def test_directly_constructed(self):
x = Constant(1.0, 'x')
y = Constant(2.0, 'y')
s = Summation('summation', [x, y])
v = s()
with self.test_session() as sess:
assert sess.run(v) == 3.0
def get_data(self):
path = self.resource_path() / 'model' / 'projection' / 'ToR'
image = np.load(path / 'test_image.npy')
with open(path / 'config.json') as fin:
config = json.load(fin)
image = Constant(image, 'image')
assert tuple(image.shape) == tuple(config['grid'])
image = Image(image, config['center'], config['size'])
def get_data(self):
path = self.resource_path / 'physics' / 'ToRMap'
image = np.load(path / 'test_image.npy').astype(np.float32)
with open(path / 'config.json') as fin:
config = json.load(fin)
image = Constant(image, 'image')
assert tuple(image.shape) == tuple(config['grid'])
image = Image(image, config['center'], config['size'])
lors = np.load(path / 'test_lors.npy')
def get_data(self):
path = self.resource_path / 'physics' / 'Siddon'
image = np.load(path / 'test_image.npy').astype(np.float32)
with open(path / 'config.json') as fin:
config = json.load(fin)
image = Constant(image, 'image')
assert tuple(image.shape) == tuple(config['grid'])
image = Image(image, config['center'], config['size'])
lors = np.load(path / 'test_lors.npy').astype(np.float32)
lors = Constant(lors, 'lors')
projected = np.load(path / 'test_projection.npy')
back_projected = np.load(path / 'test_backprojection.npy')
lors_value = Constant(projected, 'lors_value')
return {
'image': image,
'lors': lors,
'lors_value': lors_value,
'projected': projected,
'backprojected': back_projected
}
def test_inputs(self):
x = Constant(1.0, 'x')
s = Summation('summation', [x] * 3)
assert s.tensors['input'] == [x] * 3
def test_construct_with_none_input(self):
x = Constant(1.0, 'x')
s = Summation('summation')
v = s([x] * 3)
with self.test_session() as sess:
assert sess.run(v) == 3.0
def test_summation_of_same_tensor(self):
x = Constant(1.0, 'x')
s = Summation('summation', [x, x, x])
v = s()
with self.test_session() as sess:
assert sess.run(v) == 3.0
def test_run_list(self):
c = Constant(1.0, 'const')
with self.test_session() as sess:
assert sess.run([c, c]) == [1.0, 1.0]
def load(self, target_graph):
return {
'projection_data':
Constant(np.ones([30, 7]), 'projection_data')
}, ()
def kernel(self, inputs):
return Constant(5 * np.ones([5] * 3, dtype=np.float32),
'result')
def get_subset(self):
s = Constant(1, 'subset')
return s
def get_dummy_input(self):
return Constant(1.0, 'x')
def make_dummy_tensor(self, info=None):
from dxl.learn.core import Constant
if info is None:
info = str(uuid.uuid4())
return Constant(0.0, info)