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makeWeights.py
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makeWeights.py
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import os
import numpy as np
import glob
import argparse
import pickle
import json
import sys
"""
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.autograd import Variable
import torch.utils.data as data
from torch.utils.data import WeightedRandomSampler
class nodData(data.Dataset):
def __init__(self, fileList, weights,transform=None,maxDiam = -1):
self.transform = transform
self.dataset = fileList
self.weights = weights
#np.random.shuffle(self.dataset)
self.maxDiam = maxDiam
self.len = len(self.dataset)
def __getitem__(self, index):
dum = np.load(self.dataset[index],allow_pickle=True).item()
if self.maxDiam > 0:
dum['features'][-1] /= self.maxDiam
img, target, feat, weight = dum['image'], dum['label'],dum['features'], self.weights[index]
if self.transform is not None:
img = self.transform(img)
return img, target, feat, weight
def __len__(self):
return self.len
if __name__ == '__main__':
fold = 0
maxEpochs = 5
if len(sys.argv)>1:
fold = int(sys.argv[1])
if len(sys.argv)>2:
maxEpochs = int(sys.argv[2])
SEED = 42
patchesPath = './patchesTr/'
savemodelpath = './clsModels/'
logName = savemodelpath + 'log_' + str(fold) + '.txt'
saveFreq = 10
batchSize = 16
numWorkers = 8
lr = 0.01
neptime = 0.3
CROPSIZE = 17
gbtdepth = 2
patchesList = np.asarray([f for f in sorted(glob.glob(patchesPath + '*.npy')) if 'statistics' not in os.path.basename(f) and 'weights' not in os.path.basename(f)])
weightsFileName = patchesPath + 'weights.npy'
assert os.path.isfile(weightsFileName)==True,'no file with patches weights'
samples_weight = np.asarray(np.load(weightsFileName,allow_pickle=True))
numAll = len(patchesList)
numFolds = 5
foldSize = numAll//numFolds
np.random.seed(SEED)
ids = np.arange(0,len(patchesList))
np.random.shuffle(ids)
patchesList = patchesList[ids]
samples_weight = samples_weight[ids]
val_keys = [patchesList[i] for i in range(fold*foldSize,min((fold+1)*foldSize,len(patchesList)))]
train_keys = [i for i in patchesList if i not in val_keys]
val_weights = np.asarray([samples_weight[i] for i in range(fold*foldSize,min((fold+1)*foldSize,len(patchesList)))])
train_weights = np.asarray([samples_weight[i] for i in range(0,len(patchesList)) if i not in range(fold*foldSize,min((fold+1)*foldSize,len(patchesList)))])
statsFileName = patchesPath + 'statistics.npy'
assert os.path.isfile(statsFileName)==True,'no file with patches stats'
stats = np.load(statsFileName,allow_pickle=True).item()
transform_train = None
transform_val = None
train_weights = torch.from_numpy(train_weights)
train_weigths = train_weights.double()
trainSampler = WeightedRandomSampler(train_weights, len(train_weights),replacement=True)
val_weights = torch.from_numpy(val_weights)
val_weigths = val_weights.double()
valSampler = WeightedRandomSampler(val_weights, len(val_weights),replacement = True)
trainSet = nodData(train_keys,train_weigths,transform=transform_train,maxDiam = stats['maxDiam'])
valSet = nodData(val_keys,val_weigths,transform=transform_val,maxDiam = stats['maxDiam'])
trainLoader = torch.utils.data.DataLoader(trainSet, batch_size=batchSize, num_workers=numWorkers,sampler=trainSampler)
valLoader = torch.utils.data.DataLoader(valSet, batch_size=batchSize, num_workers=numWorkers,sampler=valSampler)
num = 0
pos = 0
for batch_idx, (inputs, targets, feat, weight) in enumerate(trainLoader):
print(targets[0],weight[0])
num += targets.shape[0]
pos += torch.sum(targets).numpy()
if batch_idx > 200:
break
print(pos/num)
"""
if __name__ == '__main__':
fold = 0
if len(sys.argv)>1:
fold = int(sys.argv[1])
patchesPath = './patchesTr_fold' + str(fold) + '/'
patchesList = np.asarray([f for f in sorted(glob.glob(patchesPath + '*.npy')) if 'statistics' not in os.path.basename(f) and 'weights' not in os.path.basename(f)])
pos = 0
neg = 0
labels = []
for num, patchName in enumerate(patchesList):
if num%10 == 0:
print(num,flush=True)
dum = np.load(patchName,allow_pickle=True).item()
if dum['label'] == 0:
neg += 1
else:
pos += 1
labels.append(dum['label'])
wPos = 1/pos
wNeg = 1/neg
weights = [wNeg if l==0 else wPos for l in labels]
np.save(patchesPath + 'weights.npy',weights)
print(pos,neg)