def main(args):
in_channels = 3
in_height = 224
in_width = 224
print("Importing ONNX model")
net = eddl.import_net_from_onnx_file(args.model_fn,
[in_channels, in_height, in_width])
# Add a softmax layer to get probabilities directly from the model
input_ = net.lin[0] # getLayer(net,"input_layer_name")
output = net.lout[0] # getLayer(net,"output_layer_name")
new_output = eddl.Softmax(output)
net = eddl.Model([input_], [new_output])
eddl.build(
net,
eddl.adam(0.001), # not used for prediction
["softmax_cross_entropy"], # not used for prediction
["categorical_accuracy"], # not used for prediction
eddl.CS_GPU() if args.gpu else eddl.CS_CPU(),
False # Disable model initialization, we want to use the ONNX weights
)
eddl.summary(net)
image = Tensor.load(args.img_fn)
image_preprocessed = preprocess_input_resnet34(image,
[in_height, in_width])
outputs = eddl.predict(net, [image_preprocessed])
print("Reading class names...")
with open(args.classes_fn, "rt") as f:
class_names = [_.strip() for _ in f]
print("Top 5 predictions:")
print(eddl.get_topk_predictions(outputs[0], class_names, 5))
def main(args):
eddl.download_mnist()
in_ = eddl.Input([784])
layer = in_
layer = eddl.Activation(eddl.Dense(layer, 256), "relu")
layer = eddl.Activation(eddl.Dense(layer, 128), "relu")
layer = eddl.Activation(eddl.Dense(layer, 64), "relu")
layer = eddl.Activation(eddl.Dense(layer, 128), "relu")
layer = eddl.Activation(eddl.Dense(layer, 256), "relu")
out = eddl.Dense(layer, 784)
net = eddl.Model([in_], [out])
eddl.build(
net, eddl.sgd(0.001, 0.9), ["mean_squared_error"],
["mean_squared_error"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem))
eddl.summary(net)
eddl.plot(net, "model.pdf")
x_train = Tensor.load("mnist_trX.bin")
if args.small:
x_train = x_train.select([":6000"])
x_train.div_(255.0)
eddl.fit(net, [x_train], [x_train], args.batch_size, args.epochs)
tout = eddl.predict(net, [x_train])
tout[0].info()
print("All done")
def main(args):
if not os.path.isfile(args.input):
raise RuntimeError("input file '%s' not found" % args.input)
eddl.download_mnist()
net = eddl.import_net_from_onnx_file(args.input)
eddl.build(
net,
eddl.rmsprop(0.01),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem),
False # do not initialize weights to random values
)
net.resize(args.batch_size) # resize manually since we don't use "fit"
eddl.summary(net)
x_test = Tensor.load("mnist_tsX.bin")
x_test.div_(255.0)
sys.stderr.write("forward...\n")
eddl.forward(net, [x_test])
sys.stderr.write("forward done\n")
sys.stderr.write("lout: %r\n" % (net.lout, ))
out = eddl.getOut(net)
sys.stderr.write("getOut done\n")
sys.stderr.write("out: %r\n" % (out, ))
print("All done")
def main(args):
eddl.download_mnist()
in_ = eddl.Input([784])
layer = in_
layer = eddl.Activation(eddl.Dense(layer, 256), "relu")
layer = eddl.Activation(eddl.Dense(layer, 128), "relu")
layer = eddl.Activation(eddl.Dense(layer, 64), "relu")
layer = eddl.Activation(eddl.Dense(layer, 128), "relu")
layer = eddl.Activation(eddl.Dense(layer, 256), "relu")
out = eddl.Dense(layer, 784)
net = eddl.Model([in_], [out])
mse_loss = MSELoss()
mse_metric = MSEMetric()
net.build(
eddl.sgd(0.001, 0.9), [mse_loss], [mse_metric],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem))
eddl.summary(net)
eddl.plot(net, "model.pdf")
x_train = Tensor.load("mnist_trX.bin")
x_train.div_(255.0)
eddl.fit(net, [x_train], [x_train], args.batch_size, args.epochs)
print("All done")
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.Reshape(layer, [1, 28, 28])
layer = eddl.RandomCropScale(layer, [0.9, 1.0])
layer = eddl.Reshape(layer, [-1])
layer = eddl.ReLu(
eddl.GaussianNoise(
eddl.BatchNormalization(eddl.Dense(layer, 1024), True), 0.3))
layer = eddl.ReLu(
eddl.GaussianNoise(
eddl.BatchNormalization(eddl.Dense(layer, 1024), True), 0.3))
layer = eddl.ReLu(
eddl.GaussianNoise(
eddl.BatchNormalization(eddl.Dense(layer, 1024), True), 0.3))
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net, eddl.sgd(0.01, 0.9), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem))
eddl.summary(net)
eddl.plot(net, "model.pdf")
x_train = Tensor.load("mnist_trX.bin")
y_train = Tensor.load("mnist_trY.bin")
x_test = Tensor.load("mnist_tsX.bin")
y_test = Tensor.load("mnist_tsY.bin")
if args.small:
x_train = x_train.select([":6000"])
y_train = y_train.select([":6000"])
x_test = x_test.select([":1000"])
y_test = y_test.select([":1000"])
x_train.div_(255.0)
x_test.div_(255.0)
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
# LR annealing
if args.epochs < 4:
return
eddl.setlr(net, [0.005, 0.9])
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs // 2)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
eddl.setlr(net, [0.001, 0.9])
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs // 2)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
eddl.setlr(net, [0.0001, 0.9])
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs // 4)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
print("All done")
def main(args):
if not os.path.isfile(args.input):
raise RuntimeError("input file '%s' not found" % args.input)
eddl.download_mnist()
print("importing net from", args.input)
