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diff --git a/DAMASK_3/processing/post/DP1000/Generation_Data/grain_data_input.csv b/DAMASK_3/processing/post/DP1000/Generation_Data/grain_data_input.csv
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--- a/DAMASK_3/processing/post/DP1000/Generation_Data/grain_data_input.csv
+++ /dev/null
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diff --git a/DAMASK_3/processing/post/DP1000/Generation_Data/grain_data_output_conti.csv b/DAMASK_3/processing/post/DP1000/Generation_Data/grain_data_output_conti.csv
deleted file mode 100644
index 64f671c4965254c91d1e02276fcaef8d75eb562f..0000000000000000000000000000000000000000
--- a/DAMASK_3/processing/post/DP1000/Generation_Data/grain_data_output_conti.csv
+++ /dev/null
@@ -1,102 +0,0 @@
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diff --git a/DAMASK_3/processing/post/DP1000/Generation_Data/grain_data_output_discrete.csv b/DAMASK_3/processing/post/DP1000/Generation_Data/grain_data_output_discrete.csv
deleted file mode 100644
index 64a8b490247f378537ea46805c9c99e33af65a81..0000000000000000000000000000000000000000
--- a/DAMASK_3/processing/post/DP1000/Generation_Data/grain_data_output_discrete.csv
+++ /dev/null
@@ -1,102 +0,0 @@
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diff --git a/DAMASK_3/processing/post/DP1000/MartensiteSize.png b/DAMASK_3/processing/post/DP1000/MartensiteSize.png
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diff --git a/DAMASK_3/processing/post/DP1000/Postprocessing/vol_distribution_phase1vs2_discrete.png b/DAMASK_3/processing/post/DP1000/Postprocessing/vol_distribution_phase1vs2_discrete.png
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diff --git a/DAMASK_3/processing/post/DP1000/RVE_Numpy.npy b/DAMASK_3/processing/post/DP1000/RVE_Numpy.npy
deleted file mode 100644
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diff --git a/DAMASK_3/processing/post/DP1000/Specs.txt b/DAMASK_3/processing/post/DP1000/Specs.txt
deleted file mode 100644
index 755e8b0fc93e51a6eb2076ccd3bbe1287f0911ba..0000000000000000000000000000000000000000
--- a/DAMASK_3/processing/post/DP1000/Specs.txt
+++ /dev/null
@@ -1,20 +0,0 @@
------------------------------------------------------------
----------------------RVE-Specifications--------------------
------------------------------------------------------------
-
-
-Created at: 14/11/2021
-
-Size of the RVE 16µm - 32
-Solver Typ: Spectral
-
-Number of Bands: 0.0
-Phase ratios:
- Overall-percentage: 25.495613945855027%
- Band-Percentage: 0.0%
- Island-Percentage: 25.495613945855027%
-No inclusions in the RVE!
-
-For informations, contributing etc. please contact the DRAGEN-Team:
- DRAGen@iehk.rwth-aachen.de
-
diff --git a/DAMASK_3/processing/post/DP1000/debug.yaml b/DAMASK_3/processing/post/DP1000/debug.yaml
deleted file mode 100644
index 5b87ff5b9b2c4e7ea9c36042198649bdad1877cc..0000000000000000000000000000000000000000
--- a/DAMASK_3/processing/post/DP1000/debug.yaml
+++ /dev/null
@@ -1,12 +0,0 @@
-phase: [basic]
-CPFEM: [basic]
-
-# options for selective debugging
-element: 1
-integrationpoint: 1
-constituent: 1
-
-# solver-specific
-mesh: [PETSc]
-grid: [basic]
-Marc: [basic]
diff --git a/DAMASK_3/processing/post/DP1000/grid.vti b/DAMASK_3/processing/post/DP1000/grid.vti
deleted file mode 100644
index 0a0caf2a003cc826671581968cde8073b3e1bc37..0000000000000000000000000000000000000000
--- a/DAMASK_3/processing/post/DP1000/grid.vti
+++ /dev/null
@@ -1,19 +0,0 @@
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- AAAAAACAAAAAAAAA
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XyXv7DnUuq/N7yX3zX+6kau25L35T7do76HrP9r92pJ7lp/mQHsf9VhKzmzP7SfvKP+3oO7W88kxquSN/PtScpM/ej5d96iSN9p/qvp879cf7b234tc5MHmzfi/5af89aw5+/k/eXn80h5+D6nFdH3laG82BzoHJTY3cXr8+8mTbuwfq9WfnEF2nznvImXVn/dn3fbbkV+m8LzuPXj95WjvKH82h131pfpXcW/Fr/zXKXTsHcl+6388Bt+rXufds/1b336P2npfmV0fvf7fu1z4k2nuRixq5vYdSd+tzODlaS/7eOZDb/Wr0PYw82d5byd86B3LL5f5ne3yU392X5u+dR68/Owdyqzr/y77/e+fg7l0peWkf/mLNur2z57DKH82Bzv17/TSPXdBWdzSH2tc9mgP5so3cfu5d66XWumftvbL+0XuPVv+oOUTu1+6nvd/oOZBzlj8quW/+041ctSXvzX+6RXsfXf/R7vd2lT87B9p/HUvJle0qN/nJ3eu/lv33sXRfSu5r83+2+uf87Bz8vHu1X+ff5PXqdac5RHOhfY+ep+sjR2t9D0DuXr8q578uGavseF7t2rGKHFcQRmFLtrElMMKOFBjBsAJJiWAiZ2aTDSwUKjVG7/8SCvZW4B8Vp6pu9Z3umd3k0NIE/dV20HNrX/zw+PMC+nNzz9JfR3+BPh99K9d/SZ87JadXuz9yRavuWX+25N1qDlW3+mdLTq8/OSW3Vd2X8lfn4Pmj89ib/8tol9ubg+e+dr/1x9Gq+1r8WvJu7bfeup/mQL6sO+un+57tH07JRz26f3YOUf97uV7l/zBK/uocyG99LzX3s1FyZPtBSu7V/mdOyRVt1R+dA3nJv9UcZt3ROZDX+vto1l+dQ5ffe7//dzTq1mb92XmY+yQl71H8NAd1R/30vVZb9escyBmt517tJ7eWXFRy/yadddMcyHtpv5Vc0VbdW/s9d7ffSs6t/NQn//dLnmrJW/V/Dn7uKP5XTu2+7+W6u+SYbdatvR8lh1fvnOfa/X9LPf+qOVhfjpJb93y675v1r34OrOQmP+099bknv5Xuu6vkjs6Bvv+Rd7X/LCW39e1odg7k9Tq79yW3detzYHJSyUWtuvfin30O1H2pPSA5qXreR271V/eg5IqWfFTv3JP8R91/k1v7UWrnAFX3Uf10Dkjeo/qjewDybu23kvvJ//hD3q38dP+z1XPfW/HTub/Og7xRd3YO5KiW3FpyV/2r9/9Vf3QO5CW/nluQI1vyUVf7u+dAvmhn3Xbut2r/3+X33u/J6/m9OZC/OgdznOQ62lm/d/5b9UfncHJKXnJn57CV35uD5476ydvl1zmQk7raTXMgt5Z8VPLb57rde/fr57rd3hzIq70bJWfUT58nR7WX8ldLnmrJq8991J/d/+v32r35bc9jfm///1madXtzIMds1ftSSvuv+1HyZv1Wuv+uqp/cVT95L/UcZH/vuv/13nNn/aueg1k/zcFKTio5ZnseJXd1/08+Kt1/tWep+aLngFH/3uagbjr/7DoHJR+VXFR1Z/2zcyBftOZ5LddUdWf3oOSjkitau+/X0q3cXf6uOdh5p7ntWt3eHvha/VZv/68l71Z+a3b/TX51791PTvJXf+978VfnQPuf6D6EvE/+/5cc1ZI36yd31k/3P1s6B79WP7m9OXT5rbr3v1X/qv1/1U/zIGfUr3MgR7bkyvbNKDmr/u7ngTzR2nv+av/sHOz+T3IdrX7Pqfr1HDzrz87h5JS8nltL3q38NIet3dk50P6n6vfmUfWTcys/uaL13Fb7XJeb5kBu652UnFm/fq7bTc9B1l+dg+elkivbS/m13ve5VX7Pa/uvqv/a9t9Rv7k/Of8fdWvp/ruq7uge9H70k3TWbf1zlO5/tlU/Peez/lXPQdZtpfdcK/modP+z3bt/6zl4fjv3ifofnH/vmgM5qj1L6RxQz8MenHb7u+dQdXvngfT738sc1N3lj5Zc0f43Gt1/e/7sHph8VHJFq27y2/uvue2avHv1e3NQN+1Dydvt756D5yf30f3qvrX9v87h1vf/WffR/d4csnsw8j75H3/o/mdrHjsHJXeXPzoHuv/Z0h7gWv3kpjm8kZLTq+693o3S/c+WvDSHLr951W/nWOSolpxZf3UO6l/1dyDk3Ju/ew7k9Grv+V1+c0X9s3M4jZJTvV4v5c/O4SQl9z+j5Nfv+dF6++6sn+ah7q382Tl4e7A7KXm9em6rfS77vFPJnfVX50BubffeK+vu9lv1ve3Sfp3Dar83j24/zcHbf3p+bw7eHpjc3hzIk23UTX5yz/qtWz0HVvJ/lUb92eeeSo5qs36r7v8999H95vT89J5nJV+05KmW/LT3ppIrWnJUq+7sOdCD06PMoboH7vJ/A+UJmeB4Xu3aS25bRxCFYb/iRzLLyAPDQCBYmhkIsoMgBqxsIDN7/7vIwF0DH+jgr+qupi4pavKD9AP8iqTJ2+V3z378vEv2++gvo+/ldrZ/St8U+yLZb6Pkqjbm8F5ade/2a8lF/S7N+l+OrrrPzf8SSs5z9Yfvs3TVT75syUlVd1S9zn31//gh7y7/6hxW3Ufxd82h6r5U/6tRcl+K/5Upuat+evyrJSd11p+dAz3+rpLT9eMoOcl/J93t/0/6uzTrXvWrO/p8lByrVXd2DuqfncPV/3Cfm5KnWvLrHJz7VP6uOfwxSm53XbvLT+6uOaj/9ei9dLeffNlmvdrXUvVX50Be7a+j4biRkpvm4dzx+9RffR1kzzucW9vljzq31s2B3Lv9XXOgrvqrczi1Pz6/3O0uf3YOp/LT99io88/Ooeomv5vDv+b+qv9Ur4Pd/g+j5HWNP380v3NHP5iSN/t+ICc1669+Djr3ql9fB+TLltzV85But5Y81Xb73RzIlW38feTKdtbt/F/M/d37X3JVG3vQWf8X013+36S0/9TGv3+re2C6zlN/3CZftlW/fv7Nuqt+Lbmydfs/twd2Jee5+bPuS/XH+yJ8tA8kZ5e7ew7nvv8mHzXr7379k6tacmb91ef90v3kPop/dg7OT95L8Wt3++nxr5Z81Lej5J2dAz3+2cbee3UOs3uP6J3pqfy6/yevulf3P84f5zvkqFbdXf//YZd/1xyitP/Nunf7u+dB5/1H3/+vzoH87txzt5+81TncmvvJfT/qzjW6/eTLlp73qt/Nocsf53/kWm244zb56X2gcyCnc8/uPVZL3qy/eu5T9Z/LHMj72P74/Nrlr85htz+7DyLvUfzZOczuA8md9dMcaP/X7e6eA7lP7Sdv1p2dA7lpDuTPfg6Sd5d/dg5RctN5SNVPTmqXP/Yjq/74Hkfuo/h1b9zlpzns2n+Tl+Yw66bzsHDH7W6/Pv9u/5dtt1/bvf//Our2/+R1nwPknfVryUcNP+1/nVdLznPzR8n9WP6uOdDzHz7aC5Gz273bX90Dk3eXf3UOWX/365881ZLz6n+4q+6j+Gfn8NT3/6f20+Pv6l+mNI9z3f+v+lf3HtE7091+3X8fzR+N8w7yVBvuuJ7L+nfvP9WtJVe27lzj6P6uOdD5XlTPVY7ip3ncQsnt5rDbT07qLv+9tMtPntl2+6Nxntflj3Mg8lTb7Va/mwN51a3+Gyk5qyVftm6/k21273PUOdCei3r1P9xuP+2DyNntp/3Pqn92H0berjl0+8l7bn43h93+2Tm4PV94ae9F/k+ju93d/uzezzWu/8Mft8m56q/OgbzVOej5z2P5s3MgZ7c/rm/Iu+rO+rP7X/LTOaA7D3Purv3/0f3ZktP1q1Tdq37yHs3v9uDO6b4HkLPbPzuHWT99DyJnt3vWr3PI7v+p5L36fy65qiWvq/OHK7sPJO8u/z+j5Kz6s+6j+GdfB1f/z626H9vvmt3/qX92/03e6N+j9Phn69y0/131Z+dAj3+1s/7Zc3/tnWn447yDHLM9ql/Pe8hRrV7HZf2795/O3z0Pdz3r5hDX1Ufx0xxuoXRdrz2Vn7zZOTw1f5Tcs/7sHMirJU+15L50P82BfNnqHMipdee/5JrtLj85q/4bKbmqJVe15Fz1d8+BPNVm955H8e+aA3nJ3e2P96f79fg+S65syb3bT3sw2oeRj+rOP2nvQ3Nw++8ud7efSm7n1zmQt+qfnQN5Z+cQ1/9Pza/nP+qP6xvyVt2zcyBnl1/Pw8i96s/OgZxZvzv/c35tePV+5/ofjR+tL3he7dq9bhRBEEVhfiTA8AIECI9lYWckJGRIiACERIxEht//HQjcFXDl0qmq7t7tWa+To4VkvvIYZrp88eT+6wL6Avqm2NfSl8E+g/5y/pycXu161f1WSl7PT95R/t45fG+t+p9LyVn1U8lJfnWTX93H9lfnEPV73l4/ebIlr1fPT95V/N9ayXnq/up9cPY/XPKu6r9qJbfn/9hK3qr/ayu5olX/J6dR/6tW8lbnMNqv9fzeHE7Nb89zWf97KTm93jp9KiVHtvo8T/fBK+mh/aPn4L3PVf3VOWT9vXOg91ntO6nnHuUnd3QON1Bye3NYza8ld9UfnQN5teTJltxn//0XOam9bqudA5Er21lu64/Wql/P/2bNQUuubMl59j9cz2+9biVPteTKlrxn//0XudU/aw7kyZbcVf8HKbmyJVe0dP7v7X2O5df3WvJRyZ312/t/1K/vKVZyH9ofnYO6Pb/nzvqrcyDnaL++50X95K7OgZxZv55/kZ+6F793/mdu+0ze3v139v8Fcq7iz87BrtNzR/e/VPJH3VF/9OdBr3OWn9zVOVDJ782B9t9aeh4k7yy//f8wyz/aPdofvQ+ifvL2zoEc0er+LzuHUX7yrua3vcdj81+2eu5T8V9JL6Wee2/7X+/3Hzy31bv/e/2/W8k/eg5Rv+49R/sP/f23es+1UX+v2/N75xvkyZb89nyk/tH7f/Pdymcq+bJur6fiv2klb3QOo/299bxacnrVn4dV979Wz987h737yRdtr3vvfr0PyKnV8y/yZDvbbyVn1D9rDlryZEvOs//h0v6DHNWSp1ryRt2z/bPmQO6s/7qVHNWSJ1vv3JP2HuSnvW9vyRUtnfNn/XZ9tP/tLbmiJXd2DtH9d2/JFS15V/Pb+WbvHMg5y2/veeQkf+8cyJn12zlQ9PcftFm3lryr+739r3ff9/67QM6o3zv/jLqj/mxn+XUOdA4c3YNE9/9UvU510/43WvJG3dHqv59fnHrXG91/a73nYXKP9ms9v51vVP30HnCqfvIe26/7P+9832r7EPKTc1U/zSG6Bybn3vzR/W/1vl/Vv+r+m1zRmn+TP79qXdU/eg6bNOvXnxdy9vrJE+3m1J7raP8/yq0///Q8qs9t1W5Sfa41t90