Datasets
Models
Downloads: 1
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{
"cells": [
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
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"text": [
"/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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" <th></th>\n",
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" <th>intraparenchymal</th>\n",
" <th>intraventricular</th>\n",
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" <th>SeriesI</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": 14,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/reina/anaconda3/envs/RSNA/lib/python3.6/site-packages/ipykernel_launcher.py:34: ResourceWarning: unclosed file <_io.BufferedReader name='/media/hd/notebooks/data/RSNA/outputs/model_se_resnext101_32x4d_version_classifier_splits_type_features_train_tta_split_1.pkl'>\n"
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"se_resnext101_32x4d classifier_splits features_train_tta features_test_tta 0\n"
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"data": {
"text/plain": [
"torch.Size([2697008, 256])"
]
},
"execution_count": 14,
"metadata": {},
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"data": {
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(14526,) (7218,)\n",
"502569 250228\n"
]
},
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 14,
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"torch.Size([151014, 6])\n"
]
},
{
"data": {
"text/plain": [
"tensor(0.0702)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multi=3\n",
"\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_train=['features_train_tta',\n",
" 'features_train_tta',\n",
" 'features_train_tta2',\n",
" 'features_train_tta2',\n",
" 'features_train_tta',\n",
" 'features_train_tta',\n",
" 'features_train_tta']\n",
"types_test=['features_test_tta',\n",
" 'features_test',\n",
" 'features_test2',\n",
" 'features_test2',\n",
" 'features_test',\n",
" 'features_test',\n",
" '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,type_tr,type_ts,version_,n_splits,SEED in zip(model_names,types_train,types_test,versions,num_splits,seeds):\n",
" pickle_file=open(outputs_dir+'PID_splits_{}_stage_2.pkl'.format(n_splits),'rb')\n",
" split_sid,splits=pickle.load(pickle_file)\n",
" pickle_file.close()\n",
" for num_split in tqdm_notebook(range(n_splits)):\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],dtype=torch.float)\n",
" pred_list=[]\n",
" print(model_name,version_,type_tr,type_ts,num_split) \n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version_,type_tr,num_split),'rb')\n",
" features=pickle.load(pickle_file)\n",
" pickle_file.close()\n",
" features.shape\n",
" features=features.reshape(features.shape[0]//4,4,-1)\n",
" features.shape\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version_,type_ts,num_split),'rb')\n",
" featurest=pickle.load(pickle_file)\n",
" pickle_file.close()\n",
" featurest.shape\n",
" featurest=featurest.reshape(featurest.shape[0]//8,8,-1)\n",
" featurest.shape\n",
" features=torch.cat([features,featurest[:,:4]])\n",
" features.shape\n",
" dfm=pd.concat([old_train_df,old_test_df],sort=False,axis=0).reset_index(drop=True)[['PatientID']]\n",
" atrain_df=dfm.merge(train_df,on='PatientID')\n",
" split_train = atrain_df[atrain_df.PID.isin(set(split_sid[splits[num_split][0]]))].SeriesI.unique()\n",
" split_validate = atrain_df[atrain_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
" print (split_train.shape,split_validate.shape)\n",
" print (train_df[train_df.SeriesI.isin(set(split_train))].shape[0],\n",
" train_df[train_df.SeriesI.isin(set(split_validate))].shape[0],)\n",
"\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(atrain_df,\n",
" split_validate,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" multi =3)\n",
"\n",
" win_dataset=FullHeadDataset(atrain_df,\n",
" split_validate,\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",
" y_true = torch.cat(win_list,0)[wins].to(torch.float)\n",