net = eddl.import_net_from_onnx_file(args.input)
print("input.shape:", net.layers[0].input.shape)
print("output size =", len(net.lout))
eddl.build(
net,
eddl.rmsprop(0.01),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem),
False # do not initialize weights to random values
)
net.resize(args.batch_size) # resize manually since we don't use "fit"
eddl.summary(net)
x_test = Tensor.load("mnist_tsX.bin")
y_test = Tensor.load("mnist_tsY.bin")
x_test.div_(255.0)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
print("All done")
def main(args):
slide_fn = args.slide_fn
level = args.level
## Load model
net = models.tissue_detector_DNN()
eddl.build(net, eddl.rmsprop(0.00001), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU() if args.gpu else eddl.CS_CPU())
eddl.load(net, args.weights_fn, "bin")
eddl.summary(net)
## Load Slide
slide_T, s = read_slide(slide_fn, level)
## Compute tissue mask
#len_T = slide_T.getShape()[0]
#bs = args.batch_size
#nb = int(np.ceil((len_T / bs)))
#print ("n. batches: %d" % nb)
#output_l = []
#for b in range(nb):
# start = bs*b
# stop = bs*(b+1) if bs*(b+1) < len_T else len_T
# b_T = slide_T.select(["%d:%d" % (start, stop)])
output_l = eddl.predict(net, [slide_T])
print(output_l)
## Binarize the output
mask = get_mask(output_l, s, args.threshold)
np.save("mask", mask)
def main(args):
eddl.download_cifar10()
num_classes = 10
in_ = eddl.Input([3, 32, 32])
layer = in_
layer = eddl.RandomCropScale(layer, [0.8, 1.0])
layer = eddl.RandomHorizontalFlip(layer)
layer = eddl.ReLu(BG(eddl.Conv(layer, 64, [3, 3], [1, 1], "same", False)))
layer = eddl.Pad(layer, [0, 1, 1, 0])
for i in range(3):
layer = ResBlock(layer, 64, 0, i == 0)
for i in range(4):
layer = ResBlock(layer, 128, i == 0)
for i in range(6):
layer = ResBlock(layer, 256, i == 0)
for i in range(3):
layer = ResBlock(layer, 512, i == 0)
layer = eddl.MaxPool(layer, [4, 4])
layer = eddl.Reshape(layer, [-1])
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net, eddl.sgd(0.001, 0.9), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem))
eddl.summary(net)
eddl.plot(net, "model.pdf", "TB")
x_train = Tensor.load("cifar_trX.bin")
y_train = Tensor.load("cifar_trY.bin")
x_train.div_(255.0)
x_test = Tensor.load("cifar_tsX.bin")
y_test = Tensor.load("cifar_tsY.bin")
x_test.div_(255.0)
if args.small:
# this is slow, make it really small
x_train = x_train.select([":500"])
y_train = y_train.select([":500"])
x_test = x_test.select([":100"])
y_test = y_test.select([":100"])
lr = 0.01
for j in range(3):
lr /= 10.0
eddl.setlr(net, [lr, 0.9])
for i in range(args.epochs):
eddl.fit(net, [x_train], [y_train], args.batch_size, 1)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
print("All done")
def main(args):
eddl.download_cifar10()
num_classes = 10
in_ = eddl.Input([3, 32, 32])
layer = in_
layer = eddl.MaxPool(eddl.ReLu(Normalization(
eddl.Conv(layer, 32, [3, 3], [1, 1])
)), [2, 2])
layer = eddl.MaxPool(eddl.ReLu(Normalization(
eddl.Conv(layer, 64, [3, 3], [1, 1])
)), [2, 2])
layer = eddl.MaxPool(eddl.ReLu(Normalization(
eddl.Conv(layer, 128, [3, 3], [1, 1])
)), [2, 2])
layer = eddl.MaxPool(eddl.ReLu(Normalization(
eddl.Conv(layer, 256, [3, 3], [1, 1])
)), [2, 2])
layer = eddl.GlobalMaxPool(layer)
layer = eddl.Flatten(layer)
layer = eddl.Activation(eddl.Dense(layer, 128), "relu")
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.adam(0.001),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)
)
eddl.summary(net)
eddl.plot(net, "model.pdf")
x_train = Tensor.load("cifar_trX.bin")
y_train = Tensor.load("cifar_trY.bin")
x_train.div_(255.0)
x_test = Tensor.load("cifar_tsX.bin")
y_test = Tensor.load("cifar_tsY.bin")
x_test.div_(255.0)
if args.small:
x_train = x_train.select([":5000"])
y_train = y_train.select([":5000"])
x_test = x_test.select([":1000"])
y_test = y_test.select([":1000"])
for i in range(args.epochs):
eddl.fit(net, [x_train], [y_train], args.batch_size, 1)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
print("All done")
def main(args):
eddl.download_cifar10()
num_classes = 10
in_ = eddl.Input([3, 32, 32])
layer = in_
layer = eddl.RandomCropScale(layer, [0.8, 1.0])
layer = eddl.RandomFlip(layer, 1)
layer = eddl.ReLu(BG(eddl.Conv(layer, 64, [3, 3], [1, 1])))
layer = eddl.Pad(layer, [0, 1, 1, 0])
layer = ResBlock(layer, 64, 2, True)
layer = ResBlock(layer, 64, 2, False)
layer = ResBlock(layer, 128, 2, True)
layer = ResBlock(layer, 128, 2, False)
layer = ResBlock(layer, 256, 2, True)
layer = ResBlock(layer, 256, 2, False)
layer = ResBlock(layer, 256, 2, True)
layer = ResBlock(layer, 256, 2, False)
layer = eddl.Reshape(layer, [-1])
layer = eddl.ReLu(BG(eddl.Dense(layer, 512)))
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.sgd(0.01, 0.9),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)
)
eddl.summary(net)
eddl.plot(net, "model.pdf", "TB")
x_train = Tensor.load("cifar_trX.bin")
y_train = Tensor.load("cifar_trY.bin")
x_train.div_(255.0)
x_test = Tensor.load("cifar_tsX.bin")
y_test = Tensor.load("cifar_tsY.bin")
x_test.div_(255.0)
if args.small:
x_train = x_train.select([":5000"])
y_train = y_train.select([":5000"])
x_test = x_test.select([":1000"])
y_test = y_test.select([":1000"])
