def build(config, classes, softmax=True, scale_adjust_wb=None):
model = Sequential()
input_shape = config.input_shape
# 1. CONV => RELU => BN => POOL
model.add(
Conv2D(48, (7, 7), activation='relu', input_shape=input_shape))
# model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
# 2. CONV => RELU => BN => POOL
model.add(Conv2D(96, (5, 5), activation='relu'))
# model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
# 3. CONV => RELU => BN => POOL
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# 4. CONV => RELU => BN => POOL
model.add(Conv2D(96, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# 5. CONV => RELU => BN => POOL
model.add(Conv2D(64, (1, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
# model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(16, activation='relu'))
# final layer
model.add(Dense(classes))
if softmax:
model.add(Activation("softmax"))
if scale_adjust_wb is not None:
# The below line doesn't save/load well as this is a custom object. Thus replaced by dense layer
# model.add(Lambda(lambda x: scale_adjust_wb[0] * x + scale_adjust_wb[1]))
input_shape = (None, classes)
scale_layer = Dense(
classes,
trainable=False,
input_shape=input_shape,
)
scale_layer.build(input_shape=input_shape)
scale_layer.set_weights(
[np.diag(scale_adjust_wb[0]), scale_adjust_wb[1]])
model.add(scale_layer)
# return the constructed network architecture
return model
class VAE_Encoder(Model):
def __init__(self, latent_num):
super(VAE_Encoder, self).__init__()
self.latent_num = latent_num
self.conv_layers = [ # (32, 32, 3)
Conv2D(filters=32,
kernel_size=(4, 4),
strides=(2, 2),
activation='relu'), # (16, 16, 32)
Conv2D(filters=64,
kernel_size=(4, 4),
strides=(2, 2),
activation='relu'), # (6, 6, 64)
Conv2D(filters=128, kernel_size=(3, 3),
activation='relu'), # (4, 4, 128)
Conv2D(filters=256, kernel_size=(3, 3),
activation='relu'), # (2, 2, 256)
]
self.flatten = Flatten()
self.mean_out = Dense(latent_num, name='mean')
# self.std_out = Dense(latent_num, activation='sigmoid', name='std')
def __call__(self, x):
#returns out, mean, std
m, s = self.encode(x)
# if sampling:
# noise = np.random.normal(size=self.latent_num)
# z = m + s * noise
# else:
z = m
return z, m, s
def forward(self, x):
m, _ = self.encode(x)
return m
def encode(self, x):
for layer in self.conv_layers:
x = layer(x)
x = self.flatten(x)
m = self.mean_out(x)
# s = self.std_out(x)
return m, None
def set_weights_by_list(self, l):
for i, layer in enumerate(self.conv_layers):
layer.set_weights(l[i])
self.mean_out.set_weights(l[-2])
self.std_out.set_weights(l[-1])
def save(self, dir, name):
self.save_weights(dir + name + '.h5')
def _create_dense_layer(self, _, normalized_weights, num_classes):
input_shape = tf.TensorShape([None, 512])
dense_layer = Dense(
input_shape=(512, ),
units=num_classes,
use_bias=False,
name='fully_connected_to_softmax_crossentropy',
dtype='float32',
trainable=False,
)
dense_layer.build(input_shape)
dense_layer.set_weights([normalized_weights.read_value()])
return dense_layer
def sparse_fc_mapping(x, input_idxs):
num_units = len(input_idxs)
d = Dense(num_units, use_bias=False)
d.trainable = False
x = d(x)
w = d.get_weights()
w[0].fill(0)
for i in range(num_units):
w[0][input_idxs[i], i] = 1.
d.set_weights(w)
return x
def wider(self, added_size=1, pos_layer=None):
layers_size = len(self.layers)
if layers_size < 2:
raise ValueError("Number of layer must be greater than 2.")
