Datasets
Models
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{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\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": 15,
"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": 16,
"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": 17,
"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": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(674510, 15)"
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},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(674252, 15)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PatientID</th>\n",
" <th>epidural</th>\n",
" <th>intraparenchymal</th>\n",
" <th>intraventricular</th>\n",
" <th>subarachnoid</th>\n",
" <th>subdural</th>\n",
" <th>any</th>\n",
" <th>PID</th>\n",
" <th>StudyI</th>\n",
" <th>SeriesI</th>\n",
" <th>WindowCenter</th>\n",
" <th>WindowWidth</th>\n",
" <th>ImagePositionZ</th>\n",
" <th>ImagePositionX</th>\n",
" <th>ImagePositionY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>63eb1e259</td>\n",
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" <td>['00036', '00036']</td>\n",
" <td>['00080', '00080']</td>\n",
" <td>180.199951</td>\n",
" <td>-125.0</td>\n",
" <td>-8.000000</td>\n",
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" <tr>\n",
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" <td>['00080', '00080']</td>\n",
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" <td>['00080', '00080']</td>\n",
" <td>100.000000</td>\n",
" <td>-99.5</td>\n",
" <td>28.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7858edd88</td>\n",
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" <td>c73e81ed3a</td>\n",
" <td>28e0531b3a</td>\n",
" <td>40</td>\n",
" <td>100</td>\n",
" <td>145.793000</td>\n",
" <td>-125.0</td>\n",
" <td>-132.190000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df = pd.read_csv(data_dir+'train.csv')\n",
"train_df.shape\n",
"train_df=train_df[~train_df.PatientID.isin(bad_images)].reset_index(drop=True)\n",
"train_df=train_df.drop_duplicates().reset_index(drop=True)\n",
"train_df.shape\n",
"train_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PatientID</th>\n",
" <th>epidural</th>\n",
" <th>intraparenchymal</th>\n",
" <th>intraventricular</th>\n",
" <th>subarachnoid</th>\n",
" <th>subdural</th>\n",
" <th>any</th>\n",
" <th>SeriesI</th>\n",
" <th>PID</th>\n",
" <th>StudyI</th>\n",
" <th>WindowCenter</th>\n",
" <th>WindowWidth</th>\n",
" <th>ImagePositionZ</th>\n",
" <th>ImagePositionX</th>\n",
" <th>ImagePositionY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>-125.0</td>\n",
" <td>-133.300003</td>\n",
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" <td>c497ac5bad</td>\n",
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" <td>55.388000</td>\n",
" <td>-125.0</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>a130c4d2f</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>faeb7454f3</td>\n",
" <td>69affa42</td>\n",
" <td>854e4fbc01</td>\n",
" <td>30</td>\n",
" <td>80</td>\n",
" <td>33.516888</td>\n",
" <td>-125.0</td>\n",
" <td>-118.689819</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df = pd.read_csv(data_dir+'test.csv')\n",
"test_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"split_sid = train_df.PID.unique()\n",
"splits=list(KFold(n_splits=3,shuffle=True, random_state=SEED).split(split_sid))\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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": 22,
"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": 23,
"metadata": {},
"outputs": [],
"source": [
"#features=(features-features.mean())/features.std()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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": 25,
"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",
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" def no_grad(self):\n",
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" def do_grad(self):\n",
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"multi=3\n",
"model_names=['se_resnet101']\n",
"types=['features_train_tta']\n",
"versions=['new_splits']\n",
"num_splits=[5]\n",
"seeds=[432]\n",
"for model_name,type_,version_,n,SEED in zip(model_names,types,versions,num_splits,seeds):\n",
" for num_split in tqdm_notebook(range(n)):\n",
" split_sid = train_df.PID.unique()\n",
" splits=list(KFold(n_splits=n,shuffle=True, random_state=SEED).split(split_sid))\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]//4,4,-1)\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version_+'_fullhead_resmodel_pool2_{}'.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(train_df,\n",
" split_validate,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" multi =3)\n",
"\n",
" win_dataset=FullHeadDataset(train_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",
" 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,'OOF_pred',num_split),'wb')\n",
" pickle.dump(pred_list,pickle_file,protocol=4)\n",
" pickle_file.close()\n"
]
},
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"source": [
"multi=3\n",
"\n",
"model_names=['se_resnext101_32x4d','se_resnext101_32x4d','Densenet161_3','Densenet169_3','Densenet201_3','se_resnet101']\n",
"types=['features_train_tta','features_train_tta','features_train_tta2','features_train_tta2','features_train_tta','features_train_tta']\n",
"versions=['classifier_splits','new_splits','classifier_splits','classifier_splits','classifier_splits','classifier_splits']\n",
"num_splits=[3,5,3,3,3,3]\n",
"seeds=[8153,432,8153,8153,8153,8153]\n",
"for model_name,type_,version_,n,SEED in zip(model_names,types,versions,num_splits,seeds):\n",
" for num_split in tqdm_notebook(range(n)):\n",
" split_sid = train_df.PID.unique()\n",
" splits=list(KFold(n_splits=n,shuffle=True, random_state=SEED).split(split_sid))\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]//4,4,-1)\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version_+'_fullhead_resmodel_pool2_{}'.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(train_df,\n",
