def task_train_filter(args):
ff, mapper = getattr(__import__('dataset'), args.dataset)()
dataset = Dataset(root_path=args.path,
verbose=args.verbose,
name='base-model',
mapper=mapper,
filename_filter=ff,
rotation=False)
dataset_batches = BatchGenerator(dataset, args.batch)
from mitosis import model_base
TT.debug("Compile base model.")
model = model_base(args.lr)
model_saved_weights_path = os.path.join(args.path,
'base-model.weights.npy')
if os.path.exists(model_saved_weights_path):
TT.info("Loading weights from %s" % model_saved_weights_path)
model.load_weights(model_saved_weights_path)
train_start = time.time()
log = LearnLog("filter", args.path)
for epoch in xrange(args.epoch):
TT.debug(epoch + 1, "of", args.epoch, "epochs")
log.on_dataset_epoch_begin(epoch + 1)
for x, y in dataset_batches:
model.fit(x,
y,
batch_size=args.mini_batch,
nb_epoch=1,
validation_split=.1,
callbacks=[log],
show_accuracy=True,
shuffle=True)
log.on_dataset_epoch_end(epoch + 1)
log.on_dataset_train_end()
TT.success("Training finished in %.2f hours." %
((time.time() - train_start) / 3600.))
def task_test_cnn(args):
dataset = ImageIterator(args.input, args.output)
dataset_batches = BatchGenerator(dataset, args.batch)
from mitosis import model_base, model_1, model_2
TT.debug("Compile base model.")
model = model_base(0)
TT.debug("Compile model 1.")
model1 = model_1(0)
TT.debug("Compile model 2.")
model2 = model_2(0)
model_saved_weights_path = os.path.join(args.path,
'base-model.weights.npy')
model1_saved_weights_path = os.path.join(args.path, 'model1.weights.npy')
model2_saved_weights_path = os.path.join(args.path, 'model2.weights.npy')
TT.info("Loading weights from %s" % model_saved_weights_path)
model.load_weights(model_saved_weights_path)
TT.info("Loading weights from %s" % model1_saved_weights_path)
model1.load_weights(model1_saved_weights_path)
TT.info("Loading weights from %s" % model2_saved_weights_path)
model2.load_weights(model2_saved_weights_path)
test_start = time.time()
out = out1 = out2 = None
for x, y in dataset_batches:
tmp = model.predict(x, args.mini_batch, args.verbose)
local1 = numpy.zeros(tmp.shape)
local2 = numpy.zeros(tmp.shape)
out = np_append(out, tmp)
x = 1. - x
x_new = []
indices = []
for i in range(len(tmp)):
if tmp[i][0] > .6:
x_new.append(x[i])
indices.append(i)
x_new = numpy.asarray(x_new)
if len(x_new):
tmp1 = model1.predict(x_new, args.mini_batch, args.verbose)
local1[indices] = tmp1
out1 = np_append(out1, local1)
if len(x_new):
tmp2 = model2.predict(x_new, args.mini_batch, args.verbose)
local2[indices] = tmp2
out2 = np_append(out2, local2)
width, height = dataset.image_size
out = numpy.reshape(out[:, 0], (height, width))
out1 = numpy.reshape(out1[:, 0], (height, width))
out2 = numpy.reshape(out2[:, 0], (height, width))
numpy.save(change_ext(args.input, 'predicted.npy'), out)
numpy.save(change_ext(args.input, 'model1.predicted.npy'), out1)
numpy.save(change_ext(args.input, 'model2.predicted.npy'), out2)
numpy.save(change_ext(args.input, 'expected.npy'), dataset.output)
TT.success("Testing finished in %.2f minutes." %
((time.time() - test_start) / 60.))
def task_train_cnn(args):
ff, mapper = getattr(__import__('dataset'), args.dataset)()
dataset = Dataset(root_path=args.path, verbose=args.verbose, name='cnn',
mapper=mapper, filename_filter=ff, ratio=9)
dataset_batches = BatchGenerator(dataset, args.batch)
from mitosis import model_base, model_1, model_2
TT.debug("Compile base model.")
model = model_base(lr=0)
TT.debug("Compile model 1.")
model1 = model_1(args.lr)
TT.debug("Compile model 2.")