if pos_layer is None:
pos_layer = max(layers_size - 2, 0)
elif pos_layer >= layers_size - 1 or pos_layer < 0:
raise ValueError(
f"pos_layer is expected less than length of layers (pos_layer in [0, layers_size-2])"
)
# TODO: get biggest value to divide for new weights
weights, bias = self.layers[pos_layer].get_weights()
weights_next_layer, bias_next_layer = self.layers[pos_layer +
1].get_weights()
new_weights, new_bias, new_weights_next_layer = net2wider(
weights, bias, weights_next_layer, added_size)
src_units, des_units = weights.shape[0], weights.shape[1] + added_size
next_des_units = weights_next_layer.shape[1]
wider_layer = Dense(units=des_units,
activation=tf.nn.relu,
kernel_regularizer=regularizers.l1_l2(l1=self.l1,
l2=self.l2))
# input_shape = (batch_size, input_features).
# input_features = number of units in layer = length(layer) = output of previous layer
wider_layer.build(input_shape=(None, src_units))
wider_layer.set_weights([new_weights, new_bias])
next_layer = Dense(units=next_des_units,
activation=tf.nn.relu,
kernel_regularizer=regularizers.l1_l2(l1=self.l1,
l2=self.l2))
next_layer.build(input_shape=(None, des_units))
next_layer.set_weights([new_weights_next_layer, bias_next_layer])
self.layers[pos_layer] = wider_layer
self.layers[pos_layer + 1] = next_layer
def create_keras_model(self, nclasses, warming_up=False):
input = Input(shape=self.nfeatures)
x = input
classifier = Dense(
nclasses - 1, use_bias=True, kernel_initializer='he_normal',
bias_initializer='zeros', input_shape=K.int_shape(x)[-1:],
kernel_regularizer=regularizers.l2(self.mu)
)
output = classifier(x)
if warming_up:
kernel_values = self.sklearn_model.coef_.T
kernel_values = np.concatenate((np.mean(-1. * kernel_values, axis=-1)[:, None], kernel_values), axis=-1)
beta = self.sklearn_model.intercept_
classifier.set_weights([kernel_values, beta])
self.model = Model(input, output)
self.output_shape = self.model.output_shape
self.output = self.model.output
self.layers = self.model.layers
self.input = self.model.input
optimizer = optimizers.SGD(0.1)
self.model.compile(loss=self.loss_function('square_hinge'), optimizer=optimizer, metrics=['acc'])
def deeper(self, pos_layer=None):
layers_size = len(self.layers)
if pos_layer is None:
pos_layer = max(layers_size - 2, 0)
elif pos_layer >= layers_size - 1 or pos_layer < 0:
raise ValueError(
f"pos_layer is expected less than length of layers (pos_layer in [0, layers_size-2])."
)
weights, bias = self.layers[pos_layer].get_weights()
new_weights, new_bias = net2deeper(weights)
des_units = weights.shape[1]
# TODO: add initial kernel
layer = Dense(
units=des_units,
activation=tf.nn.relu,
kernel_regularizer=regularizers.l1_l2(l1=self.l1, l2=self.l2),
)
layer.build(input_shape=(None, des_units))
layer.set_weights([new_weights, new_bias])
self.layers.insert(pos_layer + 1, layer)
def last_insert_layer(self, layer_dim):
prev_weights, prev_bias = self.layers[len(self.layers) -
1].get_weights()
prev_units = prev_weights.shape[1]
replace_prev_layer = Dense(
units=prev_units,
activation=tf.nn.relu,
kernel_regularizer=regularizers.l1_l2(l1=self.l1, l2=self.l2),
)
replace_prev_layer.build(input_shape=(None, prev_weights.shape[0]))
replace_prev_layer.set_weights([prev_weights, prev_bias])
added_layer = Dense(
units=layer_dim,
activation=tf.nn.sigmoid,
kernel_regularizer=regularizers.l1_l2(l1=self.l1, l2=self.l2),
kernel_initializer=initializers.GlorotNormal(seed=self.seed),
bias_initializer=initializers.Zeros())
added_layer.build(input_shape=(None, prev_units))
del self.layers[len(self.layers) - 1]
self.layers.append(replace_prev_layer)
self.layers.append(added_layer)
class CNNLayerwise(tf.keras.Model):
def __init__(self, hyperp, run_options, data_input_shape, label_dimensions, num_channels, kernel_regularizer, bias_regularizer):
super(CNNLayerwise, self).__init__()
###############################################################################
