def get_value():
modelpath = A.get()
picinit = B.get()
outpath = F.get()
#pic = list(pic)
pic = picinit.split(' ')
sign1 = sign.get()
num_class1 = num_class.get()
if not modelpath or not pic:
change.set('请选择文件')
if modelpath and pic:
change.set('执行中...')
root.update()
#allpic = glob.glob(os.path.join(pic,'*.tif'))
lastpic = pic[-1]
model = Unet((256, 256, 3), num_class1)
#model = myunet((256,256,3),num_class1)
model.load_weights(modelpath)
d, n = os.path.split(lastpic)
lastpic_save = os.path.join(outpath + '/' + n) #最后一个文件
# delete_path = os.path.join(d,'result')#保存文件目录
# if os.path.exists(delete_path):
# pp = os.listdir(delete_path)
# for x in pp:
# delete = os.path.join(delete_path,x)
# os.remove(delete)
# os.removedirs(delete_path)
W = P(num_class1)
W.main_p(model, pic, outpath, changes=sign1)
if os.path.exists(lastpic_save):
change.set('识别完成！')
os.startfile(outpath)
def __init__(self,
image_size,
learning_rate=2e-5,
batch_size=1,
ngf=64,
):
"""
Args:
input_size：list [N, H, W, C]
batch_size: integer, batch size
learning_rate: float, initial learning rate for Adam
ngf: number of base gen filters in conv layer
"""
self.learning_rate = learning_rate
self.input_shape = [int(batch_size / 4), image_size[0], image_size[1], image_size[2]]
self.code_shape = [int(batch_size / 4), int(image_size[0] / 8), int(image_size[1] / 8), 4]
self.ones_code = tf.ones(self.code_shape, name="ones_code")
self.tenaor_name = {}
self.G_X = Unet('G_X', ngf=ngf)
self.D_X = Discriminator('D_X', ngf=ngf)
self.G_Y = Unet('G_Y', ngf=ngf)
self.D_Y = Discriminator('D_Y', ngf=ngf)
self.G_Z = Unet('G_Z', ngf=ngf)
self.D_Z = Discriminator('D_Z', ngf=ngf)
self.G_W = Unet('G_W', ngf=ngf)
self.D_W = Discriminator('D_W', ngf=ngf)
def train(args):
model = Unet(3, 1).to(device)
batch_size = args.batch_size
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters())
liver_dataset = LiverDataset("data/train",transform=x_transforms,target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
train_model(model, criterion, optimizer, dataloaders)
def train(self):
model = Unet(in_ch=2, out_ch=2).to(device)
# batch_size = 1
# criterion = nn.BCEWithLogitsLoss()
criterion = nn.BCELoss()
# criterion = nn.CrossEntropyLoss()
# optimizer = optim.Adam(model.parameters(),lr = 0.01)
optimizer = optim.SGD(model.parameters(), lr=0.005, momentum=0.9)
# data_set = Train_Data(data_root='./train', mask_root='./Train_GT')
self.train_model(model, criterion, optimizer)
def __init__(
self,
input_channels=1,
num_classes=1,
num_filters=[32, 64, 128, 192],
latent_dim=6,
no_convs_fcomb=4,
beta=10.0,
):
super(ProbabilisticUnet, self).__init__()
self.input_channels = input_channels
self.num_classes = num_classes
self.num_filters = num_filters
self.latent_dim = latent_dim
self.no_convs_per_block = 3
self.no_convs_fcomb = no_convs_fcomb
self.initializers = {"w": "he_normal", "b": "normal"}
self.beta = beta
self.z_prior_sample = 0
self.unet = Unet(
self.input_channels,
self.num_classes,
self.num_filters,
self.initializers,
apply_last_layer=False,
padding=True,
).to(device)
self.prior = AxisAlignedConvGaussian(
self.input_channels,
self.num_filters,
self.no_convs_per_block,
self.latent_dim,
self.initializers,
).to(device)
self.posterior = AxisAlignedConvGaussian(
self.input_channels,
self.num_filters,
self.no_convs_per_block,
self.latent_dim,
self.initializers,
posterior=True,
).to(device)
self.fcomb = Fcomb(
self.num_filters,
self.latent_dim,
self.input_channels,
self.num_classes,
self.no_convs_fcomb,
{
"w": "orthogonal",
"b": "normal"
},
use_tile=True,
).to(device)
def train():
model = Unet(5, 2).to(device)
model.train()
batch_size = args.batch_size
criterion = torch.nn.BCELoss()
optimizer = optim.Adam(model.parameters())
