Datasets
Models
Downloads: 1
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"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": 3,
"metadata": {},
"outputs": [],
"source": [
"SEED = 432\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": 4,
"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": 5,
"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": 6,
"metadata": {},
"outputs": [
{
"data": {
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"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
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"data": {
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"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
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" <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",
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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": 6,
"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": 7,
"metadata": {},
"outputs": [
{
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" <th>PatientID</th>\n",
" <th>epidural</th>\n",
" <th>intraparenchymal</th>\n",
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" <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",
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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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df = pd.read_csv(data_dir+'test.csv')\n",
"test_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"class PosSeriesSampler():\n",
" def __init__(self,df,split,pos_r,neg_r):\n",
" self.df=df[df.SeriesI.isin(split)]\n",
" self.pos_r=max(min(pos_r,1.0),0)\n",
" self.neg_r=max(min(neg_r,1.0),0)\n",
" cols=['SeriesI','any']\n",
" self.series_df=self.df[cols].groupby('SeriesI').sum().reset_index()\n",
" self.series_df['any']=self.series_df['any']>0\n",
" self.sids=self.series_df.SeriesI.values\n",
" self.any_col=self.series_df['any'].values\n",
" self.any_col=self.align(self.any_col,split,self.sids)\n",
" self.pos=np.where(self.any_col)[0]\n",
" self.neg=np.where(~self.any_col)[0]\n",
"\n",
" def align(self,arr,index1,index2):\n",
" return arr[np.argsort(index2)[np.argsort(np.argsort(index1))]]\n",
" def __call__(self):\n",
" tmpp=self.pos.copy()\n",
" np.random.shuffle(tmpp)\n",
" tmpn=self.neg.copy()\n",
" np.random.shuffle(tmpn)\n",
" return np.concatenate([tmpp[:int(self.pos_r*tmpp.shape[0])],tmpn[:int(self.neg_r*tmpn.shape[0])]])\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"split_sid = train_df.PID.unique()\n",
"splits=list(KFold(n_splits=5,shuffle=True, random_state=SEED).split(split_sid))\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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": "raw",
"metadata": {},
"source": [
"def my_loss(y_pred,y_true,weights):\n",
" if len(y_pred.shape)==len(y_true.shape):\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",
" else:\n",
" window0=(y_true[...,0]>=0).to(torch.float)\n",
" window1=(y_true[...,1]>=0).to(torch.float)\n",
" loss0 = F.binary_cross_entropy_with_logits(y_pred,y_true[...,0],reduction='none')*window0*weights.expand_as(y_true[...,0])/(window0.mean()+1e-7)\n",
" loss1 = F.binary_cross_entropy_with_logits(y_pred,y_true[...,1],reduction='none')*window1*weights.expand_as(y_true[...,1])/(window1.mean()+1e-7)\n",
" loss = (y_true[...,2]*loss0+(1.0-y_true[...,2])*loss1).mean() \n",
" return loss"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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": 12,
"metadata": {},
"outputs": [],
"source": [
"#features=(features-features.mean())/features.std()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"class SimpleModel(nn.Module):\n",
" def __init__(self,in_size):\n",
" super(SimpleModel, self).__init__()\n",
" self.dont_do_grad=[]\n",
" self.conv2d1=torch.nn.Conv2d(1, 128, (7,in_size), padding=(3,0))\n",
" self.bn0=torch.nn.BatchNorm1d(128)\n",
" self.relu0=torch.nn.ReLU()\n",
" self.conv1d1=torch.nn.Conv1d(128, 128, 5, padding=2)\n",
" self.bn1=torch.nn.BatchNorm1d(128)\n",
" self.relu1=torch.nn.ReLU()\n",
" self.conv1d2=torch.nn.Conv1d(128, 64, 3, padding=1)\n",
" self.bn2=torch.nn.BatchNorm1d(64)\n",
" self.relu2=torch.nn.ReLU()\n",
" self.conv1d3=torch.nn.Conv1d(64, 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",
" x = self.relu0(x)\n",
" x = self.conv1d1(x)\n",
" x = self.bn1(x)\n",
" x = self.relu1(x)\n",
" x = self.conv1d2(x)\n",
" x = self.bn2(x)\n",
" x = self.relu2(x)\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": {},
"outputs": [],
"source": [
"class SimpleModel2(nn.Module):\n",
" def __init__(self,in_size):\n",
" super(SimpleModel2, self).__init__()\n",
" self.dont_do_grad=[]\n",
" self.conv2d1=torch.nn.Conv2d(1, 128, (9,in_size), padding=(4,0))\n",
" self.bn0=torch.nn.BatchNorm1d(128)\n",
" self.relu0=torch.nn.ReLU()\n",
" self.conv1d1=torch.nn.Conv1d(128, 128, 7, padding=3)\n",
" self.bn1=torch.nn.BatchNorm1d(128)\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(64, 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",
" x = self.relu0(x)\n",
" x = self.conv1d1(x)\n",
" x = self.bn1(x)\n",
" x = self.relu1(x)\n",
" x = self.conv1d2(x)\n",
" x = self.bn2(x)\n",
" x = self.relu2(x)\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": 15,
"metadata": {},
"outputs": [],
"source": [
"class ClassModel(nn.Module):\n",
" def __init__(self,in_size):\n",
" super(ClassModel, self).__init__()\n",
" self.dont_do_grad=[]\n",
" self.conv2d1=torch.nn.Conv2d(1, 128, (9,in_size), padding=(4,0))\n",
" self.bn0=torch.nn.BatchNorm1d(128)\n",
" self.relu0=torch.nn.ReLU()\n",
" self.conv1d1=torch.nn.Conv1d(128, 128, 7, padding=3)\n",
" self.bn1=torch.nn.BatchNorm1d(128)\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(128, 6, 3, padding=1)\n",
" \n",
" self.conv2d1class=torch.nn.Conv2d(1, 128, (9,in_size), padding=(4,0))\n",
" self.bn0class=torch.nn.BatchNorm1d(128)\n",
" self.maxpool1class=torch.nn.MaxPool1d(3)\n",
" self.conv1d1class=torch.nn.Conv1d(128, 128, 3, padding=1)\n",
" self.bn1class=torch.nn.BatchNorm1d(128)\n",
" self.maxpool2class=torch.nn.MaxPool1d(3)\n",
" self.conv1d2class=torch.nn.Conv1d(128, 64, 2, padding=1)\n",
" self.bn2class=torch.nn.BatchNorm1d(64)\n",
"\n",
" \n",
" \n",
" def forward(self, x):\n",
" z=x\n",
" x = self.conv2d1(x.unsqueeze(1)).squeeze(-1)\n",
" x = self.bn0(x)\n",
" x = self.relu0(x)\n",
" x = self.conv1d1(x)\n",
" x = self.bn1(x)\n",
" x = self.relu1(x)\n",
" x = self.conv1d2(x)\n",
" x = self.bn2(x)\n",
" x = self.relu2(x)\n",
" z=self.conv2d1class(z.unsqueeze(1)).squeeze(-1)\n",
" z=self.bn0class(z)\n",
" z=self.maxpool1class(z)\n",
" z=self.conv1d1class(z)\n",
" z=self.maxpool2class(z)\n",
" z=self.conv1d2class(z)\n",
" z=self.bn2class(z)\n",
" z=F.max_pool1d(z,kernel_size=z.shape[-1])\n",
" z=z.expand_as(x)\n",
" x=torch.cat([x,z],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": 16,
"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=x.unsqueeze(1)\n",
" x = self.conv2d1(x).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": 17,
"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": 18,
"metadata": {},
"outputs": [],
"source": [
"class ResDropModel(nn.Module):\n",
" def __init__(self,in_size,dropout=0.2):\n",
" super(ResDropModel, 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",
" self.dropout=dropout\n",
