Datasets
Models
Downloads: 1
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{
"cells": [
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"name": "stderr",
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"/home/reina/anaconda3/envs/RSNA/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n",
" return f(*args, **kwds)\n",
"/home/reina/anaconda3/envs/RSNA/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n",
" return f(*args, **kwds)\n"
]
}
],
"source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import os\n",
"import datetime\n",
"import seaborn as sns\n",
"\n",
"#import pydicom\n",
"import time\n",
"from functools import partial\n",
"import gc\n",
"import operator \n",
"import matplotlib.pyplot as plt\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.utils.data as D\n",
"import torch.nn.functional as F\n",
"from sklearn.model_selection import KFold\n",
"from tqdm import tqdm, tqdm_notebook\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\"\n",
"import warnings\n",
"warnings.filterwarnings(action='once')\n",
"import pickle\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline\n",
"from skimage.io import imread,imshow\n",
"from helper import *\n",
"import helper\n",
"import torchvision.models as models\n",
"from torch.optim import Adam\n",
"from defenitions import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"SEED = 8153\n",
"device=device_by_name(\"Tesla\")\n",
"#device=device_by_name(\"RTX\")\n",
"#device=device_by_name(\"1060\")\n",
"torch.cuda.set_device(device)\n",
"#device = \"cpu\"\n",
"sendmeemail=Email_Progress(my_gmail,my_pass,to_email,'Densenet161-Copy2-2 results')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def get_submission(test_df,pred):\n",
" epidural_df=pd.DataFrame(data={'ID':'ID_'+test_df.PatientID.values+'_epidural','Label':torch.sigmoid(pred[:,0])})\n",
" intraparenchymal_df=pd.DataFrame(data={'ID':'ID_'+test_df.PatientID.values+'_intraparenchymal','Label':torch.sigmoid(pred[:,1])})\n",
" intraventricular_df=pd.DataFrame(data={'ID':'ID_'+test_df.PatientID.values+'_intraventricular','Label':torch.sigmoid(pred[:,2])})\n",
" subarachnoid_df=pd.DataFrame(data={'ID':'ID_'+test_df.PatientID.values+'_subarachnoid','Label':torch.sigmoid(pred[:,3])})\n",
" subdural_df=pd.DataFrame(data={'ID':'ID_'+test_df.PatientID.values+'_subdural','Label':torch.sigmoid(pred[:,4])})\n",
" any_df=pd.DataFrame(data={'ID':'ID_'+test_df.PatientID.values+'_any','Label':torch.sigmoid(pred[:,5])}) \n",
" return pd.concat([epidural_df,\n",
" intraparenchymal_df,\n",
" intraventricular_df,\n",
" subarachnoid_df,\n",
" subdural_df,\n",
" any_df]).sort_values('ID').reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def get_submission_ids(image_ids,pred,do_sigmoid=True):\n",
" if do_sigmoid:\n",
" func = lambda x:torch.sigmoid(x)\n",
" else:\n",
" func = lambda x:x\n",
" epidural_df=pd.DataFrame(data={'ID':'ID_'+image_ids+'_epidural','Label':func(pred[:,0])})\n",
" intraparenchymal_df=pd.DataFrame(data={'ID':'ID_'+image_ids+'_intraparenchymal','Label':func(pred[:,1])})\n",
" intraventricular_df=pd.DataFrame(data={'ID':'ID_'+image_ids+'_intraventricular','Label':func(pred[:,2])})\n",
" subarachnoid_df=pd.DataFrame(data={'ID':'ID_'+image_ids+'_subarachnoid','Label':func(pred[:,3])})\n",
" subdural_df=pd.DataFrame(data={'ID':'ID_'+image_ids+'_subdural','Label':func(pred[:,4])})\n",
