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):
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):
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_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 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 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 classificate(args):
args = dotdict(args)
ckpts_dir = opjoin(settings.TRAINING_DIR, 'ckpts')
outputfile = None
inference = None
train = True if args.mode == 'training' else False
batch_size = args.batch_size if args.mode == 'training' else args.test_batch_size
weight_id = args.weight_id
weight = dj_models.ModelWeights.objects.get(id=weight_id)
if train:
pretrained = None
if weight.pretrained_on:
pretrained = weight.pretrained_on.location
else:
inference_id = args.inference_id
inference = dj_models.Inference.objects.get(id=inference_id)
pretrained = weight.location
save_stdout = sys.stdout
size = [args.input_h, args.input_w] # Height, width
try:
model = bindings.models_binding[args.model_id]
except KeyError:
raise Exception(
f'Model with id: {args.model_id} not found in bindings.py')
try:
dataset_path = str(
dj_models.Dataset.objects.get(id=args.dataset_id).path)
except KeyError:
raise Exception(
f'Dataset with id: {args.dataset_id} not found in bindings.py')
dataset = bindings.dataset_binding.get(args.dataset_id)
if dataset is None and not train:
# Binding does not exist. it's a single image dataset
# Use as dataset "stub" the dataset on which model has been trained
dataset = bindings.dataset_binding.get(weight.dataset_id.id)
elif dataset is None and train:
raise Exception(
f'Dataset with id: {args.dataset_id} not found in bindings.py')
basic_augs = ecvl.SequentialAugmentationContainer(
[ecvl.AugResizeDim(size)])
train_augs = basic_augs
val_augs = basic_augs
test_augs = basic_augs
if args.train_augs:
train_augs = ecvl.SequentialAugmentationContainer([
ecvl.AugResizeDim(size),
ecvl.AugmentationFactory.create(args.train_augs)
])
if args.val_augs:
val_augs = ecvl.SequentialAugmentationContainer([
ecvl.AugResizeDim(size),
ecvl.AugmentationFactory.create(args.val_augs)
])
if args.test_augs:
test_augs = ecvl.SequentialAugmentationContainer([
ecvl.AugResizeDim(size),
ecvl.AugmentationFactory.create(args.test_augs)
])
logging.info('Reading dataset')
print('Reading dataset', flush=True)
dataset = dataset(
dataset_path, batch_size,
ecvl.DatasetAugmentations([train_augs, val_augs, test_augs]))
d = dataset.d
num_classes = dataset.num_classes
in_ = eddl.Input([d.n_channels_, size[0], size[1]])
out = model(in_,
num_classes) # out is already softmaxed in classific models
net = eddl.Model([in_], [out])
if train:
logfile = open(Path(weight.logfile), 'w')
else:
logfile = open(inference.logfile, 'w')
outputfile = open(inference.outputfile, 'w')
with redirect_stdout(logfile):
# Save args to file
print('args: ' + json.dumps(args, indent=2, sort_keys=True),
flush=True)
logging.info('args: ' + json.dumps(args, indent=2, sort_keys=True))
eddl.build(
net, eddl.sgd(args.lr,
0.9), [bindings.losses_binding.get(args.loss)],
[bindings.metrics_binding.get(args.metric)],
eddl.CS_GPU([1], mem='low_mem') if args.gpu else eddl.CS_CPU())
eddl.summary(net)
if pretrained and os.path.exists(pretrained):
eddl.load(net, pretrained)
logging.info('Weights loaded')
# Create tensor for images and labels
images = eddlT.create([batch_size, d.n_channels_, size[0], size[1]])
labels = eddlT.create([batch_size, num_classes])
logging.info(f'Starting {args.mode}')
print(f'Starting {args.mode}', flush=True)
if train:
num_samples_train = len(d.GetSplit(ecvl.SplitType.training))
num_batches_train = num_samples_train // batch_size
num_samples_val = len(d.GetSplit(ecvl.SplitType.validation))
num_batches_val = num_samples_val // batch_size
indices = list(range(batch_size))
for e in range(args.epochs):
eddl.reset_loss(net)
d.SetSplit(ecvl.SplitType.training)
s = d.GetSplit()
random.shuffle(s)
d.split_.training_ = s
d.ResetCurrentBatch()