" \n",
" def forward(self, x):\n",
" x = self.conv2d1(x.unsqueeze(1)).squeeze(-1)\n",
" x = self.bn0(x)\n",
" x = F.dropout(x,self.dropout)\n",
" x0 = self.relu0(x) \n",
" x = self.conv1d1(x0)\n",
"# x = self.bn1(x)\n",
" x = F.dropout(x,self.dropout)\n",
" x1 = self.relu1(x)\n",
" x = torch.cat([x0,x1],1)\n",
" x = self.conv1d2(x)\n",
"# x = self.bn2(x)\n",
" x = F.dropout(x,self.dropout)\n",
" x2 = self.relu2(x)\n",
" x = torch.cat([x0,x1,x2],1)\n",
" x = F.dropout(x,self.dropout)\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": 19,
"metadata": {},
"outputs": [],
"source": [
"fn = partial(torch.clamp,min=0,max=1)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"class GenReLU(nn.Module):\n",
" def __init__(self,leak=0,add=0,clamp=None):\n",
" super(GenReLU, self).__init__()\n",
" self.leak,self.add=leak,add\n",
" if isinstance(clamp,tuple):\n",
" self.clamp = partial(torch.clamp,min=clamp[0],max=clamp[1])\n",
" elif clamp:\n",
" self.clamp = partial(torch.clamp,min=-clamp,max=clamp)\n",
" else:\n",
" self.clamp=None\n",
" \n",
" \n",
" def forward(self,x):\n",
" x = F.leaky_relu(x,self.leak)\n",
" if self.add:\n",
" x=x+self.add\n",
" if self.clamp:\n",
" x = self.clamp(x)\n",
" return x\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"\n",
"class ResModelIn(nn.Module):\n",
" def __init__(self,in_size):\n",
" super(ResModelIn, 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, 3, padding=1)\n",
" self.bn1=torch.nn.BatchNorm1d(64)\n",
" self.relu1=torch.nn.ReLU()\n",
" self.conv1d2=torch.nn.Conv1d(128, 64, 3, padding=1)\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",
" \n",
" def forward(self, x):\n",
" x = x.unsqueeze(1)\n",
" x = self.conv2d1(x).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",
" x = self.conv1d3(x)\n",
" out = 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": 22,
"metadata": {},
"outputs": [],
"source": [
"\n",
"class ResModelInR(nn.Module):\n",
" def __init__(self,in_size):\n",
" super(ResModelInR, 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= GenReLU(leak=0.01,add=-0.3,clamp=(-1,3))\n",
" self.conv1d1=torch.nn.Conv1d(64, 64, 3, padding=1)\n",
" self.bn1=torch.nn.BatchNorm1d(64)\n",
" self.relu1=GenReLU(leak=0.01,add=-0.3,clamp=(-1,3))\n",
" self.conv1d2=torch.nn.Conv1d(128, 64, 3, padding=1)\n",
" self.bn2=torch.nn.BatchNorm1d(64)\n",
" self.relu2=GenReLU(leak=0,add=0,clamp=(0,2))\n",
" self.conv1d3=torch.nn.Conv1d(192, 6, 3, padding=1)\n",
"\n",
" \n",
" \n",
" def forward(self, x):\n",
" x = x.unsqueeze(1)\n",
" x = self.conv2d1(x).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",
" x = self.conv1d3(x)\n",
" out = 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": 23,
"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": 23,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"se_resnext101_32x4d new_splits_focal 0\n"
]
},
{
"data": {
"text/plain": [
"torch.Size([2697008, 256])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"torch.Size([674252, 4, 256])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<torch._C.Generator at 0x7f82c9b2e7b0>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, max=24), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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},
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{
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"HBox(children=(IntProgress(value=0, max=245), HTML(value='')))"
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},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'loss': 0.067308146525303, 'mloss': tensor(0.0673), 'val_loss': 0.07460971067906642, 'val_mloss_tot': tensor(0.0745)}\n"
]
},
{
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"source": [
"%matplotlib nbagg\n",
"for num_split in [0]: #range(5):\n",
" multi=3\n",
" model_name,version = 'se_resnext101_32x4d' , 'new_splits_focal'\n",
" print (model_name,version,num_split)\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.shape\n",
"\n",
" features=features.reshape(features.shape[0]//4,4,-1)\n",
" features.shape\n",
" split_train = train_df[train_df.PID.isin(set(split_sid[splits[num_split][0]]))].SeriesI.unique()\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
"\n",
" np.random.seed(SEED+num_split)\n",
" torch.manual_seed(SEED+num_split)\n",
" torch.cuda.manual_seed(SEED+num_split)\n",
" torch.backends.cudnn.deterministic = True\n",
" batch_size=16\n",
" num_workers=18\n",
" num_epochs=24\n",
" klr=1\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],device=device)\n",
" train_dataset=FullHeadDataset(train_df,\n",
" split_train,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types,\n",
" multi=multi) \n",
" validate_dataset=FullHeadDataset(train_df,\n",
" split_validate,\n",
" torch.cat([features[:,i,:] for i in range(4)],-1),\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types) \n",
"\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version+'_fullhead_resmodel_pool2_over{}'.format(multi)\n",
" _=model.to(device)\n",
" #mixup=Mixup(device=device)\n",
" loss_func=my_loss\n",
" #fig,ax = plt.subplots(figsize=(10,7))\n",
" #gr=loss_graph(fig,ax,num_epochs,len(train_dataset)//batch_size+1,limits=[0.02,0.06])\n",
"# sampling=PosSeriesSampler(train_df,split_train,1,0.75)\n",
" sample_ratio= 1.0 #1.01*float(sampling().shape[0])/split_train.shape[0]\n",
" num_train_optimization_steps = sample_ratio*num_epochs*(len(train_dataset)//batch_size+int(len(train_dataset)%batch_size>0))\n",
" sched=WarmupExpCosineWithWarmupRestartsSchedule( t_total=num_train_optimization_steps, cycles=2,tau=1)\n",
" optimizer = BertAdam(model.parameters(),lr=klr*1e-3,schedule=sched)\n",
" history,best_model= model_train(model,\n",
" optimizer,\n",
" train_dataset,\n",
" batch_size,\n",
" num_epochs,\n",
" loss_func,\n",
" weights=weights,\n",
" do_apex=False,\n",
" validate_dataset=validate_dataset,\n",
" param_schedualer=None,\n",
" weights_data=None,\n",
" metric=Metric(torch.tensor([1.,1.,1.,1.,1.,2.])),\n",
" return_model=True,\n",
" best_average=3,\n",
" num_workers=num_workers,\n",
" sampler=None,\n",
" graph=None)\n",
" torch.save(best_model.state_dict(), models_dir+models_format.format(model_name,version,num_split))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": false
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"se_resnext101_32x4d new_splits_focal 1\n"
]
},
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},
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"source": [
"%matplotlib nbagg\n",
"for num_split in [1]: #range(5):\n",
" multi=3\n",
" model_name,version = 'se_resnext101_32x4d' , 'new_splits_focal'\n",
" print (model_name,version,num_split)\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.shape\n",
"\n",
" features=features.reshape(features.shape[0]//4,4,-1)\n",
" features.shape\n",
" split_train = train_df[train_df.PID.isin(set(split_sid[splits[num_split][0]]))].SeriesI.unique()\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
"\n",
" np.random.seed(SEED+num_split)\n",
" torch.manual_seed(SEED+num_split)\n",
" torch.cuda.manual_seed(SEED+num_split)\n",
" torch.backends.cudnn.deterministic = True\n",
" batch_size=16\n",
" num_workers=18\n",
" num_epochs=24\n",
" klr=1\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],device=device)\n",
" train_dataset=FullHeadDataset(train_df,\n",