Ps/3kHT2HrZXea6yr+rNzICfV81f3f6P8NI8bKTn37requ9fvzeGx+XUO5KOSo9rV/Xaduu+Y1dF+Kzm1f1u9fc/skidb8kb9h5oDeaold9Q/ew7kqJbcVnLP9s+aA7lP3e+df3rn/VS7ztF7z1lzoPPvXv+sOZArWnJn50D731ElF5Wcq/t750DOWX57vyNXtuSd7bfzoOr+m3xU8o7ye3Po9Y/af5O710/ff51D7x6AvNU5kJPquWftPzyn7T3ts16nee9ao3tvKrln+6P7/zsp7b+13vMwuUf7tbP89B6wut/Od9S/9/2/1pzqtp76/ts71/b2gGf//63e98f2b1Jy29+P3n8fyq3dpMfyr7L/tkb9o/agnl+fU8gV7QZV98/WVfa/5BvlVr83B/JRyUslN/np/Wa17391DubZ5HO0q/u9OXyWkpO6ul/nMMq9N791tF/nQD4qXf+okifbXr9d1173XVZyevX2PbNLnmzJGfUfag7kqZa8q/hnzYG85D70HMiTLblP3e+d+/6Rz+RW/6p7z6jfK/mts/e/5IqWvFk/7X9HlVxUcq7u750DOc/+h9vr1/dc8kVL3tl+O/+r7v+jc/kH0baO1nhe7dy9rt1UEIZh/gQRIKpIAVFkNWlOQ0vHBdAgpaTl/q+BIp7ifNKnd2Z5vGzvs9O82tIp/Ix3DvaaiPdfffnz3vT7ZH+a7I+mP0g/bf1G+oep/tzXUufVxs8f5dc5qLvL7+ZA89Dr7Ha/20r+qHNl/W4Of5nS9f+a/Lnot9Lwk5v81Wb9cX3OrSXvXv/LVvJVm/V/3Ep+cmvJfbT/HfSj9Lut6ifn3fzqdv74TM5Z/4uUPNWOrVW/K3mv4h9Scp/tJ0+2A7rKTX797xS5sh1Q54/vR7ffPZe5km/WHVX331I3B3J2+WfnMKDuvjt/lHxUclLJTX59rg93fD7KT65qq356r9O65wRyrvK7efwpJeej+NW91+/mQE4qOWbb7XZzIB+VHF0lT7V7/XS9R5Vc1ZLT1Z3/0fXvLXmqJeej+6tzcO5V/qPmQO6sf9UcyFOt8/4rn8m9yt89B3fuG/74TO5H82vJrf0kJU+15MqW3NU5xPVd3U/OWb+6j/LvnQM5j/Lr3ot8Wt1zzs6BnKv81Tns3Xuv9usekPa/s/7qPMhZ9es5sPqdm+ZAzqqffFTdf5Of7jvtwclJ7fK7c6TqHiTr7voekMtVn4f3+qNVv/u9EL7Ye2fd9O8f6D0o61e3fg9mq3Nw9yU8tAfLutVfve9dfufVHr0HPMtPc1i1Bz3bP7aSn9yr/OTJdkj33vdZ/4et6o7fS93+AV3tD6fz6xzIRx3Qs/zZkm/WHXX77273rH92DgOa3f93+clX7V5/PNe7/f9d9r9ZP73Xau/i1+oeeGwl7939z/3/65Kr2r1+uv7ukqfau/mPmgM5XfXci667q+SplpxZ/13nUD33cO67+qvfA/LfdQ7kfnS/Owd97v9fl9zqv+red9ZfncPV999H+fX+u/0vuVzdfuuzfKaSc9bv9t+096u6ac9HJWeXX+971U/u2TmQs8sf56Kr/Nk5kHPWH96q/xcp+bLt8uv5gXO7PUicZ2X9R82BnOR2JX+U9t9H+cl5tNt9D9y5Z/j3/h4Ijzv/Vrc+9x/ln211Htk9EL3/XMX/s9RdV9ad9esc3P7zbP+q/d8H6Sr/2Er3fbU/6tzd/qhzn+WP30/d/gE926/PrV3uq/rpfc2VnFV3dNZd9ZOPSt6q3933+L3Q5e7yV+cxoM6tvaqf5jFM6f2WnlvJeZY/+tz/vy45qXf1kyvbu/hdyZft0//lDzld9dyLrrur5KqWnFn/Xefgznuq7rv6q98D8t91DuR+dL/+PZjde9zdTyX3an/XHMhd9UeP3vvrXqe6/676s3OI6+v2u/P9z1Lyzrq7/Nm9L7ndHMh9tF/3v+R3cyAv9Wp+2n93+2kO5NTGe4Nz/2b6n3wOn9t3d+9/9/rpHCma9ce5Frm7/FFyzrrJr9XzPfJ3zUHPPbvc0fh7r94X6Wo/nX+7c2DyOv/s/pP8Ud3/7nXvnUN1D0rn/VW/7sHJR6X3QHLv9bt5xGfyH70HI3e3X/vW/UMa/r3736w/Owfn/l0+Vzuk7r6f5dfnWHffq3MY0FVu9dP7m/bufvJlm/UP6OrvPbmqpTkMaLzPuP1/l/sov2v44/OQ0vu9e167i79r///PVjcP8l7FT85H9cdnclLv5teSj/rW/W4O5NXquR9dd3fJR511O//qOZAv2zjvIi+5V/u75vDW/VFyZ/2r50CubMl9VX/XHGgPkN17Pf2vS9c92+x+i0reWT9df7W034gevf8mt/q7/v0DuWe/D+Sd9bv/D2i15KR2+7NzcPvfaPhoz+Nc/wOQs5gMeF7t3E1vHEUUhWG+hBAgEol8CLHAa/AqwhISWWcdthH//3ewcJ+Fjzi8t6qru6rNePNqPDHq507LsesOefXZ48erYr+2/h76Xeg3od9af2rsD8WSj+qeqj+5U8nb6x89B/KTc5a/dw5fWZOffFTyql9sJSeV3MlPJWfq263u/dU6yv+9tde71/821N3u752Du1VdD/moX1pb3a3+1jkk//ut5KO630vuXj/NI7lVXf8vVvKe5ZfrZ3tcLfndvbq/dQ5y3tnj5CfnUf7PoaP86qets/3k3uu/Cx3lJ7dKvmpb53AXutc9y+9z0GN59fhh653Vf6+7t5J3FX/qQ6i7k7/15yFyr+p3t/s/2mMveWf5/9ja6vfnr+r3+yDd99Sr+1VyUq/u3zuHN1v3+v3cj653dMlZ9ZOT3FedQ+/r/1z8qs47yX3zP36Q/2pz8PP+/5vf5/A6VM+T+6r+5F7dr/Pu3jmQezU/7TnI23rfe8l9lJ/cvXPwvd8of9p7uYvO+Xv9NAff+5G/ev/T3q9370nOVM2P9r5q8qffm+TWY3LPmkPV73OgvZ+XvHv9vXPQ9ZFbJedsf3UO6ZxvtHuWP+2/yU8lp9f3IGe70x60t+Qlv88h7TnIubqf9oC+B017HvJW/eSqNv3+1+r2vSDtu8h9lp/mQN60DyU/zeHo+57ce/2tc0j779Sz9qC97l6/vs69n6yz/eROfpqDvu7DVne7n3xU8pOv2uTX8773U5+L3+fgn09+n0Pa/6rkrbrP8pNb1fv9yK/956r+1tddJXfyaw/W6l7Fr3MccuvnNfnfhJL3bD/Nwc+z3K3Pf7Tu9b/bStc9uuRPTd7eOczye8nd6ie39+jz7mrJX50DeZN79hzI/dz91Tnc/P/eo/bff22l6x5V8vsc9Nj9vvd4bn4v7b285J7lr87hbyt5e/10nUeX3Ff3+56j1a/Pk7d3Di+26vyfPKrOOavu1DQHNe07q01u2vv07n9a/em+oD0wufX75Nl+lbzJ7XubVj/tgcg7yl+dxyg/uXvnQK5q0x7A/X7Olfzko5L7bD+d98ntnyfnbD/tgchNJWfqbf//tO72fQc5V/fTHpD23+Sc5ac5kLfq753D0W41nQOQt7r/b30/DL3uo/79h6P8aQ7V90Gc5adzoF433Qd6nvb+q/rJXfWnkp9c1ZKfrrPaqv+DdfbrT65qq/6Hrcl/H0rOVvdoP83hwSq/nq/u/8k920+vu1fPk1ul/W91D3L2/nOvXz+vXdWv/mmt+uW6uj+VzvvJ+1z95NzrP/u8n3qWf9a+g3q0/+VW2nvQdR7VvX79PdH7+t/8Tzt7/0ted9Pep/r/f872q6Pc1b3fav50Hxy99539fSDNwffg5KSS/4WVrvPo7n3fQ6s/7T38utI5J3nSnpPa+n6fVj/tffbuf9xPe9/kp71vqr5vzvLTefCPxbZ+/6c9kEruo/00B7r+Xvdqfr/v6dxLJR+V3KP8ye1+8j5XPzmp5Ey97f+fVt5Re9/V/LQHpP03OWf5q3MgN/lb53CWW9Xv/Xvd5Nd/d69/1P7P/aoc5KZ9cHXvvZpf7XXTfZD+/G9bV/eTu9evyul74PdWclWb/HSd1Vb91f0/eaqlc1ByVTvKfx9Kztl+moP2nb3+1jmQ++z9n+99veRW6TyY9j9X9evnCnKT/52VrntU9/rJW/Wv9vrT17W+7q3+2XOgP0/Oq/lbS86q/6V11fc/jPa7e/X9/16/vl/c/I8fvX519v6XvO6u7j/IfTU/uat7v6v5yUkl/6z9N+39VPJRb/7/rvvTHOi8k7yr+Wnvs3f/k9w6ByB37/5b9XPws/3qPySoxXM=
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diff --git a/DAMASK_3/processing/post/DP1000/load.yaml b/DAMASK_3/processing/post/DP1000/load.yaml
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- - constituents:
- - phase: Martensite
- O: [0.5535957303273183, -0.01875069001865881, 0.8234329029091495, 0.12303834115347222]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.3876140183021386, 0.8055650111800067, -0.2256394854781512, 0.3871785223529069]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Ferrite
- O: [0.49798372447168154, -0.6931496239952856, -0.24758417040804293, 0.4585388614718145]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.6216205155911253, -0.5861831110763206, -0.3969337097894477, 0.3352922977312422]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.6068009815824997, 0.6813690642215852, -0.40920121043903074, -0.009117919036071918]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.4538963065407538, -0.7326313090251688, -0.004759703083234113, -0.5071556498465624]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.23305908443137469, 0.9183144486004229, -0.3167287031164532, -0.04544188902191915]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Ferrite
- O: [0.9363431782648309, 0.19340040146294543, 0.19377489553565605, 0.21979314614207693]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.16329712118907738, -0.2509207419737857, -0.07682122578918518, -0.9510369765297938]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.8884857139472508, -0.18836911618515467, 0.3718244952698446, -0.19198113682493098]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Ferrite
- O: [0.4329225025662389, -0.708203037562605, 0.23785405437491328, -0.5044323672964495]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Ferrite
- O: [0.6200565855658137, -0.38214272707098457, -0.35614726187883383, -0.5853681702137769]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.7709578926897427, -0.06891761328904925, 0.5287544684265495, 0.34827144929909115]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.3699631184405217, 0.10970588458368373, -0.8446328764960803, -0.37106227755382654]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.3499320023144287, 0.929363953552007, -0.04358245582925607, 0.10922822500749803]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.5218488900805288, 0.3358006742482453, 0.7024572103513631, -0.34851902490613745]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.4280463804742962, -0.40192940923196896, -0.21426994735652163, 0.7805878783437057]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.7312566770487792, 0.03810737503543031, 0.02547947620897595, -0.6805602813356088]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.48769708787383415, -0.8286620861349445, -0.22276980252586334, -0.16076166385196206]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.771949862343683, -0.1437110050515257, -0.03730158773287648, 0.6181012446252278]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.6285637818981972, 0.43792126296278117, -0.3068114991253678, -0.5648001801835432]
- v: 1.0
- homogenization: SX
- - constituents:
- - phase: Martensite
- O: [0.4430015814114614, -0.08919907143180258, -0.5962377318846888, -0.6635462995146989]
- v: 1.0
- homogenization: SX
-
-homogenization:
- SX:
- N_constituents: 1
- mechanical: {type: pass}
-
-phase:
- Ferrite:
- lattice: cI
- mechanical:
- output: [F, P, F_e, F_p, L_p, O]
- elastic: {type: Hooke, C_11: 233300000000.0, C_12: 135500000000.0, C_44: 128000000000.0}
- plastic:
- type: phenopowerlaw
- N_sl: [12]
- a_sl: 1.3
- atol_xi: 1
- dot_gamma_0_sl: 0.001
- h_0_sl-sl: 4500000000.0
- h_sl-sl: [1, 1, 1.4, 1.4, 1.4, 1.4, 1.4]
- n_sl: 20
- xi_0_sl: [200000000.0]
- xi_inf_sl: [370000000.0]
- Martensite:
- lattice: cI
- mechanical:
- output: [F, P, F_e, F_p, L_p, O]
- elastic: {type: Hooke, C_11: 417400000000.0, C_12: 242400000000.0, C_44: 211100000000.0}
- plastic:
- type: phenopowerlaw
- N_sl: [12]
- a_sl: 2.5
- atol_xi: 1
- dot_gamma_0_sl: 0.001
- h_0_sl-sl: 40000000000.0
- h_sl-sl: [1, 1, 1.4, 1.4, 1.4, 1.4, 1.4]
- n_sl: 20
- xi_0_sl: [680000000.0]
- xi_inf_sl: [700000000.0]
diff --git a/DAMASK_3/processing/post/DP1000/numerics.yaml b/DAMASK_3/processing/post/DP1000/numerics.yaml
deleted file mode 100644
index 344a4ec691cfe54eb517d079e73c7138cfeb7344..0000000000000000000000000000000000000000
--- a/DAMASK_3/processing/post/DP1000/numerics.yaml
+++ /dev/null
@@ -1,84 +0,0 @@
-# Available numerical parameters
-# Case sensitive keys
-
-homogenization:
- mech:
- RGC:
- atol: 1.0e+4 # absolute tolerance of RGC residuum (in Pa)
- rtol: 1.0e-3 # relative ...
- amax: 1.0e+10 # absolute upper-limit of RGC residuum (in Pa)
- rmax: 1.0e+2 # relative ...