# total_loss = 0.
# total_metric = 0.
for i in range(num_batches_train):
d.LoadBatch(images, labels)
images.div_(255.0)
eddl.train_batch(net, [images], [labels], indices)
total_loss = net.fiterr[0]
total_metric = net.fiterr[1]
print(
f'Train Epoch: {e + 1}/{args.epochs} [{i + 1}/{num_batches_train}] {net.lout[0].name}'
f'({net.losses[0].name}={total_loss / net.inferenced_samples:1.3f},'
f'{net.metrics[0].name}={total_metric / net.inferenced_samples:1.3f})',
flush=True)
logging.info(
f'Train Epoch: {e + 1}/{args.epochs} [{i + 1}/{num_batches_train}] {net.lout[0].name}'
f'({net.losses[0].name}={total_loss / net.inferenced_samples:1.3f},'
f'{net.metrics[0].name}={total_metric / net.inferenced_samples:1.3f})'
)
eddl.save(net, opjoin(ckpts_dir, f'{weight_id}.bin'))
logging.info('Weights saved')
print('Weights saved', flush=True)
if len(d.split_.validation_) > 0:
logging.info(f'Validation {e}/{args.epochs}')
print(f'Validation {e}/{args.epochs}', flush=True)
d.SetSplit(ecvl.SplitType.validation)
d.ResetCurrentBatch()
for i in range(num_batches_val):
d.LoadBatch(images, labels)
images.div_(255.0)
eddl.eval_batch(net, [images], [labels], indices)
# eddl.evaluate(net, [images], [labels])
total_loss = net.fiterr[0]
total_metric = net.fiterr[1]
print(
f'Val Epoch: {e + 1}/{args.epochs} [{i + 1}/{num_batches_val}] {net.lout[0].name}'
f'({net.losses[0].name}={total_loss / net.inferenced_samples:1.3f},'
f'{net.metrics[0].name}={total_metric / net.inferenced_samples:1.3f})',
flush=True)
logging.info(
f'Val Epoch: {e + 1}/{args.epochs} [{i + 1}/{num_batches_val}] {net.lout[0].name}'
f'({net.losses[0].name}={total_loss / net.inferenced_samples:1.3f},'
f'{net.metrics[0].name}={total_metric / net.inferenced_samples:1.3f})'
)
else:
d.SetSplit(ecvl.SplitType.test)
num_samples_test = len(d.GetSplit())
num_batches_test = num_samples_test // batch_size
preds = np.empty((0, num_classes), np.float64)
for b in range(num_batches_test):
d.LoadBatch(images)
images.div_(255.0)
eddl.forward(net, [images])
print(f'Infer Batch {b + 1}/{num_batches_test}', flush=True)
logging.info(f'Infer Batch {b + 1}/{num_batches_test}')
# print(
# f'Evaluation {b + 1}/{num_batches} {net.lout[0].name}({net.losses[0].name}={total_loss / net.inferenced_samples:1.3f},'
# f'{net.metrics[0].name}={total_metric / net.inferenced_samples:1.3f})')
# logging.info(
# f'Evaluation {b + 1}/{num_batches} {net.lout[0].name}({net.losses[0].name}={total_loss / net.inferenced_samples:1.3f},'
# f'{net.metrics[0].name}={total_metric / net.inferenced_samples:1.3f})')
# Save network predictions
for i in range(batch_size):
pred = np.array(eddlT.select(eddl.getTensor(out), i),
copy=False)
# gt = np.argmax(np.array(labels)[indices])
# pred = np.append(pred, gt).reshape((1, num_classes + 1))
preds = np.append(preds, pred, axis=0)
pred_name = d.samples_[d.GetSplit()[b * batch_size +
i]].location_
# print(f'{pred_name};{pred}')
outputfile.write(f'{pred_name};{pred.tolist()}\n')
outputfile.close()
print('<done>')
logfile.close()
del net
del out
del in_
return
def main(args):
num_classes = 8
size = [224, 224] # size of images
in_ = eddl.Input([3, size[0], size[1]])
out = VGG16(in_, num_classes)
net = eddl.Model([in_], [out])
eddl.build(net, eddl.sgd(0.001, 0.9), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU())
eddl.summary(net)
eddl.setlogfile(net, "skin_lesion_classification")
training_augs = ecvl.SequentialAugmentationContainer([
ecvl.AugResizeDim(size),
ecvl.AugMirror(.5),
ecvl.AugFlip(.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, len(d.classes_)])
num_samples_train = len(d.GetSplit())
num_batches_train = num_samples_train // args.batch_size