" split_train,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types,\n",
" multi=multi) \n",
" validate_dataset=FullHeadDataset(train_df,\n",
" split_validate,\n",
" torch.cat([features[:,i,:] for i in range(4)],-1),\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types) \n",
"\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version+'_fullhead_resmodel_pool2_over{}'.format(multi)\n",
" _=model.to(device)\n",
" #mixup=Mixup(device=device)\n",
" loss_func=my_loss\n",
" #fig,ax = plt.subplots(figsize=(10,7))\n",
" #gr=loss_graph(fig,ax,num_epochs,len(train_dataset)//batch_size+1,limits=[0.02,0.06])\n",
"# sampling=PosSeriesSampler(train_df,split_train,1,0.75)\n",
" sample_ratio= 1.0 #1.01*float(sampling().shape[0])/split_train.shape[0]\n",
" num_train_optimization_steps = sample_ratio*num_epochs*(len(train_dataset)//batch_size+int(len(train_dataset)%batch_size>0))\n",
" sched=WarmupExpCosineWithWarmupRestartsSchedule( t_total=num_train_optimization_steps, cycles=2,tau=1)\n",
" optimizer = BertAdam(model.parameters(),lr=klr*1e-3,schedule=sched)\n",
" history,best_model= model_train(model,\n",
" optimizer,\n",
" train_dataset,\n",
" batch_size,\n",
" num_epochs,\n",
" loss_func,\n",
" weights=weights,\n",
" do_apex=False,\n",
" validate_dataset=validate_dataset,\n",
" param_schedualer=None,\n",
" weights_data=None,\n",
" metric=Metric(torch.tensor([1.,1.,1.,1.,1.,2.])),\n",
" return_model=True,\n",
" best_average=3,\n",
" num_workers=num_workers,\n",
" sampler=None,\n",
" graph=None)\n",
" torch.save(best_model.state_dict(), models_dir+models_format.format(model_name,version,num_split))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"se_resnext101_32x4d new_splits_focal 2\n"
]
},
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"data": {
"text/plain": [
"torch.Size([2697008, 256])"
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},
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"metadata": {},
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"source": [
"%matplotlib nbagg\n",
"for num_split in [2,3]: #range(5):\n",
" multi=3\n",
" model_name,version = 'se_resnext101_32x4d' , 'new_splits_focal'\n",
" print (model_name,version,num_split)\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.shape\n",
"\n",
" features=features.reshape(features.shape[0]//4,4,-1)\n",
" features.shape\n",
" split_train = train_df[train_df.PID.isin(set(split_sid[splits[num_split][0]]))].SeriesI.unique()\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
"\n",
" np.random.seed(SEED+num_split)\n",
" torch.manual_seed(SEED+num_split)\n",
" torch.cuda.manual_seed(SEED+num_split)\n",
" torch.backends.cudnn.deterministic = True\n",
" batch_size=16\n",
" num_workers=18\n",
" num_epochs=24\n",
" klr=1\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],device=device)\n",
" train_dataset=FullHeadDataset(train_df,\n",
" split_train,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types,\n",
" multi=multi) \n",
" validate_dataset=FullHeadDataset(train_df,\n",
" split_validate,\n",
" torch.cat([features[:,i,:] for i in range(4)],-1),\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types) \n",
"\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version+'_fullhead_resmodel_pool2_over{}'.format(multi)\n",
" _=model.to(device)\n",
" #mixup=Mixup(device=device)\n",
" loss_func=my_loss\n",
" #fig,ax = plt.subplots(figsize=(10,7))\n",
" #gr=loss_graph(fig,ax,num_epochs,len(train_dataset)//batch_size+1,limits=[0.02,0.06])\n",
"# sampling=PosSeriesSampler(train_df,split_train,1,0.75)\n",
" sample_ratio= 1.0 #1.01*float(sampling().shape[0])/split_train.shape[0]\n",
" num_train_optimization_steps = sample_ratio*num_epochs*(len(train_dataset)//batch_size+int(len(train_dataset)%batch_size>0))\n",
" sched=WarmupExpCosineWithWarmupRestartsSchedule( t_total=num_train_optimization_steps, cycles=2,tau=1)\n",
" optimizer = BertAdam(model.parameters(),lr=klr*1e-3,schedule=sched)\n",
" history,best_model= model_train(model,\n",
" optimizer,\n",
" train_dataset,\n",
" batch_size,\n",
" num_epochs,\n",
" loss_func,\n",
" weights=weights,\n",
" do_apex=False,\n",
" validate_dataset=validate_dataset,\n",
" param_schedualer=None,\n",
" weights_data=None,\n",
" metric=Metric(torch.tensor([1.,1.,1.,1.,1.,2.])),\n",
" return_model=True,\n",
" best_average=3,\n",
" num_workers=num_workers,\n",
" sampler=None,\n",
" graph=None)\n",
" torch.save(best_model.state_dict(), models_dir+models_format.format(model_name,version,num_split))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"se_resnext101_32x4d new_splits_focal 4\n",
"se_resnext101_32x4d new_splits_focal 4\n"
]
},
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"\n",
"(0.07146647619649105, {'val_loss': 0.07146647619649105, 'val_mloss_tot': tensor(0.0714)})\n",
"\n",
"(0.07146647619649105, {'val_loss': 0.07146647619649105, 'val_mloss_tot': tensor(0.0714)})\n"
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],
"source": [
"%matplotlib nbagg\n",
"for num_split in [4]: #range(5):\n",
" multi=3\n",
" model_name,version = 'se_resnext101_32x4d' , 'new_splits_focal'\n",
" print (model_name,version,num_split)\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.shape\n",
"\n",
" features=features.reshape(features.shape[0]//4,4,-1)\n",
" features.shape\n",
" split_train = train_df[train_df.PID.isin(set(split_sid[splits[num_split][0]]))].SeriesI.unique()\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
"\n",
" np.random.seed(SEED+num_split)\n",
" torch.manual_seed(SEED+num_split)\n",
" torch.cuda.manual_seed(SEED+num_split)\n",
" torch.backends.cudnn.deterministic = True\n",
" batch_size=16\n",
" num_workers=18\n",
" num_epochs=24\n",
" klr=1\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],device=device)\n",
" train_dataset=FullHeadDataset(train_df,\n",
" split_train,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types,\n",
" multi=multi) \n",
" validate_dataset=FullHeadDataset(train_df,\n",
" split_validate,\n",
" torch.cat([features[:,i,:] for i in range(4)],-1),\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types) \n",
"\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version+'_fullhead_resmodel_pool2_over{}'.format(multi)\n",
" _=model.to(device)\n",
" #mixup=Mixup(device=device)\n",
" loss_func=my_loss\n",
" #fig,ax = plt.subplots(figsize=(10,7))\n",
" #gr=loss_graph(fig,ax,num_epochs,len(train_dataset)//batch_size+1,limits=[0.02,0.06])\n",
"# sampling=PosSeriesSampler(train_df,split_train,1,0.75)\n",
" sample_ratio= 1.0 #1.01*float(sampling().shape[0])/split_train.shape[0]\n",
" num_train_optimization_steps = sample_ratio*num_epochs*(len(train_dataset)//batch_size+int(len(train_dataset)%batch_size>0))\n",
" sched=WarmupExpCosineWithWarmupRestartsSchedule( t_total=num_train_optimization_steps, cycles=2,tau=1)\n",
" optimizer = BertAdam(model.parameters(),lr=klr*1e-3,schedule=sched)\n",
" history,best_model= model_train(model,\n",
" optimizer,\n",
" train_dataset,\n",
" batch_size,\n",
" num_epochs,\n",
" loss_func,\n",
" weights=weights,\n",
" do_apex=False,\n",
" validate_dataset=validate_dataset,\n",
" param_schedualer=None,\n",
" weights_data=None,\n",
" metric=Metric(torch.tensor([1.,1.,1.,1.,1.,2.])),\n",
" return_model=True,\n",
" best_average=3,\n",
" num_workers=num_workers,\n",
" sampler=None,\n",
" graph=None)\n",