# Construct Initial Neural Network Architecture #
###############################################################################
#=== Defining Attributes ===#
self.data_input_shape = data_input_shape
self.architecture = [] # storage for layer information, each entry is [filter_size, num_filters]
self.num_filters = hyperp.num_filters
self.kernel_size = hyperp.filter_size
self.activation = hyperp.activation
self.kernel_regularizer = kernel_regularizer
self.bias_regularizer = bias_regularizer
self.hidden_layers_list = [] # This will be a list of Keras layers
#=== Define Initial Architecture and Create Layer Storage ===#
self.architecture.append([self.data_input_shape[0], num_channels]) # input information
self.architecture.append([3, self.num_filters]) # 3x3 convolutional layer for upsampling data
self.architecture.append([hyperp.filter_size, self.num_filters]) # First hidden layer
self.architecture.append([3, num_channels]) # 3x3 convolutional layer for downsampling features
self.architecture.append(label_dimensions) # fully-connected output layer
print(self.architecture)
#=== Weights and Biases Initializer ===#
kernel_initializer = RandomNormal(mean=0.0, stddev=0.05)
bias_initializer = 'zeros'
#=== Linear Upsampling Layer to Map to Feature Space ===#
l = 1
self.upsampling_layer = Conv2D(self.architecture[l][1], (3, 3), padding = 'same',
activation = 'linear', use_bias = True,
input_shape = self.data_input_shape,
kernel_initializer = kernel_initializer, bias_initializer = bias_initializer,
kernel_regularizer = self.kernel_regularizer, bias_regularizer = self.bias_regularizer,
name='upsampling_layer')
#=== Define Hidden Layers ===#
l = 2
conv_layer = Conv2D(self.architecture[l][1], (self.architecture[l][0], self.architecture[l][0]), padding = 'same',
activation = self.activation, use_bias = True,
input_shape = (None, self.data_input_shape[0], self.data_input_shape[1], self.num_filters),
kernel_initializer = kernel_initializer, bias_initializer = bias_initializer,
kernel_regularizer = self.kernel_regularizer, bias_regularizer = self.bias_regularizer,
name = "W" + str(l))
self.hidden_layers_list.append(conv_layer)
#=== Linear Downsampling Layer to Map to Data Space ===#
l = 3
self.downsampling_layer = Conv2D(self.architecture[l][1], (3, 3), padding = 'same',
activation = "linear", use_bias = True,
input_shape = (None, self.data_input_shape[0], self.data_input_shape[1], self.num_filters),
kernel_initializer = kernel_initializer, bias_initializer = bias_initializer,
kernel_regularizer = self.kernel_regularizer, bias_regularizer = self.bias_regularizer,
name = "downsampling_layer")
#=== Classification Layer ===#
self.classification_layer = Dense(units = label_dimensions,
activation = 'linear', use_bias = True,
kernel_initializer = kernel_initializer, bias_initializer = bias_initializer,
kernel_regularizer = self.kernel_regularizer, bias_regularizer = self.bias_regularizer,
name = 'classification_layer')
###############################################################################
# Network Propagation #
###############################################################################
def call(self, inputs):
#=== Upsampling ===#
output = self.upsampling_layer(inputs)
for hidden_layer in self.hidden_layers_list:
#=== Hidden Layers ===#
prev_output = output
output = prev_output + hidden_layer(output)
#=== Downsampling ===#
output = self.downsampling_layer(output)
#=== Classification ===#
output = Flatten()(output)
output = self.classification_layer(output)
return output
###############################################################################
# Add Layer #
###############################################################################
def add_layer(self, trainable_hidden_layer_index, freeze = True, add = True):
kernel_initializer = 'zeros'
bias_initializer = 'zeros'
if add:
conv_layer = Conv2D(self.num_filters, (self.kernel_size, self.kernel_size), padding = 'same',
activation = self.activation, use_bias = True,
input_shape = (None, self.data_input_shape[0], self.data_input_shape[1], self.num_filters),
kernel_initializer = kernel_initializer, bias_initializer = bias_initializer,
kernel_regularizer = self.kernel_regularizer, bias_regularizer = self.bias_regularizer,
name = "W" + str(trainable_hidden_layer_index))
self.hidden_layers_list.append(conv_layer)
if freeze:
self.upsampling_layer.trainable = False
for index in range(0, trainable_hidden_layer_index-2):
self.hidden_layers_list[index].trainable = False
else:
self.upsampling_layer.trainable = True
for index in range(0, trainable_hidden_layer_index-2):
self.hidden_layers_list[index].trainable = True
###############################################################################
# Sparsify Weights #
###############################################################################
def sparsify_weights_and_get_relative_number_of_zeros(self, threshold = 1e-6):
#=== Downsampling Layer ===#
down_weights = self.downsampling_layer.get_weights()
sparsified_weights = self.sparsify_weights(down_weights, threshold)
self.downsampling_layer.set_weights(sparsified_weights)
#=== Classification Layer ===#
class_weights = self.classification_layer.get_weights()
sparsified_weights = self.sparsify_weights(class_weights, threshold)
self.classification_layer.set_weights(sparsified_weights)
#=== Trained Hidden Layer ===#
trained_weights = self.hidden_layers_list[-1].get_weights()
sparsified_weights = self.sparsify_weights(trained_weights, threshold)
self.hidden_layers_list[-1].set_weights(sparsified_weights)
#=== Compute Relative Number of Zeros ===#
total_number_of_zeros = 0
total_number_of_elements = 0
for i in range(0, len(sparsified_weights)):
total_number_of_zeros += np.count_nonzero(sparsified_weights[i]==0)
total_number_of_elements += sparsified_weights[i].flatten().shape[0]
relative_number_zeros = np.float64(total_number_of_zeros/total_number_of_elements)
return relative_number_zeros
def sparsify_weights(self, weights, threshold = 1e-6):
sparsified_weights = []
if isinstance(weights, float):
if abs(weights) > threshold:
sparsified_weights = weights
else:
sparsified_weights = 0
else:
for w in weights:
bool_mask = (abs(w) > threshold).astype(int)
sparsified_weights.append(w*bool_mask)
return sparsified_weights
EarlyStopping(monitor='val_loss',
patience=20,
restore_best_weights=True)
],
validation_data=(X_train_val, y_labels_train_val))
new_weights = np.empty([100, 37])
biases = outLayer.get_weights()[1]
for i in range(len(outLayer.get_weights()[0])):
weights = outLayer.get_weights()[0][i]
for j in range(len(weights)):
if j not in (32, 33, 34, 35, 36): #subtypes
weights[j] = 0
new_weights[i] = weights
outLayer.set_weights([new_weights, biases])
score = model.evaluate(X_test, y_labels_test)
print(model.predict(X_test).argmax(axis=1))
conf_matrix = pd.DataFrame(
confusion_matrix(y_labels_test.values.argmax(axis=1),
model.predict(X_test).argmax(axis=1)))
conf_matrix.to_csv(
"../results/fully_con/all_cancer/feed_forward_300_100_conf_matrix_test.csv"
)
classify_df = classify_df.append({"accuracy": score[1]}, ignore_index=True)
print(score)
scores.append(score[1])
class DenseApproximator(Approximator):
def __init__(self, num_layers, num_units, output_dim, internal_dim,
activation):
super().__init__(output_dim, internal_dim)
self.activation = activation
self.dense_layers = []
self.batch_layers = []
for _ in range(num_layers - 1):
self.dense_layers.append(Dense(units=num_units, use_bias=False))
self.batch_layers.append(BatchNormalization())
self.output_layer = Dense(units=output_dim + internal_dim)
def _evaluate_layer(self, x, dense, batch, training):
x = dense(x, training=training)
x = batch(x, training=training)
return self.activation(x)
def _call(self, inputs, training=False):
"""Implementation of call for Strategy.
