def test_Conv2DTranspose_pickle(): tg = TensorGraph() feature = Feature(shape=(tg.batch_size, 10, 10, 1)) layer = Conv2DTranspose(num_outputs=3, in_layers=feature) tg.add_output(layer) tg.set_loss(layer) tg.build() tg.save()
def test_conv_2D_transpose(self):
"""Test that Conv2DTranspose can be invoked."""
length = 4
width = 5
in_channels = 2
out_channels = 3
batch_size = 20
in_tensor = np.random.rand(batch_size, length, width, in_channels)
with self.session() as sess:
in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32)
out_tensor = Conv2DTranspose(out_channels, kernel_size=1,
stride=2)(in_tensor)
sess.run(tf.global_variables_initializer())
out_tensor = out_tensor.eval()
assert out_tensor.shape == (batch_size, 2 * length, 2 * width,
out_channels)
def __init__(self,
img_rows=512,
img_cols=512,
filters=[64, 128, 256, 512, 1024],
model=dc.models.TensorGraph(),
**kwargs):
super(UNet, self).__init__(use_queue=False, **kwargs)
self.img_cols = img_cols
self.img_rows = img_rows
self.filters = filters
self.model = dc.models.TensorGraph()
input = Feature(shape=(None, self.img_rows, self.img_cols))
conv1 = Conv2D(num_outputs=self.filters[0],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[input])
conv1 = Conv2D(num_outputs=self.filters[0],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv1])
pool1 = MaxPool2D(ksize=2, in_layers=[conv1])
conv2 = Conv2D(num_outputs=self.filters[1],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[pool1])
conv2 = Conv2D(num_outputs=self.filters[1],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv2])
pool2 = MaxPool2D(ksize=2, in_layers=[conv2])
conv3 = Conv2D(num_outputs=self.filters[2],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[pool2])
conv3 = Conv2D(num_outputs=self.filters[2],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv3])
pool3 = MaxPool2D(ksize=2, in_layers=[conv3])
conv4 = Conv2D(num_outputs=self.filters[3],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[pool3])
conv4 = Conv2D(num_outputs=self.filters[3],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv4])
pool4 = MaxPool2D(ksize=2, in_layers=[conv4])
conv5 = Conv2D(num_outputs=self.filters[4],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[pool4])
conv5 = Conv2D(num_outputs=self.filters[4],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv5])
up6 = Conv2DTranspose(num_outputs=self.filters[3],
kernel_size=2,
in_layers=[conv5])
concat6 = Concat(in_layers=[conv4, up6], axis=1)
conv6 = Conv2D(num_outputs=self.filters[3],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[concat6])
conv6 = Conv2D(num_outputs=self.filters[3],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv6])
up7 = Conv2DTranspose(num_outputs=self.filters[2],
kernel_size=2,
in_layers=[conv6])
concat7 = Concat(in_layers=[conv3, up7], axis=1)
conv7 = Conv2D(num_outputs=self.filters[2],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[concat7])
conv7 = Conv2D(num_outputs=self.filters[2],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv7])
up8 = Conv2DTranspose(num_outputs=self.filters[1],
kernel_size=2,
in_layers=[conv7])
concat8 = Concat(in_layers=[conv2, up8], axis=1)
conv8 = Conv2D(num_outputs=self.filters[1],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[concat8])
conv8 = Conv2D(num_outputs=self.filters[1],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv8])
up9 = Conv2DTranspose(num_outputs=self.filters[0],
kernel_size=2,
in_layers=[conv8])
concat9 = Concat(in_layers=[conv1, up9], axis=1)
conv9 = Conv2D(num_outputs=self.filters[0],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[concat9])
conv9 = Conv2D(num_outputs=self.filters[0],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv9])
conv10 = Conv2D(num_outputs=1,
kernel_size=1,
activation='sigmoid',
in_layers=[conv9])
model.add_output(conv10)
def __init__(self,
img_rows=512,
img_cols=512,
filters=[64, 128, 256, 512, 1024],
**kwargs):
super(UNet, self).__init__(use_queue=False, **kwargs)
self.img_cols = img_cols
self.img_rows = img_rows
self.filters = filters
input = Feature(shape=(None, self.img_rows, self.img_cols, 3))
labels = Label(shape=(None, self.img_rows * self.img_cols))
conv1 = Conv2D(num_outputs=self.filters[0],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[input])
conv1 = Conv2D(num_outputs=self.filters[0],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv1])
pool1 = MaxPool2D(ksize=[1, 2, 2, 1], in_layers=[conv1])
conv2 = Conv2D(num_outputs=self.filters[1],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[pool1])
conv2 = Conv2D(num_outputs=self.filters[1],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv2])
pool2 = MaxPool2D(ksize=[1, 2, 2, 1], in_layers=[conv2])
conv3 = Conv2D(num_outputs=self.filters[2],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[pool2])
conv3 = Conv2D(num_outputs=self.filters[2],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv3])
pool3 = MaxPool2D(ksize=[1, 2, 2, 1], in_layers=[conv3])
conv4 = Conv2D(num_outputs=self.filters[3],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[pool3])
conv4 = Conv2D(num_outputs=self.filters[3],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv4])
pool4 = MaxPool2D(ksize=[1, 2, 2, 1], in_layers=[conv4])
conv5 = Conv2D(num_outputs=self.filters[4],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[pool4])
conv5 = Conv2D(num_outputs=self.filters[4],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv5])
up6 = Conv2DTranspose(num_outputs=self.filters[3],
kernel_size=2,
stride=2,
in_layers=[conv5])
concat6 = Concat(in_layers=[conv4, up6], axis=3)
conv6 = Conv2D(num_outputs=self.filters[3],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[concat6])
conv6 = Conv2D(num_outputs=self.filters[3],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv6])
up7 = Conv2DTranspose(num_outputs=self.filters[2],
kernel_size=2,
stride=2,
in_layers=[conv6])
concat7 = Concat(in_layers=[conv3, up7], axis=3)
conv7 = Conv2D(num_outputs=self.filters[2],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[concat7])
conv7 = Conv2D(num_outputs=self.filters[2],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv7])
up8 = Conv2DTranspose(num_outputs=self.filters[1],
kernel_size=2,
stride=2,
in_layers=[conv7])
concat8 = Concat(in_layers=[conv2, up8], axis=3)
conv8 = Conv2D(num_outputs=self.filters[1],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[concat8])
conv8 = Conv2D(num_outputs=self.filters[1],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv8])
up9 = Conv2DTranspose(num_outputs=self.filters[0],
kernel_size=2,
stride=2,
in_layers=[conv8])
concat9 = Concat(in_layers=[conv1, up9], axis=3)
conv9 = Conv2D(num_outputs=self.filters[0],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[concat9])
conv9 = Conv2D(num_outputs=self.filters[0],
kernel_size=3,
activation='relu',
padding='same',
in_layers=[conv9])
conv10 = Conv2D(num_outputs=1,
kernel_size=1,
activation='sigmoid',
in_layers=[conv9])
loss = SoftMaxCrossEntropy(in_layers=[labels, conv10])
loss = ReduceMean(in_layers=[loss])
self.set_loss(loss)
self.add_output(conv10)