" torch.save(best_model.state_dict(), models_dir+models_format.format(model_name,version,num_split))"
]
},
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{
"name": "stdout",
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"text": [
"se_resnet101 new_splits_focal 0\n"
]
},
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"source": [
"%matplotlib nbagg\n",
"for num_split in [0]: #range(5):\n",
" multi=3\n",
" model_name,version = 'se_resnet101' , 'new_splits_focal'\n",
" print (model_name,version,num_split)\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.shape\n",
"\n",
" features=features.reshape(features.shape[0]//4,4,-1)\n",
" features.shape\n",
" split_train = train_df[train_df.PID.isin(set(split_sid[splits[num_split][0]]))].SeriesI.unique()\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
"\n",
" np.random.seed(SEED+num_split)\n",
" torch.manual_seed(SEED+num_split)\n",
" torch.cuda.manual_seed(SEED+num_split)\n",
" torch.backends.cudnn.deterministic = True\n",
" batch_size=16\n",
" num_workers=18\n",
" num_epochs=24\n",
" klr=1\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],device=device)\n",
" train_dataset=FullHeadDataset(train_df,\n",
" split_train,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types,\n",
" multi=multi) \n",
" validate_dataset=FullHeadDataset(train_df,\n",
" split_validate,\n",
" torch.cat([features[:,i,:] for i in range(4)],-1),\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types) \n",
"\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version+'_fullhead_resmodel_pool2_over{}'.format(multi)\n",
" _=model.to(device)\n",
" #mixup=Mixup(device=device)\n",
" loss_func=my_loss\n",
" #fig,ax = plt.subplots(figsize=(10,7))\n",
" #gr=loss_graph(fig,ax,num_epochs,len(train_dataset)//batch_size+1,limits=[0.02,0.06])\n",
"# sampling=PosSeriesSampler(train_df,split_train,1,0.75)\n",
" sample_ratio= 1.0 #1.01*float(sampling().shape[0])/split_train.shape[0]\n",
" num_train_optimization_steps = sample_ratio*num_epochs*(len(train_dataset)//batch_size+int(len(train_dataset)%batch_size>0))\n",
" sched=WarmupExpCosineWithWarmupRestartsSchedule( t_total=num_train_optimization_steps, cycles=2,tau=1)\n",
" optimizer = BertAdam(model.parameters(),lr=klr*1e-3,schedule=sched)\n",
" history,best_model= model_train(model,\n",
" optimizer,\n",
" train_dataset,\n",
" batch_size,\n",
" num_epochs,\n",
" loss_func,\n",
" weights=weights,\n",
" do_apex=False,\n",
" validate_dataset=validate_dataset,\n",
" param_schedualer=None,\n",
" weights_data=None,\n",
" metric=Metric(torch.tensor([1.,1.,1.,1.,1.,2.])),\n",
" return_model=True,\n",
" best_average=3,\n",
" num_workers=num_workers,\n",
" sampler=None,\n",
" graph=None)\n",
" torch.save(best_model.state_dict(), models_dir+models_format.format(model_name,version,num_split))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"se_resnet101 new_splits_focal 1\n"
]
},
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"data": {
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"source": [
"%matplotlib nbagg\n",
"for num_split in [1]: #range(5):\n",
" multi=3\n",
" model_name,version = 'se_resnet101' , 'new_splits_focal'\n",
" print (model_name,version,num_split)\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.shape\n",
"\n",
" features=features.reshape(features.shape[0]//4,4,-1)\n",
" features.shape\n",
" split_train = train_df[train_df.PID.isin(set(split_sid[splits[num_split][0]]))].SeriesI.unique()\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
"\n",
" np.random.seed(SEED+num_split)\n",
" torch.manual_seed(SEED+num_split)\n",
" torch.cuda.manual_seed(SEED+num_split)\n",
" torch.backends.cudnn.deterministic = True\n",
" batch_size=16\n",
" num_workers=18\n",
" num_epochs=24\n",
" klr=1\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],device=device)\n",
" train_dataset=FullHeadDataset(train_df,\n",
" split_train,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types,\n",
" multi=multi) \n",
" validate_dataset=FullHeadDataset(train_df,\n",
" split_validate,\n",
" torch.cat([features[:,i,:] for i in range(4)],-1),\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types) \n",
"\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version+'_fullhead_resmodel_pool2_over{}'.format(multi)\n",
" _=model.to(device)\n",
" #mixup=Mixup(device=device)\n",
" loss_func=my_loss\n",
" #fig,ax = plt.subplots(figsize=(10,7))\n",
" #gr=loss_graph(fig,ax,num_epochs,len(train_dataset)//batch_size+1,limits=[0.02,0.06])\n",
"# sampling=PosSeriesSampler(train_df,split_train,1,0.75)\n",
" sample_ratio= 1.0 #1.01*float(sampling().shape[0])/split_train.shape[0]\n",
" num_train_optimization_steps = sample_ratio*num_epochs*(len(train_dataset)//batch_size+int(len(train_dataset)%batch_size>0))\n",
" sched=WarmupExpCosineWithWarmupRestartsSchedule( t_total=num_train_optimization_steps, cycles=2,tau=1)\n",
" optimizer = BertAdam(model.parameters(),lr=klr*1e-3,schedule=sched)\n",
" history,best_model= model_train(model,\n",
" optimizer,\n",
" train_dataset,\n",
" batch_size,\n",
" num_epochs,\n",
" loss_func,\n",
" weights=weights,\n",
" do_apex=False,\n",
" validate_dataset=validate_dataset,\n",
" param_schedualer=None,\n",
" weights_data=None,\n",
" metric=Metric(torch.tensor([1.,1.,1.,1.,1.,2.])),\n",
" return_model=True,\n",
" best_average=3,\n",
" num_workers=num_workers,\n",
" sampler=None,\n",
" graph=None)\n",
" torch.save(best_model.state_dict(), models_dir+models_format.format(model_name,version,num_split))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"se_resnet101 new_splits_focal 2\n"
]
},
{
"data": {
"text/plain": [
"torch.Size([2697008, 256])"
]
},
"execution_count": 26,
"metadata": {},
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},
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"source": [
"%matplotlib nbagg\n",
"for num_split in [2]: #range(5):\n",
" multi=3\n",
" model_name,version = 'se_resnet101' , 'new_splits_focal'\n",
" print (model_name,version,num_split)\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.shape\n",
"\n",
" features=features.reshape(features.shape[0]//4,4,-1)\n",
" features.shape\n",
" split_train = train_df[train_df.PID.isin(set(split_sid[splits[num_split][0]]))].SeriesI.unique()\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
"\n",
" np.random.seed(SEED+num_split)\n",
" torch.manual_seed(SEED+num_split)\n",
" torch.cuda.manual_seed(SEED+num_split)\n",
" torch.backends.cudnn.deterministic = True\n",
" batch_size=16\n",
" num_workers=18\n",
" num_epochs=24\n",
" klr=1\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],device=device)\n",
" train_dataset=FullHeadDataset(train_df,\n",
" split_train,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types,\n",
" multi=multi) \n",
" validate_dataset=FullHeadDataset(train_df,\n",
" split_validate,\n",
" torch.cat([features[:,i,:] for i in range(4)],-1),\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types) \n",
"\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version+'_fullhead_resmodel_pool2_over{}'.format(multi)\n",
" _=model.to(device)\n",
" #mixup=Mixup(device=device)\n",
" loss_func=my_loss\n",
" #fig,ax = plt.subplots(figsize=(10,7))\n",
" #gr=loss_graph(fig,ax,num_epochs,len(train_dataset)//batch_size+1,limits=[0.02,0.06])\n",
"# sampling=PosSeriesSampler(train_df,split_train,1,0.75)\n",
