Unet-Segmentation
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lmb-freiburg
Use the weights available at https://lmb.informatik.uni-freiburg.de/resources/opensource/unet/ for Tensorflow 2 (Keras)
Hi everyone!
I am trying to use the Caffe weights available at https://lmb.informatik.uni-freiburg.de/resources/opensource/unet/ for a model implemented in Tensorflow.
To be more specific, I read the 2d_cell_net_v0.modeldef.h5 file in order to get the layer names in the right order:
layer_names=[]
mod=h5py.File('2d_cell_net_v0.modeldef.h5', 'r')
for line in mod['model_prototxt'].value.decode('utf8').split('\n'):
if line.startswith('layer'):
name=line.split('name:')[1].split(' ')[1].replace('\'','')
layer_names.append(name)
Then, I loop through the layers available in the 2d_cell_net_v0.caffemodel.h5 file and try to find the corresponding layers in TensorFlow Keras. Doing this, I saw that the shape of the layers were different and I corrected this. In the end, I extract the weights from the Caffe layer and set the Tensorflow layer with them.
size_image=512
unet = networks.UNet_Freiburg((size_image,size_image,1))
caffe_weights=[]
unet_weights=h5py.File('2d_cell_net_v0.caffemodel.h5', 'r')
data=unet_weights['data']
for layer_name in layer_names:
for layer in data:
l=unet_weights['/data/'+layer]
name=l.name.split('/')[-1]
if name==layer_name:
if '0' in l:
weight_array=np.array(l['0']).T
bias_array=np.array(l['1']).T
if 'up' in name:
weight_array=np.swapaxes(weight_array,2,3)
shape_weights=weight_array.shape
shape_bias=bias_array.shape
for ul in unet.layers:
if ul.name==name:
ul.set_weights([weight_array,bias_array])
The results are not what I expected:
Could anyone help me with this? What am I missing?
In principle your approach is fine. caffe stores the weights in order (cOut, cIn, y, x) for convolution and (cIn, cOut, y, x) for up-convolution. Probably dimensions must be permuted a little more to work with the corresponding keras layers? I don't know the native dimension order of keras/tensorflow by heart. But this is my first guess of what might cause your strange outputs.
What kind of padding do you use? The plugin processes the image in overlapping tiles and uses mirroring to extrapolate data across image boundaries. You probably used zero padding leading to this wide bright border?
In TensorFlow, I use the Conv2D object with padding='same'. I changed it to 'valid', but it causes some problem now with layer concatenations. In my network, it is done like this:
def UNet_Freiburg(shape):
inputs = tfk.layers.Input(shape=shape, name='input')
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_d0a-b')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_d0b-c')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2), name='maxpool1')(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_d1a-b')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_d1b-c')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2), name='maxpool2')(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_d2a-b')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_d2b-c')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2), name='maxpool3')(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_d3a-b')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_d3b-c')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2), name='maxpool4')(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_d4a-b')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_d4b-c')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='upconv_d4c_u3a')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_u3b-c')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_u3c-d')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='upconv_u3d_u2a')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_u2b-c')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_u2c-d')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='upconv_u2d_u1a')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_u1b-c')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_u1c-d')(conv8)
up9 = Conv2D(128, 2, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='upconv_u1d_u0a')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_u0b-c')(merge9)
conv9 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_u0c-d')(conv9)
conv9 = Conv2D(2, 1, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal', name='conv_u0d-score')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
model = Model(inputs, conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
return model
and I get this error message:
ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 56, 56, 512), (None, 47, 47, 512)]
I guess I need to rearrange the whole network in order to adapt to the padding change.
Ah, OK, you used padding, you're right U-Net uses valid convolutions. The left blob must be cropped to match the spatial shape of the right blob.
This is my keras implementation of U-Net:
class Unet2D:
def __init__(self, snapshot=None, n_channels=1, n_classes=2, n_levels=4,
n_features=64, name="U-Net"):
self.concat_blobs = []
self.n_channels = n_channels
self.n_classes = n_classes
self.n_levels = n_levels
self.n_features = n_features
self.name = name
self.trainModel, self.padding = self._createModel(True)
self.testModel, _ = self._createModel(False)
if snapshot is not None:
self.trainModel.load_weights(snapshot)
self.testModel.load_weights(snapshot)
def _weighted_categorical_crossentropy(self, y_true, y_pred, weights):
return tf.losses.softmax_cross_entropy(
y_true, y_pred, weights=weights, reduction=tf.losses.Reduction.MEAN)
def _createModel(self, training):