for i in range(args.epochs):
eddl.fit(net, [x_train], [y_train], args.batch_size, 1)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
print("All done")
def test_build_net(opt_cls):
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.LeakyReLu(eddl.Dense(layer, 1024))
layer = eddl.LeakyReLu(eddl.Dense(layer, 1024))
layer = eddl.LeakyReLu(eddl.Dense(layer, 1024))
out = eddl.Softmax(eddl.Dense(layer, num_classes), -1)
net = eddl.Model([in_], [out])
eddl.build(net, opt_cls(0.01), ["soft_cross_entropy"],
["categorical_accuracy"], eddl.CS_CPU(mem="low_mem"))
def main(args):
eddl.download_cifar10()
num_classes = 10
in_ = eddl.Input([3, 32, 32])
layer = in_
layer = eddl.RandomCropScale(layer, [0.8, 1.0])
layer = eddl.RandomFlip(layer, 1)
layer = eddl.RandomCutout(layer, [0.1, 0.3], [0.1, 0.3])
layer = eddl.MaxPool(Block3_2(layer, 64))
layer = eddl.MaxPool(Block3_2(layer, 128))
layer = eddl.MaxPool(Block1(Block3_2(layer, 256), 256))
layer = eddl.MaxPool(Block1(Block3_2(layer, 512), 512))
layer = eddl.MaxPool(Block1(Block3_2(layer, 512), 512))
layer = eddl.Reshape(layer, [-1])
layer = eddl.Activation(eddl.Dense(layer, 512), "relu")
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.sgd(0.001, 0.9),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)
)
eddl.setlogfile(net, "vgg16")
eddl.summary(net)
eddl.plot(net, "model.pdf")
x_train = Tensor.load("cifar_trX.bin")
y_train = Tensor.load("cifar_trY.bin")
x_train.div_(255.0)
x_test = Tensor.load("cifar_tsX.bin")
y_test = Tensor.load("cifar_tsY.bin")
x_test.div_(255.0)
if args.small:
x_train = x_train.select([":5000"])
y_train = y_train.select([":5000"])
x_test = x_test.select([":1000"])
y_test = y_test.select([":1000"])
for i in range(args.epochs):
eddl.fit(net, [x_train], [y_train], args.batch_size, 1)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
print("All done")
def load_model(self, model_weights):
## Load the ANN tissue detector model implemented by using pyeddl
## Create ANN topology (Same used during training phase)
## The model is assumed being trained by using RGB triplets.
net = models.tissue_detector_DNN()
eddl.build(net, eddl.rmsprop(0.00001), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU() if self.gpu else eddl.CS_CPU())
# Load weights
eddl.load(net, model_weights, "bin")
self.model = net
def get_net(net_name='vgg16',
in_size=[256, 256],
num_classes=2,
lr=1e-5,
augs=False,
gpus=[1],
lsb=1,
init=eddl.HeNormal,
dropout=None,
l2_reg=None):
## Network definition
in_ = eddl.Input([3, in_size[0], in_size[1]])
if net_name == 'vgg16':
out = models.VGG16_promort(in_,
num_classes,
init=init,
l2_reg=l2_reg,
dropout=dropout)
else:
print('model %s not available' % net_name)
sys.exit(-1)
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.rmsprop(lr),
["soft_cross_entropy"],
["categorical_accuracy"],
#eddl.CS_GPU([1,1], mem="low_mem") if gpu else eddl.CS_CPU()
eddl.CS_GPU(gpus, mem="low_mem", lsb=lsb) if gpus else eddl.CS_CPU()
#eddl.CS_GPU(gpus, mem="low_mem") if gpus else eddl.CS_CPU()
)
eddl.summary(net)
eddl.setlogfile(net, "promort_VGG16_classification")
if augs:
## Set augmentations
training_augs = ecvl.SequentialAugmentationContainer(
[ecvl.AugMirror(.5),
ecvl.AugFlip(.5),
ecvl.AugRotate([-10, 10])])
validation_augs = ecvl.SequentialAugmentationContainer([])
dataset_augs = [training_augs, validation_augs, None]
else:
dataset_augs = [None, None, None]
return net, dataset_augs
def main(args):
eddl.download_mnist()
in_ = eddl.Input([784])
target = eddl.Reshape(in_, [1, 28, 28])
layer = in_
layer = eddl.Reshape(layer, [1, 28, 28])
layer = eddl.ReLu(eddl.Conv(layer, 8, [3, 3]))
layer = eddl.ReLu(eddl.Conv(layer, 16, [3, 3]))
layer = eddl.ReLu(eddl.Conv(layer, 8, [3, 3]))
out = eddl.Sigmoid(eddl.Conv(layer, 1, [3, 3]))
net = eddl.Model([in_], [])
eddl.build(
net,
eddl.adam(0.001),
[],
[],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)
)
eddl.summary(net)
x_train = Tensor.load("mnist_trX.bin")
if args.small:
x_train = x_train.select([":6000"])
x_train.div_(255.0)
mse = eddl.newloss(mse_loss, [out, target], "mse_loss")
dicei = eddl.newloss(dice_loss_img, [out, target], "dice_loss_img")
dicep = eddl.newloss(dice_loss_pixel, [out, target], "dice_loss_pixel")
batch = Tensor([args.batch_size, 784])
num_batches = x_train.shape[0] // args.batch_size
for i in range(args.epochs):
print("Epoch %d/%d (%d batches)" % (i + 1, args.epochs, num_batches))
diceploss = 0.0
diceiloss = 0.0
mseloss = 0
for j in range(num_batches):
print("Batch %d " % j, end="", flush=True)
eddl.next_batch([x_train], [batch])
eddl.zeroGrads(net)
eddl.forward(net, [batch])
diceploss += eddl.compute_loss(dicep) / args.batch_size
print("diceploss = %.6f " % (diceploss / (j + 1)), end="")
diceiloss += eddl.compute_loss(dicei) / args.batch_size
print("diceiloss = %.6f " % (diceiloss / (j + 1)), end="")
mseloss += eddl.compute_loss(mse) / args.batch_size
print("mseloss = %.6f\r" % (mseloss / (j + 1)), end="")
eddl.optimize(dicep)
eddl.update(net)
print()
print("All done")
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.Reshape(layer, [1, 784]) # image as a 1D signal with depth 1
layer = eddl.MaxPool1D(eddl.ReLu(eddl.Conv1D(layer, 16, [3], [1])), [4],