" sample_ratio= 1.0 #1.01*float(sampling().shape[0])/split_train.shape[0]\n",
" num_train_optimization_steps = sample_ratio*num_epochs*(len(train_dataset)//batch_size+int(len(train_dataset)%batch_size>0))\n",
" sched=WarmupExpCosineWithWarmupRestartsSchedule( t_total=num_train_optimization_steps, cycles=2,tau=1)\n",
" optimizer = BertAdam(model.parameters(),lr=klr*1e-3,schedule=sched)\n",
" history,best_model= model_train(model,\n",
" optimizer,\n",
" train_dataset,\n",
" batch_size,\n",
" num_epochs,\n",
" loss_func,\n",
" weights=weights,\n",
" do_apex=False,\n",
" validate_dataset=validate_dataset,\n",
" param_schedualer=None,\n",
" weights_data=None,\n",
" metric=Metric(torch.tensor([1.,1.,1.,1.,1.,2.])),\n",
" return_model=True,\n",
" best_average=3,\n",
" num_workers=num_workers,\n",
" sampler=None,\n",
" graph=None)\n",
" torch.save(best_model.state_dict(), models_dir+models_format.format(model_name,version,num_split))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"se_resnet101 new_splits_focal 3\n"
]
},
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"data": {
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},
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"source": [
"%matplotlib nbagg\n",
"for num_split in [3]: #range(5):\n",
" multi=3\n",
" model_name,version = 'se_resnet101' , 'new_splits_focal'\n",
" print (model_name,version,num_split)\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.shape\n",
"\n",
" features=features.reshape(features.shape[0]//4,4,-1)\n",
" features.shape\n",
" split_train = train_df[train_df.PID.isin(set(split_sid[splits[num_split][0]]))].SeriesI.unique()\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
"\n",
" np.random.seed(SEED+num_split)\n",
" torch.manual_seed(SEED+num_split)\n",
" torch.cuda.manual_seed(SEED+num_split)\n",
" torch.backends.cudnn.deterministic = True\n",
" batch_size=16\n",
" num_workers=18\n",
" num_epochs=24\n",
" klr=1\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],device=device)\n",
" train_dataset=FullHeadDataset(train_df,\n",
" split_train,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types,\n",
" multi=multi) \n",
" validate_dataset=FullHeadDataset(train_df,\n",
" split_validate,\n",
" torch.cat([features[:,i,:] for i in range(4)],-1),\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types) \n",
"\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version+'_fullhead_resmodel_pool2_over{}'.format(multi)\n",
" _=model.to(device)\n",
" #mixup=Mixup(device=device)\n",
" loss_func=my_loss\n",
" #fig,ax = plt.subplots(figsize=(10,7))\n",
" #gr=loss_graph(fig,ax,num_epochs,len(train_dataset)//batch_size+1,limits=[0.02,0.06])\n",
"# sampling=PosSeriesSampler(train_df,split_train,1,0.75)\n",
" sample_ratio= 1.0 #1.01*float(sampling().shape[0])/split_train.shape[0]\n",
" num_train_optimization_steps = sample_ratio*num_epochs*(len(train_dataset)//batch_size+int(len(train_dataset)%batch_size>0))\n",
" sched=WarmupExpCosineWithWarmupRestartsSchedule( t_total=num_train_optimization_steps, cycles=2,tau=1)\n",
" optimizer = BertAdam(model.parameters(),lr=klr*1e-3,schedule=sched)\n",
" history,best_model= model_train(model,\n",
" optimizer,\n",
" train_dataset,\n",
" batch_size,\n",
" num_epochs,\n",
" loss_func,\n",
" weights=weights,\n",
" do_apex=False,\n",
" validate_dataset=validate_dataset,\n",
" param_schedualer=None,\n",
" weights_data=None,\n",
" metric=Metric(torch.tensor([1.,1.,1.,1.,1.,2.])),\n",
" return_model=True,\n",
" best_average=3,\n",
" num_workers=num_workers,\n",
" sampler=None,\n",
" graph=None)\n",
" torch.save(best_model.state_dict(), models_dir+models_format.format(model_name,version,num_split))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"se_resnet101 new_splits_focal 4\n"
]
},
{
"data": {
"text/plain": [
"torch.Size([2697008, 256])"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
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]
},
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},
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"source": [
"%matplotlib nbagg\n",
"for num_split in [4]: #range(5):\n",
" multi=3\n",
" model_name,version = 'se_resnet101' , 'new_splits_focal'\n",
" print (model_name,version,num_split)\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.shape\n",
"\n",
" features=features.reshape(features.shape[0]//4,4,-1)\n",
" features.shape\n",
" split_train = train_df[train_df.PID.isin(set(split_sid[splits[num_split][0]]))].SeriesI.unique()\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
"\n",
" np.random.seed(SEED+num_split)\n",
" torch.manual_seed(SEED+num_split)\n",
" torch.cuda.manual_seed(SEED+num_split)\n",
" torch.backends.cudnn.deterministic = True\n",
" batch_size=16\n",
" num_workers=18\n",
" num_epochs=24\n",
" klr=1\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],device=device)\n",
" train_dataset=FullHeadDataset(train_df,\n",
" split_train,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types,\n",
" multi=multi) \n",
" validate_dataset=FullHeadDataset(train_df,\n",
" split_validate,\n",
" torch.cat([features[:,i,:] for i in range(4)],-1),\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" hemorrhage_types) \n",
"\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version+'_fullhead_resmodel_pool2_over{}'.format(multi)\n",
" _=model.to(device)\n",
" #mixup=Mixup(device=device)\n",
" loss_func=my_loss\n",
" #fig,ax = plt.subplots(figsize=(10,7))\n",
" #gr=loss_graph(fig,ax,num_epochs,len(train_dataset)//batch_size+1,limits=[0.02,0.06])\n",
"# sampling=PosSeriesSampler(train_df,split_train,1,0.75)\n",
" sample_ratio= 1.0 #1.01*float(sampling().shape[0])/split_train.shape[0]\n",
" num_train_optimization_steps = sample_ratio*num_epochs*(len(train_dataset)//batch_size+int(len(train_dataset)%batch_size>0))\n",
" sched=WarmupExpCosineWithWarmupRestartsSchedule( t_total=num_train_optimization_steps, cycles=2,tau=1)\n",
" optimizer = BertAdam(model.parameters(),lr=klr*1e-3,schedule=sched)\n",
" history,best_model= model_train(model,\n",
" optimizer,\n",
" train_dataset,\n",
" batch_size,\n",
" num_epochs,\n",
" loss_func,\n",
" weights=weights,\n",
" do_apex=False,\n",
" validate_dataset=validate_dataset,\n",
" param_schedualer=None,\n",
" weights_data=None,\n",
" metric=Metric(torch.tensor([1.,1.,1.,1.,1.,2.])),\n",
" return_model=True,\n",
" best_average=3,\n",
" num_workers=num_workers,\n",
" sampler=None,\n",
" graph=None)\n",
" torch.save(best_model.state_dict(), models_dir+models_format.format(model_name,version,num_split))"
]
},
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"text": [
"se_resnext101_32x4d new_splits_focal features_train_tta 0\n"
]