" print(train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].shape[0],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",
" print(pred_list[0].shape)\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version,'OOF_pred',num_split),'wb')\n",
" pickle.dump(pred_list,pickle_file,protocol=4)\n",
" pickle_file.close()\n",
" y_pred=torch.cat([p[None] for p in pred_list],0).mean(0)\n",
" my_loss(y_pred,y_true,weights)\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"17079"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"1859"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(18938,)\n",
"(12625,) (12625,) (6313,) (6313,)\n",
"(12625,) (12625,) (6313,) (6313,)\n",
"(12626,) (12626,) (6312,) (6312,)\n"
]
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"250629\n",
"torch.Size([250187, 6])\n",
"251768\n",
"torch.Size([251760, 6])\n",
"250400\n",
"torch.Size([250850, 6])\n"
]
},
{
"data": {
"text/plain": [
"17079"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
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"1859"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(18938,)\n",
"(12625,) (12625,) (6313,) (6313,)\n",
"(12625,) (12625,) (6313,) (6313,)\n",
"(12626,) (12626,) (6312,) (6312,)\n"
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]
},
"metadata": {},
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"250629\n",
"torch.Size([250170, 6])\n",
"251768\n",
"torch.Size([251852, 6])\n",
"250400\n",
"torch.Size([250775, 6])\n"
]
},
{
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"text/plain": [
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]
},
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]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(18938,)\n",
"(12625,) (12625,) (6313,) (6313,)\n",
"(12625,) (12625,) (6313,) (6313,)\n",
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{
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"HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"250629\n",
"torch.Size([250170, 6])\n",
"251768\n",
"torch.Size([251852, 6])\n",
"250400\n",
"torch.Size([250775, 6])\n"
]
},
{
"data": {
"text/plain": [
"17079"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
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{
"data": {
"text/plain": [
"1859"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(18938,)\n",
"(12625,) (12625,) (6313,) (6313,)\n",
"(12625,) (12625,) (6313,) (6313,)\n",
"(12626,) (12626,) (6312,) (6312,)\n"
]
},
{
"data": {
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"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"250629\n",
"torch.Size([250170, 6])\n",
"251768\n",
"torch.Size([251852, 6])\n",
"250400\n",
"torch.Size([250775, 6])\n"
]
}
],
"source": [
"multi=3\n",
"\n",
"model_names=['se_resnext101_32x4d',\n",
" 'Densenet161_3',\n",
" 'Densenet169_3',\n",
" 'se_resnet101']\n",
"types_train=['features_train_tta',\n",
" 'features_train_tta2',\n",
" 'features_train_tta2',\n",
" 'features_train_tta']\n",
"types_test=['features_test',\n",
" 'features_test2',\n",
" 'features_test2',\n",
" 'features_test']\n",
"versions=['classifier_splits',\n",
" 'classifier_splits',\n",
" 'classifier_splits',\n",
" 'classifier_splits']\n",
"num_splits=[3,3,3,3]\n",
"seeds=[8153,8153,8153,8153]\n",
"# model_names=['se_resnext101_32x4d',\n",
"# 'se_resnet101',\n",
"# 'se_resnet101']\n",
"# types_train=['features_train_tta',\n",
"# 'features_train_tta',\n",
"# 'features_train_tta']\n",
"# types_test=['features_test',\n",
"# 'features_test',\n",
"# 'features_test']\n",
"# versions=['new_splits',\n",
"# 'new_splits',\n",
"# 'new_splits_focal']\n",
"# num_splits=[5,5,5]\n",
"# seeds=[432,432,432]\n",
"for model_name,type_tr,type_ts,version_,n_splits,SEED in zip(model_names,types_train,types_test,versions,num_splits,seeds):\n",
" old_split_sid = old_train_df.PID.unique()\n",
" old_split_sid.shape[0]\n",
" old_splits=list(KFold(n_splits=n_splits,shuffle=True, random_state=SEED).split(old_split_sid))\n",
" new_split_sid=np.array(list(set(train_df.PID.unique()).difference(old_train_df.PID.unique())))\n",
