from time import time
import numpy as np
import datatest
from mxboard import SummaryWriter
import tools_for_ex_seg as tool
import dice_entropy_loss
batch_size = 96
img_path = "/home/sz/hard_ex_segmentation/HEI-MED/data4/test/img/"
label_path = "/home/sz/hard_ex_segmentation/HEI-MED/data4/test/label/"
img_root = "/home/sz/hard_ex_segmentation/HEI-MED/data4/train_crop/img/"
label_root = "/home/sz/hard_ex_segmentation/HEI-MED/data4/train_crop/label/"
dataset = datatest.get_dataset(img_root, label_root)
train_data = mx.gluon.data.DataLoader(dataset,
batch_size,
shuffle=True,
last_batch='rollover',
num_workers=8)
ctx = [mx.gpu(4), mx.gpu(5), mx.gpu(6), mx.gpu(7)]
net = multi_att_net.set_network()
net.collect_params().reset_ctx(ctx)
net.load_parameters('pretrain2_double.params', ctx=ctx)
def get_batch(batch, ctx):
if isinstance(batch, mx.io.DataBatch):
batch_size=150
img_path="/home/sz/hard_ex_segmentation/e_dataset/data2/test/img/"
label_path="/home/sz/hard_ex_segmentation/e_dataset/data2/test/label/"
img_root="/home/sz/hard_ex_segmentation/IDRID/train_crop/img/"
label_root="/home/sz/hard_ex_segmentation/IDRID/train_crop/label/"
dataset=data_enhance.get_dataset(img_root,label_root)
train_data=mx.gluon.data.DataLoader(dataset, batch_size,shuffle=True,
last_batch='rollover',num_workers=8)
img_root1="/home/sz/hard_ex_segmentation/IDRID/test_crop/img/"
label_root1="/home/sz/hard_ex_segmentation/IDRID/test_crop/label/"
testset=datatest.get_dataset(img_root1,label_root1)
test_data=mx.gluon.data.DataLoader(testset, batch_size=100,shuffle=False,
last_batch='keep',num_workers=8)
ctx=[mx.gpu(4),mx.gpu(5)]
net=res_u_net_attention.set_network()
net.collect_params().reset_ctx(ctx)
net.hybridize()
def get_batch(batch, ctx):
if isinstance(batch, mx.io.DataBatch):
data = batch.data[0]
label = batch.data[1]
else:
data, label = batch