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"\u001b[0;32m<ipython-input-25-91dc370428ee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0mwins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mleave\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0mpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalid_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdo_apex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0mpred_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwins\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0mpickle_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs_dir\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0moutputs_format\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'OOF_pred'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnum_split\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/kaggle/RSNA/Production/helper/mytraining.py\u001b[0m in \u001b[0;36mmodel_run\u001b[0;34m(model, dataset, do_apex, batch_size, num_workers)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0mres_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0mdata_loader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnum_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_workers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mbatchs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 186\u001b[0m \u001b[0my_preds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbatchs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatchs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatchs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0mres_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0my_preds\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_preds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0my_preds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/RSNA/lib/python3.6/site-packages/tqdm/_tqdm_notebook.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 222\u001b[0m \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/RSNA/lib/python3.6/site-packages/tqdm/_tqdm.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 977\u001b[0m \"\"\", fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m 978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 979\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 980\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 981\u001b[0m \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/RSNA/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 802\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 803\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtasks_outstanding\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 804\u001b[0;31m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 805\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtasks_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 806\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/RSNA/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 769\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 770\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 771\u001b[0;31m \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 772\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 773\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/RSNA/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_try_get_data\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 722\u001b[0m \u001b[0;31m# (bool: whether successfully get data, any: data if successful else None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 723\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 724\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 725\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/RSNA/lib/python3.6/multiprocessing/queues.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeadline\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 105\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/RSNA/lib/python3.6/multiprocessing/connection.py\u001b[0m in \u001b[0;36mpoll\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_readable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/RSNA/lib/python3.6/multiprocessing/connection.py\u001b[0m in \u001b[0;36m_poll\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 415\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/RSNA/lib/python3.6/multiprocessing/connection.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(object_list, timeout)\u001b[0m\n\u001b[1;32m 909\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 910\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 911\u001b[0;31m \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 912\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfileobj\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/RSNA/lib/python3.6/selectors.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 374\u001b[0m \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 375\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m \u001b[0mfd_event_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 377\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mInterruptedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 378\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"multi=3\n",
"model_names=['se_resnext101_32x4d']\n",
"types=['features_train_tta']\n",
"versions=['new_splits_focal']\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_over{}'.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"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"se_resnet101 new_splits_focal features_train_tta 0\n"
]
},
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
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"source": [
"multi=3\n",
"model_names=['se_resnet101']\n",
"types=['features_train_tta']\n",
"versions=['new_splits_focal']\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_over{}'.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"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"pickle_file=open(outputs_dir+'OOF_validation_image_ids_5.pkl','rb')\n",
"image_id_folds=pickle.load(pickle_file)\n",
"pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"\n",
"targets=res_df.loc[image_id_folds[0]]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(134734, 6)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"targets.shape"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.0680)"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"tensor(0.0683)"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"tensor(0.0693)"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0710)"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0698)"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0693)"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multi=3\n",
"cols=['PatientID']\n",
"cols.extend(hemorrhage_types)\n",
"res_df=train_df[cols].set_index('PatientID')\n",
"model_names=['se_resnet101']\n",
"types=['OOF_pred']\n",
"versions=['new_splits_focal_fullhead_resmodel_pool2_over3']\n",
"num_splits=[5]\n",
"#model_names=['se_resnext101_32x4d','se_resnet101']\n",
"#types=['test_pred_ensamble','test_pred_ensamble']\n",
"#versions=['new_splits_fullhead_resmodel_pool2_3','new_splits_fullhead_resmodel_pool2_3']\n",
"#num_splits=[5,5]\n",
"#model_names=['se_resnet101']\n",
"#types=['OOF_pred']\n",
"#versions=['new_splits_focal_fullhead_resmodel_pool2_over3']\n",
"#num_splits=[5]\n",
"l=0.\n",
"for num_split in range(n):\n",
" pred_list=[]\n",
" for model_name,type_,version_,n in zip(model_names,types,versions,num_splits):\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version_,type_,num_split),'rb')\n",
" pred_list.extend(pickle.load(pickle_file))\n",
" pickle_file.close()\n",
" preds=torch.cat([p[None] for p in pred_list],0).mean(0)\n",
" targets=torch.tensor(res_df.loc[image_id_folds[num_split]][hemorrhage_types].values,dtype=torch.float)\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],dtype=torch.float)\n",