model2 = model_2(args.lr)
model_saved_weights_path = os.path.join(args.path, 'base-model.weights.npy')
model1_saved_weights_path = os.path.join(args.path, 'model1.weights.npy')
model2_saved_weights_path = os.path.join(args.path, 'model2.weights.npy')
if os.path.exists(model_saved_weights_path):
TT.info("Loading weights from %s" % model_saved_weights_path)
model.load_weights(model_saved_weights_path)
if os.path.exists(model1_saved_weights_path):
TT.info("Loading weights from %s" % model1_saved_weights_path)
model1.load_weights(model1_saved_weights_path)
if os.path.exists(model2_saved_weights_path):
TT.info("Loading weights from %s" % model2_saved_weights_path)
model2.load_weights(model2_saved_weights_path)
train_start = time.time()
log1 = LearnLog("model1", args.path)
log2 = LearnLog("model2", args.path)
for epoch in xrange(args.epoch):
TT.debug(epoch + 1, "of", args.epoch, "epochs")
log1.on_dataset_epoch_begin(epoch + 1)
log2.on_dataset_epoch_begin(epoch + 1)
for x, y in dataset_batches:
outputs = model.predict(x, batch_size=args.mini_batch, verbose=args.verbose)
# Multiply each window with it's prediction and then pass it to the next layer
# x_new = []
# y_new = []
x = 1. - x
for i in range(len(outputs)):
if y[i][0] < 1.:
# x_new.append(x[i])
# y_new.append(y[i])
x[i] *= outputs[i][0]
TT.debug("Model 1 on epoch %d" % (epoch + 1))
model1.fit(numpy.asarray(x), numpy.asarray(y), batch_size=args.mini_batch, nb_epoch=1, validation_split=.1,
callbacks=[log1], show_accuracy=True, shuffle=True)
TT.debug("Model 2 on epoch %d" % (epoch + 1))
model2.fit(numpy.asarray(x), numpy.asarray(y), batch_size=args.mini_batch, nb_epoch=1, validation_split=.1,
callbacks=[log2], show_accuracy=True, shuffle=True)
log1.on_dataset_epoch_end(epoch + 1)
log2.on_dataset_epoch_end(epoch + 1)
log1.on_dataset_train_end()
log2.on_dataset_train_end()
TT.success("Training finished in %.2f hours." % ((time.time() - train_start) / 3600.))
def task_test_cnn(args):
dataset = ImageIterator(args.input, args.output)
dataset_batches = BatchGenerator(dataset, args.batch)
from mitosis import model_base, model_1, model_2
TT.debug("Compile base model.")
model = model_base(0)
TT.debug("Compile model 1.")
model1 = model_1(0)
TT.debug("Compile model 2.")
model2 = model_2(0)
model_saved_weights_path = os.path.join(args.path, 'base-model.weights.npy')
model1_saved_weights_path = os.path.join(args.path, 'model1.weights.npy')
model2_saved_weights_path = os.path.join(args.path, 'model2.weights.npy')
TT.info("Loading weights from %s" % model_saved_weights_path)
model.load_weights(model_saved_weights_path)
TT.info("Loading weights from %s" % model1_saved_weights_path)
model1.load_weights(model1_saved_weights_path)
TT.info("Loading weights from %s" % model2_saved_weights_path)
model2.load_weights(model2_saved_weights_path)
test_start = time.time()
out = out1 = out2 = None
for x, y in dataset_batches:
tmp = model.predict(x, args.mini_batch, args.verbose)
local1 = numpy.zeros(tmp.shape)
local2 = numpy.zeros(tmp.shape)
out = np_append(out, tmp)
x = 1. - x
x_new = []
indices = []
for i in range(len(tmp)):
if tmp[i][0] > .6:
x_new.append(x[i])
indices.append(i)
x_new = numpy.asarray(x_new)
if len(x_new):
tmp1 = model1.predict(x_new, args.mini_batch, args.verbose)
local1[indices] = tmp1
out1 = np_append(out1, local1)
if len(x_new):
tmp2 = model2.predict(x_new, args.mini_batch, args.verbose)
local2[indices] = tmp2
out2 = np_append(out2, local2)
width, height = dataset.image_size
out = numpy.reshape(out[:, 0], (height, width))
out1 = numpy.reshape(out1[:, 0], (height, width))
out2 = numpy.reshape(out2[:, 0], (height, width))
numpy.save(change_ext(args.input, 'predicted.npy'), out)
numpy.save(change_ext(args.input, 'model1.predicted.npy'), out1)
numpy.save(change_ext(args.input, 'model2.predicted.npy'), out2)
numpy.save(change_ext(args.input, 'expected.npy'), dataset.output)
TT.success("Testing finished in %.2f minutes." % ((time.time() - test_start) / 60.))
def task_test_filter(args):
dataset = ImageIterator(args.input, args.output)
dataset_batches = BatchGenerator(dataset, args.batch)
from mitosis import model_base
TT.debug("Compile base model.")
model = model_base(args.lr)
model_saved_weights_path = os.path.join(args.path, 'base-model.weights.npy')
TT.info("Loading weights from %s" % model_saved_weights_path)
model.load_weights(model_saved_weights_path)
test_start = time.time()
out = None
for x, y in dataset_batches:
tmp = model.predict(x, args.mini_batch, args.verbose)
out = np_append(out, tmp)
width, height = dataset.image_size
out = numpy.reshape(out[:, 0], (height, width))
numpy.save(change_ext(args.input, 'predicted.npy'), out)
numpy.save(change_ext(args.input, 'expected.npy'), dataset.output)
TT.success("Testing finished in %.2f minutes." % ((time.time() - test_start) / 60.))
