4pXvEIB/Zmf+RlVVX3yk5+sT37yk6/u/+mf/mkF9Md+7Mfu8bP//M//XAH9iZ/4iSP7AH3pS1/6Ibfjw21L43g+3PuvevQeXvn9v/KVr7y67TnPeY4C+nM/93N3e+8znvEMfchDHnL173/7b/+tAvof/sN/uNtxr33taxXQH/qhHzq2/aUUjTHq7/zO7yigr3/964+c+8d//Mf/j9fhpPBX/+pf1fve9746TdPVbQcHB3ru3Dm9p0d3zlljjPrTP/3Taq3V8+fPX91366236nOe85wj73n//vJ5n/d5+uhHP/oDtm21WukLXvCCD3jMc57zHL311lvvcf8HauuTn/xkBfQ1r3nN3d7z8Ic/XJ/+9Kd/wPM2PniaCfgTjJQS3/Ed38HDH/5wQgg45wgh8OY3v5k/+ZM/OXL8F33RF93t71e/+tWcP3+e5zznOaSUrr5KKfzNv/k3ee1rX3vVfPOhnqtxbfiKr/iKu/39JV/yJTjneOUrX3ns8b/6q79K3/f87b/9tz+m7bg323KS+HCu+weLiPDMZz7zbtse+chH8va3v/3q37/8y7/M6dOneeYzn3m3MeLRj340N910093Myn/2Z3/Gl3/5l3PTTTdhrcV7z5Of/GSAD2o8Oqms12t+//d/ny/4gi8ghHB1+2q1OnJ//vAP/5DP//zP59y5c1ev8Vd/9VeTc+ZNb3rTh3zuxz3ucbz+9a/n+c9/Pr/+67/O5cuXjz3mJ3/yJ3nZy17G7/7u7x4x4d4TH0pbb7rpJh73uMfdbdv798XGR0YzAX+C8cIXvpAf/MEf5B/9o3/Ek5/8ZM6cOYMxhq/7uq9ju90eOf7mm2++299XzAtf/MVffI/nOH/+PMvl8kM+V+PacNNNN93tb+cc586du0fzyx133MF97nMfjLm2870PtR33ZltOEh/Odf9gWSwW9H1/t21d1zGO49W/b7/9di5evHg3YfK+XElTc3h4yBOf+ET6vudlL3sZD37wg1ksFld9GN9/jFgsFuzv73/E3+ETgQsXLqCq3HjjjUf2ve+22267jSc+8Yk85CEP4fu///u
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
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" Figure\n",
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" </div>\n",
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" <img src='data:image/png;base64,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
|
||
|
|
" </div>\n",
|
||
|
|
" "
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"i = 0\n",
|
||
|
|
"for interpolation in interpolation_modes:\n",
|
||
|
|
"\tplt.subplot(2, len(interpolation_modes) // 2, i+1)\n",
|
||
|
|
"\tplt.title(interpolation)\n",
|
||
|
|
"\tresult = do_upsample_test(tensor_in, interpolation=interpolation)\n",
|
||
|
|
"\t\n",
|
||
|
|
"\tplt.imshow(result)\n",
|
||
|
|
"\ti += 1"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"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": "3.10.6"
|
||
|
|
},
|
||
|
|
"vscode": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|