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xwang233xwang233
committedMar 30, 2020
convtranspose2d dilation check performance 2
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‎convtranspose2d-dilation/convtranspose-dilation-cudnn-check.ipynb

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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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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.6.0a0+77b4e2d\n"
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]
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}
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],
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"source": [
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"import torch \n",
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"import time\n",
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"\n",
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"print(torch.__version__)\n",
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"\n",
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"warmup_iter_raw = 1000\n",
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"prof_iter_raw = 1000\n",
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"\n",
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"def test_convtranspose2d(bs, c, hw, ks, stride, pad, outpad, dilation):\n",
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" warmup_iter = warmup_iter_raw\n",
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" prof_iter = prof_iter_raw\n",
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" \n",
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" if c >= 256: \n",
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" warmup_iter //= 10 \n",
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" prof_iter //= 10 \n",
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" \n",
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" print(bs, c, hw, ks, stride, pad, outpad, dilation)\n",
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" x = torch.randn(bs, c, hw, hw, device='cuda', dtype=torch.half, requires_grad=True) \n",
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" conv = torch.nn.ConvTranspose2d(\n",
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" in_channels=c, \n",
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" out_channels=c, \n",
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" kernel_size=ks, \n",
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" stride=stride, \n",
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" padding=pad, \n",
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" output_padding=outpad, \n",
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" groups=c,\n",
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" bias=False, \n",
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" dilation=dilation\n",
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" ).half().cuda()\n",
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"\n",
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" y:torch.Tensor = conv(x)\n",
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" g = torch.ones_like(y)\n",
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"\n",
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" for warm_up in range(warmup_iter): \n",
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" y = conv(x)\n",
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" y.backward(g)\n",
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"\n",
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" torch.cuda.synchronize()\n",
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" ts = time.time() \n",
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"\n",
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" for it in range(prof_iter): \n",
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" y = conv(x)\n",
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"\n",
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" torch.cuda.synchronize()\n",
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" te = time.time()\n",
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"\n",
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" t_forward = (te-ts)/prof_iter\n",
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" print(f'forward {t_forward: .3e}')\n",
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"\n",
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" ts = time.time()\n",
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" torch.cuda.synchronize() \n",
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"\n",
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" for it in range(prof_iter): \n",
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" y.backward(g, retain_graph=True)\n",
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"\n",
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" torch.cuda.synchronize()\n",
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" te = time.time()\n",
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"\n",
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" t_backward = (te-ts)/prof_iter\n",
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" print(f'backward {t_backward: .3e}')\n",
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"\n",
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" print(f'total {t_forward + t_backward: .3e}')\n",
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" \n",
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" print()"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def test(): \n",
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" print('bs c hw ks stride pad outpad dilation\\n')\n",
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" # def test_convtranspose2d(bs, c, hw, ks, stride, pad, outpad, dilation)\n",
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" \n",
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" test_convtranspose2d(32, 128, 7, 1, 2, 0, 0, 2)\n",
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" test_convtranspose2d(32, 128, 7, 1, 1, 0, 0, 2)\n",
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" test_convtranspose2d(8, 128, 14, 1, 1, 0, 0, 3)\n",
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" test_convtranspose2d(1, 128, 7, 1, 3, 0, 1, 2)\n",
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" \n",
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" test_convtranspose2d(32, 512, 7, 1, 3, 0, 2, 2)\n",
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" test_convtranspose2d(8, 512, 14, 3, 2, 1, 1, 1)\n",
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" test_convtranspose2d(32, 256, 7, 1, 3, 0, 1, 1)\n",
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" test_convtranspose2d(1, 512, 14, 3, 3, 1, 1, 1)"
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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": 15,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"master\n",
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"bs c hw ks stride pad outpad dilation\n",
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"\n",
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"32 128 7 1 2 0 0 2\n",
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"forward 7.415e-05\n",
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"backward 2.432e-03\n",
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"total 2.506e-03\n",
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"\n",
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"32 128 7 1 1 0 0 2\n",
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"forward 7.270e-05\n",
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"backward 3.238e-03\n",
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"total 3.311e-03\n",
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"\n",
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"8 128 14 1 1 0 0 3\n",
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"forward 7.214e-05\n",
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"backward 3.232e-03\n",
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"total 3.304e-03\n",
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"\n",
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"1 128 7 1 3 0 1 2\n",
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"forward 7.211e-05\n",
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"backward 3.110e-03\n",
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"total 3.182e-03\n",
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"\n",
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"32 512 7 1 3 0 2 2\n",
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"forward 1.974e-01\n",
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"backward 3.896e-01\n",
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"total 5.870e-01\n",
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"\n",
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"8 512 14 3 2 1 1 1\n",
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"forward 8.204e-02\n",
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"backward 1.598e-01\n",
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"total 2.418e-01\n",
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"\n",
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"32 256 7 1 3 0 1 1\n",
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"forward 9.928e-02\n",
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"backward 1.947e-01\n",
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"total 2.940e-01\n",
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"\n",
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"1 512 14 3 3 1 1 1\n",
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"forward 3.833e-02\n",
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"backward 8.089e-02\n",
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"total 1.192e-01\n",
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"\n"
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]
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}
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],
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"source": [
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"print('master')\n",
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"test()"
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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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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"without dilation check\n",
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"bs c hw ks stride pad outpad dilation\n",
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"\n",
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"32 128 7 1 2 0 0 2\n",
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"forward 9.304e-05\n",
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"backward 2.358e-03\n",
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"total 2.451e-03\n",
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"\n",
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"32 128 7 1 1 0 0 2\n",
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"forward 7.569e-05\n",
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"backward 3.104e-03\n",
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"total 3.180e-03\n",
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"\n",
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"8 128 14 1 1 0 0 3\n",
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"forward 7.390e-05\n",
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"backward 3.123e-03\n",
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"total 3.197e-03\n",
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"\n",
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"1 128 7 1 3 0 1 2\n",
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"forward 7.711e-05\n",
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"backward 2.982e-03\n",
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"total 3.059e-03\n",
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"\n",
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"32 512 7 1 3 0 2 2\n",
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"forward 2.085e-04\n",
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"backward 1.152e-02\n",
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"total 1.173e-02\n",
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"\n",
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"8 512 14 3 2 1 1 1\n",
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"forward 1.887e-04\n",
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"backward 1.424e-04\n",
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"total 3.311e-04\n",
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"\n",
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"32 256 7 1 3 0 1 1\n",
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"forward 1.264e-04\n",
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"backward 3.502e-03\n",
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"total 3.629e-03\n",
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"\n",
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"1 512 14 3 3 1 1 1\n",
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"forward 7.291e-05\n",
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"backward 1.455e-04\n",
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"total 2.184e-04\n",
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"\n"
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]
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}
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],
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"source": [
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"print('without dilation check')\n",
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"test()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.7.6 64-bit",
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"language": "python",
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"name": "python37664bitfce950e88ea94256bae6c6f663f53e68"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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