def resnet_block(l0, nf, bn, reps, downsample):
for i in range(reps):
stri = 2 if (downsample and i == 0) else 1
l1 = eddl.GlorotUniform(
eddl.Conv(l0, nf, [1, 1], [stri, stri], "same", False))
if (bn):
l1 = eddl.BatchNormalization(l1, 0.99, 0.001, True, "")
l1 = eddl.ReLu(l1)
l1 = eddl.GlorotUniform(
eddl.Conv(l1, nf, [3, 3], [1, 1], "same", False))
if (bn):
l1 = eddl.BatchNormalization(l1, 0.99, 0.001, True, "")
l1 = eddl.ReLu(l1)
l1 = eddl.GlorotUniform(
eddl.Conv(l1, nf * 4, [1, 1], [1, 1], "same", False))
if (bn):
l1 = eddl.BatchNormalization(l1, 0.99, 0.001, True, "")
if (i == 0):
l0 = eddl.GlorotUniform(
eddl.Conv(l0, nf * 4, [1, 1], [stri, stri], "same", False))
l0 = eddl.Add([l0, l1])
l0 = eddl.ReLu(l0)
return l0
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 Block3_2(layer, filters):
layer = eddl.ReLu(eddl.BatchNormalization(
eddl.Conv(layer, filters, [3, 3], [1, 1]), True
))
layer = eddl.ReLu(eddl.BatchNormalization(
eddl.Conv(layer, filters, [3, 3], [1, 1]), True
))
return layer
def DoubleConv(x, out_channels, mid_channels=None):
if not mid_channels:
mid_channels = out_channels
x = eddl.Conv2D(x, mid_channels, kernel_size=[3, 3])
x = eddl.BatchNormalization(x, True)
x = eddl.ReLu(x)
x = eddl.Conv2D(x, out_channels, kernel_size=[3, 3])
x = eddl.BatchNormalization(x, True)
x = eddl.ReLu(x)
return x
def UNetWithPadding(layer):
x = layer
depth = 32
x = LBC(x, depth, [3, 3], [1, 1], "same")
x = LBC(x, depth, [3, 3], [1, 1], "same")
x2 = eddl.MaxPool(x, [2, 2], [2, 2])
x2 = LBC(x2, 2*depth, [3, 3], [1, 1], "same")
x2 = LBC(x2, 2*depth, [3, 3], [1, 1], "same")
x3 = eddl.MaxPool(x2, [2, 2], [2, 2])
x3 = LBC(x3, 4*depth, [3, 3], [1, 1], "same")
x3 = LBC(x3, 4*depth, [3, 3], [1, 1], "same")
x4 = eddl.MaxPool(x3, [2, 2], [2, 2])
x4 = LBC(x4, 8*depth, [3, 3], [1, 1], "same")
x4 = LBC(x4, 8*depth, [3, 3], [1, 1], "same")
x5 = eddl.MaxPool(x4, [2, 2], [2, 2])
x5 = LBC(x5, 8*depth, [3, 3], [1, 1], "same")
x5 = LBC(x5, 8*depth, [3, 3], [1, 1], "same")
x5 = eddl.BatchNormalization(eddl.Conv(
eddl.UpSampling(x5, [2, 2]), 8*depth, [3, 3], [1, 1], "same"
), True)
x4 = eddl.Concat([x4, x5]) if USE_CONCAT else eddl.Add([x4, x5])
x4 = LBC(x4, 8*depth, [3, 3], [1, 1], "same")
x4 = LBC(x4, 8*depth, [3, 3], [1, 1], "same")
x4 = eddl.BatchNormalization(eddl.Conv(
eddl.UpSampling(x4, [2, 2]), 4*depth, [3, 3], [1, 1], "same"
), True)
x3 = eddl.Concat([x3, x4]) if USE_CONCAT else eddl.Add([x3, x4])
x3 = LBC(x3, 4*depth, [3, 3], [1, 1], "same")
x3 = LBC(x3, 4*depth, [3, 3], [1, 1], "same")
x3 = eddl.Conv(
eddl.UpSampling(x3, [2, 2]), 2*depth, [3, 3], [1, 1], "same"
)
x2 = eddl.Concat([x2, x3]) if USE_CONCAT else eddl.Add([x2, x3])
x2 = LBC(x2, 2*depth, [3, 3], [1, 1], "same")
x2 = LBC(x2, 2*depth, [3, 3], [1, 1], "same")
x2 = eddl.BatchNormalization(eddl.Conv(
eddl.UpSampling(x2, [2, 2]), depth, [3, 3], [1, 1], "same"
), True)
x = eddl.Concat([x, x2]) if USE_CONCAT else eddl.Add([x, x2])
x = LBC(x, depth, [3, 3], [1, 1], "same")
x = LBC(x, depth, [3, 3], [1, 1], "same")
x = eddl.BatchNormalization(eddl.Conv(x, 1, [1, 1]), True)
return x
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 defblock(l, bn, nf, reps, initializer):
for i in range(reps):
l = initializer(eddl.Conv(l, nf, [3, 3]))
if bn:
l = eddl.BatchNormalization(l, 0.99, 0.001, True, "")
l = eddl.ReLu(l)
l = eddl.MaxPool(l, [2, 2], [2, 2], "valid")
return l
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_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.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.ReLu(eddl.BatchNormalization(eddl.Dense(layer, 512), True))
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 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 BG(layer):
return eddl.GaussianNoise(eddl.BatchNormalization(layer, True), 0.3)
bn = int(sys.argv[1]) == 1
initializer = eddl.GlorotUniform if bn else eddl.HeUniform
inp = eddl.Input([3, 32, 32])
l = inp
l = defblock(l, bn, 64, 2, initializer)
l = defblock(l, bn, 128, 2, initializer)
l = defblock(l, bn, 256, 4, initializer)
l = defblock(l, bn, 512, 4, initializer)
l = defblock(l, bn, 512, 4, initializer)
l = eddl.Flatten(l)
for i in range(2):
l = initializer(eddl.Dense(l, 4096))
if (bn):
l = eddl.BatchNormalization(l, 0.99, 0.001, True, "")
l = eddl.ReLu(l)
out = eddl.Softmax(initializer(eddl.Dense(l, num_classes)))
net = eddl.Model([inp], [out])
eddl.plot(net, "model.pdf")
eddl.build(net, eddl.adam(0.00001), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU() if gpu else eddl.CS_CPU())
eddl.summary(net)
x_train = Tensor.load("cifar_trX.bin")
y_train = Tensor.load("cifar_trY.bin")
def SegNetBN(x, num_classes):
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same")))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same")))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same")))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same")))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same")))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same")))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same")))
x = eddl.Conv(x, num_classes, [3, 3], [1, 1], "same")
return x
def Normalization(layer):
return eddl.BatchNormalization(layer, True)
def Block1(layer, filters):
return eddl.ReLu(eddl.BatchNormalization(
eddl.Conv(layer, filters, [1, 1], [1, 1]), True
))
def LBC(layer, *args, **kwargs):
return eddl.LeakyReLu(eddl.BatchNormalization(eddl.Conv(
layer, *args, **kwargs
), True))