PAVE_dataset = SSFPDataset("train", transform=1, target_transform=1)
dataloaders = DataLoader(PAVE_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
train_model(model, criterion, optimizer, dataloaders)
def train():
model = Unet(3, 1).to(device)
batch_size = 8
criterion = torch.nn.BCELoss()
optimizer = optim.Adam(model.parameters())
liver_dataset = LiverDataset("/home/xm/Program/ALL-Data/unetdata/test",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4)
train_model(model, criterion, optimizer, dataloaders)
def train(args):
model = Unet(1, 1).to(device)
batch_size = args.batch_size
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)
liver_dataset = LiverDataset("/gs/home/majg/liupeng/code",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=10)
train_model(model, criterion, optimizer, dataloaders)
def train():
model = Unet(3, 3).to(device)
batch_size = 3
criterion = torch.nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=1e-5)
liver_dataset = LiverDataset("data/train_xin/liver_bmp",
"data/train_xin/mask_bw",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
train_model(model, criterion, optimizer, dataloaders)
def train():
model = Unet(1, 1).to(device)
batch_size = 1
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters())
train_dataset = TrainDataset("dataset/train/image",
"dataset/train/label",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4)
train_model(model, criterion, optimizer, dataloaders)
def train():
model = Unet(3, 1).to(device)
batch_size = args.batch_size
criterion = torch.nn.BCELoss()
optimizer = optim.Adam(model.parameters())
liver_dataset = LiverDataset(
r"H:\BaiduNetdisk\BaiduDownload\u_net_liver-master\data\train",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
train_model(model, criterion, optimizer, dataloaders)
def train():
#logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
model = Unet(3, 1).to(device)
batch_size = 1
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters())
liver_dataset = LiverDataset("data/train",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4)
train_model(model, criterion, optimizer, dataloaders)
def main():
args = build_parser().parse_args()
image_size = [args.img_height, args.img_width]
# config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 1.0
# sess = tf.Session(config=config)
sess = tf.Session()
unet = Unet(input_shape=image_size,
sess=sess,
filter_num=args.filter_num,
batch_norm=args.batch_norm)
unet.build_net()
if args.checkpoint_path:
unet.load_weights(args.checkpoint_path)
images, masks = read_data(args.train_dir,
args.train_mask_dir,
n_images=args.n_images,
image_size=image_size)
val_images, val_masks = read_data(args.val_dir,
args.val_mask_dir,
n_images=args.n_images // 4,
image_size=image_size)
unet.train(images=images,
masks=masks,
val_images=val_images,
val_masks=val_masks,
epochs=args.epochs,
batch_size=args.batch_size,
learning_rate=args.learning_rate,
dice_loss=args.dice_loss,
always_save=args.always_save)
def train():
model = Unet(3, 1).to(device)
model.load_state_dict(torch.load(r"./results/weights4_13_40.pth"))
batch_size = 5
criterion = torch.nn.BCELoss()
optimizer = optim.Adam(model.parameters())
liver_dataset = LiverDataset(r"D:\project\data_sets\data_sci\train",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
train_model(model, criterion, optimizer, dataloaders)
def train():
model = Unet(3, 1).to(device)
#summary(model,(3,512,512))
batch_size = 1
criterion = torch.nn.BCELoss()
optimizer = optim.Adam(model.parameters())
liver_dataset = LiverDataset("data/image",
"data/mask",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4)
train_model(model, criterion, optimizer, dataloaders)