" any_df=pd.DataFrame(data={'ID':'ID_'+image_ids+'_any','Label':func(pred[:,5])}) \n",
" return pd.concat([epidural_df,\n",
" intraparenchymal_df,\n",
" intraventricular_df,\n",
" subarachnoid_df,\n",
" subdural_df,\n",
" any_df]).sort_values('ID').reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
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"execution_count": 5,
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"execution_count": 5,
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" <th></th>\n",
" <th>PatientID</th>\n",
" <th>epidural</th>\n",
" <th>intraparenchymal</th>\n",
" <th>intraventricular</th>\n",
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" <th>PID</th>\n",
" <th>StudyI</th>\n",
" <th>SeriesI</th>\n",
" <th>WindowCenter</th>\n",
" <th>WindowWidth</th>\n",
" <th>ImagePositionZ</th>\n",
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"text/plain": [
" PatientID epidural intraparenchymal intraventricular subarachnoid \\\n",
"0 63eb1e259 0 0 0 0 \n",
"1 2669954a7 0 0 0 0 \n",
"2 52c9913b1 0 0 0 0 \n",
"3 4e6ff6126 0 0 0 0 \n",
"4 7858edd88 0 0 0 0 \n",
"\n",
" subdural any PID StudyI SeriesI WindowCenter \\\n",
"0 0 0 a449357f 62d125e5b2 0be5c0d1b3 ['00036', '00036'] \n",
"1 0 0 363d5865 a20b80c7bf 3564d584db ['00047', '00047'] \n",
"2 0 0 9c2b4bd7 3e3634f8cf 973274ffc9 40 \n",
"3 0 0 3ae81c2d a1390c15c2 e5ccad8244 ['00036', '00036'] \n",
"4 0 0 c1867feb c73e81ed3a 28e0531b3a 40 \n",
"\n",
" WindowWidth ImagePositionZ ImagePositionX ImagePositionY \n",
"0 ['00080', '00080'] 180.199951 -125.0 -8.000000 \n",
"1 ['00080', '00080'] 922.530821 -156.0 45.572849 \n",
"2 150 4.455000 -125.0 -115.063000 \n",
"3 ['00080', '00080'] 100.000000 -99.5 28.500000 \n",
"4 100 145.793000 -125.0 -132.190000 "
]
},
"execution_count": 5,
"metadata": {},
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"execution_count": 5,
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" <th></th>\n",
" <th>PatientID</th>\n",
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],
"text/plain": [
" PatientID epidural intraparenchymal intraventricular subarachnoid \\\n",
"0 28fbab7eb 0.5 0.5 0.5 0.5 \n",
"1 877923b8b 0.5 0.5 0.5 0.5 \n",
"2 a591477cb 0.5 0.5 0.5 0.5 \n",
"3 42217c898 0.5 0.5 0.5 0.5 \n",
"4 a130c4d2f 0.5 0.5 0.5 0.5 \n",
"\n",
" subdural any SeriesI PID StudyI WindowCenter WindowWidth \\\n",
"0 0.5 0.5 ebfd7e4506 cf1b6b11 93407cadbb 30 80 \n",
"1 0.5 0.5 6d95084e15 ad8ea58f a337baa067 30 80 \n",
"2 0.5 0.5 8e06b2c9e0 ecfb278b 0cfe838d54 30 80 \n",
"3 0.5 0.5 e800f419cf e96e31f4 c497ac5bad 30 80 \n",
"4 0.5 0.5 faeb7454f3 69affa42 854e4fbc01 30 80 \n",
"\n",
" ImagePositionZ ImagePositionX ImagePositionY \n",
"0 158.458000 -125.0 -135.598000 \n",
"1 138.729050 -125.0 -101.797981 \n",
"2 60.830002 -125.0 -133.300003 \n",
"3 55.388000 -125.0 -146.081000 \n",
"4 33.516888 -125.0 -118.689819 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"old_train_df = pd.read_csv(data_dir+'train.csv')\n",
"old_train_df.shape\n",
"old_train_df=old_train_df[~old_train_df.PatientID.isin(bad_images)].reset_index(drop=True)\n",
"old_train_df=old_train_df.drop_duplicates().reset_index(drop=True)\n",
"old_train_df.shape\n",
"old_train_df.head()\n",
"\n",
"train_df = pd.read_csv(data_dir+'train_stage2.csv')\n",
"train_df.shape\n",
"\n",
"\n",
"old_test_df = pd.read_csv(data_dir+'test.csv')\n",