d.SetSplit(ecvl.SplitType.validation)
num_samples_val = len(d.GetSplit())
num_batches_val = num_samples_val // args.batch_size
indices = list(range(args.batch_size))
metric = eddl.getMetric("categorical_accuracy")
print("Starting training")
for e in range(args.epochs):
print("Epoch {:d}/{:d} - Training".format(e + 1, args.epochs),
flush=True)
if args.out_dir:
current_path = os.path.join(args.out_dir, "Epoch_%d" % e)
for c in d.classes_:
c_dir = os.path.join(current_path, c)
os.makedirs(c_dir, exist_ok=True)
d.SetSplit(ecvl.SplitType.training)
eddl.reset_loss(net)
total_metric = []
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)
tx, ty = [x], [y]
eddl.train_batch(net, tx, ty, indices)
eddl.print_loss(net, b)
print()
print("Saving weights")
eddl.save(net, "isic_classification_checkpoint_epoch_%s.bin" % e,
"bin")
print("Epoch %d/%d - Evaluation" % (e + 1, args.epochs), flush=True)
d.SetSplit(ecvl.SplitType.validation)
for b in range(num_batches_val):
n = 0
print("Epoch {:d}/{:d} (batch {:d}/{:d}) - ".format(
e + 1, args.epochs, b + 1, num_batches_val),
end="",
flush=True)
d.LoadBatch(x, y)
x.div_(255.0)
eddl.forward(net, [x])
output = eddl.getOutput(out)
sum_ = 0.0
for k in range(args.batch_size):
result = output.select([str(k)])
target = y.select([str(k)])
ca = metric.value(target, result)
total_metric.append(ca)
sum_ += ca
if args.out_dir:
result_a = np.array(result, copy=False)
target_a = np.array(target, copy=False)
classe = np.argmax(result_a).item()
gt_class = np.argmax(target_a).item()
single_image = x.select([str(k)])
img_t = ecvl.TensorToView(single_image)
img_t.colortype_ = ecvl.ColorType.BGR
single_image.mult_(255.)
filename = d.samples_[d.GetSplit()[n]].location_[0]
head, tail = os.path.splitext(os.path.basename(filename))
bname = "%s_gt_class_%s.png" % (head, gt_class)
cur_path = os.path.join(current_path, d.classes_[classe],
bname)
ecvl.ImWrite(cur_path, img_t)
n += 1
print("categorical_accuracy:", sum_ / args.batch_size)
total_avg = sum(total_metric) / len(total_metric)
print("Total categorical accuracy:", total_avg)
def main(args):
num_classes = 10
size = [28, 28] # size of images
ctype = ecvl.ColorType.GRAY
in_ = eddl.Input([1, size[0], size[1]])
out = LeNet(in_, num_classes)
net = eddl.Model([in_], [out])
eddl.build(net, eddl.sgd(0.001, 0.9), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU())
eddl.summary(net)
eddl.setlogfile(net, "mnist")
training_augs = ecvl.SequentialAugmentationContainer([
ecvl.AugRotate([-5, 5]),
ecvl.AugAdditivePoissonNoise([0, 10]),
ecvl.AugGaussianBlur([0, 0.8]),
ecvl.AugCoarseDropout([0, 0.3], [0.02, 0.05], 0),
])
dataset_augs = ecvl.DatasetAugmentations([training_augs, None, None])
print("Reading dataset")
d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs, ctype)
x_train = Tensor([args.batch_size, d.n_channels_, size[0], size[1]])
y_train = Tensor([args.batch_size, len(d.classes_)])
num_samples = len(d.GetSplit())
num_batches = num_samples // args.batch_size
indices = list(range(args.batch_size))
print("Training")
for i in range(args.epochs):
eddl.reset_loss(net)
s = d.GetSplit()
random.shuffle(s)
d.split_.training_ = s
d.ResetCurrentBatch()
for j in range(num_batches):
print("Epoch %d/%d (batch %d/%d) - " %
(i + 1, args.epochs, j + 1, num_batches),
end="",
flush=True)
d.LoadBatch(x_train, y_train)
x_train.div_(255.0)
tx, ty = [x_train], [y_train]
eddl.train_batch(net, tx, ty, indices)
eddl.print_loss(net, j)
print()
print("Saving weights")
eddl.save(net, "mnist_checkpoint.bin", "bin")
print("Evaluation")
d.SetSplit(ecvl.SplitType.test)
num_samples = len(d.GetSplit())
num_batches = num_samples // args.batch_size