def train():
model = Unet(3, 1).to(device)
#model.load_state_dict(torch.load('./checkpoints/weights_39.pth'))
batch_size = args.batch_size
criterion = torch.nn.BCELoss()
optimizer = optim.Adam(model.parameters())
liver_dataset = LiverDataset("data/train",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4) #4
train_model(model, criterion, optimizer, dataloaders)
def __init__(self,
image_size,
learning_rate=2e-5,
batch_size=1,
ngf=64,
units=4096
):
"""
Args:
input_size：list [H, W, C]
batch_size: integer, batch size
learning_rate: float, initial learning rate for Adam
ngf: number of gen filters in first conv layer
"""
self.learning_rate = learning_rate
self.input_shape = [int(batch_size / 4), image_size[0], image_size[1], image_size[2]]
self.ones = tf.ones(self.input_shape, name="ones")
self.tenaor_name = {}
self.EC_S = VEncoder('EC_S', ngf=ngf, units=units, keep_prob=0.85)
self.DC_S = VDecoder('DC_S', ngf=ngf, output_channl=2, units=units)
self.G_M = Unet('G_M', ngf=ngf / 2, keep_prob=0.9, output_channl=2)
self.D_S = Discriminator('D_S', ngf=ngf, keep_prob=0.85)
self.FD_Z = FeatureDiscriminator('FD_Z', ngf=ngf)
def __init__(
self,
image_size,
learning_rate=2e-5,
batch_size=1,
classes_size=2,
ngf=64,
):
"""
Args:
input_size：list [H, W, C]
batch_size: integer, batch size
learning_rate: float, initial learning rate for Adam
ngf: number of gen filters in first conv layer
"""
self.learning_rate = learning_rate
self.input_shape = [
int(batch_size / 4), image_size[0], image_size[1], image_size[2]
]
self.tenaor_name = {}
self.classes_size = classes_size
self.G_X = Unet('G_X',
ngf=ngf,
output_channl=image_size[2],
keep_prob=0.97)
self.D_X = Discriminator('D_X', ngf=ngf, keep_prob=0.9)
self.G_L_X = Detector('G_L_X',
ngf,
classes_size=classes_size,
keep_prob=0.99,
input_channl=image_size[2])
def infer():
model = Unet(3, 1)
model.load_state_dict(torch.load('weights_19.pth', map_location='cpu'))
liver_dataset = LiverDataset("./../data/val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
import matplotlib.pyplot as plt
plt.ion()
with torch.no_grad():
for x, _ in dataloaders:
y = model(x)
img_y = torch.squeeze(y).numpy()
plt.imshow(img_y)
plt.pause(0.01)
plt.show()
def dnp(run_name,
noisy_file,
samples_dir,
LR=0.001,
num_iter=5000,
save_every=50):
# Initiate model
nlayers = 6
model = Unet(nlayers=nlayers, nefilters=60).cuda()
samples_dir = os.path.join(samples_dir, run_name)
utils.makedirs(samples_dir)
# load data
target, sr = utils.load_wav_to_torch(noisy_file)
target = target[:(len(target) // 2**nlayers) * 2**nlayers]
target = target / utils.MAX_WAV_VALUE
input = torch.rand_like(target)
input = (input - 0.5) * 2
target, input = target.cuda(), input.cuda()
criterion = torch.nn.MSELoss()
# Initialize accumulator
nfft = 512
residual = 10**(-30 / 10) # -18 db lower gain
low_cut = 10
high_cut = 90
center = False
bandpass = int(round(3 / 512 * nfft))
accumulator = utils.Accumulator(target, low_cut, high_cut, nfft, center,
residual, sr, bandpass)
# Run the algorithm
optimize(model, criterion, input, target, samples_dir, LR, num_iter, sr,
save_every, accumulator)
def __init__(self,
input_channels=1,
num_classes=1,
num_filters=[32, 64, 128, 192],
latent_dim=6,
no_convs_fcomb=4,
beta=10.0):
super(ProbabilisticUnet, self).__init__()
self.input_channels = input_channels # 输入图像通道数
self.num_classes = num_classes # 分割类别数
self.num_filters = num_filters # filter数
self.latent_dim = latent_dim # 隐空间维度
self.no_convs_per_block = 3
self.no_convs_fcomb = no_convs_fcomb
self.initializers = {'w': 'he_normal', 'b': 'normal'} # 初始化
self.beta = beta
self.z_prior_sample = 0
self.unet = Unet(self.input_channels,
self.num_classes,
self.num_filters,
self.initializers,
