def main():
# setup experiment logging to comet.ml
if expConfig.LOG_COMETML:
hyper_params = {"experimentName": expConfig.EXPERIMENT_NAME,
"epochs": expConfig.EPOCHS,
"batchSize": expConfig.BATCH_SIZE,
"channels": expConfig.CHANNELS,
"virualBatchsize": expConfig.VIRTUAL_BATCHSIZE}
expConfig.experiment.log_parameters(hyper_params)
expConfig.experiment.add_tags([expConfig.EXPERIMENT_NAME, "ID{}".format(expConfig.id)])
if hasattr(expConfig, "EXPERIMENT_TAGS"): expConfig.experiment.add_tags(expConfig.EXPERIMENT_TAGS)
print(bcolors.OKGREEN + "Logging to comet.ml" + bcolors.ENDC)
else:
print(bcolors.WARNING + "Not logging to comet.ml" + bcolors.ENDC)
# log parameter count
if expConfig.LOG_PARAMCOUNT:
paramCount = sum(p.numel() for p in expConfig.net.parameters() if p.requires_grad)
print("Parameters: {:,}".format(paramCount).replace(",", "'"))
#load data
randomCrop = expConfig.RANDOM_CROP if hasattr(expConfig, "RANDOM_CROP") else None
trainset = bratsDataset.BratsDataset(systemsetup.BRATS_PATH, expConfig, mode="train", randomCrop=randomCrop)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=expConfig.BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=expConfig.DATASET_WORKERS)
valset = bratsDataset.BratsDataset(systemsetup.BRATS_PATH, expConfig, mode="validation")
valloader = torch.utils.data.DataLoader(valset, batch_size=1, shuffle=False, pin_memory=True, num_workers=expConfig.DATASET_WORKERS)
challengeValset = bratsDataset.BratsDataset(systemsetup.BRATS_VAL_PATH, expConfig, mode="validation", hasMasks=False, returnOffsets=True)
challengeValloader = torch.utils.data.DataLoader(challengeValset, batch_size=1, shuffle=False, pin_memory=True, num_workers=expConfig.DATASET_WORKERS)
seg = segmenter.Segmenter(expConfig, trainloader, valloader, challengeValloader)
if hasattr(expConfig, "VALIDATE_ALL") and expConfig.VALIDATE_ALL:
seg.validateAllCheckpoints()
elif hasattr(expConfig, "PREDICT") and expConfig.PREDICT:
seg.makePredictions()
elif hasattr(expConfig, "VISUALIZE_PROB_MAP") and expConfig.VISUALIZE_PROB_MAP:
seg.visualize_prob_maps()
else:
seg.train()
import os
import torch
import bratsDataset
import segmenter
import systemsetup
import matplotlib.pyplot as plt
from dataProcessing import utils
import numpy as np
import experiments.noNewNet as expConfig
trainset = bratsDataset.BratsDataset(systemsetup.BRATS_PATH,
expConfig,
mode="train")
valset = bratsDataset.BratsDataset(systemsetup.BRATS_PATH,
expConfig,
mode="validation")
# sum_inputs = 0
# count = 0
# for i in range(len(trainset)):
# inputs, pid, labels = trainset[i]
# print("processing no.{} {}".format(i, pid))
# inputs = inputs.numpy()
# sum_inputs += inputs
# count += 1
# print(count)
#
# for i in range(len(valset)):
# inputs, pid, labels = valset[i]
# print("processing no.{} {}".format(i, pid))