Datasets
Models
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{
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"/home/reina/anaconda3/envs/RSNA/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n",
" return f(*args, **kwds)\n",
"/home/reina/anaconda3/envs/RSNA/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n",
" return f(*args, **kwds)\n"
]
}
],
"source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import os\n",
"import datetime\n",
"import seaborn as sns\n",
"\n",
"#import pydicom\n",
"import time\n",
"from functools import partial\n",
"import gc\n",
"import operator \n",
"import matplotlib.pyplot as plt\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.utils.data as D\n",
"import torch.nn.functional as F\n",
"from sklearn.model_selection import KFold\n",
"from tqdm import tqdm, tqdm_notebook\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\"\n",
"import warnings\n",
"warnings.filterwarnings(action='once')\n",
"import pickle\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline\n",
"from skimage.io import imread,imshow\n",
"from helper import *\n",
"import helper\n",
"import torchvision.models as models\n",
"from torch.optim import Adam\n",
"from defenitions import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# here you should set which model parameters you want to choose (see definitions.py) and what GPU to use\n",
"\n",
"device=device_by_name(\"Tesla\") # RTX , cpu\n",
"torch.cuda.set_device(device)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
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"execution_count": 3,
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"data": {
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"execution_count": 3,
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PatientID</th>\n",
" <th>epidural</th>\n",
" <th>intraparenchymal</th>\n",
" <th>intraventricular</th>\n",
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" <th>StudyI</th>\n",
" <th>SeriesI</th>\n",
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"text/plain": [
" PatientID epidural intraparenchymal intraventricular subarachnoid \\\n",
"0 63eb1e259 0 0 0 0 \n",
"1 2669954a7 0 0 0 0 \n",
"2 52c9913b1 0 0 0 0 \n",
"3 4e6ff6126 0 0 0 0 \n",
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"\n",
" subdural any PID StudyI SeriesI WindowCenter \\\n",
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"\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": 3,
"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": 12,
"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",
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" <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",
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"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": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df = pd.read_csv(data_dir+'test.csv')\n",
"test_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def my_loss(y_pred,y_true,weights):\n",
" window=(y_true>=0).to(torch.float)\n",
" loss = (F.binary_cross_entropy_with_logits(y_pred,y_true,reduction='none')*window*weights.expand_as(y_true)).mean()/(window.mean()+1e-7)\n",
" return loss"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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": 7,
"metadata": {},
"outputs": [],
"source": [
"model_names=[]\n",
"types_train=[]\n",
"types_test=[]\n",
"versions=[]\n",
"num_splits =[]\n",
"seeds=[]\n",
"for key in parameters.keys():\n",
" model_names.append(parameters[key]['model_name'])\n",
" types_train.append(parameters[key]['train_features'])\n",
" types_test.append(parameters[key]['test_features'])\n",
" versions.append(parameters[key]['version'])\n",
" num_splits.append(parameters[key]['n_splits'])\n",
" seeds.append(parameters[key]['SEED']) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"multi=3\n",
"for model_name,type_,version_,n,SEED in zip(model_names,types_train,versions,num_splits,seeds):\n",
" for num_split in tqdm_notebook(range(n)):\n",
" pickle_file=open(outputs_dir+'PID_splits_{}.pkl'.format(n_splits),'rb')\n",
" split_sid,splits=pickle.load(pickle_file)\n",
" pickle_file.close()\n",
" pred_list=[]\n",
" print(model_name,version_,type_,num_split) \n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version_,type_,num_split),'rb')\n",
" features=pickle.load(pickle_file)\n",
" pickle_file.close()\n",
" features=features.reshape(features.shape[0]//4,4,-1)\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[num_split][1]]))].SeriesI.unique()\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version_+'_fullhead_resmodel_pool2_{}'.format(multi)\n",
"\n",
" model.load_state_dict(torch.load(models_dir+models_format.format(model_name,version,num_split),map_location=torch.device(device)))\n",
"\n",
" valid_dataset=FullHeadDataset(train_df,\n",
" split_validate,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" multi =3)\n",
"\n",
" win_dataset=FullHeadDataset(train_df,\n",
" split_validate,\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" target_columns=hemorrhage_types)\n",
" win_list=[]\n",
" dl = D.DataLoader(win_dataset,batch_size=128,num_workers=16)\n",
" for _,win in tqdm_notebook(dl):\n",