Args:
inputs: (batch_size, None)
training: bool
Returns:
output: see Strategy._call
"""
for dense, batch in zip(self.dense_layers, self.batch_layers):
inputs = self._evaluate_layer(inputs, dense, batch, training)
output = self.output_layer(inputs, training=training)
return output
def initialise(self, inputs, sample_size):
batch_size = tf.shape(inputs)[0]
iterator = zip(self.dense_layers, self.batch_layers)
for k, (dense, batch) in enumerate(iterator):
sample_idx = tf.random.shuffle(tf.range(batch_size))[:sample_size]
sample = tf.gather(inputs, sample_idx, axis=0)
for i in tf.range(k + 1):
sample = self._evaluate_layer(sample, self.dense_layers[i],
self.batch_layers[i], False)
mean, variance = tf.nn.moments(sample, 0)
# dense.set_weights([dense.get_weights()[0] / tf.sqrt(variance)])
batch.set_weights([
batch.get_weights()[0] / tf.sqrt(variance),
(batch.get_weights()[1] - mean) / tf.sqrt(variance),
batch.get_weights()[2],
batch.get_weights()[3]
])
sample_idx = tf.random.shuffle(tf.range(batch_size))[:sample_size]
sample = tf.gather(inputs, sample_idx, axis=0)
sample = self._call(sample, False)
mean, variance = tf.nn.moments(sample, 0)
self.output_layer.set_weights([
self.output_layer.get_weights()[0] / tf.sqrt(variance),
self.output_layer.get_weights()[1]
])
class AlexnetModel(Model):
def __init__(self):
super(AlexnetModel, self).__init__()
# OPS
self.relu = Activation('relu')
self.maxpool = MaxPooling2D(pool_size=(3, 3),
strides=(2, 2),
padding='valid')
# self.dropout = Dropout(0.4)
self.softmax = Activation('softmax', )
# Conv layers
self.conv1 = Conv2D(name="conv1",
filters=96,
input_shape=(224, 224, 3),
kernel_size=(11, 11),
strides=(4, 4),
padding='same')
self.conv2a = Conv2D(name="conv2a",
filters=128,
kernel_size=(5, 5),
strides=(1, 1),
padding='same')
self.conv2b = Conv2D(name="conv2b",
filters=128,
kernel_size=(5, 5),
strides=(1, 1),
padding='same')
self.conv3 = Conv2D(name="conv3",
filters=384,
kernel_size=(3, 3),
strides=(1, 1),
padding='same')
self.conv4a = Conv2D(name="conv4a",
filters=192,
kernel_size=(3, 3),
strides=(1, 1),
padding='same')
self.conv4b = Conv2D(name="conv4b",
filters=192,
kernel_size=(3, 3),
strides=(1, 1),
padding='same')
self.conv5a = Conv2D(name="conv5a",
filters=128,
kernel_size=(3, 3),
strides=(1, 1),
padding='same')
self.conv5b = Conv2D(name="conv5b",
filters=128,
kernel_size=(3, 3),
strides=(1, 1),
padding='same')
# Fully-connected layers
self.flatten = Flatten()
self.dense1 = Dense(4096, name="dense1", input_shape=(100, ))
self.dense2 = Dense(4096, name="dense2")
self.dense3 = Dense(1000, name="dense3")
def setAlexnetWeights(self, wdir):
self.conv1.set_weights(
(np.load(wdir + 'conv1.npy'), np.load(wdir + 'conv1b.npy')))
self.conv2a.set_weights(
(np.load(wdir + 'conv2_a.npy'), np.load(wdir + 'conv2b_a.npy')))
self.conv2b.set_weights(
(np.load(wdir + 'conv2_b.npy'), np.load(wdir + 'conv2b_b.npy')))
self.conv3.set_weights(
(np.load(wdir + 'conv3.npy'), np.load(wdir + 'conv3b.npy')))
self.conv4a.set_weights(
(np.load(wdir + 'conv4_a.npy'), np.load(wdir + 'conv4b_a.npy')))
self.conv5a.set_weights(
(np.load(wdir + 'conv5_a.npy'), np.load(wdir + 'conv5b_a.npy')))
self.conv4b.set_weights(
(np.load(wdir + 'conv4_b.npy'), np.load(wdir + 'conv4b_b.npy')))
self.conv5b.set_weights(
(np.load(wdir + 'conv5_b.npy'), np.load(wdir + 'conv5b_b.npy')))
self.dense1.set_weights(
(np.load(wdir + 'dense1.npy'), np.load(wdir + 'dense1b.npy')))
self.dense2.set_weights(
