Exemplos de get_study_level_data em Python

Exemplo n.º 1

Arquivo: main.py Projeto: mrkovaliv/X-Ray

import time
import copy
import pandas as pd
import torch
from torch.autograd import Variable
from densenet import densenet169
from utils import plot_training, n_p, get_count
from train import train_model, get_metrics
from pipeline import get_study_level_data, get_dataloaders

# #### load study level dict data
study_data = get_study_level_data(study_type='XR_WRIST')

# #### Create dataloaders pipeline
data_cat = ['train', 'valid']  # data categories
dataloaders = get_dataloaders(study_data, batch_size=1)
dataset_sizes = {x: len(study_data[x]) for x in data_cat}

# #### Build model
# tai = total abnormal images, tni = total normal images
tai = {x: get_count(study_data[x], 'positive') for x in data_cat}
tni = {x: get_count(study_data[x], 'negative') for x in data_cat}
Wt1 = {x: n_p(tni[x] / (tni[x] + tai[x])) for x in data_cat}
Wt0 = {x: n_p(tai[x] / (tni[x] + tai[x])) for x in data_cat}

print('tai:', tai)
print('tni:', tni, '\n')
print('Wt0 train:', Wt0['train'])
print('Wt0 valid:', Wt0['valid'])
print('Wt1 train:', Wt1['train'])
print('Wt1 valid:', Wt1['valid'])

Exemplo n.º 2

    def __init__(self, Wt1, Wt0):
        super(Loss, self).__init__()
        self.Wt1 = Wt1
        self.Wt0 = Wt0

    def forward(self, inputs, targets, phase):
        loss = -(self.Wt1[phase] * targets * inputs.log() + self.Wt0[phase] *
                 (1 - targets) * (1 - inputs).log())
        return loss


if __name__ == '__main__':

    # #### load study level dict data
    study_data = get_study_level_data(
        study_type='XR_WRIST'
    )  #选择某一部位的图像，返回值是一个字典，有train和valid两类，value是dataframe数据

    # #### Create dataloaders pipeline
    data_cat = ['train', 'valid']  # data categories
    dataloaders = get_dataloaders(study_data, batch_size=1)
    dataset_sizes = {x: len(study_data[x]) for x in data_cat}  #样本的数量

    # #### Build model
    # tai = total abnormal images, tni = total normal images
    tai = {x: get_count(study_data[x], 'positive') for x in data_cat}
    tni = {x: get_count(study_data[x], 'negative') for x in data_cat}
    Wt1 = {x: n_p(tni[x] / (tni[x] + tai[x])) for x in data_cat}
    Wt0 = {x: n_p(tai[x] / (tni[x] + tai[x])) for x in data_cat}

    print('tai:', tai)

Exemplo n.º 3

import time
import copy
import pandas as pd
import torch
from torch.autograd import Variable
from mvdensenet import densenet169
from utils import plot_training, n_p, get_count
from train import train_model, get_metrics
from pipeline import get_study_level_data, get_dataloaders

# #### load study level dict data
study_data = get_study_level_data(study_type='XR_ELBOW')

# #### Create dataloaders pipeline
data_cat = ['train', 'valid']  # data categories
dataloaders = get_dataloaders(study_data, batch_size=1)
dataset_sizes = {x: len(study_data[x]) for x in data_cat}

# #### Build model
# tai = total abnormal images, tni = total normal images
tai = {x: get_count(study_data[x], 'positive') for x in data_cat}
tni = {x: get_count(study_data[x], 'negative') for x in data_cat}
Wt1 = {x: n_p(tni[x] / (tni[x] + tai[x])) for x in data_cat}
Wt0 = {x: n_p(tai[x] / (tni[x] + tai[x])) for x in data_cat}

print('tai:', tai)
print('tni:', tni, '\n')
print('Wt0 train:', Wt0['train'])
print('Wt0 valid:', Wt0['valid'])
print('Wt1 train:', Wt1['train'])
print('Wt1 valid:', Wt1['valid'])

Exemplo n.º 4

import copy
import pandas as pd
import torch
from torch.autograd import Variable
from densenet import densenet169
from utils import plot_training, n_p, get_count
from train import train_model, get_metrics
from pipeline import get_study_level_data, get_dataloaders

# #### load study level dict data

assert len(sys.argv) == 2

study_type=sys.argv[1]

study_data = get_study_level_data(study_type=study_type)

# #### Create dataloaders pipeline
data_cat = ['train', 'valid'] # data categories
dataloaders = get_dataloaders(study_data, batch_size=1)
dataset_sizes = {x: len(study_data[x]) for x in data_cat}

# #### Build model
# tai = total abnormal images, tni = total normal images
tai = {x: get_count(study_data[x], 'positive') for x in data_cat}
tni = {x: get_count(study_data[x], 'negative') for x in data_cat}
Wt1 = {x: n_p(tni[x] / (tni[x] + tai[x])) for x in data_cat}
Wt0 = {x: n_p(tai[x] / (tni[x] + tai[x])) for x in data_cat}

print('tai:', tai)
print('tni:', tni, '\n')