apply_last_layer=False,
padding=True).to(device)
self.prior = AxisAlignedConvGaussian(
self.input_channels,
self.num_filters,
self.no_convs_per_block,
self.latent_dim,
self.initializers,
).to(device)
self.posterior = AxisAlignedConvGaussian(self.input_channels,
self.num_filters,
self.no_convs_per_block,
self.latent_dim,
self.initializers,
posterior=True).to(device)
self.fcomb = Fcomb(self.num_filters,
self.latent_dim,
self.input_channels,
self.num_classes,
self.no_convs_fcomb, {
'w': 'orthogonal',
'b': 'normal'
},
use_tile=True).to(device)
def test_unet():
model = Unet(
layers_n_channels=[4, 8],
layers_n_non_lins=1,
)
shape = [1, 32, 32, 1]
res = model(tf.zeros(shape))
assert res.shape.as_list() == shape
def train(args):
model = Unet(3, 1).to(device)
#begin add
# checkpoint = torch.load("./weights_19.pth",map_location=device)
# model.load_state_dict(checkpoint['model_state_dict'])
#end add
batch_size = args.batch_size
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters())
liver_dataset = LiverDataset("./data/train",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4)
train_model(model, criterion, optimizer, dataloaders)
def train(args):
model = Unet(3, 1).to(device) # 输入3通道，输出1通道
batch_size = args.batch_size
criterion = nn.BCEWithLogitsLoss() # 损失函数
optimizer = optim.Adam(model.parameters()) # 获得模型的参数
liver_dataset = LiverDataset("data/train",
transform=x_transforms,
target_transform=y_transforms)
# 加载数据集，返回的是一对原图+掩膜，和所有图片的数目
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4)
# DataLoader接口是自定义数据接口输出输入接口，将已有的数据输入按照batch size封装成Tensor
# batch_size=4,epoch=10,共100个minbatch
# shuffle，每个epoch将数据打乱
# num_workers: 多个进程倒入数据，加速倒入速度
train_model(model, criterion, optimizer, dataloaders) # 训练
def train(args):
model = Unet(3, 1).to(device)
batch_size = args.batch_size
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters())
liver_dataset = LiverDataset("data/train",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
# shuffle = True, # 乱序
# num_workers = 2 # 多进程
# DataLoader:该接口主要用来将自定义的数据读取接口的输出或者PyTorch已有的数据读取接口的输入按照batch size封装成Tensor
# batch_size：how many samples per minibatch to load，这里为4，数据集大小400，所以一共有100个minibatch
# shuffle:每个epoch将数据打乱，这里epoch=10。一般在训练数据中会采用
# num_workers：表示通过多个进程来导入数据，可以加快数据导入速度
train_model(model, criterion, optimizer, dataloaders)
def main(argv):
with CytomineJob.from_cli(argv) as job:
model_path = os.path.join(str(Path.home()), "models", "thyroid-unet")
model_filepath = pick_model(model_path, job.parameters.tile_size,
job.parameters.cytomine_zoom_level)
device = torch.device(job.parameters.device)
unet = Unet(job.parameters.init_fmaps, n_classes=1)
unet.load_state_dict(torch.load(model_filepath, map_location=device))
unet.to(device)
unet.eval()
segmenter = UNetSegmenter(device=job.parameters.device,
unet=unet,
classes=[0, 1],
threshold=job.parameters.threshold)
working_path = os.path.join(str(Path.home()), "tmp")
tile_builder = CytomineTileBuilder(working_path)
builder = SSLWorkflowBuilder()
builder.set_n_jobs(1)
builder.set_overlap(job.parameters.tile_overlap)
builder.set_tile_size(job.parameters.tile_size,
job.parameters.tile_size)
builder.set_tile_builder(tile_builder)
builder.set_border_tiles(Workflow.BORDER_TILES_EXTEND)
builder.set_background_class(0)
builder.set_distance_tolerance(1)
builder.set_seg_batch_size(job.parameters.batch_size)
builder.set_segmenter(segmenter)
workflow = builder.get()
slide = CytomineSlide(img_instance=ImageInstance().fetch(
job.parameters.cytomine_id_image),
zoom_level=job.parameters.cytomine_zoom_level)
results = workflow.process(slide)
print("-------------------------")
print(len(results))
print("-------------------------")
collection = AnnotationCollection()