" new_split_sid.shape[0]\n",
" new_splits=list(KFold(n_splits=n_splits,shuffle=True, random_state=SEED).split(new_split_sid))\n",
" split_sid=np.concatenate([old_split_sid,new_split_sid])\n",
" print(split_sid.shape)\n",
" splits=[]\n",
" for i in range(len(old_splits)):\n",
" splits.append((np.concatenate([old_splits[i][0],new_splits[i][0]+old_split_sid.shape[0]]),\n",
" np.concatenate([old_splits[i][1],new_splits[i][1]+old_split_sid.shape[0]])))\n",
" print(splits[-1][0].shape,np.unique(splits[-1][0]).shape,splits[-1][1].shape,np.unique(splits[-1][1]).shape)\n",
"\n",
" for num_split in tqdm_notebook(range(n_splits)):\n",
" \n",
"\n",
" print(train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].shape[0])\n",
" version=version_+'_fullhead_resmodel_pool2_stage2_{}'.format(multi)\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version,'OOF_pred',num_split),'rb')\n",
" tmp=pickle.load(pickle_file)\n",
" pickle_file.close()\n",
" print(tmp[0].shape)\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b9e7cb19e34a4350bae04ba162f089e8",
"version_major": 2,
"version_minor": 0
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"HBox(children=(IntProgress(value=0, max=7218), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(250228,) 250228\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8ebda623d1ea4d96a9a05a8770ae2e6c",
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},
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"HBox(children=(IntProgress(value=0, max=7298), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(251792,) 251792\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8cd991b2b52a44d3ba7fcf6db0832e4d",
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"version_minor": 0
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"HBox(children=(IntProgress(value=0, max=7228), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(250777,) 250777\n"
]
}
],
"source": [
"OOF_ids={}\n",
"SEED=8153\n",
"n_splits=3\n",
"pickle_file=open(outputs_dir+'PID_splits_{}_stage_2.pkl'.format(n_splits),'rb')\n",
"split_sid,splits=pickle.load(pickle_file)\n",
"pickle_file.close()\n",
"dfm=pd.concat([old_train_df,old_test_df],sort=False,axis=0).reset_index(drop=True)[['PatientID']]\n",
"atrain_df=dfm.merge(train_df,on='PatientID')\n",
"\n",
"for i in range( n_splits):\n",
" images_id_list=[]\n",
" split_validate = atrain_df[atrain_df.PID.isin(set(split_sid[splits[i][1]]))].SeriesI.unique()\n",
" image_arr=atrain_df.PatientID.values\n",
" ref_arr=atrain_df.SeriesI.values\n",
" order_arr=atrain_df.ImagePositionZ.values\n",
" for s in tqdm_notebook(split_validate):\n",
" head_idx = np.where(ref_arr==s)[0]\n",
" sorted_head_idx=head_idx[np.argsort(order_arr[head_idx])]\n",
" images_id_list.append(image_arr[sorted_head_idx])\n",
" image_ids=np.concatenate(images_id_list)\n",
" print(image_ids.shape,atrain_df[atrain_df.PID.isin(set(split_sid[splits[i][1]]))].shape[0])\n",
" OOF_ids[i]=image_ids\n",
"\n",
"\n",
"pickle_file=open(outputs_dir+'OOF_validation_image_ids_{}_stage2.pkl'.format(n_splits),'wb')\n",
"pickle.dump(OOF_ids,pickle_file,protocol=4)\n",
"pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"n_splits=3\n",
"pickle_file=open('/media/hd/notebooks/data/RSNA/outputs/OOF_validation_image_ids_3_stage2.pkl','rb')\n",
"OOF_ids=pickle.load(pickle_file)\n",
"pickle_file.close()\n",
"pickle_file=open(outputs_dir+'PID_splits_{}_stage_2.pkl'.format(n_splits),'rb')\n",
"split_sid,splits=pickle.load(pickle_file)\n",
"pickle_file.close()\n",
"\n",
"\n",
"[OOF_ids[i].shape[0] for i in range(3)]\n",
"[train_df[train_df.PID.isin(set(split_sid[splits[i][1]]))].shape[0] for i in range(3)]\n",
"[len(set(train_df[train_df.PID.isin(set(split_sid[splits[i][1]]))].PatientID.values).difference(set(OOF_ids[i]))) for i in range(3)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
"version_minor": 0
},
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"HBox(children=(IntProgress(value=0, max=4375), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(151238,) 151238\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "402a3109534f4e45bd98434da96e15ac",