data = keras.layers.Input(shape=(None, None, self.n_channels), name="data")
concat_blobs = []
if training:
labels = keras.layers.Input(
shape=(None, None, self.n_classes), name="labels")
weights = keras.layers.Input(shape=(None, None), name="weights")
# Modules of the analysis path consist of two convolutions and max pooling
for l in range(self.n_levels):
t = keras.layers.LeakyReLU(alpha=0.1)(
keras.layers.Conv2D(
2**l * self.n_features, 3, padding="valid",
kernel_initializer="he_normal",
name="conv_d{}a-b".format(l))(data if l == 0 else t))
concat_blobs.append(
keras.layers.LeakyReLU(alpha=0.1)(
keras.layers.Conv2D(
2**l * self.n_features, 3, padding="valid",
kernel_initializer="he_normal", name="conv_d{}b-c".format(l))(t)))
t = keras.layers.MaxPooling2D(pool_size=(2, 2))(concat_blobs[-1])
# Deepest layer has two convolutions only
t = keras.layers.LeakyReLU(alpha=0.1)(
keras.layers.Conv2D(
2**self.n_levels * self.n_features, 3, padding="valid",
kernel_initializer="he_normal",
name="conv_d{}a-b".format(self.n_levels))(t))
t = keras.layers.LeakyReLU(alpha=0.1)(
keras.layers.Conv2D(
2**self.n_levels * self.n_features, 3, padding="valid",
kernel_initializer="he_normal",
name="conv_d{}b-c".format(self.n_levels))(t))
pad = 8
# Modules in the synthesis path consist of up-convolution,
# concatenation and two convolutions
for l in range(self.n_levels - 1, -1, -1):
name = "upconv_{}{}{}_u{}a".format(
*(("d", l+1, "c", l) if l == self.n_levels - 1 else ("u", l+1, "d", l)))
t = keras.layers.LeakyReLU(alpha=0.1)(
keras.layers.Conv2D(
2**l * self.n_features, 2, padding="same",
kernel_initializer="he_normal", name=name)(
keras.layers.UpSampling2D(size = (2,2))(t)))
t = keras.layers.Concatenate()(
[keras.layers.Cropping2D(cropping=int(pad / 2))(concat_blobs[l]), t])
pad = 2 * (pad + 8)
t = keras.layers.LeakyReLU(alpha=0.1)(
keras.layers.Conv2D(
2**l * self.n_features, 3, padding="valid",
kernel_initializer="he_normal", name="conv_u{}b-c".format(l))(t))
t = keras.layers.LeakyReLU(alpha=0.1)(
keras.layers.Conv2D(
2**l * self.n_features, 3, padding="valid",
kernel_initializer="he_normal", name="conv_u{}c-d".format(l))(t))
pad /= 2
score = keras.layers.Conv2D(
self.n_classes, 1, kernel_initializer = 'he_normal',
name="conv_u0d-score")(t)
softmax_score = keras.layers.Softmax()(score)
if training:
model = keras.Model(inputs=[data, labels, weights], outputs=softmax_score)
model.add_loss(
self._weighted_categorical_crossentropy(labels, score, weights))
adam = keras.optimizers.Adam(
lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0,
amsgrad=False)
model.compile(optimizer=adam, loss=None)
else:
model = keras.Model(inputs=data, outputs=softmax_score)
return model, int(pad)
def loadCaffeModelH5(self, path):
train_layer_dict = dict([(layer.name, layer)
for layer in self.trainModel.layers])
test_layer_dict = dict([(layer.name, layer)
for layer in self.testModel.layers])
pre = h5py.File(path, 'a')
l = list(pre['data'].keys())
for i in l:
kernel = pre['data'][i]['0'][()]
bias = pre['data'][i]['1'][()]
train_layer_dict[i].set_weights([kernel,bias])
test_layer_dict[i].set_weights([kernel,bias])
pre.close()
def train(self, sample_generator, validation_generator=None,
n_epochs=100, snapshot_interval=1, snapshot_prefix=None):
callbacks = [TensorBoard(log_dir="logs/{}-{}".format(self.name, time()))]
if snapshot_prefix is not None:
callbacks.append(keras.callbacks.ModelCheckpoint(
(snapshot_prefix if snapshot_prefix is not None else self.name) +
".{epoch:04d}.h5", mode='auto', period=snapshot_interval))
self.trainModel.fit_generator(
sample_generator, epochs=n_epochs, validation_data=validation_generator,
verbose=1, callbacks=callbacks)
def predict(self, tile_generator):
smscores = []
segmentations = []
for tileIdx in range(tile_generator.__len__()):
tile = tile_generator.__getitem__(tileIdx)
outIdx = tile[0]["image_index"]
outShape = tile[0]["image_shape"]
outSlice = tile[0]["out_slice"]
inSlice = tile[0]["in_slice"]
softmax_score = self.testModel.predict(tile[0]["data"], verbose=1)
if len(smscores) < outIdx + 1:
smscores.append(np.empty((*outShape, self.n_classes)))
segmentations.append(np.empty(outShape))
smscores[outIdx][outSlice] = softmax_score[0][inSlice]
segmentations[outIdx][outSlice] = np.argmax(
softmax_score[0], axis=-1)[inSlice]
return smscores, segmentations
I think this should make everything clear. It even includes loading the weights.
All the best, Thorsten
This is unfortunately not TensorFlow 2, I will have to do some work to adapt it to my environment.
Hi again! Now what I did is simply go back to TensorFlow 1.14, rather than porting the code to TF2. I have now a problem that I already have before, namely that in
def loadCaffeModelH5(self, path):
train_layer_dict = dict([(layer.name, layer)
for layer in self.trainModel.layers])
test_layer_dict = dict([(layer.name, layer)
for layer in self.testModel.layers])
pre = h5py.File(path, 'a')
l = list(pre['data'].keys())
for i in l:
print(i)
print(pre['data'][i].keys())
try:
kernel = pre['data'][i]['0'][()]
bias = pre['data'][i]['1'][()]
train_layer_dict[i].set_weights([kernel, bias])
test_layer_dict[i].set_weights([kernel, bias])
except KeyError:
pass
pre.close()
there is a conflict between the shapes of the layers in Caffe and the shapes expected by TF:
ValueError: Layer weight shape (3, 3, 1, 64) not compatible with provided weight shape (64, 1, 3, 3)
This could maybe be solved by taking the transpose of the arrays, but then there is the issue with the upconv layers:
ValueError: Layer weight shape (3, 3, 128, 64) not compatible with provided weight shape (3, 3, 192, 128)
These are the problems I had already before setting up this issue. Am I missing something?