- perturbpenalty: 1.0e-7 # perturbation for computing penalty tangent
- relevantmismatch: 1.0e-5 # minimum threshold of mismatch
- viscositypower: 1.0e+0 # power (sensitivity rate) of numerical viscosity in RGC scheme
- viscositymodulus: 0.0e+0 # stress modulus of RGC numerical viscosity (zero = without numerical viscosity)
- # suggestion: larger than the aTol_RGC but still far below the expected flow stress of material
- refrelaxationrate: 1.0e-3 # reference rate of relaxation (about the same magnitude as straining rate, possibly a bit higher)
- maxrelaxationrate: 1.0e+0 # threshold of maximum relaxation vector increment (if exceed this then cutback)
- maxvoldiscrepancy: 1.0e-5 # maximum allowable relative volume discrepancy
- voldiscrepancymod: 1.0e+12
- discrepancypower: 5.0
-
- generic:
- subStepMin: 1.0e-3 # minimum (relative) size of sub-step allowed during cutback in homogenization
- subStepSize: 0.25 # size of substep when cutback introduced in homogenization (value between 0 and 1)
- stepIncrease: 1.5 # increase of next substep size when previous substep converged in homogenization (value higher than 1)
- nMPstate: 10 # materialpoint state loop limit
-
-grid:
- eps_div_atol: 1.0e-3 # absolute tolerance for fulfillment of stress equilibrium
- eps_div_rtol: 5.0e-4 # relative tolerance for fulfillment of stress equilibrium
- eps_curl_atol: 1.0e-12 # absolute tolerance for fulfillment of strain compatibility
- eps_curl_rtol: 5.0e-4 # relative tolerance for fulfillment of strain compatibility
- eps_stress_atol: 1.0e3 # absolute tolerance for fulfillment of stress BC
- eps_stress_rtol: 0.01 # relative tolerance for fulfillment of stress BC
- eps_damage_atol: 1.0e-2 # absolute tolerance for damage evolution
- eps_damage_rtol: 1.0e-6 # relative tolerance for damage evolution
- eps_thermal_atol: 1.0e-2 # absolute tolerance for thermal equilibrium
- eps_thermal_rtol: 1.0e-6 # relative tolerance for thermal equilibrium
- itmax: 75 # Maximum iteration number
- itmin: 2 # Minimum iteration number
- fftw_timelimit: -1.0 # timelimit of plan creation for FFTW, see manual on www.fftw.org, Default -1.0: disable timelimit
- fftw_plan_mode: FFTW_PATIENT # reads the planing-rigor flag, see manual on www.fftw.org, Default FFTW_PATIENT: use patient planner flag
- maxCutBack: 10 # maximum cut back level (0: 1, 1: 0.5, 2: 0.25, etc)
- maxStaggeredIter: 10 # max number of field level staggered iterations
- memory_efficient: 1 # Precalculate Gamma-operator (81 double per point)
- update_gamma: false # Update Gamma-operator with current dPdF (not possible if memory_efficient=1)
- divergence_correction: 2 # Use size-independent divergence criterion
- derivative: continuous # Approximation used for derivatives in Fourier space
- petsc_options: -snes_ngmres_anderson # PetSc solver options
- alpha: 1.0 # polarization scheme parameter 0.0 < alpha < 2.0. alpha = 1.0 ==> AL scheme, alpha = 2.0 ==> accelerated scheme
- beta: 1.0 # polarization scheme parameter 0.0 < beta < 2.0. beta = 1.0 ==> AL scheme, beta = 2.0 ==> accelerated scheme
-
-mesh:
- maxCutBack: 3 # maximum cut back level (0: 1, 1: 0.5, 2: 0.25, etc)
- maxStaggeredIter: 10 # max number of field level staggered iterations
- structorder: 2 # order of displacement shape functions (when mesh is defined)
- bbarstabilisation: false
- integrationorder: 2 # order of quadrature rule required (when mesh is defined)
- itmax: 250 # Maximum iteration number
- itmin: 2 # Minimum iteration number
- eps_struct_atol: 1.0e-10 # absolute tolerance for mechanical equilibrium
- eps_struct_rtol: 1.0e-4 # relative tolerance for mechanical equilibrium
-
-crystallite:
- subStepMin: 1.0e-3 # minimum (relative) size of sub-step allowed during cutback in crystallite
- subStepSize: 0.25 # size of substep when cutback introduced in crystallite (value between 0 and 1)
- stepIncrease: 1.5 # increase of next substep size when previous substep converged in crystallite (value higher than 1)
- subStepSizeLp: 0.5 # size of first substep when cutback in Lp calculation
- subStepSizeLi: 0.5 # size of first substep when cutback in Li calculation
- nState: 10 # state loop limit
- nStress: 40 # stress loop limit
- rtol_State: 1.0e-6 # relative tolerance in crystallite state loop (abs tol provided by constitutive law)
- rtol_Stress: 1.0e-6 # relative tolerance in crystallite stress loop (Lp residuum)
- atol_Stress: 1.0e-8 # absolute tolerance in crystallite stress loop (Lp residuum!)
- integrator: FPI # integration method (FPI = Fixed Point Iteration, Euler = Euler, AdaptiveEuler = Adaptive Euler, RK4 = classical 4th order Runge-Kutta, RKCK45 = 5th order Runge-Kutta Cash-Karp)
- iJacoLpresiduum: 1 # frequency of Jacobian update of residuum in Lp
-
-commercialFEM:
- unitlength: 1 # physical length of one computational length unit
-
-generic:
- charLength: 1.0 # characteristic length scale for gradient problems.
- random_seed: 0 # fixed seeding for pseudo-random number generator, Default 0: use random seed.
- residualStiffness: 1.0e-6 # non-zero residual damage.
diff --git a/DAMASK_3/processing/post/DP1000/rve.sta b/DAMASK_3/processing/post/DP1000/rve.sta
deleted file mode 100644
index 37ffb36e9f3c131008ed288abd83365ad2f54c97..0000000000000000000000000000000000000000
--- a/DAMASK_3/processing/post/DP1000/rve.sta
+++ /dev/null
@@ -1,59 +0,0 @@
-------------------------------------------------------------------------------
-----------------------------RVE Generation started----------------------------
-------------------------------------------------------------------------------
-
-
-Starttime: 14/11/2021 15:37:30
-
-
-Calculating the phase ratio...
-The phase ratio for the rest of the volume is: 0.2549561394585503
-
-Needed 65-Loops to sample points for the bands!
-RVE generation process continues with placing of the ferrite grains and martensite islands
-Sampled 36 Ferrite-Points for the matrix!
-The total conti volume is: 3070.170019827009
-The total discrete volume is: 2922.875
-Sampled 65 Martensite-Islands for the matrix!
-The total conti volume is: 1101.4793390480595
-The total discrete volume is: 1050.5
-
-Elapsed time for normal RSA: 0:01:34.343063
-
-
-
-RVE generation process continues with the tesselation of grains!
-Packingratio: 61.047363%
-Packingratio: 74.603271%
-Packingratio: 86.151123%
-Packingratio: 94.027710%
-Packingratio: 96.707153%
-Packingratio: 97.387695%
-Packingratio: 98.059082%
-Packingratio: 98.153687%
-Packingratio: 98.284912%
-Packingratio: 98.315430%
-Packingratio: 98.358154%
-Packingratio: 98.413086%
-Packingratio: 98.590088%
-Packingratio: 98.880005%
-Packingratio: 99.316406%
-Packingratio: 99.349976%
-Packingratio: 99.383545%
-Packingratio: 99.414062%
-Packingratio: 99.447632%
-Packingratio: 99.450684%
-Packingratio: 99.951172%
-Packingratio: 100.000000%
-Elapsed time for Tesselation: 0:00:30.449399
-
-
-
-RVE generation process nearly complete: Creating input for DAMASK Spectral now:
-Attention: Discrete and continuous Output are equal for the spectral grid!
-
-
-RVE generation process has successfully completed...
-------------------------------------------------------------------------------
------------------------------RVE Generation ended-----------------------------
-------------------------------------------------------------------------------
diff --git a/DAMASK_3/processing/post/config.txt b/DAMASK_3/processing/post/config.txt
index 358763ff4b442dbfcb463b2933197b4b805a7d9d..22b1b45a8a46b47ec86a232aebe3b8494654726d 100644
--- a/DAMASK_3/processing/post/config.txt
+++ b/DAMASK_3/processing/post/config.txt
@@ -1,5 +1,6 @@
# Enter hdf5 filename. Must be in same directory as process_results
filename = grid_load.hdf5
+folderpath = E:/Data/damask3/DAMASK_3/processing/post/grid_load
# Index of increments which should be exported.
increments = 100
# Exported phases, Options:
diff --git a/DAMASK_3/processing/post/grid_load.hdf5 b/DAMASK_3/processing/post/grid_load.hdf5
index bec48f7f54ce992de8bae06debfdc848c62f913e..061fe00ec005a4eb3a041cc77fb08cf296cd81d6 100644
Binary files a/DAMASK_3/processing/post/grid_load.hdf5 and b/DAMASK_3/processing/post/grid_load.hdf5 differ
diff --git a/DAMASK_3/processing/post/grid_load/Results_1.vti b/DAMASK_3/processing/post/grid_load/Results_1.vti
new file mode 100644
index 0000000000000000000000000000000000000000..7af57ff8c764744643b5a2eec3ad97f4918dde5b
Binary files /dev/null and b/DAMASK_3/processing/post/grid_load/Results_1.vti differ
diff --git a/DAMASK_3/processing/post/grid_load/Results_inc100.vti b/DAMASK_3/processing/post/grid_load/Results_inc100.vti
new file mode 100644
index 0000000000000000000000000000000000000000..a818af02960ef767735f1039629767f4f231e446
Binary files /dev/null and b/DAMASK_3/processing/post/grid_load/Results_inc100.vti differ
diff --git a/DAMASK_3/processing/post/grid_load_1.hdf5 b/DAMASK_3/processing/post/grid_load_1.hdf5
new file mode 100644
index 0000000000000000000000000000000000000000..959ff72fc33687e5e980b97052baf7e232ea52ac
Binary files /dev/null and b/DAMASK_3/processing/post/grid_load_1.hdf5 differ
diff --git a/DAMASK_3/processing/post/process_results.py b/DAMASK_3/processing/post/process_results.py
index 91f9f9ab05c21228c197aa58ca87f8d6cab3e30c..583b6b8eb1e2a4233402f10c8ce06f3f2ef4e051 100644
--- a/DAMASK_3/processing/post/process_results.py
+++ b/DAMASK_3/processing/post/process_results.py
@@ -1,3 +1,4 @@
+from matplotlib.pyplot import title
import pyvista as pv
import numpy as np
import damask # the damask version has been customized. use git version of damask. Install it by: cd damask/python; pip install .
