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 LeNet(in_layer, num_classes): x = in_layer x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 20, [5, 5])), [2, 2], [2, 2]) x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 50, [5, 5])), [2, 2], [2, 2]) x = eddl.Reshape(x, [-1]) x = eddl.ReLu(eddl.Dense(x, 500)) x = eddl.Softmax(eddl.Dense(x, num_classes)) return x
def Block3_2(layer, filters): layer = eddl.ReLu(Normalization(eddl.Conv( layer, filters, [3, 3], [1, 1], "same", False ))) layer = eddl.ReLu(Normalization(eddl.Conv( layer, filters, [3, 3], [1, 1], "same", False ))) return layer
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 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 ResBlock(layer, filters, nconv, half): in_ = layer strides = [2, 2] if half else [1, 1] layer = eddl.ReLu(BG(eddl.Conv(layer, filters, [3, 3], strides))) for i in range(nconv - 1): layer = eddl.ReLu(BG(eddl.Conv(layer, filters, [3, 3], [1, 1]))) if (half): return eddl.Add(BG(eddl.Conv(in_, filters, [1, 1], [2, 2])), layer) else: return eddl.Add(layer, in_)
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 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 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_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.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 main(args): eddl.download_mnist() num_classes = 10 in_ = eddl.Input([784]) layer = in_ layer = eddl.Reshape(layer, [1, 28, 28]) layer = eddl.MaxPool(eddl.ReLu(eddl.Conv(layer, 32, [3, 3], [1, 1])), [3, 3], [1, 1], "same") layer = eddl.MaxPool(eddl.ReLu(eddl.Conv(layer, 64, [3, 3], [1, 1])), [2, 2], [2, 2], "same") layer = eddl.MaxPool(eddl.ReLu(eddl.Conv(layer, 128, [3, 3], [1, 1])), [3, 3], [2, 2], "none") layer = eddl.MaxPool(eddl.ReLu(eddl.Conv(layer, 256, [3, 3], [1, 1])), [2, 2], [2, 2], "none") 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 ResBlock(layer, filters, half, expand=0): in_ = layer layer = eddl.ReLu( BG(eddl.Conv(layer, filters, [1, 1], [1, 1], "same", False))) strides = [2, 2] if half else [1, 1] layer = eddl.ReLu( BG(eddl.Conv(layer, filters, [3, 3], strides, "same", False))) layer = eddl.ReLu( BG(eddl.Conv(layer, 4 * filters, [1, 1], [1, 1], "same", False))) if (half): return eddl.ReLu( eddl.Add( BG(eddl.Conv(in_, 4 * filters, [1, 1], [2, 2], "same", False)), layer)) else: if expand: return eddl.ReLu( eddl.Add( BG( eddl.Conv(in_, 4 * filters, [1, 1], [1, 1], "same", False)), layer)) else: return eddl.ReLu(eddl.Add(in_, layer))
def SegNet(x, num_classes): x = eddl.ReLu(eddl.Conv(x, 64, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 64, [3, 3], [1, 1], "same")) x = eddl.MaxPool(x, [2, 2], [2, 2]) x = eddl.ReLu(eddl.Conv(x, 128, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 128, [3, 3], [1, 1], "same")) x = eddl.MaxPool(x, [2, 2], [2, 2]) x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same")) x = eddl.MaxPool(x, [2, 2], [2, 2]) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same")) x = eddl.MaxPool(x, [2, 2], [2, 2]) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same")) x = eddl.ReLu(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.Conv(x, 512, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same")) x = eddl.UpSampling(x, [2, 2]) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same")) x = eddl.UpSampling(x, [2, 2]) x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 128, [3, 3], [1, 1], "same")) x = eddl.UpSampling(x, [2, 2]) x = eddl.ReLu(eddl.Conv(x, 128, [3, 3], [1, 1], "same")) x = eddl.ReLu(eddl.Conv(x, 64, [3, 3], [1, 1], "same")) x = eddl.UpSampling(x, [2, 2]) x = eddl.ReLu(eddl.Conv(x, 64, [3, 3], [1, 1], "same")) x = eddl.Conv(x, num_classes, [3, 3], [1, 1], "same") return x
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 LBC(layer, *args, **kwargs): return eddl.LeakyReLu(eddl.BatchNormalization(eddl.Conv( layer, *args, **kwargs ), True))