for i in range(num_batches):
print("batch %d / %d - " % (i, num_batches), end="", flush=True)
d.LoadBatch(x_train, y_train)
x_train.div_(255.0)
eddl.evaluate(net, [x_train], [y_train])
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
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.sgd(0.001, 0.9), ["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")
eddl.setlogfile(net, "mnist")
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)
s = x_train.shape
num_batches = s[0] // args.batch_size
for i in range(args.epochs):
eddl.reset_loss(net)
print("Epoch %d/%d (%d batches)" % (i + 1, args.epochs, num_batches))
for j in range(num_batches):
indices = np.random.randint(0, s[0], args.batch_size)
eddl.train_batch(net, [x_train], [y_train], indices)
losses1 = eddl.get_losses(net)
metrics1 = eddl.get_metrics(net)
for l, m in zip(losses1, metrics1):
print("Loss: %.6f\tMetric: %.6f" % (l, m))
s = x_test.shape
num_batches = s[0] // args.batch_size
for j in range(num_batches):
indices = np.arange(j * args.batch_size,
j * args.batch_size + args.batch_size)
eddl.eval_batch(net, [x_test], [y_test], indices)
losses2 = eddl.get_losses(net)
metrics2 = eddl.get_metrics(net)
for l, m in zip(losses2, metrics2):
print("Loss: %.6f\tMetric: %.6f" % (l, m))
last_batch_size = s[0] % args.batch_size
if last_batch_size:
indices = np.arange(j * args.batch_size,
j * args.batch_size + args.batch_size)
eddl.eval_batch(net, [x_test], [y_test], indices)
losses3 = eddl.get_losses(net)
metrics3 = eddl.get_metrics(net)
for l, m in zip(losses3, metrics3):
print("Loss: %.6f\tMetric: %.6f" % (l, m))
print("All done")
def main(args):
num_classes = 8
size = [224, 224] # size of images
in_ = eddl.Input([3, size[0], size[1]])
out = VGG16(in_, num_classes)
net = eddl.Model([in_], [out])
eddl.build(net, eddl.sgd(0.001, 0.9), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU())
eddl.summary(net)
eddl.setlogfile(net, "skin_lesion_classification_inference")
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)
if args.out_dir:
for c in d.classes_:
os.makedirs(os.path.join(args.out_dir, c), exist_ok=True)
x = Tensor([args.batch_size, d.n_channels_, size[0], size[1]])
y = Tensor([args.batch_size, len(d.classes_)])
d.SetSplit(ecvl.SplitType.test)
num_samples = len(d.GetSplit())
num_batches = num_samples // args.batch_size
metric = eddl.getMetric("categorical_accuracy")
total_metric = []
if not os.path.exists(args.ckpts):
raise RuntimeError('Checkpoint "{}" not found'.format(args.ckpts))
eddl.load(net, args.ckpts, "bin")
print("Testing")
for b in range(num_batches):
n = 0
print("Batch {:d}/{:d}".format(b + 1, num_batches))
d.LoadBatch(x, y)
x.div_(255.0)
eddl.forward(net, [x])
output = eddl.getOutput(out)
sum_ = 0.0
for j in range(args.batch_size):
result = output.select([str(j)])
target = y.select([str(j)])
ca = metric.value(target, result)
total_metric.append(ca)
sum_ += ca
if args.out_dir:
result_a = np.array(result, copy=False)
target_a = np.array(target, copy=False)
classe = np.argmax(result_a).item()
gt_class = np.argmax(target_a).item()
single_image = x.select([str(j)])
img_t = ecvl.TensorToView(single_image)
img_t.colortype_ = ecvl.ColorType.BGR
single_image.mult_(255.)
filename = d.samples_[d.GetSplit()[n]].location_[0]
head, tail = os.path.splitext(os.path.basename(filename))
bname = "%s_gt_class_%s.png" % (head, gt_class)
cur_path = os.path.join(args.out_dir, d.classes_[classe],
bname)
ecvl.ImWrite(cur_path, img_t)
n += 1
print("categorical_accuracy:", sum_ / args.batch_size)
total_avg = sum(total_metric) / len(total_metric)
print("Total categorical accuracy:", total_avg)