[4])
layer = eddl.MaxPool1D(
eddl.ReLu(eddl.Conv1D(layer, 32, [3], [1])),
[4],
[4],
)
layer = eddl.MaxPool1D(
eddl.ReLu(eddl.Conv1D(layer, 64, [3], [1])),
[4],
[4],
)
layer = eddl.MaxPool1D(
eddl.ReLu(eddl.Conv1D(layer, 64, [3], [1])),
[4],
[4],
)
layer = eddl.Reshape(layer, [-1])
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net, eddl.rmsprop(0.01), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem))
eddl.summary(net)
x_train = Tensor.load("mnist_trX.bin")
y_train = Tensor.load("mnist_trY.bin")
x_test = Tensor.load("mnist_tsX.bin")
y_test = Tensor.load("mnist_tsY.bin")
if args.small:
x_train = x_train.select([":6000"])
y_train = y_train.select([":6000"])
x_test = x_test.select([":1000"])
y_test = y_test.select([":1000"])
x_train.div_(255.0)
x_test.div_(255.0)
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
print("All done")
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.Activation(eddl.Dense(layer, 1024), "relu")
layer = eddl.Activation(eddl.Dense(layer, 1024), "relu")
layer = eddl.Activation(eddl.Dense(layer, 1024), "relu")
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
acc = CategoricalAccuracy()
net.build(
eddl.sgd(0.01, 0.9),
[eddl.getLoss("soft_cross_entropy")],
[acc],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)
)
eddl.summary(net)
eddl.plot(net, "model.pdf")
x_train = Tensor.load("mnist_trX.bin")
y_train = Tensor.load("mnist_trY.bin")
x_test = Tensor.load("mnist_tsX.bin")
# y_test = Tensor.load("mnist_tsY.bin")
x_train.div_(255.0)
x_test.div_(255.0)
num_samples = x_train.shape[0]
num_batches = num_samples // args.batch_size
test_samples = x_test.shape[0]
test_batches = test_samples // args.batch_size
eddl.set_mode(net, TRMODE)
for i in range(args.epochs):
for j in range(num_batches):
print("Epoch %d/%d (batch %d/%d)" %
(i + 1, args.epochs, j + 1, num_batches))
indices = np.random.randint(0, num_samples, args.batch_size)
eddl.train_batch(net, [x_train], [y_train], indices)
for j in range(test_batches):
print("Epoch %d/%d (batch %d/%d)" %
(i + 1, args.epochs, j + 1, test_batches))
indices = np.random.randint(0, num_samples, args.batch_size)
eddl.eval_batch(net, [x_train], [y_train], indices)
print("All done")
def get_net(in_size=[256, 256], num_classes=2, lr=1e-5, gpu=True):
## Network definition
in_ = eddl.Input([3, in_size[0], in_size[1]])
out = models.VGG16_promort(in_, num_classes)
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.rmsprop(lr),
["soft_cross_entropy"],
["categorical_accuracy"],
#eddl.CS_GPU([1,1], mem="low_mem") if gpu else eddl.CS_CPU()
eddl.CS_GPU([1], mem="low_mem") if gpu else eddl.CS_CPU())
return net
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.Reshape(layer, [-1])
layer = eddl.ReLu(eddl.Dense(layer, 1024))
layer = eddl.BatchNormalization(layer, True)
layer = eddl.ReLu(eddl.Dense(layer, 1024))
layer = eddl.BatchNormalization(layer, True)
layer = eddl.ReLu(eddl.Dense(layer, 1024))
layer = eddl.BatchNormalization(layer, True)
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.rmsprop(0.01),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem),
True # initialize weights to random values
)
eddl.summary(net)
x_train = Tensor.load("mnist_trX.bin")
y_train = Tensor.load("mnist_trY.bin")
x_test = Tensor.load("mnist_tsX.bin")
y_test = Tensor.load("mnist_tsY.bin")
if args.small:
x_train = x_train.select([":6000"])
y_train = y_train.select([":6000"])
x_test = x_test.select([":1000"])
y_test = y_test.select([":1000"])
x_train.div_(255.0)
x_test.div_(255.0)
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
eddl.save_net_to_onnx_file(net, args.output)
print("saved net to", args.output)
print("All done")
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([28])
layer = in_
layer = eddl.LeakyReLu(eddl.Dense(layer, 32))
layer = eddl.L2(eddl.LSTM(layer, 128), 0.001)
ls = layer
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net, eddl.rmsprop(0.001), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem))
eddl.summary(net)
eddl.plot(net, "model.pdf")
x_train = Tensor.load("mnist_trX.bin")
y_train = Tensor.load("mnist_trY.bin")
x_test = Tensor.load("mnist_tsX.bin")
y_test = Tensor.load("mnist_tsY.bin")
if args.small:
x_train = x_train.select([":6000"])
y_train = y_train.select([":6000"])
x_test = x_test.select([":1000"])
y_test = y_test.select([":1000"])
x_train.reshape_([x_train.shape[0], 28, 28])
x_test.reshape_([x_test.shape[0], 28, 28])
y_train.reshape_([y_train.shape[0], 1, 10])
y_test.reshape_([y_test.shape[0], 1, 10])
x_train.div_(255.0)
x_test.div_(255.0)
for i in range(args.epochs):
eddl.fit(net, [x_train], [y_train], args.batch_size, 1)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
ls_in = eddl.getInput(ls)
ls_in.info()
ls_out = eddl.getOutput(ls)
ls_out.info()
print("All done")
def main(args):
eddl.download_imdb_2000()
epochs = 2 if args.small else 10
length = 250
embdim = 33
vocsize = 2000
in_ = eddl.Input([1]) # 1 word
layer = in_
layer = eddl.RandomUniform(eddl.Embedding(layer, vocsize, 1, embdim),
-0.05, 0.05)
layer = eddl.GRU(layer, 37)
layer = eddl.ReLu(eddl.Dense(layer, 256))
out = eddl.Sigmoid(eddl.Dense(layer, 1))
net = eddl.Model([in_], [out])