" my_loss(preds,targets,weights)\n",
" l=l+my_loss(preds,targets,weights)\n",
"l/5\n"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.0675)"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0683)"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"tensor(0.0690)"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0716)"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0701)"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0693)"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multi=3\n",
"cols=['PatientID']\n",
"cols.extend(hemorrhage_types)\n",
"res_df=train_df[cols].set_index('PatientID')\n",
"model_names=['se_resnet101']\n",
"types=['OOF_pred']\n",
"versions=['new_splits_fullhead_resmodel_pool2_3']\n",
"num_splits=[5]\n",
"#model_names=['se_resnext101_32x4d','se_resnet101']\n",
"#types=['test_pred_ensamble','test_pred_ensamble']\n",
"#versions=['new_splits_fullhead_resmodel_pool2_3','new_splits_fullhead_resmodel_pool2_3']\n",
"#num_splits=[5,5]\n",
"#model_names=['se_resnet101']\n",
"#types=['OOF_pred']\n",
"#versions=['new_splits_focal_fullhead_resmodel_pool2_over3']\n",
"#num_splits=[5]\n",
"l=0.\n",
"for num_split in range(n):\n",
" pred_list=[]\n",
" for model_name,type_,version_,n in zip(model_names,types,versions,num_splits):\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version_,type_,num_split),'rb')\n",
" pred_list.extend(pickle.load(pickle_file))\n",
" pickle_file.close()\n",
" preds=torch.cat([p[None] for p in pred_list],0).mean(0)\n",
" targets=torch.tensor(res_df.loc[image_id_folds[num_split]][hemorrhage_types].values,dtype=torch.float)\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],dtype=torch.float)\n",
" my_loss(preds,targets,weights)\n",
" l=l+my_loss(preds,targets,weights)\n",
"l/5\n"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.0664)"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0672)"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0678)"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0701)"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0687)"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0680)"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multi=3\n",
"cols=['PatientID']\n",
"cols.extend(hemorrhage_types)\n",
"res_df=train_df[cols].set_index('PatientID')\n",
"model_names=['se_resnext101_32x4d','se_resnet101','se_resnet101']\n",
"types=['OOF_pred','OOF_pred','OOF_pred']\n",
"versions=['new_splits_fullhead_resmodel_pool2_3','new_splits_fullhead_resmodel_pool2_3','new_splits_focal_fullhead_resmodel_pool2_over3']\n",
"num_splits=[5,5,5]\n",
"#model_names=['se_resnext101_32x4d','se_resnet101']\n",
"#types=['test_pred_ensamble','test_pred_ensamble']\n",
"#versions=['new_splits_fullhead_resmodel_pool2_3','new_splits_fullhead_resmodel_pool2_3']\n",
"#num_splits=[5,5]\n",
"#model_names=['se_resnet101']\n",
"#types=['OOF_pred']\n",
"#versions=['new_splits_focal_fullhead_resmodel_pool2_over3']\n",
"#num_splits=[5]\n",
"l=0.\n",
"for num_split in range(n):\n",
" pred_list=[]\n",
" for model_name,type_,version_,n in zip(model_names,types,versions,num_splits):\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version_,type_,num_split),'rb')\n",
" pred_list.extend(pickle.load(pickle_file))\n",
" pickle_file.close()\n",
" preds=torch.cat([p[None] for p in pred_list],0).mean(0)\n",
" targets=torch.tensor(res_df.loc[image_id_folds[num_split]][hemorrhage_types].values,dtype=torch.float)\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],dtype=torch.float)\n",
" my_loss(preds,targets,weights)\n",
" l=l+my_loss(preds,targets,weights)\n",
"l/5\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.0662)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0672)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0675)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0702)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0687)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0680)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multi=3\n",
"cols=['PatientID']\n",
"cols.extend(hemorrhage_types)\n",
"res_df=train_df[cols].set_index('PatientID')\n",
"model_names=['se_resnext101_32x4d','se_resnext101_32x4d','se_resnet101','se_resnet101']\n",
"types=['OOF_pred','OOF_pred','OOF_pred','OOF_pred']\n",
"versions=['new_splits_fullhead_resmodel_pool2_3','new_splits_focal_fullhead_resmodel_pool2_over3','new_splits_fullhead_resmodel_pool2_3','new_splits_focal_fullhead_resmodel_pool2_over3']\n",
"num_splits=[5,5,5,5]\n",
"#model_names=['se_resnext101_32x4d','se_resnet101']\n",
"#types=['test_pred_ensamble','test_pred_ensamble']\n",
"#versions=['new_splits_fullhead_resmodel_pool2_3','new_splits_fullhead_resmodel_pool2_3']\n",
"#num_splits=[5,5]\n",
"#model_names=['se_resnet101']\n",
"#types=['OOF_pred']\n",
"#versions=['new_splits_focal_fullhead_resmodel_pool2_over3']\n",
"#num_splits=[5]\n",
"l=0.\n",
"for num_split in range(n):\n",
" pred_list=[]\n",
" for model_name,type_,version_,n in zip(model_names,types,versions,num_splits):\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version_,type_,num_split),'rb')\n",
" pred_list.extend(pickle.load(pickle_file))\n",
" pickle_file.close()\n",
" preds=torch.cat([p[None] for p in pred_list],0).mean(0)\n",
" targets=torch.tensor(res_df.loc[image_id_folds[num_split]][hemorrhage_types].values,dtype=torch.float)\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],dtype=torch.float)\n",
" my_loss(preds,targets,weights)\n",
" l=l+my_loss(preds,targets,weights)\n",
"l/5\n"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.0664)"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0675)"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor(0.0678)"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"source": [
"multi=3\n",
"cols=['PatientID']\n",
"cols.extend(hemorrhage_types)\n",
"res_df=train_df[cols].set_index('PatientID')\n",
"#model_names=['se_resnext101_32x4d','se_resnet101','se_resnet101']\n",
"#types=['OOF_pred','OOF_pred','OOF_pred']\n",
"#versions=['new_splits_fullhead_resmodel_pool2_3','new_splits_fullhead_resmodel_pool2_3','new_splits_focal_fullhead_resmodel_pool2_over3']\n",
"#num_splits=[5,5,5]\n",
"model_names=['se_resnext101_32x4d','se_resnet101']\n",
"types=['OOF_pred','OOF_pred']\n",
"versions=['new_splits_fullhead_resmodel_pool2_3','new_splits_fullhead_resmodel_pool2_3']\n",
"num_splits=[5,5]\n",
"l=0\n",
"#model_names=['se_resnet101']\n",
"#types=['OOF_pred']\n",
"#versions=['new_splits_focal_fullhead_resmodel_pool2_over3']\n",
"#num_splits=[5]\n",
"for num_split in range(n):\n",
" pred_list=[]\n",
" for model_name,type_,version_,n in zip(model_names,types,versions,num_splits):\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version_,type_,num_split),'rb')\n",
" pred_list.extend(pickle.load(pickle_file))\n",
" pickle_file.close()\n",
" preds=torch.cat([p[None] for p in pred_list],0).mean(0)\n",
" targets=torch.tensor(res_df.loc[image_id_folds[num_split]][hemorrhage_types].values,dtype=torch.float)\n",
" weights = torch.tensor([1.,1.,1.,1.,1.,2.],dtype=torch.float)\n",
" my_loss(preds,targets,weights)\n",
" l=l+my_loss(preds,targets,weights)\n",
"l/5\n"
]
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"text": [
"se_resnet101 new_splits_focal features_test 4\n",
"torch.Size([78545, 8, 256])\n"
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},