"old_test_df.head()\n",
"assert old_test_df.shape[0]+old_train_df.shape[0]==train_df.shape[0]\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
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" <tr>\n",
" <th>4</th>\n",
" <td>fc45b2151</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>d16ad143cb</td>\n",
" <td>39eb6eb6</td>\n",
" <td>6a15b35eae</td>\n",
" <td>['00036', '00036']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>424.099976</td>\n",
" <td>-102.5</td>\n",
" <td>14.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PatientID epidural intraparenchymal intraventricular subarachnoid \\\n",
"0 0fbf6a978 0.5 0.5 0.5 0.5 \n",
"1 d62ec3412 0.5 0.5 0.5 0.5 \n",
"2 cb544194b 0.5 0.5 0.5 0.5 \n",
"3 0d62513ec 0.5 0.5 0.5 0.5 \n",
"4 fc45b2151 0.5 0.5 0.5 0.5 \n",
"\n",
" subdural any SeriesI PID StudyI WindowCenter \\\n",
"0 0.5 0.5 48eaa460c6 3556ae0e 3710aff1c5 40 \n",
"1 0.5 0.5 389cec2e11 c667d610 8e38d86ede ['00036', '00036'] \n",
"2 0.5 0.5 a6ebe3ca4f 9389d3b6 7040866911 ['00036', '00036'] \n",
"3 0.5 0.5 01c388cfe3 79cdc6e4 07c298b5be 30 \n",
"4 0.5 0.5 d16ad143cb 39eb6eb6 6a15b35eae ['00036', '00036'] \n",
"\n",
" WindowWidth ImagePositionZ ImagePositionX ImagePositionY \n",
"0 100 37.690857 -125.0 -117.830185 \n",
"1 ['00080', '00080'] 762.200043 -66.0 -10.000000 \n",
"2 ['00080', '00080'] 818.400024 -105.0 6.000000 \n",
"3 80 31.857132 -125.0 -112.984024 \n",
"4 ['00080', '00080'] 424.099976 -102.5 14.500000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df = pd.read_csv(data_dir+'test_stage2.csv')\n",
"test_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def my_loss(y_pred,y_true,weights):\n",
" window=(y_true>=0).to(torch.float)\n",
" loss = (F.binary_cross_entropy_with_logits(y_pred,y_true,reduction='none')*window*weights.expand_as(y_true)).mean()/(window.mean()+1e-7)\n",
" return loss"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"class Metric():\n",
" def __init__(self,weights,k=0.03):\n",
" self.weights=weights\n",
" self.k=k\n",
" self.zero()\n",
" \n",
" def zero(self):\n",
" self.loss_sum=0.\n",
" self.loss_count=0.\n",
" self.lossf=0.\n",
" \n",
" def calc(self,y_pred,y_true,prefix=\"\"):\n",
" window=(y_true>=0).to(torch.float)\n",
" loss = (F.binary_cross_entropy_with_logits(y_pred,y_true,reduction='none')*window*self.weights.expand_as(y_true)).mean()/(window.mean()+1e-5)\n",
" self.lossf=self.lossf*(1-self.k)+loss*self.k\n",
" self.loss_sum=self.loss_sum+loss*window.sum()\n",
" self.loss_count=self.loss_count+window.sum()\n",
" return({prefix+'mloss':self.lossf}) \n",
" \n",
" def calc_sums(self,prefix=\"\"):\n",
" return({prefix+'mloss_tot':self.loss_sum/self.loss_count}) \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#features=(features-features.mean())/features.std()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"class ResModel(nn.Module):\n",
" def __init__(self,in_size):\n",
" super(ResModel, self).__init__()\n",
" self.dont_do_grad=[]\n",
" self.conv2d1=torch.nn.Conv2d(1, 64, (9,in_size), padding=(4,0))\n",
" self.bn0=torch.nn.BatchNorm1d(64)\n",
" self.relu0=torch.nn.ReLU()\n",
" self.conv1d1=torch.nn.Conv1d(64, 64, 7, padding=3)\n",
" self.bn1=torch.nn.BatchNorm1d(64)\n",
" self.relu1=torch.nn.ReLU()\n",
" self.conv1d2=torch.nn.Conv1d(128, 64, 5, padding=2)\n",
" self.bn2=torch.nn.BatchNorm1d(64)\n",