eddl.build(
net, eddl.adam(0.001), ["cross_entropy"], ["binary_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem))
eddl.summary(net)
x_train = Tensor.load("imdb_2000_trX.bin")
y_train = Tensor.load("imdb_2000_trY.bin")
x_test = Tensor.load("imdb_2000_tsX.bin")
y_test = Tensor.load("imdb_2000_tsY.bin")
# batch x timesteps x input_dim
x_train.reshape_([x_train.shape[0], length, 1])
x_test.reshape_([x_test.shape[0], length, 1])
y_train.reshape_([y_train.shape[0], 1, 1])
y_test.reshape_([y_test.shape[0], 1, 1])
if args.small:
x_train = x_train.select([":64", ":", ":"])
y_train = y_train.select([":64", ":", ":"])
x_test = x_test.select([":64", ":", ":"])
y_test = y_test.select([":64", ":", ":"])
for i in range(epochs):
eddl.fit(net, [x_train], [y_train], args.batch_size, 1)
eddl.evaluate(net, [x_test], [y_test])
print("All done")
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.BatchNormalization(
eddl.Activation(eddl.L2(eddl.Dense(layer, 1024), 0.0001), "relu"), True
)
layer = eddl.BatchNormalization(
eddl.Activation(eddl.L2(eddl.Dense(layer, 1024), 0.0001), "relu"), True
)
layer = eddl.BatchNormalization(
eddl.Activation(eddl.L2(eddl.Dense(layer, 1024), 0.0001), "relu"), True
)
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
acc = CategoricalAccuracy()
net.build(
eddl.sgd(0.01, 0.9),
[eddl.getLoss("soft_cross_entropy")],
[acc],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)
)
eddl.summary(net)
eddl.plot(net, "model.pdf")
x_train = Tensor.load("mnist_trX.bin")
y_train = Tensor.load("mnist_trY.bin")
x_test = Tensor.load("mnist_tsX.bin")
y_test = Tensor.load("mnist_tsY.bin")
x_train.div_(255.0)
x_test.div_(255.0)
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
print("All done")
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.ReLu(eddl.L2(eddl.Dense(layer, 1024), 0.0001))
layer = eddl.ReLu(eddl.L1(eddl.Dense(layer, 1024), 0.0001))
layer = eddl.ReLu(eddl.L1L2(eddl.Dense(layer, 1024), 0.00001, 0.0001))
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.sgd(0.01, 0.9),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)
)
eddl.summary(net)
eddl.plot(net, "model.pdf")
x_train = Tensor.load("mnist_trX.bin")
y_train = Tensor.load("mnist_trY.bin")
x_test = Tensor.load("mnist_tsX.bin")
y_test = Tensor.load("mnist_tsY.bin")
if args.small:
x_train = x_train.select([":6000"])
y_train = y_train.select([":6000"])
x_test = x_test.select([":1000"])
y_test = y_test.select([":1000"])
x_train.div_(255.0)
x_test.div_(255.0)
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
print("All done")
def main(args):
## Read input dataset
x_train, y_train, x_test, y_test = read_input(args.in_ds)
## Net architecture
net = models.tissue_detector_DNN()
## Net compilation
eddl.build(
net,
eddl.rmsprop(0.00001),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU() if args.gpu else eddl.CS_CPU()
)
eddl.summary(net)
## Fit and evaluation
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs)
eddl.evaluate(net, [x_test], [y_test])
eddl.save(net, "tissue_detector_model.bin")
def main(args):
size = 256 // 2
# Conv3D expects (B, C, dim1, dim2, dim3)
in_ = eddl.Input([3, 10, size, size])
layer = in_
layer = eddl.MaxPool3D(eddl.ReLu(eddl.Conv3D(
layer, 4, [1, 3, 3], [1, 1, 1], "same"
)), [1, 2, 2], [1, 2, 2], "same")
layer = eddl.MaxPool3D(eddl.ReLu(eddl.Conv3D(
layer, 8, [1, 3, 3], [1, 1, 1], "same"
)), [1, 2, 2], [1, 2, 2], "same")
layer = eddl.MaxPool3D(eddl.ReLu(eddl.Conv3D(
layer, 16, [1, 3, 3], [1, 1, 1], "same"
)), [1, 2, 2], [1, 2, 2], "same")
layer = eddl.GlobalMaxPool3D(layer)
layer = eddl.Reshape(layer, [-1])
layer = eddl.LSTM(layer, 128)
layer = eddl.Dense(layer, 100)
layer = eddl.ReLu(layer)
layer = eddl.Dense(layer, 2)
out = eddl.ReLu(layer)
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.adam(),
["mse"],
["mse"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)
)
eddl.summary(net)
seqImages = Tensor.randu([32, 10, 3, 10, size, size])
seqLabels = Tensor.randu([32, 7, 2])
eddl.fit(net, [seqImages], [seqLabels], 4, 2 if args.small else 10)
def main(args):
eddl.download_cifar10()
num_classes = 10
in_ = eddl.Input([3, 32, 32])
layer = in_
layer = eddl.RandomHorizontalFlip(layer)
layer = eddl.RandomCropScale(layer, [0.8, 1.0])
layer = eddl.RandomCutout(layer, [0.1, 0.5], [0.1, 0.5])
layer = eddl.MaxPool(eddl.ReLu(eddl.BatchNormalization(
eddl.HeUniform(eddl.Conv(layer, 32, [3, 3], [1, 1], "same", False)),
True)), [2, 2])
layer = eddl.MaxPool(eddl.ReLu(eddl.BatchNormalization(
eddl.HeUniform(eddl.Conv(layer, 64, [3, 3], [1, 1], "same", False)),
True)), [2, 2])
layer = eddl.MaxPool(eddl.ReLu(eddl.BatchNormalization(
eddl.HeUniform(eddl.Conv(layer, 128, [3, 3], [1, 1], "same", False)),
True)), [2, 2])
layer = eddl.MaxPool(eddl.ReLu(eddl.BatchNormalization(
eddl.HeUniform(eddl.Conv(layer, 256, [3, 3], [1, 1], "same", False)),
True)), [2, 2])
layer = eddl.Reshape(layer, [-1])
layer = eddl.Activation(eddl.BatchNormalization(
eddl.Dense(layer, 128), True
), "relu")
out = eddl.Softmax(eddl.BatchNormalization(
eddl.Dense(layer, num_classes), True
))
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.adam(0.001),
["softmax_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)