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"HBox(children=(IntProgress(value=0, max=4276), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(147313,) 147313\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ca74bbe8a4a24d26b83cd32f63a9d3fa",
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"HBox(children=(IntProgress(value=0, max=4389), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(152135,) 152135\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "109e2933546e4dcc90254e06b168984c",
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"version_minor": 0
},
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"HBox(children=(IntProgress(value=0, max=4336), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(151097,) 151097\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "70939261001d49cba8eb3cb89ab2c2b6",
"version_major": 2,
"version_minor": 0
},
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"HBox(children=(IntProgress(value=0, max=4368), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(151014,) 151014\n"
]
}
],
"source": [
"OOF_ids={}\n",
"SEED=432\n",
"n_splits=5\n",
"pickle_file=open(outputs_dir+'PID_splits_{}_stage_2.pkl'.format(n_splits),'rb')\n",
"split_sid,splits=pickle.load(pickle_file)\n",
"pickle_file.close()\n",
"dfm=pd.concat([old_train_df,old_test_df],sort=False,axis=0).reset_index(drop=True)[['PatientID']]\n",
"atrain_df=dfm.merge(train_df,on='PatientID')\n",
"\n",
"for i in range( n_splits):\n",
" images_id_list=[]\n",
" split_validate = atrain_df[atrain_df.PID.isin(set(split_sid[splits[i][1]]))].SeriesI.unique()\n",
" image_arr=atrain_df.PatientID.values\n",
" ref_arr=atrain_df.SeriesI.values\n",
" order_arr=atrain_df.ImagePositionZ.values\n",
" for s in tqdm_notebook(split_validate):\n",
" head_idx = np.where(ref_arr==s)[0]\n",
" sorted_head_idx=head_idx[np.argsort(order_arr[head_idx])]\n",
" images_id_list.append(image_arr[sorted_head_idx])\n",
" image_ids=np.concatenate(images_id_list)\n",
" print(image_ids.shape,atrain_df[atrain_df.PID.isin(set(split_sid[splits[i][1]]))].shape[0])\n",
" OOF_ids[i]=image_ids\n",
"\n",
"\n",
"pickle_file=open(outputs_dir+'OOF_validation_image_ids_{}_stage2.pkl'.format(n_splits),'wb')\n",
"pickle.dump(OOF_ids,pickle_file,protocol=4)\n",
"pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[151238, 147313, 152135, 151097, 151014]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"[151238, 147313, 152135, 151097, 151014]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"[0, 0, 0, 0, 0]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n_splits=5\n",
"pickle_file=open(outputs_dir+'OOF_validation_image_ids_{}_stage2.pkl'.format(n_splits),'rb')\n",
"OOF_ids=pickle.load(pickle_file)\n",
"pickle_file.close()\n",
"pickle_file=open(outputs_dir+'PID_splits_{}_stage_2.pkl'.format(n_splits),'rb')\n",
"split_sid,splits=pickle.load(pickle_file)\n",
"pickle_file.close()\n",
"\n",
"\n",
"[OOF_ids[i].shape[0] for i in range(n_splits)]\n",
"[train_df[train_df.PID.isin(set(split_sid[splits[i][1]]))].shape[0] for i in range(n_splits)]\n",
"[len(set(train_df[train_df.PID.isin(set(split_sid[splits[i][1]]))].PatientID.values).difference(set(OOF_ids[i]))) for i in range(n_splits)]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "095c0b30f3284554918073688d5701fb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, max=19530), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(674252,)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"images_id_list=[]\n",
"split_all = train_df.SeriesI.unique()\n",
"image_arr=train_df.PatientID.values\n",
"ref_arr=train_df.SeriesI.values\n",
"order_arr=train_df.ImagePositionZ.values\n",
"for s in tqdm_notebook(split_all):\n",
" head_idx = np.where(ref_arr==s)[0]\n",
" sorted_head_idx=head_idx[np.argsort(order_arr[head_idx])]\n",
" images_id_list.append(image_arr[sorted_head_idx])\n",
"image_ids=np.concatenate(images_id_list)\n",