def task_test_filter(args):
dataset = ImageIterator(args.input, args.output)
dataset_batches = BatchGenerator(dataset, args.batch)
from mitosis import model_base
TT.debug("Compile base model.")
model = model_base(args.lr)
model_saved_weights_path = os.path.join(args.path, 'base-model.weights.npy')
TT.info("Loading weights from %s" % model_saved_weights_path)
model.load_weights(model_saved_weights_path)
test_start = time.time()
out = None
for x, y in dataset_batches:
tmp = model.predict(x, args.mini_batch, args.verbose)
out = np_append(out, tmp)
width, height = dataset.image_size
out = numpy.reshape(out[:, 0], (height, width))
numpy.save(change_ext(args.input, 'predicted.npy'), out)
numpy.save(change_ext(args.input, 'expected.npy'), dataset.output)
TT.success("Testing finished in %.2f minutes." % ((time.time() - test_start) / 60.))
def task_train_filter(args):
ff, mapper = getattr(__import__('dataset'), args.dataset)()
dataset = Dataset(root_path=args.path, verbose=args.verbose, name='base-model',
mapper=mapper, filename_filter=ff, rotation=False)
dataset_batches = BatchGenerator(dataset, args.batch)
from mitosis import model_base
TT.debug("Compile base model.")
model = model_base(args.lr)
model_saved_weights_path = os.path.join(args.path, 'base-model.weights.npy')
if os.path.exists(model_saved_weights_path):
TT.info("Loading weights from %s" % model_saved_weights_path)
model.load_weights(model_saved_weights_path)
train_start = time.time()
log = LearnLog("filter", args.path)
for epoch in xrange(args.epoch):
TT.debug(epoch + 1, "of", args.epoch, "epochs")
log.on_dataset_epoch_begin(epoch + 1)
for x, y in dataset_batches:
model.fit(x, y, batch_size=args.mini_batch, nb_epoch=1, validation_split=.1,
callbacks=[log], show_accuracy=True, shuffle=True)
log.on_dataset_epoch_end(epoch + 1)
log.on_dataset_train_end()
TT.success("Training finished in %.2f hours." % ((time.time() - train_start) / 3600.))
def task_train_cnn(args):
ff, mapper = getattr(__import__('dataset'), args.dataset)()
dataset = Dataset(root_path=args.path,
verbose=args.verbose,
name='cnn',
mapper=mapper,
filename_filter=ff,
ratio=9)
dataset_batches = BatchGenerator(dataset, args.batch)
from mitosis import model_base, model_1, model_2
TT.debug("Compile base model.")
model = model_base(lr=0)
TT.debug("Compile model 1.")
model1 = model_1(args.lr)
TT.debug("Compile model 2.")
model2 = model_2(args.lr)
model_saved_weights_path = os.path.join(args.path,
'base-model.weights.npy')
model1_saved_weights_path = os.path.join(args.path, 'model1.weights.npy')
model2_saved_weights_path = os.path.join(args.path, 'model2.weights.npy')
if os.path.exists(model_saved_weights_path):
TT.info("Loading weights from %s" % model_saved_weights_path)
model.load_weights(model_saved_weights_path)
if os.path.exists(model1_saved_weights_path):
TT.info("Loading weights from %s" % model1_saved_weights_path)
model1.load_weights(model1_saved_weights_path)
if os.path.exists(model2_saved_weights_path):
TT.info("Loading weights from %s" % model2_saved_weights_path)
model2.load_weights(model2_saved_weights_path)
train_start = time.time()
log1 = LearnLog("model1", args.path)
log2 = LearnLog("model2", args.path)
for epoch in xrange(args.epoch):
TT.debug(epoch + 1, "of", args.epoch, "epochs")
log1.on_dataset_epoch_begin(epoch + 1)
log2.on_dataset_epoch_begin(epoch + 1)
for x, y in dataset_batches:
outputs = model.predict(x,
batch_size=args.mini_batch,
verbose=args.verbose)
# Multiply each window with it's prediction and then pass it to the next layer
# x_new = []
# y_new = []
x = 1. - x
for i in range(len(outputs)):
if y[i][0] < 1.:
# x_new.append(x[i])
# y_new.append(y[i])
x[i] *= outputs[i][0]
TT.debug("Model 1 on epoch %d" % (epoch + 1))
model1.fit(numpy.asarray(x),
numpy.asarray(y),
batch_size=args.mini_batch,
nb_epoch=1,
validation_split=.1,
callbacks=[log1],
show_accuracy=True,
shuffle=True)
TT.debug("Model 2 on epoch %d" % (epoch + 1))
model2.fit(numpy.asarray(x),
numpy.asarray(y),
batch_size=args.mini_batch,
nb_epoch=1,
validation_split=.1,
callbacks=[log2],
show_accuracy=True,
shuffle=True)
log1.on_dataset_epoch_end(epoch + 1)
log2.on_dataset_epoch_end(epoch + 1)
log1.on_dataset_train_end()
log2.on_dataset_train_end()
TT.success("Training finished in %.2f hours." %
((time.time() - train_start) / 3600.))