@@ -76,7 +77,7 @@ def export_VTK(config: dir):
-def display_Data(config: dict):
+def display_Data_clipped(config: dict):
vtk_list = []
indices = config['increments'].split(',')
phase_namebase = config['folderpath']
@@ -118,9 +119,59 @@ def display_Data(config: dict):
plotter.add_mesh_clip_plane(mesh, scalars=mesh.array_names[field_index], show_edges=True)
plotter.show()
+def display_Data_whole(config: dict):
+ pv.global_theme.font.color = 'black'
+ vtk_list = []
+ indices = config['increments'].split(',')
+ phase_namebase = config['folderpath']
+ for ind in indices: # iterate through the increments
+ ind = int(ind)
+ if ind < 10:
+ vtk_list.append(f'{phase_namebase}/Results_inc00{int(ind)}.vti') # build a list containing the names of the vti-files, which should be plotted
+ elif ind < 100:
+ vtk_list.append(f'{phase_namebase}/Results_inc0{int(ind)}.vti')
+ else:
+ vtk_list.append(f'{phase_namebase}/Results_inc{int(ind)}.vti')
+ num_plots = len(vtk_list)
+ if num_plots > 0 and num_plots <3:
+ plotter = pv.Plotter(shape=(1,num_plots),off_screen=True)
+ elif num_plots < 5:
+ plotter = pv.Plotter(shape=(2,2))
+ else:
+ print('Invalid number of displayed increments. Must be between 1 and 4')
+ iter_vtk = 0 #set Counter for the increments
+ sargs = dict(height=0.5, vertical=True, position_x=0.05, position_y=0.05,title='')
+
+ for vtk_element in vtk_list:
+ iter_vtk = iter_vtk + 1 #increase iterater
+ _ , increment_name = vtk_element.split('Results_') # trenne die Increment Nummer aus dem Pfad zum VTK-Element
+ increment_name, _= increment_name.split('.')
+ phase_name = config['phase']
+ mesh = pv.read(f'{vtk_element}') #lade vtk file und transformiere es in ein Uniform grid
+ new_pos = mesh.points + mesh.get_array('u') # add the disposition of the grid points to the original position
+ mesh = mesh.cast_to_structured_grid() # change it from uniform grid to structured grid to be able to edit position
+ mesh.points = new_pos #change the position of the gridpoints
+ mesh = pv.wrap(mesh) #wrap it up for visualization
+ name_array = mesh.array_names #get list of all active fields
+ field_index = name_array.index(config['field']) # ermittele den Index des gewählten Feldes
+ if iter_vtk == 2: # position des zweiten Increments im Ausgabebildschirm
+ plotter.subplot(0,1)
+ elif iter_vtk == 3:
+ plotter.subplot(1,0)
+ elif iter_vtk == 4:
+ plotter.subplot(1,1)
+ #plotter.add_text(f'{increment_name} phase {phase_name}') # Füge Name zum Subplot hinzu
+ mesh[mesh.array_names[field_index]]=mesh[mesh.array_names[field_index]]/1000000000
+ plotter.add_mesh(mesh, scalars=mesh.array_names[field_index], show_edges=True,scalar_bar_args=sargs)
+ #plotter.remove_scalar_bar()
+ plotter
+ #plotter.show('F:/RWTH/HiWi_IEHK/DAMASK3/Bericht/vtk/64_volume.png')
+ #plotter.savefig('F:/RWTH/HiWi_IEHK/DAMASK3/Bericht/vtk/difference.png',transparent_background=True)
+ plotter.screenshot('64_volume.png', transparent_background=True)
if __name__ == '__main__':
config = read_config(f'{pathlib.Path(__file__).parent.resolve()}\config.txt')
export_VTK(config)
- display_Data(config)
+ #display_Data_clipped(config)
+ #display_Data_whole(config)
diff --git a/RVE_Generator/check_grainconsistency.py b/RVE_Generator/check_grainconsistency.py
index 4ba6ded518635c31cf38a6f9bbbc55759a24fddf..955dca483303d4b5aaf40cdf64058cb2e4bed3e3 100644
--- a/RVE_Generator/check_grainconsistency.py
+++ b/RVE_Generator/check_grainconsistency.py
@@ -7,18 +7,26 @@ from yaml.loader import SafeLoader
def read_material(path : str):
grid = pv.read(f'{path}/grid.vti')
grid = grid['material']
+
grid_grains = np.unique(grid, return_counts=False)
with open(f'{path}/material.yaml') as file:
material = yaml.load(file, Loader=SafeLoader)
material = material['material']
if len(grid_grains) != len(material): # check if amount of grains in material.yaml and grid.vti is equal
print(f'number of grains inconsistent in file:{path}/material.yaml. there are {grid_grains} in grid.vti and {len(material)} in material.yml')
+def check_max_stress(path : str):
+ grid = pv.read(f'{path}/Results_inc100.vti')
+ grid = grid['phase/mechanical/sigma_vM / Pa']
+
+ if grid.max() > 4e9: # check if amount of grains in material.yaml and grid.vti is equal
+ print(f'stress to high:{path}/Results_inc100.vti. The maximum stress is {grid.max()}')
if __name__ == '__main__':
Input_path = 'E:/Data/Simulation_Output/OutputData_64'
for folder_id, folder in enumerate(os.listdir(Input_path)):
folder_path = f'{Input_path}/{folder}'
- input_matrix = read_material(folder_path)
+ #input_matrix = read_material(folder_path)
+ check_max_stress(folder_path)
diff --git a/UNet/NormalizeTrainingdata_32.ipynb b/UNet/NormalizeTrainingdata_32.ipynb
index 5b8044d618556cd6b76bce756ae86a74851de403..a24703fb7ab2bc22ec39b4d170251298cfcd7d30 100644
--- a/UNet/NormalizeTrainingdata_32.ipynb
+++ b/UNet/NormalizeTrainingdata_32.ipynb
@@ -293,7 +293,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.10"
+ "version": "3.9.5"
},
"orig_nbformat": 4
},
diff --git a/UNet/NormalizeTrainingdata_64.ipynb b/UNet/NormalizeTrainingdata_64.ipynb
index 7bcd331f5ee652cc9f2e368b7c28d9ef2329a770..de256a5ceb7ba676814eb268dc1c8814a12de10b 100644
--- a/UNet/NormalizeTrainingdata_64.ipynb
+++ b/UNet/NormalizeTrainingdata_64.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -19,16 +19,29 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: 'E:/Data/damask3/UNet/Input/Norm_min_max_64.npy'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_27568/913510190.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmin_label\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_label\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mangles_min_max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'E:/Data/damask3/UNet/Input/Norm_min_max_64.npy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_pickle\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\numpy\\lib\\npyio.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[0;32m 415\u001b[0m \u001b[0mown_fid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 416\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 417\u001b[1;33m \u001b[0mfid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menter_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 418\u001b[0m \u001b[0mown_fid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 419\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'E:/Data/damask3/UNet/Input/Norm_min_max_64.npy'"
+ ]
+ }
+ ],
"source": [
"min_label, max_label,angles_min_max = np.load('E:/Data/damask3/UNet/Input/Norm_min_max_64.npy', allow_pickle= True)"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 8,
"metadata": {
"id": "OzNQI96lq3Pi"
},
@@ -47,7 +60,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 9,
"metadata": {
"id": "lUnBE7T4q3Pi"
},
@@ -71,7 +84,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -84,8 +97,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "size of input is torch.Size([791, 2, 64, 64, 64])\n",
- "size of label is torch.Size([791, 64, 64, 64])\n"
+ "size of input is torch.Size([783, 6, 64, 64, 64])\n",
+ "size of label is torch.Size([783, 64, 64, 64])\n"
]
}
],
@@ -94,9 +107,9 @@
"phase= data[:,4,:,:,:].reshape(data.shape[0], 1,64,64,64)\n",
"new_phase = np.ones(phase.shape) - phase #input[4]: martinsite, input[5]:ferrit\n",
"#new_training_data = np.append(data,new_channel,axis=1)\n",
- "#input = np.append(angles,phase,axis=1)\n",
- "#input = np.append(input,new_phase,axis=1)\n",
- "input = np.append(phase,new_phase,axis=1)\n",
+ "input = np.append(angles,phase,axis=1)\n",
+ "input = np.append(input,new_phase,axis=1)\n",
+ "#input = np.append(phase,new_phase,axis=1)\n",
"\n",
"label = torch.from_numpy(training_label)\n",
"input = torch.from_numpy(input)\n",
@@ -112,7 +125,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 11,
"metadata": {
"id": "Kgd1WhOODim3"
},
@@ -130,15 +143,15 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 12,
"metadata": {
"id": "-Rbt8Brb9mM_"
},
"outputs": [],
"source": [
"dataset = TensorDataset(input,label_normalized) # create the pytorch dataset \n",
- "np.save('E:/Data/damask3/UNet/Input/Norm_min_max_64_subset_phase.npy',[min_label, max_label])\n",
- "torch.save(dataset,'E:/Data/damask3/UNet/Input/TD_norm_64_subset_phase.pt')\n"
+ "np.save('E:/Data/damask3/UNet/Input/Norm_min_max_64_angles.npy',[min_label, max_label])\n",
+ "torch.save(dataset,'E:/Data/damask3/UNet/Input/TD_norm_64_angles.pt')\n"
]
}
],
diff --git a/UNet/UNet_V13.py b/UNet/UNet_V13.py
index 536127b8be1a52685401165f8480dff504055146..7c4d893c8640f2459fdb5df764eba5325f76840f 100644
--- a/UNet/UNet_V13.py
+++ b/UNet/UNet_V13.py
@@ -1,4 +1,4 @@
-#like V6_2 but only the different phases as input
+#just the grains as input and for each layer just one convolution
"""UNet_V6.ipynb
Automatically generated by Colaboratory.