" self.relu2=torch.nn.ReLU()\n",
" self.conv1d3=torch.nn.Conv1d(192, 6, 3, padding=1)\n",
" \n",
" \n",
" def forward(self, x):\n",
" x = self.conv2d1(x.unsqueeze(1)).squeeze(-1)\n",
" x = self.bn0(x)\n",
" x0 = self.relu0(x)\n",
" x = self.conv1d1(x0)\n",
" x = self.bn1(x)\n",
" x1 = self.relu1(x)\n",
" x = torch.cat([x0,x1],1)\n",
" x = self.conv1d2(x)\n",
" x = self.bn2(x)\n",
" x2 = self.relu2(x)\n",
" x = torch.cat([x0,x1,x2],1)\n",
" out = self.conv1d3(x).transpose(-1,-2)\n",
" return out \n",
" \n",
" def no_grad(self):\n",
" for param in self.parameters():\n",
" param.requires_grad=False\n",
"\n",
" def do_grad(self):\n",
" for n,p in self.named_parameters():\n",
" p.requires_grad= not any(nd in n for nd in self.dont_do_grad)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"class BaseModel(nn.Module):\n",
" def __init__(self):\n",
" super(BaseModel, self).__init__()\n",
" self.dont_do_grad=[]\n",
" self.conv2d1=torch.nn.Conv2d(1, 128, (5,2208), padding=(2,0))\n",
" self.relu0=torch.nn.ReLU()\n",
" self.conv1d1=torch.nn.Conv1d(128, 6, 1)\n",
"\n",
" \n",
" \n",
" def forward(self, x):\n",
" x = self.conv2d1(x.unsqueeze(1)).squeeze(-1)\n",
" x = self.relu0(x)\n",
" out = self.conv1d1(x).transpose(-1,-2)\n",
" return out \n",
" \n",
" def no_grad(self):\n",
" for param in self.parameters():\n",
" param.requires_grad=False\n",
"\n",
" def do_grad(self):\n",
" for n,p in self.named_parameters():\n",
" p.requires_grad= not any(nd in n for nd in self.dont_do_grad)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"class ResModelPool(nn.Module):\n",
" def __init__(self,in_size):\n",
" super(ResModelPool, self).__init__()\n",
" self.dont_do_grad=[]\n",
" self.conv2d1=torch.nn.Conv2d(1, 64, (9,in_size),stride=(1,in_size), padding=(4,0))\n",
" self.bn0=torch.nn.BatchNorm1d(64)\n",
"# self.relu0=torch.nn.ReLU()\n",
" self.conv1d1=torch.nn.Conv1d(64, 64, 7, padding=3)\n",
" self.bn1=torch.nn.BatchNorm1d(64)\n",
" self.relu1=torch.nn.ReLU()\n",
" self.conv1d2=torch.nn.Conv1d(128, 64, 5, padding=2)\n",
" self.bn2=torch.nn.BatchNorm1d(64)\n",
" self.relu2=torch.nn.ReLU()\n",
" self.conv1d3=torch.nn.Conv1d(192, 6, 3, padding=1)\n",
" \n",
" \n",
" def forward(self, x):\n",
" x=x.unsqueeze(1)\n",
" x = self.conv2d1(x)\n",
" x=F.max_pool2d(x,kernel_size=(1,x.shape[-1])).squeeze(-1) \n",
" x0 = self.bn0(x)\n",
"# x0 = self.relu0(x)\n",
" x = self.conv1d1(x0)\n",
" x = self.bn1(x)\n",
" x1 = self.relu1(x)\n",
" x = torch.cat([x0,x1],1)\n",
" x = self.conv1d2(x)\n",
" x = self.bn2(x)\n",
" x2 = self.relu2(x)\n",
" x = torch.cat([x0,x1,x2],1)\n",
" out = self.conv1d3(x).transpose(-1,-2)\n",
" return out \n",
" \n",
" def no_grad(self):\n",
" for param in self.parameters():\n",
" param.requires_grad=False\n",
"\n",
" def do_grad(self):\n",
" for n,p in self.named_parameters():\n",
" p.requires_grad= not any(nd in n for nd in self.dont_do_grad)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"se_resnext101_32x4d classifier_splits features_test_stage2 0\n",
"torch.Size([121232, 8, 256])\n"
]
},
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 13,
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{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"multi=3\n",
"model_names=['se_resnext101_32x4d',\n",
" 'se_resnext101_32x4d',\n",
" 'Densenet161_3',\n",
" 'Densenet169_3',\n",
" 'se_resnet101',\n",
