Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
 Community
text
stringlengths
1
93.6k
root_container=dg._root_container,
parent_path=dg._cursor.parent_path + (dg._cursor.index,),
)
block_dg = DeltaGenerator(
root_container=dg._root_container,
cursor=block_cursor,
parent=dg,
block_type=block_type,
)
# Blocks inherit their parent form ids.
# NOTE: Container form ids aren't set in proto.
block_dg._form_data = FormData(current_form_id(dg))
# Must be called to increment this cursor's index.
dg._cursor.get_locked_cursor(last_index=None)
_enqueue_message(msg)
return block_dg
def _enqueue_message(msg):
"""Enqueues a ForwardMsg proto to send to the app."""
ctx = get_script_run_ctx()
if ctx is None:
raise NoSessionContext()
ctx.enqueue(msg)
DeltaGenerator._block = _block
# <FILESEP>
# from __future__ import print_function
import argparse, os
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import torch.optim as optim
import torch
import torch.utils.data as data_utils
from utils import *
from ResUnet3d_pytorch import UNet, ResUNet, UNet_LRes, ResUNet_LRes, Discriminator
# from Unet3d_pytorch import UNet3D
from nnBuildUnits import CrossEntropy3d, topK_RegLoss, RelativeThreshold_RegLoss, adjust_learning_rate
import time
import SimpleITK as sitk
# Training settings
parser = argparse.ArgumentParser(description="PyTorch InfantSeg")
parser.add_argument("--gpuID", type=int, default=3, help="how to normalize the data")
parser.add_argument("--isAdLoss", action="store_true", help="is adversarial loss used?", default=False)
parser.add_argument("--lambda_AD", default=0.05, type=float, help="Momentum, Default: 0.05")
parser.add_argument("--how2normalize", type=int, default=5, help="how to normalize the data")
parser.add_argument("--whichLoss", type=int, default=1, help="which loss to use: 1. LossL1, 2. lossRTL1, 3. MSE (default)")
parser.add_argument("--whichNet", type=int, default=4, help="which loss to use: 1. UNet, 2. ResUNet, 3. UNet_LRes and 4. ResUNet_LRes (default, 3)")
parser.add_argument("--lossBase", type=int, default=1, help="The base to multiply the lossG_G, Default (1)")
parser.add_argument("--batchSize", type=int, default=10, help="training batch size")
parser.add_argument("--isMultiSource", action="store_true", help="is multiple modality used?", default=False)
parser.add_argument("--numOfChannel_singleSource", type=int, default=5, help="# of channels for a 2D patch for the main modality (Default, 5)")
parser.add_argument("--numOfChannel_allSource", type=int, default=1, help="# of channels for a 2D patch for all the concatenated modalities (Default, 5)")
parser.add_argument("--numofIters", type=int, default=200000, help="number of iterations to train for")
parser.add_argument("--showTrainLossEvery", type=int, default=100, help="number of iterations to show train loss")
parser.add_argument("--saveModelEvery", type=int, default=5000, help="number of iterations to save the model")
parser.add_argument("--showValPerformanceEvery", type=int, default=1000, help="number of iterations to show validation performance")
parser.add_argument("--showTestPerformanceEvery", type=int, default=5000, help="number of iterations to show test performance")
parser.add_argument("--lr", type=float, default=5e-3, help="Learning Rate. Default=1e-4")
parser.add_argument("--dropout_rate", default=0.2, type=float, help="prob to drop neurons to zero: 0.2")
parser.add_argument("--decLREvery", type=int, default=10000, help="Sets the learning rate to the initial LR decayed by momentum every n iterations, Default: n=40000")
parser.add_argument("--cuda", action="store_true", help="Use cuda?", default=True)
parser.add_argument("--resume", default="/home/niedong/Data4LowDosePET/pytorch_UNet/resunet3d_dp_pet_BatchAug_noNorm_lres_bn_lr5e3_lrdec_base1_lossL1_0p005_0627_5000.pt", type=str, help="Path to checkpoint (default: none)")
parser.add_argument("--start_epoch", default=1, type=int, help="Manual epoch number (useful on restarts)")
parser.add_argument("--threads", type=int, default=1, help="Number of threads for data loader to use, Default: 1")
parser.add_argument("--momentum", default=0.9, type=float, help="Momentum, Default: 0.9")
parser.add_argument("--weight-decay", "--wd", default=1e-4, type=float, help="weight decay, Default: 1e-4")
parser.add_argument("--RT_th", default=0.005, type=float, help="Relative thresholding: 0.005")
parser.add_argument("--pretrained", default="", type=str, help="path to pretrained model (default: none)")
parser.add_argument("--prefixModelName", default="/home/niedong/Data4LowDosePET/pytorch_UNet/resunet3d_dp_pet_BatchAug_noNorm_lres_bn_lr5e3_lrdec_base1_lossL1_0p005_0627_", type=str, help="prefix of the to-be-saved model name")
parser.add_argument("--prefixPredictedFN", default="preSub1_pet_BatchAug_noNorm_resunet3d_dp_lres_bn_lr5e3_lrdec_base1_lossL1_0p005_0627_", type=str, help="prefix of the to-be-saved predicted filename")
global opt, model
opt = parser.parse_args()
def main():
print opt
# prefixModelName = 'Regressor_1112_'
# prefixPredictedFN = 'preSub1_1112_'
# showTrainLossEvery = 100
# lr = 1e-4
# showTestPerformanceEvery = 2000
# saveModelEvery = 2000
# decLREvery = 40000
# numofIters = 200000
# how2normalize = 0
netD = Discriminator()
netD.apply(weights_init)
netD.cuda()