"image_ids.shape\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"pickle_file=open(outputs_dir+'ensemble_test_image_ids_stage2.pkl','wb')\n",
"pickle.dump(image_ids,pickle_file,protocol=4)\n",
"pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"pred_list=[]\n",
"for num_split in range(5):\n",
" model_name,version = 'se_resnet101' , 'new_splits_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",
"for num_split in range(5):\n",
" model_name,version = 'se_resnext101_32x4d' , 'new_splits_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()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"320"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(pred_list)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"320"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"torch.Size([121232, 6])"
]
},
"execution_count": 26,
"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": 30,
"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()\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <td>0.000138</td>\n",
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" <td>ID_000000e27_subarachnoid</td>\n",
" <td>0.074892</td>\n",
" </tr>\n",
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" <td>0.013256</td>\n",
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" <th>8</th>\n",
" <td>ID_000009146_intraparenchymal</td>\n",
" <td>0.000029</td>\n",
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" <tr>\n",
" <th>9</th>\n",
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" <td>0.000031</td>\n",
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" <th>11</th>\n",
" <td>ID_000009146_subdural</td>\n",
" <td>0.000059</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" ID Label\n",
"0 ID_000000e27_any 0.086962\n",
"1 ID_000000e27_epidural 0.000254\n",
"2 ID_000000e27_intraparenchymal 0.005881\n",
"3 ID_000000e27_intraventricular 0.000138\n",
"4 ID_000000e27_subarachnoid 0.074892\n",
"5 ID_000000e27_subdural 0.013256\n",
"6 ID_000009146_any 0.000054\n",
"7 ID_000009146_epidural 0.000004\n",
"8 ID_000009146_intraparenchymal 0.000029\n",
"9 ID_000009146_intraventricular 0.000031\n",
"10 ID_000009146_subarachnoid 0.000008\n",
"11 ID_000009146_subdural 0.000059"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(727392, 2)"
]
},
"execution_count": 31,
"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",
"sub_num='_stage2_3'\n",
"submission_df.to_csv('/media/hd/notebooks/data/RSNA/submissions/submission{}.csv'.format(sub_num),\n",
" index=False, columns=['ID','Label'])\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": [],
"source": [
"sub.Label=(sub.Label>0.5).astype(np.float)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <th></th>\n",
" <th>ID</th>\n",
" <th>Label_x</th>\n",
" <th>Label_y</th>\n",
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" <td>0.086962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ID_000000e27_epidural</td>\n",
" <td>0.0</td>\n",
" <td>0.000254</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ID_000000e27_intraparenchymal</td>\n",
" <td>0.0</td>\n",
" <td>0.005881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ID_000000e27_intraventricular</td>\n",
" <td>0.0</td>\n",
" <td>0.000138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ID_000000e27_subarachnoid</td>\n",
" <td>0.0</td>\n",
" <td>0.074892</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID Label_x Label_y\n",
"0 ID_000000e27_any 0.0 0.086962\n",
"1 ID_000000e27_epidural 0.0 0.000254\n",
"2 ID_000000e27_intraparenchymal 0.0 0.005881\n",
"3 ID_000000e27_intraventricular 0.0 0.000138\n",
"4 ID_000000e27_subarachnoid 0.0 0.074892"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=sub.merge(submission_df,on='ID')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.0234)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"F.binary_cross_entropy(torch.tensor(df.Label_y.values,dtype=torch.float),\n",
" torch.tensor(df.Label_x.values,dtype=torch.float))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"display_name": "Python 3",
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