def test_invalid_mode(self):
from lasagne.layers.pool import Upscale2DLayer
inlayer = self.input_layer((128, 3, 32, 32))
with pytest.raises(ValueError):
Upscale2DLayer(inlayer, scale_factor=1, mode='')
with pytest.raises(ValueError):
Upscale2DLayer(inlayer, scale_factor=1, mode='other')
with pytest.raises(ValueError):
Upscale2DLayer(inlayer, scale_factor=1, mode=0)
def test_invalid_scale_factor(self):
from lasagne.layers.pool import Upscale2DLayer
inlayer = self.input_layer((128, 3, 32, 32))
with pytest.raises(ValueError):
Upscale2DLayer(inlayer, scale_factor=0)
with pytest.raises(ValueError):
Upscale2DLayer(inlayer, scale_factor=-1)
with pytest.raises(ValueError):
Upscale2DLayer(inlayer, scale_factor=(0, 2))
with pytest.raises(ValueError):
Upscale2DLayer(inlayer, scale_factor=(2, 0))
def layer(self, input_layer, scale_factor, mode):
from lasagne.layers.pool import Upscale2DLayer
return Upscale2DLayer(
input_layer,
scale_factor=scale_factor,
mode=mode,
)
def build_model(inp):
net = {}
net['input'] = InputLayer((None, 3, 224, 224), input_var=input_var)
net['conv1_1'] = batch_norm(
Conv2DLayer(net['input'], 64, 3, pad=1, flip_filters=False))
net['conv1_2'] = batch_norm(
Conv2DLayer(net['conv1_1'], 64, 3, pad=1, flip_filters=False))
net['pool1'] = MaxPool2DLayer(net['conv1_2'], 2)
net['conv2_1'] = batch_norm(
Conv2DLayer(net['pool1'], 128, 3, pad=1, flip_filters=False))
net['conv2_2'] = batch_norm(
Conv2DLayer(net['conv2_1'], 128, 3, pad=1, flip_filters=False))
net['pool2'] = MaxPool2DLayer(net['conv2_2'], 2)
net['conv3_1'] = batch_norm(
Conv2DLayer(net['pool2'], 256, 3, pad=1, flip_filters=False))
net['conv3_2'] = batch_norm(
Conv2DLayer(net['conv3_1'], 256, 3, pad=1, flip_filters=False))
net['conv3_3'] = batch_norm(
Conv2DLayer(net['conv3_2'], 256, 3, pad=1, flip_filters=False))
net['pool3'] = MaxPool2DLayer(net['conv3_3'], 2)
net['conv4_1'] = batch_norm(
Conv2DLayer(net['pool3'], 512, 3, pad=1, flip_filters=False))
net['conv4_2'] = batch_norm(
Conv2DLayer(net['conv4_1'], 512, 3, pad=1, flip_filters=False))
net['conv4_3'] = batch_norm(
Conv2DLayer(net['conv4_2'], 512, 3, pad=1, flip_filters=False))
net['pool4'] = MaxPool2DLayer(net['conv4_3'], 2)
net['conv5_1'] = batch_norm(
Conv2DLayer(net['pool4'], 512, 3, pad=1, flip_filters=False))
net['conv5_2'] = batch_norm(
Conv2DLayer(net['conv5_1'], 512, 3, pad=1, flip_filters=False))
net['conv5_3'] = batch_norm(
Conv2DLayer(net['conv5_2'], 512, 3, pad=1, flip_filters=False))
net['pool5'] = MaxPool2DLayer(net['conv5_3'], 2)
net['fc6'] = batch_norm(DenseLayer(net['pool5'], num_units=4096))
net['fc6_dropout'] = DropoutLayer(net['fc6'], p=0.5)
net['fc7'] = batch_norm(DenseLayer(net['fc6_dropout'], num_units=4096))
net['fc7_dropout'] = DropoutLayer(net['fc7'], p=0.5)
net['reshape'] = ReshapeLayer(net['fc7_dropout'], (-1, 4096, 1, 1))
net['deconv6'] = batch_norm(Deconv2DLayer(net['reshape'], 512, 7))
net['unpool5'] = Upscale2DLayer(net['deconv6'], 2)
net['deconv5_3'] = batch_norm(
Deconv2DLayer(net['unpool5'], 512, 3, crop=1, flip_filters=False))
net['deconv5_2'] = batch_norm(
Deconv2DLayer(net['deconv5_3'], 512, 3, crop=1, flip_filters=False))
net['deconv5_1'] = batch_norm(
Deconv2DLayer(net['deconv5_2'], 512, 3, crop=1, flip_filters=False))
net['unpool4'] = Upscale2DLayer(net['deconv5_1'], 2)
net['deconv4_3'] = batch_norm(
Deconv2DLayer(net['unpool4'], 512, 3, crop=1, flip_filters=False))
net['deconv4_2'] = batch_norm(
Deconv2DLayer(net['deconv4_3'], 512, 3, crop=1, flip_filters=False))
net['deconv4_1'] = batch_norm(
Deconv2DLayer(net['deconv4_2'], 512, 3, crop=1, flip_filters=False))
net['unpool3'] = Upscale2DLayer(net['deconv4_1'], 2)
net['deconv3_3'] = batch_norm(
Deconv2DLayer(net['unpool3'], 256, 3, crop=1, flip_filters=False))
net['deconv3_2'] = batch_norm(
Deconv2DLayer(net['deconv3_3'], 256, 3, crop=1, flip_filters=False))
net['deconv3_1'] = batch_norm(
Deconv2DLayer(net['deconv3_2'], 256, 3, crop=1, flip_filters=False))
net['unpool2'] = Upscale2DLayer(net['deconv3_1'], 2)
net['deconv2_2'] = batch_norm(
Deconv2DLayer(net['unpool2'], 128, 3, crop=1, flip_filters=False))
net['deconv2_1'] = batch_norm(
Deconv2DLayer(net['deconv2_2'], 128, 3, crop=1, flip_filters=False))
net['unpool1'] = Upscale2DLayer(net['deconv2_1'], 2)
net['deconv1_1'] = batch_norm(
Deconv2DLayer(net['unpool1'], 64, 3, crop=1, flip_filters=False))
net['deconv1_2'] = batch_norm(
Deconv2DLayer(net['deconv1_1'], 64, 3, crop=1, flip_filters=False))
net['out'] = NonlinearityLayer(DenseLayer(net['deconv1_2'], 21), softmax)
return net