)
eddl.summary(net)
eddl.plot(net, "model.pdf")
x_train = Tensor.load("cifar_trX.bin")
y_train = Tensor.load("cifar_trY.bin")
x_train.div_(255.0)
x_test = Tensor.load("cifar_tsX.bin")
y_test = Tensor.load("cifar_tsY.bin")
x_test.div_(255.0)
if args.small:
x_train = x_train.select([":5000"])
y_train = y_train.select([":5000"])
x_test = x_test.select([":1000"])
y_test = y_test.select([":1000"])
for i in range(args.epochs):
eddl.fit(net, [x_train], [y_train], args.batch_size, 1)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
eddl.setlr(net, [0.0001])
for i in range(args.epochs):
eddl.fit(net, [x_train], [y_train], args.batch_size, 1)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
print("All done")
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.Reshape(layer, [-1])
layer = eddl.ReLu(eddl.Dense(layer, 1024))
layer = eddl.ReLu(eddl.Dense(layer, 1024))
layer = eddl.ReLu(eddl.Dense(layer, 1024))
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.rmsprop(0.01),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem),
True # initialize weights to random values
)
serialized_net = eddl.serialize_net_to_onnx_string(net, False)
eddl.summary(net)
x_train = Tensor.load("mnist_trX.bin")
y_train = Tensor.load("mnist_trY.bin")
x_test = Tensor.load("mnist_tsX.bin")
y_test = Tensor.load("mnist_tsY.bin")
if args.small:
x_train = x_train.select([":6000"])
y_train = y_train.select([":6000"])
x_test = x_test.select([":1000"])
y_test = y_test.select([":1000"])
x_train.div_(255.0)
x_test.div_(255.0)
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs)
print("evaluating before import")
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
imported_net = eddl.import_net_from_onnx_string(serialized_net)
eddl.build(
imported_net,
eddl.rmsprop(0.01),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem),
False # do not initialize weights to random values
)
eddl.summary(imported_net)
print("net layers:", len(net.layers))
print("imported_net layers:", len(imported_net.layers))
print("evaluating imported net")
eddl.evaluate(imported_net, [x_test], [y_test], bs=args.batch_size)
print("All done")
def main(args):
num_classes = 1
size = [192, 192] # size of images
thresh = 0.5
if args.out_dir:
os.makedirs(args.out_dir, exist_ok=True)
in_ = eddl.Input([3, size[0], size[1]])
out = SegNet(in_, num_classes)
out_sigm = eddl.Sigmoid(out)
net = eddl.Model([in_], [out_sigm])
eddl.build(net, eddl.adam(0.0001), ["cross_entropy"],
["mean_squared_error"],
eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU())
eddl.summary(net)
eddl.setlogfile(net, "skin_lesion_segmentation")
training_augs = ecvl.SequentialAugmentationContainer([
ecvl.AugResizeDim(size),
ecvl.AugMirror(0.5),
ecvl.AugFlip(0.5),
ecvl.AugRotate([-180, 180]),
ecvl.AugAdditivePoissonNoise([0, 10]),
ecvl.AugGammaContrast([0.5, 1.5]),
ecvl.AugGaussianBlur([0, 0.8]),
ecvl.AugCoarseDropout([0, 0.3], [0.02, 0.05], 0.5)
])
validation_augs = ecvl.SequentialAugmentationContainer(
[ecvl.AugResizeDim(size)])
dataset_augs = ecvl.DatasetAugmentations(
[training_augs, validation_augs, None])
print("Reading dataset")
d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs)
x = Tensor([args.batch_size, d.n_channels_, size[0], size[1]])
y = Tensor([args.batch_size, d.n_channels_gt_, size[0], size[1]])
num_samples_train = len(d.GetSplit())
num_batches_train = num_samples_train // args.batch_size
d.SetSplit(ecvl.SplitType.validation)
num_samples_validation = len(d.GetSplit())
num_batches_validation = num_samples_validation // args.batch_size
indices = list(range(args.batch_size))
evaluator = utils.Evaluator()
print("Starting training")
for e in range(args.epochs):
print("Epoch {:d}/{:d} - Training".format(e + 1, args.epochs),
flush=True)
d.SetSplit(ecvl.SplitType.training)
eddl.reset_loss(net)
s = d.GetSplit()
random.shuffle(s)
d.split_.training_ = s
d.ResetAllBatches()
for b in range(num_batches_train):
print("Epoch {:d}/{:d} (batch {:d}/{:d}) - ".format(
e + 1, args.epochs, b + 1, num_batches_train),
end="",
flush=True)
d.LoadBatch(x, y)
x.div_(255.0)
y.div_(255.0)
tx, ty = [x], [y]
eddl.train_batch(net, tx, ty, indices)
eddl.print_loss(net, b)
print()
print("Saving weights")
eddl.save(net, "isic_segmentation_checkpoint_epoch_%s.bin" % e, "bin")
d.SetSplit(ecvl.SplitType.validation)
evaluator.ResetEval()
print("Epoch %d/%d - Evaluation" % (e + 1, args.epochs), flush=True)
for b in range(num_batches_validation):
n = 0
print("Epoch {:d}/{:d} (batch {:d}/{:d}) ".format(
e + 1, args.epochs, b + 1, num_batches_validation),
end="",
flush=True)
d.LoadBatch(x, y)
x.div_(255.0)
y.div_(255.0)
eddl.forward(net, [x])
output = eddl.getOutput(out_sigm)
for k in range(args.batch_size):
img = output.select([str(k)])
gt = y.select([str(k)])
img_np = np.array(img, copy=False)
gt_np = np.array(gt, copy=False)
iou = evaluator.BinaryIoU(img_np, gt_np, thresh=thresh)
print("- IoU: %.6g " % iou, end="", flush=True)
if args.out_dir:
# C++ BinaryIoU modifies image as a side effect
img_np[img_np >= thresh] = 1
img_np[img_np < thresh] = 0
img_t = ecvl.TensorToView(img)
img_t.colortype_ = ecvl.ColorType.GRAY
img_t.channels_ = "xyc"
img.mult_(255.)