" split_validate,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" multi =3)\n",
"\n",
" win_dataset=FullHeadDataset(train_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",
" 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,'OOF_pred',num_split),'wb')\n",
" pickle.dump(pred_list,pickle_file,protocol=4)\n",
" pickle_file.close()\n"
]
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"multi=3\n",
"model_names=['se_resnext101_32x4d','se_resnext101_32x4d','Densenet161_3','Densenet169_3','Densenet201_3','se_resnet101','se_resnet101']\n",
"types=['features_test_tta','features_test','features_test2','features_test2','features_test','features_test','features_test']\n",
"versions=['classifier_splits','new_splits','classifier_splits','classifier_splits','classifier_splits','classifier_splits','new_splits']\n",
"num_splits=[3,5,3,3,3,3,5]\n",
"seeds=[8153,432,8153,8153,8153,8153,432]\n",
"for model_name,type_,version_,n,SEED in zip(model_names,types,versions,num_splits,seeds):\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_{}'.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_ensamble',num_split),'wb')\n",
" pickle.dump(pred_list,pickle_file,protocol=4)\n",
" pickle_file.close()\n"
]
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"multi=3\n",
"for num_split in tqdm_notebook(range(3)):\n",
" pred_list=[]\n",
" model_name,version = 'se_resnet101' , 'classifier_splits'\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version,'features_train_tta',num_split),'rb')\n",
" features=pickle.load(pickle_file)\n",
" pickle_file.close()\n",
" features=features.reshape(features.shape[0]//4,4,-1)\n",
" split_all = train_df.SeriesI.unique()\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version+'_fullhead_resmodel_pool2_{}'.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(train_df,\n",
" split_all,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" multi =3)\n",
" \n",
" win_dataset=FullHeadDataset(train_df,\n",
" split_all,\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,'all_pred',num_split),'wb')\n",
" pickle.dump(pred_list,pickle_file,protocol=4)\n",
" pickle_file.close()\n"
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"source": [
"OOF_ids={}\n",
"SEED=432\n",
"n=5\n",
"split_sid = train_df.PID.unique()\n",
"splits=list(KFold(n_splits=n,shuffle=True, random_state=SEED).split(split_sid))\n",
"\n",
"for i in range( n):\n",
" images_id_list=[]\n",
" \n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[i][1]]))].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_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",
" image_ids.shape\n",
" OOF_ids[i]=image_ids\n",
"\n",
"\n",
"pickle_file=open(outputs_dir+'OOF_validation_image_ids_5.pkl','wb')\n",
"pickle.dump(OOF_ids,pickle_file,protocol=4)\n",
"pickle_file.close()\n"
]
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"OOF_ids={}\n",
"SEED=8153\n",
"n=3\n",
"split_sid = train_df.PID.unique()\n",
"splits=list(KFold(n_splits=n,shuffle=True, random_state=SEED).split(split_sid))\n",
"\n",
"for i in range( n):\n",
" images_id_list=[]\n",
" \n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[i][1]]))].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_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",
" image_ids.shape\n",
" OOF_ids[i]=image_ids\n",
"\n",
"\n",
"pickle_file=open(outputs_dir+'OOF_validation_image_ids_5.pkl','wb')\n",
"pickle.dump(OOF_ids,pickle_file,protocol=4)\n",
"pickle_file.close()\n"
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"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",
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" 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",
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{
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"name": "stderr",
"output_type": "stream",
"text": [
"/home/reina/anaconda3/envs/RSNA/lib/python3.6/site-packages/ipykernel_launcher.py:1: ResourceWarning: unclosed file <_io.BufferedWriter name='/media/hd/notebooks/data/RSNA/outputs/all_image_ids.pkl'>\n",
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"source": [
"pickle_file=open(outputs_dir+'all_image_ids.pkl','wb')\n",
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"source": [
"images_id_list=[]\n",
"dummeys=[]\n",
"image_arr=test_df.PatientID.values\n",
"ref_arr=test_df.SeriesI.values\n",
"order_arr=test_df.ImagePositionZ.values\n",
"for s in tqdm_notebook(test_df.SeriesI.unique()):\n",
" dumm=np.zeros(60)\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",
" dumm[0:head_idx.shape[0]]=1\n",
" dummeys.append(dumm)\n",
"image_ids=np.concatenate(images_id_list)\n",
"select=np.concatenate(dummeys)==1"
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},
{
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"execution_count": 23,
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"pickle.dump(image_ids,pickle_file,protocol=4)\n",
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"for num_split in tqdm_notebook(range(3)):\n",
" pred_list=[]\n",
" model_name,version = 'Densenet161_3' , 'classifier_splits'\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version,'features_test2',num_split),'rb')\n",
" features=pickle.load(pickle_file)\n",
" pickle_file.close()\n",
" features=features.reshape(features.shape[0]//8,8,-1)\n",
"\n",
" model=ResModel(features.shape[-1])\n",
" version=version+'_fullhead_resmodel'\n",
"\n",
" model.load_state_dict(torch.load(models_dir+models_format.format(model_name,version,num_split),map_location=torch.device(device)))\n",
" test_dataset=train_dataset=FullHeadDataset(test_df,\n",
" test_df.SeriesI.unique(),\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ')\n",
" for i in tqdm_notebook(range(32),leave=False):\n",
" p=model_run(model,test_dataset,do_apex=False,batch_size=128)\n",
" pred_list.append(p.reshape(p.shape[0]*p.shape[1],-1)[select])\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version,'test_pred_ensamble',num_split),'wb')\n",
" pickle.dump(pred_list,pickle_file,protocol=4)\n",
" pickle_file.close()\n",
"\n"
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