From 1f2cef8599fd02198daa9f3d5961a1803bf10ee1 Mon Sep 17 00:00:00 2001
From: eneren <engin.eren@cern.ch>
Date: Thu, 28 Apr 2022 08:51:32 +0000
Subject: [PATCH] eval for 3 energies

---
 .gitignore                 |   1 +
 interactive/eval.ipynb     | 158 +++++++++++++++++++++----------------
 pytorch_job_wgan_nccl.yaml |   4 +-
 3 files changed, 91 insertions(+), 72 deletions(-)

diff --git a/.gitignore b/.gitignore
index 507aa4b..30ccb10 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,4 +7,5 @@ models/.ipynb_checkpoints/*
 interactive/__pycache__/
 models/__pycache__/
 interactive/jsd/
+interactive/plots/
 
diff --git a/interactive/eval.ipynb b/interactive/eval.ipynb
index 8b648ec..e69c025 100644
--- a/interactive/eval.ipynb
+++ b/interactive/eval.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,102 +16,118 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib as mpl\n",
-    "import h5py"
+    "import h5py\n",
+    "import matplotlib.patches as mpatches"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "## G4 \n",
     "f40 = h5py.File('/eos/user/e/eneren/run_prod20k_40GeVp2/pion-shower_40.hdf5', 'r')\n",
-    "f50 = h5py.File('/eos/user/e/eneren/scratch/50GeV75k.hdf5', 'r')"
+    "f50 = h5py.File('/eos/user/e/eneren/50GeV75k.hdf5', 'r')\n",
+    "f60 = h5py.File('/eos/user/e/eneren/scratch/60GeV20k.hdf5', 'r')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "showers50 = f50['ecal/layers'][:1000]\n",
-    "showers40 = f40['ecal/layers'][:1000]"
+    "showers40 = f40['ecal/layers'][:1000]\n",
+    "showers60 = f60['ecal/layers'][:1000]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "showers  = {\n",
     "    '50': showers50,\n",
-    "    '40': showers40\n",
+    "    '40': showers40,\n",
+    "    '60': showers60\n",
     "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def esum_plot(real, fake, nbins, minE, maxE, name):\n",
+    "    \n",
+    "    figSE = plt.figure(figsize=(6,6*0.77/0.67))\n",
+    "    axSE = figSE.add_subplot(1,1,1)\n",
+    "\n",
+    "\n",
+    "    pSEa = axSE.hist(real, bins=nbins, \n",
+    "            weights=np.ones_like(real)/(float(len(real))), \n",
+    "            histtype='step', color='black',\n",
+    "            range=[minE, maxE])\n",
+    "    pSEb = axSE.hist(fake, bins=nbins, \n",
+    "            weights=np.ones_like(fake)/(float(len(fake))),\n",
+    "            histtype='step', color='red',\n",
+    "             range=[minE, maxE])\n",
+    "\n",
+    "    plt.title(name)\n",
+    "    \n",
+    "    plt.xlabel('MeV', fontsize=18)\n",
+    "    \n",
+    "    \n",
+    "    red_patch = mpatches.Patch(color='red', label='WGAN')\n",
+    "    grey_patch = mpatches.Patch(color='black', label='G4')\n",
+    "    \n",
+    "    axSE.legend(handles=[red_patch, grey_patch])\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    plt.savefig('./plots/esum'+str(name)+'.png')\n",
+    "\n",
+    "  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 251:  [0.3804679251946886, 0.34817149733098474]\n",
-      "Epoch 252:  [0.46051294878083926, 0.338569981047804]\n",
-      "Epoch 253:  [0.49756609463285095, 0.28872701740420126]\n",
-      "Epoch 254:  [0.5294431896884374, 0.33102840750614887]\n",