" win_list.append(win.reshape(win.shape[0]*win.shape[1],-1)) \n",
" wins = torch.cat(win_list,0).sum(1)>=0\n",
" wins.sum()\n",
" for i in tqdm_notebook(range(32),leave=False):\n",
" pr = model_run(model,valid_dataset,do_apex=False,batch_size=128)\n",
" pred_list.append(pr.reshape(pr.shape[0]*pr.shape[1],-1)[wins])\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version,'OOF_pred',num_split),'wb')\n",
" pickle.dump(pred_list,pickle_file,protocol=4)\n",
" pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"multi=3\n",
"for model_name,type_,version_,n,SEED in zip(model_names,types_test,versions,num_splits,seeds):\n",
" for num_split in tqdm_notebook(range(n)):\n",
" pred_list=[]\n",
" print(model_name,version_,type_,num_split) \n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version_,type_,num_split),'rb')\n",
" features=pickle.load(pickle_file)\n",
" pickle_file.close()\n",
" features=features.reshape(features.shape[0]//8,8,-1)\n",
" print(features.shape)\n",
" model=ResModelPool(features.shape[-1])\n",
" version=version_+'_fullhead_resmodel_pool2_{}'.format(multi)\n",
"\n",
" model.load_state_dict(torch.load(models_dir+models_format.format(model_name,version,num_split),map_location=torch.device(device)))\n",
"\n",
" valid_dataset=FullHeadDataset(test_df,\n",
" test_df.SeriesI.unique(),\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" multi =4)\n",
"\n",
" win_dataset=FullHeadDataset(test_df,\n",
" test_df.SeriesI.unique(),\n",
" features,\n",
" 'SeriesI',\n",
" 'ImagePositionZ',\n",
" target_columns=hemorrhage_types)\n",
" win_list=[]\n",
" dl = D.DataLoader(win_dataset,batch_size=128,num_workers=16)\n",
" for _,win in tqdm_notebook(dl):\n",
" win_list.append(win.reshape(win.shape[0]*win.shape[1],-1)) \n",
" wins = torch.cat(win_list,0).sum(1)>=0\n",
" wins.sum()\n",
" for i in tqdm_notebook(range(32),leave=False):\n",
" pr = model_run(model,valid_dataset,do_apex=False,batch_size=128)\n",
" pred_list.append(pr.reshape(pr.shape[0]*pr.shape[1],-1)[wins])\n",
" pickle_file=open(outputs_dir+outputs_format.format(model_name,version,'test_pred_ensemble',num_split),'wb')\n",
" pickle.dump(pred_list,pickle_file,protocol=4)\n",
" pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"OOF_ids={}\n",
"SEED=8153\n",
"n_splits=3\n",
"pickle_file=open(outputs_dir+'PID_splits_{}.pkl'.format(n_splits),'rb')\n",
"split_sid,splits=pickle.load(pickle_file)\n",
"pickle_file.close()\n",
"for i in range( n_splits):\n",
" images_id_list=[]\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[i][1]]))].SeriesI.unique()\n",
" image_arr=train_df.PatientID.values\n",
" ref_arr=train_df.SeriesI.values\n",
" order_arr=train_df.ImagePositionZ.values\n",
" for s in tqdm_notebook(split_validate):\n",
" head_idx = np.where(ref_arr==s)[0]\n",
" sorted_head_idx=head_idx[np.argsort(order_arr[head_idx])]\n",
" images_id_list.append(image_arr[sorted_head_idx])\n",
" image_ids=np.concatenate(images_id_list)\n",
" print(image_ids.shape,train_df[train_df.PID.isin(set(split_sid[splits[i][1]]))].shape[0])\n",
" OOF_ids[i]=image_ids\n",
"\n",
"\n",
"pickle_file=open(outputs_dir+'OOF_validation_image_ids_{}.pkl'.format(n_splits),'wb')\n",
"pickle.dump(OOF_ids,pickle_file,protocol=4)\n",
"pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"OOF_ids={}\n",
"SEED=432\n",
"n_splits=5\n",
"pickle_file=open(outputs_dir+'PID_splits_{}.pkl'.format(n_splits),'rb')\n",
"split_sid,splits=pickle.load(pickle_file)\n",
"pickle_file.close()\n",
"for i in range( n_splits):\n",
" images_id_list=[]\n",
" split_validate = train_df[train_df.PID.isin(set(split_sid[splits[i][1]]))].SeriesI.unique()\n",
" image_arr=train_df.PatientID.values\n",
" ref_arr=train_df.SeriesI.values\n",
" order_arr=train_df.ImagePositionZ.values\n",
" for s in tqdm_notebook(split_validate):\n",
" head_idx = np.where(ref_arr==s)[0]\n",
" sorted_head_idx=head_idx[np.argsort(order_arr[head_idx])]\n",
" images_id_list.append(image_arr[sorted_head_idx])\n",
" image_ids=np.concatenate(images_id_list)\n",
" print(image_ids.shape,train_df[train_df.PID.isin(set(split_sid[splits[i][1]]))].shape[0])\n",
" OOF_ids[i]=image_ids\n",
"\n",
"\n",
"pickle_file=open(outputs_dir+'OOF_validation_image_ids_{}.pkl'.format(n_splits),'wb')\n",
"pickle.dump(OOF_ids,pickle_file,protocol=4)\n",
"pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "05087bea7c1b4380a9c6bd7ff81eb4bd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, max=2214), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"images_id_list=[]\n",
"dummeys=[]\n",
"image_arr=test_df.PatientID.values\n",
"ref_arr=test_df.SeriesI.values\n",
"order_arr=test_df.ImagePositionZ.values\n",
"for s in tqdm_notebook(test_df.SeriesI.unique()):\n",
" dumm=np.zeros(60)\n",
" head_idx = np.where(ref_arr==s)[0]\n",
" sorted_head_idx=head_idx[np.argsort(order_arr[head_idx])]\n",
" images_id_list.append(image_arr[sorted_head_idx])\n",
" dumm[0:head_idx.shape[0]]=1\n",
" dummeys.append(dumm)\n",
"image_ids=np.concatenate(images_id_list)\n",
"select=np.concatenate(dummeys)==1\n",
"\n",
"pickle_file=open(outputs_dir+'ensemble_test_image_ids.pkl','wb')\n",
"pickle.dump(image_ids,pickle_file,protocol=4)\n",
"pickle_file.close()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}