def test_layer_structure_undefined_file_name(self):
connection = layers.Input(10) > layers.Sigmoid(1)
with reproducible_mktemp():
plots.layer_structure(connection, filepath=None, show=False)
temp_filename = tempfile.mktemp()
filesize_after = os.path.getsize(temp_filename)
self.assertGreater(filesize_after, 0)
def test_layer_structure_undefined_file_name(self):
connection = layers.Input(10) > layers.Sigmoid(1)
with reproducible_mktemp():
plots.layer_structure(connection, filepath=None, show=False)
temp_filename = tempfile.mktemp()
filesize_after = os.path.getsize(temp_filename)
self.assertGreater(filesize_after, 0)
def test_that_layer_structure_does_not_fail(self):
connection = layers.Input(10) > layers.Sigmoid(1)
with tempfile.NamedTemporaryFile() as temp:
filesize_before = os.path.getsize(temp.name)
plots.layer_structure(connection, filepath=temp.name, show=False)
filesize_after = os.path.getsize(temp.name)
self.assertEqual(filesize_before, 0)
self.assertGreater(filesize_after, filesize_before)
def test_that_layer_structure_does_not_fail(self):
connection = layers.Input(10) > layers.Sigmoid(1)
with tempfile.NamedTemporaryFile() as temp:
filesize_before = os.path.getsize(temp.name)
plots.layer_structure(connection, filepath=temp.name, show=False)
filesize_after = os.path.getsize(temp.name)
self.assertEqual(filesize_before, 0)
self.assertGreater(filesize_after, filesize_before)
def test_that_layer_structure_for_network(self):
connection = layers.Input(10) > layers.Sigmoid(1)
network = algorithms.GradientDescent(connection)
with tempfile.NamedTemporaryFile() as temp:
filesize_before = os.path.getsize(temp.name)
plots.layer_structure(network, filepath=temp.name, show=False)
filesize_after = os.path.getsize(temp.name)
self.assertEqual(filesize_before, 0)
self.assertGreater(filesize_after, filesize_before)
def test_that_layer_structure_for_network(self):
connection = layers.Input(10) > layers.Sigmoid(1)
network = algorithms.GradientDescent(connection)
with tempfile.NamedTemporaryFile() as temp:
filesize_before = os.path.getsize(temp.name)
plots.layer_structure(network, filepath=temp.name, show=False)
filesize_after = os.path.getsize(temp.name)
self.assertEqual(filesize_before, 0)
self.assertGreater(filesize_after, filesize_before)
def test_layer_structure_ignore_layers_attr(self):
input_layer = layers.Input(10)
connection = input_layer > layers.Sigmoid(1)
with tempfile.NamedTemporaryFile() as temp:
plots.layer_structure(connection, filepath=temp.name, show=False,
ignore_layers=[])
filesize_first = os.path.getsize(temp.name)
with tempfile.NamedTemporaryFile() as temp:
plots.layer_structure(connection, filepath=temp.name, show=False,
ignore_layers=[layers.Sigmoid])
filesize_second = os.path.getsize(temp.name)
# First one should have more layers to draw
# than the second one
self.assertGreater(filesize_first, filesize_second)
def test_layer_structure_ignore_layers_attr(self):
input_layer = layers.Input(10)
connection = input_layer > layers.Sigmoid(1)
with tempfile.NamedTemporaryFile() as temp:
plots.layer_structure(connection,
filepath=temp.name,
show=False,
ignore_layers=[])
filesize_first = os.path.getsize(temp.name)
with tempfile.NamedTemporaryFile() as temp:
plots.layer_structure(connection,
filepath=temp.name,
show=False,
ignore_layers=[layers.Sigmoid])
filesize_second = os.path.getsize(temp.name)
# First one should have more layers to draw
# than the second one
self.assertGreater(filesize_first, filesize_second)
]],
layers.Concatenate(),
)
googlenet = layers.join(
layers.Input((3, None, None)),
layers.Convolution((64, 7, 7), padding='half', stride=2),
layers.Relu(),
layers.MaxPooling((3, 3), stride=2),
layers.LocalResponseNorm(alpha=0.00002, k=1),
layers.Convolution((64, 1, 1)) > layers.Relu(),
layers.Convolution((192, 3, 3), padding='half') > layers.Relu(),
layers.LocalResponseNorm(alpha=0.00002, k=1),
layers.MaxPooling((3, 3), stride=2),
Inception((32, 64, 96, 128, 16, 32)),