for obj in results:
wkt = shift_poly(obj.polygon,
slide,
zoom_level=job.parameters.cytomine_zoom_level).wkt
collection.append(
Annotation(location=wkt,
id_image=job.parameters.cytomine_id_image,
id_terms=[154005477],
id_project=job.project.id))
collection.save(n_workers=job.parameters.n_jobs)
return {}
def train(args):
model = Unet(3, 1).to(device)
batch_size = args.batch_size
LR = 0.005
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=0.0005)
criterion = nn.BCEWithLogitsLoss()
lr_list = []
liver_dataset = LiverDataset("data/train",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4)
train_model(model, criterion, optimizer, dataloaders)
def main():
args = build_parser().parse_args()
assert args.checkpoint_path
result_dir = args.result_dir
checkpoint_path = args.checkpoint_path
test_dir = args.test_dir
n_imgs = args.n_images
image_size = [args.img_height, args.img_width]
sess = tf.Session()
unet = Unet(input_shape=image_size,
sess=sess,
filter_num=args.filter_num,
batch_norm=args.batch_norm)
unet.build_net(is_train=False)
unet.load_weights(checkpoint_path)
img_names = os.listdir(test_dir)
img_names.sort()
mask_names = None
total_dice = None
if args.mask_dir:
mask_names = os.listdir(args.mask_dir)
mask_names.sort()
total_dice = 0
if n_imgs <= 0:
n_imgs = len(img_names)
for i in range(n_imgs):
print('%s %d/%d' % (img_names[i], i, n_imgs))
img_mat = read_car_img(os.path.join(test_dir, img_names[i]),
image_size=image_size)
img_mat = np.expand_dims(img_mat, axis=0)
if mask_names:
mask_mat = read_mask_img(os.path.join(args.mask_dir,
mask_names[i]),
image_size=image_size)
mask_mat = np.expand_dims(mask_mat, axis=0)
res, dice = unet.predict_test(img_mat, mask_mat)
dice = np.mean(dice)
print('Dice coefficient:%.6f' % dice)
total_dice += dice
else:
res = unet.predict(img_mat)
if args.result_dir:
res = res.reshape(image_size)
misc.imsave(os.path.join(result_dir, img_names[i]), res)
if total_dice:
print('Average Dice coefficient:%.6f' % (total_dice / n_imgs))
def test():
model = Unet(3, 1).to(device) # unet输入是三通道，输出是一通道，因为不算上背景只有肝脏一个类别
weight_pre = r"./results/weights4_18_35.pth"
model.load_state_dict(torch.load(weight_pre)) # 载入训练好的模型
liver_dataset = LiverDataset(r"D:\project\data_sets\data_sci\val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion() # 开启动态模式
with torch.no_grad():
i = 0 # 验证集中第i张图
miou_total = 0
num = len(dataloaders) # 验证集图片的总数
for x, _ in dataloaders:
x = x.to(device)
y = model(x)
img_y = torch.squeeze(y).cpu().numpy(
) # 输入损失函数之前要把预测图变成numpy格式，且为了跟训练图对应，要额外加多一维表示batchsize
mask = get_data(i)[1] # 得到当前mask的路径
miou_total += get_iou(mask, img_y) # 获取当前预测图的miou，并加到总miou中
plt.subplot(121)
plt.imshow(Image.open(get_data(i)[0]))
plt.subplot(122)
plt.imshow(img_y)
plt.pause(0.01)
if i < num: i += 1 # 处理验证集下一张图
plt.show()
print('Miou=%f' % (miou_total / 10))
res_record("weights4_13_40.pth Miou=%f \n" % (miou_total / 10))
def test():
model = Unet(3, 1).to(device) #unet输入是三通道，输出是一通道，因为不算上背景只有肝脏一个类别
model.load_state_dict(torch.load(args.ckp, map_location='cpu')) #载入训练好的模型
liver_dataset = LiverDataset(
r"H:\BaiduNetdisk\BaiduDownload\u_net_liver-master\data\val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion() #开启动态模式
with torch.no_grad():
i = 0 #验证集中第i张图
miou_total = 0
num = len(dataloaders) #验证集图片的总数
for x, _ in dataloaders:
x = x.to(device)
y = model(x)
img_y = torch.squeeze(y).cpu().numpy(
) #输入损失函数之前要把预测图变成numpy格式，且为了跟训练图对应，要额外加多一维表示batchsize
mask = get_data(i)[1] #得到当前mask的路径
miou_total += get_iou(mask, img_y) #获取当前预测图的miou，并加到总miou中
plt.subplot(121)
plt.imshow(Image.open(get_data(i)[0]))
plt.subplot(122)
plt.imshow(img_y)
plt.pause(0.01)
if i < num: i += 1 #处理验证集下一张图
plt.show()
print('Miou=%f' % (miou_total / 20))