" 'se_resnet101',\n",
" 'se_resnet101']\n",
"#types=['features_test','features_test','features_test','features_test','features_test','features_test','features_test','features_test']\n",
"versions=['classifier_splits',\n",
" 'new_splits',\n",
" 'classifier_splits',\n",
" 'classifier_splits',\n",
" 'classifier_splits',\n",
" 'new_splits',\n",
" 'new_splits_focal']\n",
"num_splits=[3,5,3,3,3,5,5]\n",
"seeds=[8153,432,8153,8153,8153,432,432]\n",
"for model_name,version_,n,SEED in zip(model_names,versions,num_splits,seeds):\n",
" type_='features_test_stage2'\n",
" for num_split in tqdm_notebook(range(n)):\n",
" pred_list=[]\n",
" print(model_name,version_,type_,num_split) \n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version_,type_,num_split),'rb')\n",
" features=pickle.load(pickle_file)\n",
" pickle_file.close()\n",
" features=features.reshape(features.shape[0]//8,8,-1)\n",
" print(features.shape)\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version_+'_fullhead_resmodel_pool2_stage2_{}'.format(multi)\n",
"\n",
" model.load_state_dict(torch.load(models_dir+models_format.format(model_name,version,num_split),map_location=torch.device(device)))\n",
"\n",
" valid_dataset=FullHeadDataset(test_df,\n",
" test_df.SeriesI.unique(),\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" multi =4)\n",
"\n",
" win_dataset=FullHeadDataset(test_df,\n",
" test_df.SeriesI.unique(),\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" target_columns=hemorrhage_types)\n",
" win_list=[]\n",
" dl = D.DataLoader(win_dataset,batch_size=128,num_workers=16)\n",
" for _,win in tqdm_notebook(dl):\n",
" win_list.append(win.reshape(win.shape[0]*win.shape[1],-1)) \n",
" wins = torch.cat(win_list,0).sum(1)>=0\n",
" wins.sum()\n",
" for i in tqdm_notebook(range(32),leave=False):\n",
" pr = model_run(model,valid_dataset,do_apex=False,batch_size=128)\n",
" pred_list.append(pr.reshape(pr.shape[0]*pr.shape[1],-1)[wins])\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version,'test_pred_ensemble',num_split),'wb')\n",
" pickle.dump(pred_list,pickle_file,protocol=4)\n",
" pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"pred_list=[]\n",
"model_names=['se_resnext101_32x4d',\n",
" 'se_resnext101_32x4d',\n",
" 'Densenet161_3',\n",
" 'Densenet169_3',\n",
" 'se_resnet101',\n",
" 'se_resnet101',\n",
" 'se_resnet101']\n",
"#types=['features_test','features_test','features_test','features_test','features_test','features_test','features_test','features_test']\n",
"versions=['classifier_splits',\n",
" 'new_splits',\n",
" 'classifier_splits',\n",
" 'classifier_splits',\n",
" 'classifier_splits',\n",
" 'new_splits',\n",
" 'new_splits_focal']\n",
"num_splits=[3,5,3,3,3,5,5]\n",
"for model_name,version_,n_splits in zip(model_names,versions,num_splits):\n",
" for num_split in range(n_splits):\n",
" version=version_+'_fullhead_resmodel_pool2_stage2_3'\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version,'test_pred_ensemble',num_split),'rb')\n",
" pred_list.extend( pickle.load(pickle_file))\n",
" pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"864"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"torch.Size([121232, 6])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(pred_list)\n",
"pred=torch.sigmoid(torch.cat([p[None] for p in pred_list],0)).mean(0)\n",
