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_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.))