@@ -11,9 +11,12 @@ import torch
import torch.nn as nn
import numpy as np
import random
+from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data.dataloader import DataLoader
+from torch.utils.data import TensorDataset
import torch.nn.functional as F
from torch.utils.data import random_split
+from torch.nn.modules.activation import ReLU
class depthwise_separable_conv(nn.Module):
def __init__(self, in_c, out_1_c, out_2_c, padding, kernel_size):
@@ -53,7 +56,7 @@ class head_layer(nn.Module):
#return self.sig(self.pointwise(self.depthwise(x))) #convolution
class Encoder(nn.Module):
- def __init__(self,kernel_size, chs, padding=(("same","same"),("same","same"),("same","same"),("same","same"))):
+ def __init__(self,kernel_size, chs, padding=(("same","same"),("same","same"),("same","same"))):
super().__init__()
self.channels = chs
self.enc_blocks = nn.ModuleList([depthwise_separable_conv(chs[i][0], chs[i][1], chs[i][2], kernel_size=kernel_size, padding=padding[i]) for i in range(len(chs))])
@@ -75,7 +78,7 @@ class Encoder(nn.Module):
return ftrs
class Decoder(nn.Module):
- def __init__(self,kernel_size, chs_upsampling, chs_conv, padding=(("same","same"),("same","same"),("same","same"),("same","same"))):
+ def __init__(self,kernel_size, chs_upsampling, chs_conv, padding=(("same","same"),("same","same"),("same","same"))):
super().__init__()
assert len(chs_conv) == len(chs_upsampling)
self.chs = chs_upsampling
@@ -113,7 +116,7 @@ class UNetBase(nn.Module):
input, labels = batch
out = self(input) # Generate predictions
loss = F.l1_loss(out, labels) # Calculate loss
- acc = accuracy(out.detach(), labels.detach(),normalization=self.normalization) # Calculate accuracy
+ acc = accuracy(out.detach(), labels.detach(),self.normalization) # Calculate accuracy
return {'val_loss': loss.detach(), 'val_acc': acc}
def validation_epoch_end(self, outputs):
@@ -133,12 +136,12 @@ def accuracy(outputs, labels,normalization, threshold = 0.05):
percentage = ((right_predic/torch.numel(error))*100.)
return percentage
+
class UNet(UNetBase):
- def __init__(self,kernel_size = 7, enc_chs=((6,16,32), (32,32,64), (64,64,128), (128,128,256)), dec_chs_up=(256,256, 128, 64), dec_chs_conv=((384,256,256),(320,128,128),(160,64,64),(70,32,32)),normalization=np.array([0,1])):
+ def __init__(self,kernel_size = 5, enc_chs=((6,16,32), (32,32,64), (64,64,128),(128,128,256)), dec_chs_up=(256,256, 128, 64), dec_chs_conv=((384,256,256),(320,128, 128),(160,64,64),(70,32,32)),normalization=np.array([0,1])):
super().__init__()
self.encoder = Encoder(kernel_size = kernel_size, chs = enc_chs)
self.decoder = Decoder(kernel_size = kernel_size, chs_upsampling = dec_chs_up, chs_conv = dec_chs_conv)
- #self.head = depthwise_separable_conv(1, 1, padding = "same", kernel_size=1)
self.normalization = normalization
@@ -225,11 +228,11 @@ if __name__ == '__main__':
path_to_rep = '/home/yk138599/Hiwi/damask3'
use_seeds = False
seed = 373686838
- num_epochs = 200
- b_size = 16
+ num_epochs = 300
+ b_size = 8
opt_func = torch.optim.Adam
lr = 0.00003
- kernel = 7
+ kernel = 9
print(f'number auf epochs: {num_epochs}')
print(f'batchsize: {b_size}')
print(f'learning rate: {lr}')
@@ -248,3 +251,4 @@ if __name__ == '__main__':
model = to_device(UNet(kernel_size=kernel,normalization=normalization).double(), device)
history = fit(num_epochs, lr, model, train_dl, valid_dl,f'{path_to_rep}/UNet/output', opt_func)
+
diff --git a/UNet/UNet_V14.py b/UNet/UNet_V14.py
index 4fa51be27e463e57e01adbb56db25f5289902114..c4b3a5fd852c8c8d9d9749ee5ad6525d3b545e81 100644
--- a/UNet/UNet_V14.py
+++ b/UNet/UNet_V14.py
@@ -19,14 +19,20 @@ from torch.utils.data import random_split
from torch.nn.modules.activation import ReLU
class depthwise_separable_conv(nn.Module):
- def __init__(self, in_c, out_c, padding,kernel_size):
+ def __init__(self, in_c, out_1_c, out_2_c, padding, kernel_size):
super(depthwise_separable_conv, self).__init__()
- self.depthwise = nn.Conv3d(in_c, in_c, kernel_size= kernel_size, padding=padding, groups=in_c, bias=True)
- self.pointwise = nn.Conv3d(in_c, out_c, kernel_size=1, bias=True)
+ self.depthwise_1 = nn.Conv3d(in_c, in_c, kernel_size= kernel_size, padding=padding[0], groups=in_c, bias=True)
+ self.pointwise_1 = nn.Conv3d(in_c, out_1_c, kernel_size=1, bias=True)
+ self.batch_norm_1 = nn.BatchNorm3d(out_1_c)
self.relu = nn.ReLU()
- self.batch_norm = nn.BatchNorm3d(out_c)
+ self.droptout = nn.Dropout3d(p=0.5)
+
+ self.depthwise_2 = nn.Conv3d(out_1_c, out_1_c, kernel_size= kernel_size, padding=padding[1], groups=out_1_c, bias=True)
+ self.pointwise_2 = nn.Conv3d(out_1_c, out_2_c, kernel_size=1, bias=True)
+ self.batch_norm_2 = nn.BatchNorm3d(out_2_c)
def forward(self, x):
- return self.batch_norm(self.relu(self.pointwise(self.depthwise(x))))
+ x = self.batch_norm_1(self.relu(self.droptout(self.pointwise_1(self.depthwise_1(x)))))
+ return self.batch_norm_2(self.relu(self.droptout(self.pointwise_2(self.depthwise_2(x)))))
class convolution_Layer(nn.Module):
def __init__(self, in_c, out_1_c, out_2_c, padding, kernel_size):
@@ -50,10 +56,10 @@ class head_layer(nn.Module):
#return self.sig(self.pointwise(self.depthwise(x))) #convolution
class Encoder(nn.Module):
- def __init__(self,kernel_size, chs, padding=("same","same","same","same")):
+ def __init__(self,kernel_size, chs, padding=(("same","same"),("same","same"),("same","same"))):
super().__init__()
self.channels = chs
- self.enc_blocks = nn.ModuleList([depthwise_separable_conv(chs[i][0], chs[i][1],kernel_size=kernel_size, padding=padding[i]) for i in range(len(chs))])
+ self.enc_blocks = nn.ModuleList([depthwise_separable_conv(chs[i][0], chs[i][1], chs[i][2], kernel_size=kernel_size, padding=padding[i]) for i in range(len(chs))])
self.pool = nn.MaxPool3d(kernel_size=2, stride=2)
#self.batch_norm = nn.ModuleList([nn.BatchNorm3d( chs[i][2]) for i in range(len(chs))])
@@ -72,13 +78,13 @@ class Encoder(nn.Module):
return ftrs
class Decoder(nn.Module):
- def __init__(self,kernel_size, chs_upsampling, chs_conv, padding=("same","same","same","same")):
+ def __init__(self,kernel_size, chs_upsampling, chs_conv, padding=(("same","same"),("same","same"),("same","same"))):
super().__init__()
assert len(chs_conv) == len(chs_upsampling)
self.chs = chs_upsampling
self.upconvs = nn.ModuleList([nn.ConvTranspose3d(chs_upsampling[i], chs_upsampling[i], 2, 2) for i in range(len(chs_upsampling))])
- self.dec_blocks = nn.ModuleList([depthwise_separable_conv(chs_conv[i][0], chs_conv[i][1],kernel_size=kernel_size, padding=padding[i]) for i in range(len(chs_conv))])
- self.head = head_layer(chs_conv[-1][1])
+ self.dec_blocks = nn.ModuleList([depthwise_separable_conv(chs_conv[i][0], chs_conv[i][1], chs_conv[i][2], kernel_size=kernel_size, padding=padding[i]) for i in range(len(chs_conv))])
+ self.head = head_layer(chs_conv[-1][2])
def forward(self, x, encoder_features):
for i in range(len(self.chs)):
x = self.upconvs[i](x)
@@ -132,7 +138,7 @@ def accuracy(outputs, labels,normalization, threshold = 0.05):
class UNet(UNetBase):