(np.load(wdir + 'dense2.npy'), np.load(wdir + 'dense2b.npy')))
self.dense3.set_weights(
(np.load(wdir + 'dense3.npy'), np.load(wdir + 'dense3b.npy')))
def getOutputAtLAyer(self, layer: AlexnetLayers):
return self.get_layer(layer.name).output
# Network definition
def call(self, x, **kwargs):
layers_by_name = dict()
x = self.conv1(x)
x = add_to_dict("conv1", self.relu(x), layers_by_name)
x = tf.nn.local_response_normalization(x,
depth_radius=2,
alpha=2e-05,
beta=0.75,
bias=1.0)
x = self.maxpool(x)
x = tf.concat(
(self.conv2a(x[:, :, :, :48]), self.conv2b(x[:, :, :, 48:])), 3)
x = add_to_dict("conv2", self.relu(x), layers_by_name)
x = tf.nn.local_response_normalization(x,
depth_radius=2,
alpha=2e-05,
beta=0.75,
bias=1.0)
x = self.maxpool(x)
x = self.conv3(x)
x = add_to_dict("conv3", self.relu(x), layers_by_name)
x = tf.concat(
(self.conv4a(x[:, :, :, :192]), self.conv4b(x[:, :, :, 192:])), 3)
x = add_to_dict("conv4", self.relu(x), layers_by_name)
x = tf.concat(
(self.conv5a(x[:, :, :, :192]), self.conv5b(x[:, :, :, 192:])), 3)
x = add_to_dict("conv5", self.relu(x), layers_by_name)
x = self.maxpool(x)
x = self.flatten(x)
x = self.dense1(x)
x = add_to_dict("dense1", self.relu(x), layers_by_name)
x = self.dense2(x)
x = add_to_dict("dense2", self.relu(x), layers_by_name)
x = self.dense3(x)
x = add_to_dict("dense3", self.relu(x), layers_by_name)
x = add_to_dict("softmax", self.softmax(x), layers_by_name)
return x, layers_by_name
class AlexNet(tf.keras.Model):
def __init__(self, dropout_rate):
super(AlexNet, self).__init__()
self.dropout_rate = dropout_rate
self.conv1 = Conv2D(filters=96,
kernel_size=(11, 11),
strides=(4, 4),
padding="valid",
name="conv1")
# self.norm1 = tf.nn.local_response_normalization()
self.pool1 = MaxPool2D(pool_size=(3, 3),
strides=(2, 2),
padding="valid",
name="pool1")
self.conv2_1 = Conv2D(filters=int(256 / 2),
kernel_size=(5, 5),
strides=(1, 1),
padding="same",
name="conv2_1")
self.conv2_2 = Conv2D(filters=int(256 / 2),
kernel_size=(5, 5),
strides=(1, 1),
padding="same",
name="conv2_2")
self.pool2 = MaxPool2D(pool_size=(3, 3),
strides=(2, 2),
padding="valid",
name="pool2")
self.conv3 = Conv2D(filters=384,
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
name="conv3")
self.conv4_1 = Conv2D(filters=int(384 / 2),
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
name="conv4_1")
self.conv4_2 = Conv2D(filters=int(384 / 2),
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
name="conv4_2")
self.conv5_1 = Conv2D(filters=int(256 / 2),
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
name="conv5_1")
self.conv5_2 = Conv2D(filters=int(256 / 2),
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
name="conv5_2")
self.pool5 = MaxPool2D(pool_size=(3, 3),
strides=(2, 2),
padding="valid",
name="pool5")
self.flat6 = Flatten()
self.fc6 = Dense(units=4096,
activation=tf.nn.relu,
use_bias=True,
name="fc6")
self.dropout6 = Dropout(rate=self.dropout_rate, name="dropout6")
self.fc7 = Dense(units=4096,
activation=tf.nn.relu,
use_bias=True,
name="fc7")
self.dropout7 = Dropout(rate=self.dropout_rate, name="dropout7")
def load_weights(self, weights_path):
weights_dict = np.load(weights_path, encoding='bytes').item()
assert "conv1" in weights_dict and len(weights_dict["conv1"]) == 2
self.conv1.set_weights(weights_dict["conv1"])
conv2_weights = np.split(weights_dict["conv2"][0],
indices_or_sections=2,
axis=-1) # [5, 5, 48, 128] * 2
conv2_bias = np.split(weights_dict["conv2"][1],
indices_or_sections=2,