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"source": [
"multi=3\n",
"model_names=['se_resnet101']\n",
"types=['features_test']\n",
"versions=['new_splits_focal']\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",
" 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_over{}'.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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"text": [
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"source": [
"multi=3\n",
"model_names=['se_resnext101_32x4d']\n",
"types=['features_test']\n",
"versions=['new_splits_focal']\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(5)):\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_over{}'.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"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"multi=3\n",
"pred_list=[]\n",
"model_names=['se_resnext101_32x4d','se_resnext101_32x4d','se_resnet101','se_resnet101']\n",
"types=['test_pred_ensamble','test_pred_ensamble','test_pred_ensamble','test_pred_ensamble']\n",
"versions=['new_splits_fullhead_resmodel_pool2_3','new_splits_focal_fullhead_resmodel_pool2_over3','new_splits_fullhead_resmodel_pool2_3','new_splits_focal_fullhead_resmodel_pool2_over3']\n",
"num_splits=[5,5,5,5]\n",
"#model_names=['se_resnext101_32x4d','se_resnet101']\n",
"#types=['test_pred_ensamble','test_pred_ensamble']\n",
"#versions=['new_splits_fullhead_resmodel_pool2_3','new_splits_fullhead_resmodel_pool2_3']\n",
"#num_splits=[5,5]\n",
"#model_names=['se_resnet101']\n",
"#types=['test_pred_ensamble']\n",
"#versions=['new_splits_focal_fullhead_resmodel_pool2_over3']\n",
"#num_splits=[5]\n",
"\n",
"for model_name,type_,version_,n in zip(model_names,types,versions,num_splits):\n",
" for num_split in range(n):\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version_,type_,num_split),'rb')\n",
" pred_list.extend(pickle.load(pickle_file))\n",
" pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"preds=torch.sigmoid(torch.cat([p[None] for p in pred_list],0)).mean(0)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([78545, 6])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"tensor([[3.5711e-05, 2.7395e-05, 5.7499e-06, 7.0699e-05, 7.5931e-05, 1.5110e-04],\n",
" [3.9274e-06, 3.3226e-06, 3.7224e-07, 1.2136e-05, 1.4754e-05, 3.8673e-05],\n",
" [3.3621e-05, 1.0076e-04, 2.5102e-06, 1.9387e-04, 1.3010e-04, 5.5842e-04],\n",
" [8.2830e-05, 2.8591e-04, 5.9830e-06, 2.6872e-04, 2.9643e-04, 8.5356e-04],\n",
" [7.1110e-05, 2.6773e-04, 9.9516e-06, 1.9567e-04, 2.3143e-04, 6.7518e-04],\n",
" [5.4225e-05, 1.7603e-04, 8.6872e-06, 1.5172e-04, 2.3249e-04, 5.0158e-04],\n",
" [4.0195e-05, 1.5081e-04, 7.9949e-06, 1.3344e-04, 2.8025e-04, 5.1623e-04],\n",
" [3.5982e-05, 1.6879e-04, 1.0185e-05, 1.6445e-04, 3.8864e-04, 7.3439e-04],\n",
" [2.9345e-05, 1.2291e-04, 6.0371e-06, 1.4451e-04, 3.6284e-04, 5.9658e-04],\n",
" [3.0346e-05, 2.1097e-04, 8.2453e-06, 2.0855e-04, 3.9730e-04, 8.7336e-04]])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds.shape\n",
"preds[:10]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"pickle_file=open(outputs_dir+'ensemble_test_image_ids.pkl','rb')\n",
"image_ids=pickle.load(pickle_file)\n",
"pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['37874f3c6', '2e16b8f05', '0b758b9c0', 'b8eba0e41', 'acf5e1ac4',\n",
" 'ae3c642b0', '7edd07ece', '2cbe4682d', '7ed61df92', 'df94f381c'],\n",
" dtype=object)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image_ids[:10]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"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_000012eaf_any</td>\n",
" <td>0.001188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ID_000012eaf_epidural</td>\n",
" <td>0.000104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ID_000012eaf_intraparenchymal</td>\n",
" <td>0.000135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ID_000012eaf_intraventricular</td>\n",
" <td>0.000024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ID_000012eaf_subarachnoid</td>\n",
" <td>0.000148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ID_000012eaf_subdural</td>\n",
" <td>0.000900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>ID_0000ca2f6_any</td>\n",
" <td>0.001744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>ID_0000ca2f6_epidural</td>\n",
" <td>0.000062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>ID_0000ca2f6_intraparenchymal</td>\n",
" <td>0.000291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>ID_0000ca2f6_intraventricular</td>\n",
" <td>0.000027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>ID_0000ca2f6_subarachnoid</td>\n",
" <td>0.000251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>ID_0000ca2f6_subdural</td>\n",
" <td>0.001218</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID Label\n",
"0 ID_000012eaf_any 0.001188\n",
"1 ID_000012eaf_epidural 0.000104\n",
"2 ID_000012eaf_intraparenchymal 0.000135\n",
"3 ID_000012eaf_intraventricular 0.000024\n",
"4 ID_000012eaf_subarachnoid 0.000148\n",
"5 ID_000012eaf_subdural 0.000900\n",
"6 ID_0000ca2f6_any 0.001744\n",
"7 ID_0000ca2f6_epidural 0.000062\n",
"8 ID_0000ca2f6_intraparenchymal 0.000291\n",
"9 ID_0000ca2f6_intraventricular 0.000027\n",
"10 ID_0000ca2f6_subarachnoid 0.000251\n",
"11 ID_0000ca2f6_subdural 0.001218"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(471270, 2)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"submission_df=get_submission_ids(image_ids,preds,do_sigmoid=False)\n",
"submission_df.head(12)\n",
"submission_df.shape\n",
"sub_num=58\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": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████| 16.9M/16.9M [01:16<00:00, 206kB/s]\n",
"Successfully submitted to RSNA Intracranial Hemorrhage Detection"
]
}
],
"source": [
"#!/home/reina/anaconda3/bin/kaggle competitions submit rsna-intracranial-hemorrhage-detection -f /media/hd/notebooks/data/RSNA/submissions/submission58.csv -m \"all the 5 folds models including focal, mean after sigmoid\""
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"sub=pd.read_csv('/media/hd/notebooks/data/RSNA/submissions/submission55.csv')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"sub0=pd.read_csv('/media/hd/notebooks/data/RSNA/submissions/submission54.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.0487, dtype=torch.float64)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"F.binary_cross_entropy(torch.tensor(sub.Label.values),torch.tensor(sub0.Label.values))"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.1125)"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"F.binary_cross_entropy(torch.tensor(sub.Label.values,dtype=torch.float),torch.tensor(submission_df.Label.values,dtype=torch.float))"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.0484)"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"F.binary_cross_entropy(torch.tensor(sub.Label.values,dtype=torch.float),torch.tensor(submission_df.Label.values,dtype=torch.float))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.0486)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"F.binary_cross_entropy(torch.tensor(sub.Label.values,dtype=torch.float),torch.tensor(submission_df.Label.values,dtype=torch.float))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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