- def __init__(self,kernel_size = 7, enc_chs=((6,16), (16,32), (32,64), (64,128)), dec_chs_up=(128, 64, 32, 16), dec_chs_conv=((192, 64),(96,32),(48,16),(22,1)),normalization=np.array([0,1])):
+ def __init__(self,kernel_size = 9, enc_chs=((6,16,32), (32,32,64), (64,64,128)), dec_chs_up=(128, 128, 64), dec_chs_conv=((192,128, 128),(160,64,64),(70,32,32)),normalization=np.array([0,1])):
super().__init__()
self.encoder = Encoder(kernel_size = kernel_size, chs = enc_chs)
self.decoder = Decoder(kernel_size = kernel_size, chs_upsampling = dec_chs_up, chs_conv = dec_chs_conv)
@@ -222,8 +228,8 @@ if __name__ == '__main__':
path_to_rep = '/home/yk138599/Hiwi/damask3'
use_seeds = False
seed = 373686838
- num_epochs = 500
- b_size = 32
+ num_epochs = 300
+ b_size = 8
opt_func = torch.optim.Adam
lr = 0.00003
kernel = 9
diff --git a/UNet/grain_ numbers.ipynb b/UNet/grain_ numbers.ipynb
index 8a8966577c3eacca09c06ba6a7f3bdfe5c063ef4..f57fbc3414a7c6185be07c7a5eb6d191fabdf8e6 100644
--- a/UNet/grain_ numbers.ipynb
+++ b/UNet/grain_ numbers.ipynb
@@ -2,13 +2,65 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pyvista as pv\n",
"import numpy as np\n",
- "import os"
+ "import os\n",
+ "import numpy as np\n",
+ "import pyvista as pv\n",
+ "import torch\n",
+ "import copy\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data =torch.load('E:/Data/damask3/UNet/Input/TD_norm_32_angles.pt')\n",
+ "p_mart =np.empty(len(data))\n",
+ "for index in range(len(data)):\n",
+ " input,_ = data[index]\n",
+ " input = copy.copy(input)\n",
+ " input = input.detach().numpy()\n",
+ " p_mart[index]= np.sum(input[4,:,:,:])/input.size\n",
+ "\n",
+ " \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Minimum Mart percentage is:0.0248870849609375\n",
+ "Maximum Mart percentage is:0.060089111328125\n",
+ "Mean Mart percentage is:0.04008708634526112\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f'Minimum Mart percentage is:{p_mart.min()}')\n",
+ "print(f'Maximum Mart percentage is:{p_mart.max()}')\n",
+ "print(f'Mean Mart percentage is:{p_mart.mean()}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.save('E:/Data/damask3/UNet/percentage_mart',p_mart)"
]
},
{
@@ -44,7 +96,15 @@
"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": "3.9.5"
},
"orig_nbformat": 4
diff --git a/UNet/postprocessing_new.ipynb b/UNet/postprocessing_new.ipynb
index c25e36d37d2d65eb5b9ad361645c6338b5f83ebc..46ed8ac3ac389abb5f8d0e8684f02216cef3c512 100644
--- a/UNet/postprocessing_new.ipynb
+++ b/UNet/postprocessing_new.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -119,13 +119,6 @@
" return matrix_grains"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
{
"cell_type": "code",
"execution_count": 3,
@@ -297,38 +290,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "sample number: 83\n"
- ]
- },
- {
- "ename": "RuntimeError",
- "evalue": "[enforce fail at ..\\c10\\core\\CPUAllocator.cpp:76] data. DefaultCPUAllocator: not enough memory: you tried to allocate 14386462720 bytes.",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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- "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_23188/2786384287.py\u001b[0m in \u001b[0;36mpredict_stress\u001b[1;34m(image_id, normalization, model, dataset, grain_data, threshold)\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[0mxb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mUNet15\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_device\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevice_15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 14\u001b[0m \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprediction\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1103\u001b[0m \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32me:\\Data\\damask3\\UNet\\UNet_V15.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 150\u001b[0m \u001b[0menc_ftrs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menc_ftrs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menc_ftrs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 152\u001b[0m \u001b[1;31m#out = self.head(out)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1103\u001b[0m \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32me:\\Data\\damask3\\UNet\\UNet_V15.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x, encoder_features)\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[1;31m#print(f'size after cropping&cat: {x.size()}')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 96\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 97\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdec_blocks\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 98\u001b[0m \u001b[1;31m#print(f'size after convolution: {x.size()}')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 99\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1103\u001b[0m \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32me:\\Data\\damask3\\UNet\\UNet_V15.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatch_norm_2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBatchNorm3d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_2_c\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatch_norm_1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdroptout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpointwise_1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdepthwise_1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 34\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatch_norm_2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdroptout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpointwise_2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdepthwise_2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1103\u001b[0m \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 589\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 590\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 591\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 592\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m 583\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroups\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 584\u001b[0m )\n\u001b[1;32m--> 585\u001b[1;33m return F.conv3d(\n\u001b[0m\u001b[0;32m 586\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpadding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdilation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroups\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 587\u001b[0m )\n",
- "\u001b[1;31mRuntimeError\u001b[0m: [enforce fail at ..\\c10\\core\\CPUAllocator.cpp:76] data. DefaultCPUAllocator: not enough memory: you tried to allocate 14386462720 bytes."
- ]
- }
- ],
+ "outputs": [],
"source": [
"sample_index = np.random.randint(low=0, high=len(Training_data_64))\n",
"print(f'sample number: {sample_index}')\n",
@@ -397,7 +361,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.10"
+ "version": "3.9.5"
},
"orig_nbformat": 4
},