-      "Epoch 255:  [0.5104405441541784, 0.32231291300401893]\n",
-      "Epoch 256:  [0.48537347466233505, 0.320526721042005]\n",
-      "Epoch 257:  [0.44430318353292403, 0.33754094942412155]\n",
-      "Epoch 258:  [0.41426411913761607, 0.3383523881572018]\n",
-      "Epoch 259:  [0.4406027102881503, 0.3343011917750107]\n",
-      "Epoch 260:  [0.396682637265716, 0.3473503311876263]\n",
-      "Epoch 261:  [0.40394391363113635, 0.36565837165899845]\n",
-      "Epoch 262:  [0.42483844096641477, 0.32469973222063503]\n",
-      "Epoch 263:  [0.4019302446826285, 0.36214573291228813]\n",
-      "Epoch 264:  [0.4126153084092838, 0.38613505292409905]\n",
-      "Epoch 265:  [0.4165755030129357, 0.3519977789394489]\n",
-      "Epoch 266:  [0.3913068204960252, 0.38645762498974795]\n",
-      "Epoch 267:  [0.39439823796062096, 0.3708618579692532]\n",
-      "Epoch 268:  [0.40663386822619313, 0.37241345442978496]\n",
-      "Epoch 269:  [0.3683487445416732, 0.41097496954033574]\n",
-      "Epoch 270:  [0.3931176707467255, 0.36655001291434963]\n",
-      "Epoch 271:  [0.36265554202525746, 0.38067712153366035]\n",
-      "Epoch 272:  [0.36238044571407557, 0.3891110817723188]\n",
-      "Epoch 273:  [0.3443907936692471, 0.41633865859471425]\n",
-      "Epoch 274:  [0.3754340116921492, 0.3977884345978468]\n",
-      "Epoch 275:  [0.3552435525173964, 0.4127042666470706]\n",
-      "Epoch 276:  [0.3043411019678306, 0.4198835584501347]\n",
-      "Epoch 277:  [0.34320331541360355, 0.3928834088206806]\n",
-      "Epoch 278:  [0.31577926421268404, 0.4058244392791663]\n",
-      "Epoch 279:  [0.3009843850678768, 0.4373018678372319]\n",
-      "Epoch 280:  [0.3273260324046915, 0.4398480157854807]\n",
-      "Epoch 281:  [0.30707030715104516, 0.4339921708642802]\n",
-      "Epoch 282:  [0.2915953909635101, 0.4176873424583225]\n",
-      "Epoch 283:  [0.2924977435537306, 0.43666767755248453]\n",
-      "Epoch 284:  [0.2919643384290543, 0.45133477022104457]\n",
-      "Epoch 285:  [0.2947872964957196, 0.449896095813128]\n",
-      "Epoch 286:  [0.2897446726869525, 0.48724052437850385]\n",
-      "Epoch 287:  [0.31118635032181113, 0.4325288883457097]\n",
-      "Epoch 288:  [0.2696458856150868, 0.4629579633785893]\n",
-      "Epoch 289:  [0.28041806669044533, 0.46011915244113094]\n",
-      "Epoch 290:  [0.3074237951720241, 0.46164573769828265]\n",
-      "Epoch 291:  [0.28446450303954507, 0.463811909399871]\n",
-      "Epoch 292:  [0.2849400618724373, 0.4441966401101374]\n",
-      "Epoch 293:  [0.2826640112838286, 0.4795805721176796]\n",
-      "Epoch 294:  [0.2825341470608504, 0.4795195996538825]\n",
-      "Epoch 295:  [0.2805225945655388, 0.45946307781001905]\n",
-      "Epoch 296:  [0.28991007237838295, 0.46572437861315613]\n",
-      "Epoch 297:  [0.2668600287706407, 0.5065609989888424]\n",
-      "Epoch 298:  [0.3204394091550444, 0.4845860422924739]\n",
-      "Epoch 299:  [0.31568750383524585, 0.48608889263931343]\n",
-      "Epoch 300:  [0.2956457355782629, 0.48849089405017004]\n"
-     ]
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 480x551.642 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 480x551.642 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 480x551.642 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -120,18 +136,18 @@