def VGG16(in_layer, num_classes, seed=1234, init=eddl.HeNormal, l2_reg=None, dropout=None): x = in_layer x = eddl.ReLu(init(eddl.Conv(x, 64, [3, 3]), seed)) x = eddl.MaxPool(eddl.ReLu(init(eddl.Conv(x, 64, [3, 3]), seed)), [2, 2], [2, 2]) x = eddl.ReLu(init(eddl.Conv(x, 128, [3, 3]), seed)) x = eddl.MaxPool(eddl.ReLu(init(eddl.Conv(x, 128, [3, 3]), seed)), [2, 2], [2, 2]) x = eddl.ReLu(init(eddl.Conv(x, 256, [3, 3]), seed)) x = eddl.ReLu(init(eddl.Conv(x, 256, [3, 3]), seed)) x = eddl.MaxPool(eddl.ReLu(init(eddl.Conv(x, 256, [3, 3]), seed)), [2, 2], [2, 2]) x = eddl.ReLu(init(eddl.Conv(x, 512, [3, 3]), seed)) x = eddl.ReLu(init(eddl.Conv(x, 512, [3, 3]), seed)) x = eddl.MaxPool(eddl.ReLu(init(eddl.Conv(x, 512, [3, 3]), seed)), [2, 2], [2, 2]) x = eddl.ReLu(init(eddl.Conv(x, 512, [3, 3]), seed)) x = eddl.ReLu(init(eddl.Conv(x, 512, [3, 3]), seed)) x = eddl.MaxPool(eddl.ReLu(init(eddl.Conv(x, 512, [3, 3]), seed)), [2, 2], [2, 2]) x = eddl.Reshape(x, [-1]) x = eddl.Dense(x, 4096) if dropout: x = eddl.Dropout(x, dropout, iw=False) if l2_reg: x = eddl.L2(x, l2_reg) x = eddl.ReLu(init(x,seed)) x = eddl.Dense(x, 4096) if dropout: x = eddl.Dropout(x, dropout, iw=False) if l2_reg: x = eddl.L2(x, l2_reg) x = eddl.ReLu(init(x,seed)) x = eddl.Softmax(eddl.Dense(x, num_classes)) return x
def Block1(layer, filters): return eddl.ReLu( eddl.GroupNormalization(eddl.Conv(layer, filters, [1, 1], [1, 1]), 4))
def Block3_2(layer, filters): layer = eddl.ReLu( eddl.GroupNormalization(eddl.Conv(layer, filters, [3, 3], [1, 1]), 4)) layer = eddl.ReLu( eddl.GroupNormalization(eddl.Conv(layer, filters, [3, 3], [1, 1]), 4)) return layer
def Block1(layer, filters): return eddl.ReLu(eddl.BatchNormalization( eddl.Conv(layer, filters, [1, 1], [1, 1]), True ))
def Block1(layer, filters): return eddl.ReLu(Normalization(eddl.Conv( layer, filters, [1, 1], [1, 1], "same", False )))
def SegNetBN(x, num_classes): x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same"), True)) x = eddl.MaxPool(x, [2, 2], [2, 2]) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same"), True)) x = eddl.MaxPool(x, [2, 2], [2, 2]) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same"), True)) x = eddl.MaxPool(x, [2, 2], [2, 2]) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"), True)) x = eddl.MaxPool(x, [2, 2], [2, 2]) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"), True)) 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"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"), True)) x = eddl.UpSampling(x, [2, 2]) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same"), True)) x = eddl.UpSampling(x, [2, 2]) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same"), True)) x = eddl.UpSampling(x, [2, 2]) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same"), True)) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same"), True)) x = eddl.UpSampling(x, [2, 2]) x = eddl.ReLu( eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same"), True)) x = eddl.Conv(x, num_classes, [3, 3], [1, 1], "same") return x
def VGG16(in_layer, num_classes): x = in_layer x = eddl.ReLu(eddl.Conv(x, 64, [3, 3])) x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 64, [3, 3])), [2, 2], [2, 2]) x = eddl.ReLu(eddl.Conv(x, 128, [3, 3])) x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 128, [3, 3])), [2, 2], [2, 2]) x = eddl.ReLu(eddl.Conv(x, 256, [3, 3])) x = eddl.ReLu(eddl.Conv(x, 256, [3, 3])) x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 256, [3, 3])), [2, 2], [2, 2]) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3])) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3])) x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 512, [3, 3])), [2, 2], [2, 2]) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3])) x = eddl.ReLu(eddl.Conv(x, 512, [3, 3])) x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 512, [3, 3])), [2, 2], [2, 2]) x = eddl.Reshape(x, [-1]) x = eddl.ReLu(eddl.Dense(x, 4096)) x = eddl.ReLu(eddl.Dense(x, 4096)) x = eddl.Softmax(eddl.Dense(x, num_classes)) return x
return l0 eddl.download_cifar10() gpu = int(sys.argv[2]) == 1 if len(sys.argv) > 2 else True epochs = 10 if gpu else 1 batch_size = 50 num_classes = 10 bn = int(sys.argv[1]) == 1 inp = eddl.Input([3, 32, 32]) l = inp l = eddl.GlorotUniform(eddl.Conv(l, 64, [7, 7], [2, 2], "same", False)) l = eddl.MaxPool(l, [2, 2], [2, 2], "valid") l = resnet_block(l, 64, bn, 2, False) l = resnet_block(l, 128, bn, 2, True) l = resnet_block(l, 256, bn, 2, True) l = resnet_block(l, 512, bn, 2, True) l = eddl.GlobalAveragePool(l) l = eddl.Flatten(l) out = eddl.Softmax(eddl.GlorotUniform(eddl.Dense(l, num_classes))) net = eddl.Model([inp], [out]) eddl.plot(net, "model.pdf") eddl.build(net, eddl.adam(0.0001), ["soft_cross_entropy"], ["categorical_accuracy"],