# orig_img
orig_img = x.select([str(k)])
orig_img.mult_(255.)
orig_img_t = ecvl.TensorToImage(orig_img)
orig_img_t.colortype_ = ecvl.ColorType.BGR
orig_img_t.channels_ = "xyc"
tmp, labels = ecvl.Image.empty(), ecvl.Image.empty()
ecvl.CopyImage(img_t, tmp, ecvl.DataType.uint8)
ecvl.ConnectedComponentsLabeling(tmp, labels)
ecvl.CopyImage(labels, tmp, ecvl.DataType.uint8)
contours = ecvl.FindContours(tmp)
ecvl.CopyImage(orig_img_t, tmp, ecvl.DataType.uint8)
tmp_np = np.array(tmp, copy=False)
for cseq in contours:
for c in cseq:
tmp_np[c[0], c[1], 0] = 0
tmp_np[c[0], c[1], 1] = 0
tmp_np[c[0], c[1], 2] = 255
filename = d.samples_[d.GetSplit()[n]].location_[0]
head, tail = os.path.splitext(os.path.basename(filename))
bname = "%s.png" % head
output_fn = os.path.join(args.out_dir, bname)
ecvl.ImWrite(output_fn, tmp)
if e == 0:
gt_t = ecvl.TensorToView(gt)
gt_t.colortype_ = ecvl.ColorType.GRAY
gt_t.channels_ = "xyc"
gt.mult_(255.)
gt_filename = d.samples_[d.GetSplit()[n]].label_path_
gt_fn = os.path.join(args.out_dir,
os.path.basename(gt_filename))
ecvl.ImWrite(gt_fn, gt_t)
n += 1
print()
print("MIoU: %.6g" % evaluator.MeanMetric())
def main(args):
num_classes = 1
size = [192, 192] # size of images
thresh = 0.5
if args.out_dir:
os.makedirs(args.out_dir, exist_ok=True)
in_ = eddl.Input([3, size[0], size[1]])
out = SegNet(in_, num_classes)
out_sigm = eddl.Sigmoid(out)
net = eddl.Model([in_], [out_sigm])
eddl.build(net, eddl.adam(0.0001), ["cross_entropy"],
["mean_squared_error"],
eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU())
eddl.summary(net)
eddl.setlogfile(net, "skin_lesion_segmentation_inference")
if not os.path.exists(args.ckpts):
raise RuntimeError('Checkpoint "{}" not found'.format(args.ckpts))
eddl.load(net, args.ckpts, "bin")
training_augs = ecvl.SequentialAugmentationContainer([
ecvl.AugResizeDim(size),
])
test_augs = ecvl.SequentialAugmentationContainer([
ecvl.AugResizeDim(size),
])
dataset_augs = ecvl.DatasetAugmentations([training_augs, None, test_augs])
print("Reading dataset")
d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs)
x = Tensor([args.batch_size, d.n_channels_, size[0], size[1]])
y = Tensor([args.batch_size, d.n_channels_gt_, size[0], size[1]])
print("Testing")
d.SetSplit(ecvl.SplitType.test)
num_samples_test = len(d.GetSplit())
num_batches_test = num_samples_test // args.batch_size
evaluator = utils.Evaluator()
evaluator.ResetEval()
for b in range(num_batches_test):
n = 0
print("Batch {:d}/{:d} ".format(b + 1, num_batches_test),
end="",
flush=True)
d.LoadBatch(x, y)
x.div_(255.0)
y.div_(255.0)
eddl.forward(net, [x])
output = eddl.getOutput(out_sigm)
for k in range(args.batch_size):
img = output.select([str(k)])
gt = y.select([str(k)])
img_np, gt_np = np.array(img, copy=False), np.array(gt, copy=False)
iou = evaluator.BinaryIoU(img_np, gt_np, thresh=thresh)
print("- IoU: %.6g " % iou, end="", flush=True)
if args.out_dir:
# C++ BinaryIoU modifies image as a side effect
img_np[img_np >= thresh] = 1
img_np[img_np < thresh] = 0
img_t = ecvl.TensorToView(img)
img_t.colortype_ = ecvl.ColorType.GRAY
img_t.channels_ = "xyc"
img.mult_(255.)
# orig_img
orig_img = x.select([str(k)])
orig_img.mult_(255.)
orig_img_t = ecvl.TensorToImage(orig_img)
orig_img_t.colortype_ = ecvl.ColorType.BGR
orig_img_t.channels_ = "xyc"
tmp, labels = ecvl.Image.empty(), ecvl.Image.empty()
ecvl.CopyImage(img_t, tmp, ecvl.DataType.uint8)
ecvl.ConnectedComponentsLabeling(tmp, labels)
ecvl.CopyImage(labels, tmp, ecvl.DataType.uint8)
contours = ecvl.FindContours(tmp)
ecvl.CopyImage(orig_img_t, tmp, ecvl.DataType.uint8)
tmp_np = np.array(tmp, copy=False)
for cseq in contours:
for c in cseq:
tmp_np[c[0], c[1], 0] = 0
tmp_np[c[0], c[1], 1] = 0
tmp_np[c[0], c[1], 2] = 255
filename = d.samples_[d.GetSplit()[n]].location_[0]
head, tail = os.path.splitext(os.path.basename(filename))
bname = "%s.png" % head
output_fn = os.path.join(args.out_dir, bname)
ecvl.ImWrite(output_fn, tmp)
gt_t = ecvl.TensorToView(gt)
gt_t.colortype_ = ecvl.ColorType.GRAY
gt_t.channels_ = "xyc"
gt.mult_(255.)
