def test_fail_on_mismatching_dimensionality(self):
from lasagne.layers import SpatialPyramidPoolingLayer
with pytest.raises(ValueError) as exc:
SpatialPyramidPoolingLayer((10, 20, 30))
assert "Expected 4 input dimensions" in exc.value.args[0]
with pytest.raises(ValueError) as exc:
SpatialPyramidPoolingLayer((10, 20, 30, 40, 50))
assert "Expected 4 input dimensions" in exc.value.args[0]
def build_model_L(pyramid_dims=[6, 4, 2, 1]):
input_var = tensor.ftensor4('x') # (B, C, H, W)
input0 = InputLayer(shape=(None, None, None, None),
input_var=input_var,
name='input0')
x = SpatialPyramidPoolingLayer(input0,
pool_dims=pyramid_dims,
implementation='fast')
return x
def build_architecture(input_shape, trained_weights=None):
net = {}
net['input'] = InputLayer((None, 1, None, None))
net['large_conv1'] = batch_norm(
Conv2DLayer(net['input'],
num_filters=32,
filter_size=11,
stride=3,
pad=5,
flip_filters=False))
net['large_pool1'] = MaxPool2DLayer(net['large_conv1'],
pool_size=3,
stride=2)
net['large_conv2'] = batch_norm(
Conv2DLayer(net['large_pool1'],
num_filters=64,
filter_size=5,
pad=2,
flip_filters=False))
net['large_pool2'] = MaxPool2DLayer(net['large_conv2'], pool_size=3)
net['large_conv3'] = batch_norm(
Conv2DLayer(net['large_pool2'],
num_filters=128,
filter_size=3,
pad=1,
flip_filters=False))
net['large_conv4'] = batch_norm(
Conv2DLayer(net['large_conv3'],
num_filters=128,
filter_size=3,
pad=1,
flip_filters=False))
net['large_pool4'] = MaxPool2DLayer(net['large_conv4'], pool_size=2)
net['large_conv5'] = batch_norm(
Conv2DLayer(net['large_pool4'],
num_filters=128,
filter_size=3,
pad=1,
flip_filters=False))
net['large_pool5'] = SpatialPyramidPoolingLayer(net['large_conv5'],
implementation='kaiming')
net['fc1'] = batch_norm(DenseLayer(net['large_pool5'], num_units=2048))
net['fc2'] = batch_norm(DenseLayer(net['fc1'], num_units=2048))
if trained_weights:
lasagne.layers.set_all_param_values(net['fc2'], trained_weights)
return net
def layer(self, input_layer, pool_dims, mode='max', implementation='fast'):
from lasagne.layers import SpatialPyramidPoolingLayer
if implementation != 'kaiming':
try:
import theano.tensor as T
from lasagne.layers.pool import pool_2d
pool_2d(T.tensor4(),
ws=T.ivector(),
stride=T.ivector(),
ignore_border=True,
pad=None)
except ValueError:
pytest.skip('Old theano version')
return SpatialPyramidPoolingLayer(input_layer,
pool_dims=pool_dims,
mode=mode,
implementation=implementation)