"pred.shape"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"pickle_file=open(outputs_dir+'ensemble_test_image_ids_stage2.pkl','rb')\n",
"image_ids = pickle.load(pickle_file) \n",
"pickle_file.close()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ID_000000e27_any</td>\n",
" <td>9.501273e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ID_000000e27_epidural</td>\n",
" <td>4.105167e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ID_000000e27_intraparenchymal</td>\n",
" <td>3.959713e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ID_000000e27_intraventricular</td>\n",
" <td>3.313437e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ID_000000e27_subarachnoid</td>\n",
" <td>2.073304e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ID_000000e27_subdural</td>\n",
" <td>3.489370e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>ID_000009146_any</td>\n",
" <td>6.613446e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>ID_000009146_epidural</td>\n",
" <td>8.478917e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>ID_000009146_intraparenchymal</td>\n",
" <td>4.919742e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>ID_000009146_intraventricular</td>\n",
" <td>3.850217e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>ID_000009146_subarachnoid</td>\n",
" <td>1.723542e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>ID_000009146_subdural</td>\n",
" <td>3.993163e-05</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID Label\n",
"0 ID_000000e27_any 9.501273e-04\n",
"1 ID_000000e27_epidural 4.105167e-05\n",
"2 ID_000000e27_intraparenchymal 3.959713e-04\n",
"3 ID_000000e27_intraventricular 3.313437e-05\n",
"4 ID_000000e27_subarachnoid 2.073304e-04\n",
"5 ID_000000e27_subdural 3.489370e-04\n",
"6 ID_000009146_any 6.613446e-05\n",
"7 ID_000009146_epidural 8.478917e-06\n",
"8 ID_000009146_intraparenchymal 4.919742e-06\n",
"9 ID_000009146_intraventricular 3.850217e-07\n",
"10 ID_000009146_subarachnoid 1.723542e-05\n",
"11 ID_000009146_subdural 3.993163e-05"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(727392, 2)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"submission_df=get_submission_ids(image_ids,pred,do_sigmoid=False)\n",
"submission_df.head(12)\n",
"submission_df.shape\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"sub = pd.read_csv('/media/hd/notebooks/data/RSNA/submissions/submission_stage2_2.csv')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.5039)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"F.binary_cross_entropy(torch.tensor(submission_df.Label.values,dtype=torch.float),\n",
" torch.tensor(sub.Label.values>0.5,dtype=torch.float))"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"res_df=pd.DataFrame(data=pred.numpy(),columns=hemorrhage_types)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"res_df['PatientID']=image_ids"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"res_df=res_df.merge(test_df[['PatientID','SeriesI','ImagePositionZ']],on='PatientID')"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"positive_id=res_df[res_df['any']>0.5].SeriesI.unique()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f7a3563de10>"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"k=452\n",
"plt.scatter(res_df[res_df.SeriesI ==positive_id[k] ].ImagePositionZ,res_df[res_df.SeriesI ==positive_id[k] ]['any'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}