gt_filename = d.samples_[d.GetSplit()[n]].label_path_
gt_fn = os.path.join(args.out_dir,
os.path.basename(gt_filename))
ecvl.ImWrite(gt_fn, gt_t)
n += 1
print()
print("MIoU: %.6g" % evaluator.MeanMetric())
def main(args):
num_classes = 1
size = [512, 512] # size of images
thresh = 0.5
best_dice = -1
if args.out_dir:
os.makedirs(args.out_dir, exist_ok=True)
in_ = eddl.Input([1, size[0], size[1]])
out = SegNetBN(in_, num_classes)
out_sigm = eddl.Sigmoid(out)
net = eddl.Model([in_], [out_sigm])
eddl.build(net, eddl.adam(0.0001), ["cross_entropy"],
["mean_squared_error"],
eddl.CS_GPU([1], mem='low_mem') if args.gpu else eddl.CS_CPU())
eddl.summary(net)
eddl.setlogfile(net, "pneumothorax_segmentation_training")
if args.ckpts and os.path.exists(args.ckpts):
print("Loading checkpoints '{}'".format(args.ckpts))
eddl.load(net, args.ckpts, 'bin')
training_augs = ecvl.SequentialAugmentationContainer([
ecvl.AugResizeDim(size),
ecvl.AugMirror(0.5),
ecvl.AugRotate([-10, 10]),
ecvl.AugBrightness([0, 30]),
ecvl.AugGammaContrast([0, 3]),
])
validation_augs = ecvl.SequentialAugmentationContainer(
[ecvl.AugResizeDim(size)])
dataset_augs = ecvl.DatasetAugmentations(
[training_augs, validation_augs, None])
print("Reading dataset")
d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs,
ecvl.ColorType.GRAY)
# Prepare tensors which store batch
x = Tensor([args.batch_size, d.n_channels_, size[0], size[1]])
y = Tensor([args.batch_size, d.n_channels_gt_, size[0], size[1]])
# Retrieve indices of images with a black ground truth
# which are not include in a split
train_split = d.GetSplit(ecvl.SplitType.training)
val_split = d.GetSplit(ecvl.SplitType.validation)
test_split = d.GetSplit(ecvl.SplitType.test)
all_split = set(train_split + val_split + test_split)
images_list = set(range(len(d.samples_)))
# Obtain images with black ground truth
black_images = images_list - all_split
# Add a 25% of training samples with black ground truth.
num_samples_train = math.floor(len(train_split) * 1.25)
num_batches_train = num_samples_train // args.batch_size
# Add a 25% of validation samples with black ground truth.
num_samples_validation = math.floor(len(val_split) * 1.25)
num_batches_validation = num_samples_validation // args.batch_size
black_images = list(black_images)
black_training = black_images[0:-(num_samples_validation - len(val_split))]
black_validation = black_images[-(num_samples_validation -
len(val_split)):]
indices = list(range(args.batch_size))
evaluator = utils.Evaluator()
print("Starting training")
for e in range(args.epochs):
print("Epoch {:d}/{:d} - Training".format(e + 1, args.epochs),
flush=True)
d.SetSplit(ecvl.SplitType.training)
eddl.reset_loss(net)
s = d.GetSplit()
random.shuffle(s)
d.split_.training_ = s
random.shuffle(black_training)
d.ResetAllBatches()
# Indices to track mask and black vector in PneumothoraxLoadBatch
m_i = 0
b_i = 0
for i, b in enumerate(range(num_batches_train)):
d, images, labels, _, m_i, b_i = PneumothoraxLoadBatch(
d, black_training, m_i, b_i)
x, y = fill_tensors(images, labels, x, y)
x.div_(255.0)
y.div_(255.0)
eddl.train_batch(net, [x], [y], indices)
if i % args.log_interval == 0:
print("Epoch {:d}/{:d} (batch {:d}/{:d}) - ".format(
e + 1, args.epochs, b + 1, num_batches_train),
end="",
flush=True)
eddl.print_loss(net, b)
print()
d.SetSplit(ecvl.SplitType.validation)
evaluator.ResetEval()
print("Epoch %d/%d - Evaluation" % (e + 1, args.epochs), flush=True)
m_i = 0
b_i = 0
for b in range(num_batches_validation):
n = 0
print("Epoch {:d}/{:d} (batch {:d}/{:d}) ".format(
e + 1, args.epochs, b + 1, num_batches_validation),
end="",
flush=True)
d, images, labels, names, m_i, b_i = PneumothoraxLoadBatch(
d, black_validation, m_i, b_i)
x, y = fill_tensors(images, labels, x, y)
x.div_(255.0)
y.div_(255.0)
eddl.forward(net, [x])
output = eddl.getOutput(out_sigm)
# Compute Dice metric and optionally save the output images
for k in range(args.batch_size):
pred = output.select([str(k)])
gt = y.select([str(k)])
pred_np = np.array(pred, copy=False)
gt_np = np.array(gt, copy=False)
# DiceCoefficient modifies image as a side effect
dice = evaluator.DiceCoefficient(pred_np, gt_np, thresh=thresh)
print("- Dice: {:.6f} ".format(dice), end="", flush=True)
if args.out_dir:
# Save original image fused together with prediction and
# ground truth
pred_np *= 255
pred_ecvl = ecvl.TensorToImage(pred)
pred_ecvl.colortype_ = ecvl.ColorType.GRAY
pred_ecvl.channels_ = "xyc"
ecvl.ResizeDim(pred_ecvl, pred_ecvl, (1024, 1024),
ecvl.InterpolationType.nearest)
filename_gt = names[n + 1]
gt_ecvl = ecvl.ImRead(filename_gt,
ecvl.ImReadMode.GRAYSCALE)
filename = names[n]
# Image as BGR
img_ecvl = ecvl.ImRead(filename)
ecvl.Stack([img_ecvl, img_ecvl, img_ecvl], img_ecvl)
img_ecvl.channels_ = "xyc"
img_ecvl.colortype_ = ecvl.ColorType.BGR
image_np = np.array(img_ecvl, copy=False)
pred_np = np.array(pred_ecvl, copy=False)
gt_np = np.array(gt_ecvl, copy=False)
pred_np = pred_np.squeeze()
gt_np = gt_np.squeeze()
# Prediction summed in R channel
image_np[:, :, -1] = np.where(pred_np == 255, pred_np,
image_np[:, :, -1])
# Ground truth summed in G channel
image_np[:, :, 1] = np.where(gt_np == 255, gt_np,
image_np[:, :, 1])
n += 2
head, tail = os.path.splitext(os.path.basename(filename))
bname = "{}.png".format(head)
filepath = os.path.join(args.out_dir, bname)
ecvl.ImWrite(filepath, img_ecvl)
print()
mean_dice = evaluator.MeanMetric()
if mean_dice > best_dice:
print("Saving weights")
eddl.save(
net, "pneumothorax_segnetBN_adam_lr_0.0001_"
"loss_ce_size_512_{}.bin".format(e + 1), "bin")
best_dice = mean_dice
print("Mean Dice Coefficient: {:.6g}".format(mean_dice))