Inception((64, 128, 128, 192, 32, 96)),
layers.MaxPooling((3, 3), stride=2),
Inception((64, 192, 96, 208, 16, 48)),
Inception((64, 160, 112, 224, 24, 64)),
Inception((64, 128, 128, 256, 24, 64)),
Inception((64, 112, 144, 288, 32, 64)),
Inception((128, 256, 160, 320, 32, 128)),
layers.MaxPooling((3, 3), stride=2),
Inception((128, 256, 160, 320, 32, 128)),
Inception((128, 384, 192, 384, 48, 128)),
layers.GlobalPooling(function=T.mean),
layers.Softmax(1000),
)
plots.layer_structure(googlenet)
)
resnet50 = layers.join(
layers.Input((3, 224, 224)),
layers.Convolution((64, 7, 7), stride=2, padding=3),
layers.BatchNorm(),
layers.Relu(),
layers.MaxPooling((3, 3), stride=(2, 2), ignore_border=False),
ResidualUnit(64, 256, stride=1, has_branch=True),
ResidualUnit(64, 256, stride=1),
ResidualUnit(64, 256, stride=1),
ResidualUnit(128, 512, stride=2, has_branch=True),
ResidualUnit(128, 512, stride=1),
ResidualUnit(128, 512, stride=1),
ResidualUnit(128, 512, stride=1),
ResidualUnit(256, 1024, stride=2, has_branch=True),
ResidualUnit(256, 1024, stride=1),
ResidualUnit(256, 1024, stride=1),
ResidualUnit(256, 1024, stride=1),
ResidualUnit(256, 1024, stride=1),
ResidualUnit(256, 1024, stride=1),
ResidualUnit(512, 2048, stride=2, has_branch=True),
ResidualUnit(512, 2048, stride=1),
ResidualUnit(512, 2048, stride=1),
layers.GlobalPooling(),
layers.Reshape(),
layers.Softmax(1000),
)
plots.layer_structure(resnet50)
], [
layers.MaxPooling((3, 3), stride=(2, 2))
]],
layers.Concatenate(),
Inception_2([[192], [128, 128, 192], [128, 128, 128, 128, 192], [192]]),
Inception_2([[192], [160, 160, 192], [160, 160, 160, 160, 192], [192]]),
Inception_2([[192], [160, 160, 192], [160, 160, 160, 160, 192], [192]]),
Inception_2([[192], [192, 192, 192], [192, 192, 192, 192, 192], [192]]),
[[
ConvReluBN((192, 1, 1)),
ConvReluBN((320, 3, 3), stride=2),
], [
ConvReluBN((192, 1, 1)),
ConvReluBN((192, 1, 7), padding=(0, 3)),
ConvReluBN((192, 7, 1), padding=(3, 0)),
ConvReluBN((192, 3, 3), stride=2),
], [
layers.MaxPooling((3, 3), stride=(2, 2))
]],
layers.Concatenate(),
Inception_3(pooling='average'),
Inception_3(pooling='max'),
layers.GlobalPooling(),
layers.Softmax(1000),
)
plots.layer_structure(inception_v3)
layers.Input((3, None, None)),
layers.Convolution((64, 7, 7), padding='half', stride=2),
layers.Relu(),
layers.MaxPooling((3, 3), stride=2),
layers.LocalResponseNorm(alpha=0.00002, k=1),
layers.Convolution((64, 1, 1)) > layers.Relu(),
layers.Convolution((192, 3, 3), padding='half') > layers.Relu(),
layers.LocalResponseNorm(alpha=0.00002, k=1),
layers.MaxPooling((3, 3), stride=2),
Inception((32, 64, 96, 128, 16, 32)),
Inception((64, 128, 128, 192, 32, 96)),
layers.MaxPooling((3, 3), stride=2),
Inception((64, 192, 96, 208, 16, 48)),
Inception((64, 160, 112, 224, 24, 64)),
Inception((64, 128, 128, 256, 24, 64)),
Inception((64, 112, 144, 288, 32, 64)),
Inception((128, 256, 160, 320, 32, 128)),
layers.MaxPooling((3, 3), stride=2),
Inception((128, 256, 160, 320, 32, 128)),
Inception((128, 384, 192, 384, 48, 128)),
layers.GlobalPooling(function=T.mean),
layers.Softmax(1000),
)
plots.layer_structure(googlenet)
layers.BatchNorm(),
layers.Relu(),
layers.MaxPooling((3, 3), stride=(2, 2), ignore_border=False),
ResidualUnit(64, 256, stride=1, has_branch=True),
ResidualUnit(64, 256, stride=1),
ResidualUnit(64, 256, stride=1),
ResidualUnit(128, 512, stride=2, has_branch=True),
ResidualUnit(128, 512, stride=1),
ResidualUnit(128, 512, stride=1),
ResidualUnit(128, 512, stride=1),
ResidualUnit(256, 1024, stride=2, has_branch=True),
ResidualUnit(256, 1024, stride=1),
ResidualUnit(256, 1024, stride=1),
ResidualUnit(256, 1024, stride=1),
ResidualUnit(256, 1024, stride=1),
ResidualUnit(256, 1024, stride=1),
ResidualUnit(512, 2048, stride=2, has_branch=True),
ResidualUnit(512, 2048, stride=1),
ResidualUnit(512, 2048, stride=1),
layers.GlobalPooling(),
layers.Reshape(),
layers.Softmax(1000),
)
plots.layer_structure(resnet50)