     "mGen = DCGAN_G(ngf, nz)\n",
     "mGen = nn.parallel.DataParallel(mGen)\n",
     "exp='wganv1'\n",
-    "batch_size = 1000\n",
+    "batch_size = 100\n",
     "\n",
     "\n",
-    "for eph in range(251,301): \n",
-    "    gen_checkpoint = torch.load('/eos/user/e/eneren/experiments/' + exp + \"_generator_\"+ str(eph) + \".pt\", map_location=torch.device('cuda'))\n",
+    "for eph in range(392,393): \n",
+    "    gen_checkpoint = torch.load('/eos/user/e/eneren/experiments/' + exp + \"_generator_\"+ str(eph) + \".pt\", map_location=torch.device('cpu'))\n",
     "    mGen.load_state_dict(gen_checkpoint['model_state_dict'])\n",
     "    mGen.eval()\n",
-    "    Tensor = torch.cuda.FloatTensor \n",
+    "    Tensor = torch.FloatTensor \n",
     "    z = Variable(Tensor(np.random.uniform(-1, 1, (batch_size, nz, 1, 1, 1))))\n",
     "    \n",
     "    jsd = []\n",
-    "    for e in [40, 50]:\n",
+    "    for e in [40, 50, 60]:\n",
     "        \n",
     "        enp = torch.from_numpy(np.random.uniform(e, e, (batch_size,1,1,1,1))).float()\n",
     "        fake_data = mGen(z,enp).detach()\n",
@@ -139,11 +155,13 @@
     "        esumFake = F.getTotE(fake_data.cpu().numpy(), 30, 30, 30)\n",
     "        esumReal = F.getTotE(showers[str(e)], 30, 30, 30)\n",
     "    \n",
-    "        jsd.append(F.jsdHist(esumReal, esumFake, 50, 0, 100, eph, debug=False))\n",
+    "        esum_plot(esumReal, esumFake, 50, -10, 90, 'epoch_'+str(eph)+'_E='+str(e))\n",
+    "        \n",
+    "        #jsd.append(F.jsdHist(esumReal, esumFake, 50, 0, 100, eph, debug=False))\n",
     "        \n",
     "    \n",
     "    \n",
-    "    print ('Epoch {}: '.format(eph), jsd)\n",
+    "    #print ('Epoch {}: '.format(eph), jsd)\n",
     "    #F.plot_image2D(fake_dataG)\n",
     "    \n",
     "    "
diff --git a/pytorch_job_wgan_nccl.yaml b/pytorch_job_wgan_nccl.yaml
index 518b1d2..696a3bd 100644
--- a/pytorch_job_wgan_nccl.yaml
+++ b/pytorch_job_wgan_nccl.yaml
@@ -37,7 +37,7 @@ spec:
               command: [sh, -c] 
               args:  
                 - cp /secret/krb-secret-vol/krb5cc_1000 /tmp/krb5cc_0 && chmod 600 /tmp/krb5cc_0 
-                  && python -u wgan.py --backend nccl --epochs 50 --exp wganv1 --chpt --chpt_eph 250 --lrGen 0.00001 --ncrit 5;
+                  && python -u wgan.py --backend nccl --epochs 113 --exp wganv1 --chpt --chpt_eph 312 --lrGen 0.00001 --ncrit 5;
               volumeMounts:
               - name: eos
                 mountPath: /eos
@@ -81,7 +81,7 @@ spec:
               command: [sh, -c] 
               args:  
                 - cp /secret/krb-secret-vol/krb5cc_1000 /tmp/krb5cc_0 && chmod 600 /tmp/krb5cc_0 
-                  && python -u wgan.py --backend nccl --epochs 50 --exp wganv1 --chpt --chpt_eph 250 --lrGen 0.00001 --ncrit 5;
+                  && python -u wgan.py --backend nccl --epochs 113 --exp wganv1 --chpt --chpt_eph 312 --lrGen 0.00001 --ncrit 5;
               volumeMounts:
               - name: eos
                 mountPath: /eos
-- 
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