axis=-1)
self.conv2_1.set_weights([conv2_weights[0], conv2_bias[0]])
self.conv2_2.set_weights([conv2_weights[1], conv2_bias[1]])
self.conv3.set_weights(weights_dict["conv3"])
conv4_weights = np.split(weights_dict["conv4"][0],
indices_or_sections=2,
axis=-1) # [3, 3, 192, 192] * 2
conv4_bias = np.split(weights_dict["conv4"][1],
indices_or_sections=2,
axis=-1)
self.conv4_1.set_weights([conv4_weights[0], conv4_bias[0]])
self.conv4_2.set_weights([conv4_weights[1], conv4_bias[1]])
conv5_weights = np.split(weights_dict["conv5"][0],
indices_or_sections=2,
axis=-1) # [5, 5, 192, 128] * 2
conv5_bias = np.split(weights_dict["conv5"][1],
indices_or_sections=2,
axis=-1)
self.conv5_1.set_weights([conv5_weights[0], conv5_bias[0]])
self.conv5_2.set_weights([conv5_weights[1], conv5_bias[1]])
assert "fc6" in weights_dict and len(weights_dict["fc6"]) == 2
self.fc6.set_weights(weights_dict["fc6"])
assert "fc7" in weights_dict and len(weights_dict["fc7"]) == 2
self.fc7.set_weights(weights_dict["fc7"])
def call(self, x, training, mask=None):
# conv1
x = self.conv1(x)
x = tf.nn.local_response_normalization(x,
depth_radius=2,
alpha=2e-05,
beta=0.02,
bias=1,
name="norm1")
x = self.pool1(x)
# conv2
x = tf.split(x, num_or_size_splits=2, axis=-1)
x = tf.concat([self.conv2_1(x[0]), self.conv2_2(x[1])], axis=-1)
x = tf.nn.local_response_normalization(x,
depth_radius=2,
alpha=2e-05,
beta=0.02,
bias=1,
name="norm2")
x = self.pool2(x)
# conv3
x = self.conv3(x)
# conv4
x = tf.split(x, num_or_size_splits=2, axis=-1)
x = tf.concat([self.conv4_1(x[0]), self.conv4_2(x[1])], axis=-1)
# conv5
x = tf.split(x, num_or_size_splits=2, axis=-1)
x = tf.concat([self.conv5_1(x[0]), self.conv5_2(x[1])], axis=-1)
x = self.pool5(x)
# fc6
x = self.flat6(x)
x = self.fc6(x)
x = self.dropout6(x, training=training)
# fc7
x = self.fc7(x)
x = self.dropout7(x, training=training)
return x
class SRFR(Model):
def __init__(
self,
num_filters: int = 62,
depth: int = 50,
categories: int = 512,
num_gc: int = 32,
num_blocks: int = 23,
residual_scailing: float = 0.2,
training: bool = True,
input_shape=(28, 28, 3),
num_classes_syn: int = None,
both: bool = False,
num_classes_nat: int = None,
scale: int = 64,
):
super(SRFR, self).__init__()
self._training = training
self.scale = scale
if both:
self._natural_input = Conv2D(
input_shape=input_shape,
filters=num_filters,
kernel_size=(3, 3),
strides=1,
padding='same',
name='natural_input',
activation=mish,
)
self._synthetic_input = Conv2D(
input_shape=input_shape,
filters=num_filters,
kernel_size=(3, 3),
strides=1,
padding='same',
name='synthetic_input',
activation=mish,
)
self._super_resolution = GeneratorNetwork(
num_filters,
num_gc,
num_blocks,
residual_scailing,
)
self._face_recognition = ResNet(
depth,
categories,
training
)
if self._training:
if both:
self._fc_classification_nat = Dense(
input_shape=(categories,),
units=num_classes_nat,
activation=None,
use_bias=False,
dtype='float32',
name='fully_connected_to_softmax_crossentropy_nat',
)
self._fc_classification_nat.build(tf.TensorShape([None, 512]))
self.net_type = 'nat'
self._fc_classification_syn: Dense = Dense(
input_shape=(categories,),
units=num_classes_syn,
activation=None,
use_bias=False,
dtype='float32',
name='fully_connected_to_softmax_crossentropy_syn',
)
self._fc_classification_syn.build(tf.TensorShape([None, 512]))
@tf.function
def _call_evaluating(self, input_tensor, input_type: str = 'nat'):
if input_type == 'syn':
outputs = self._synthetic_input(input_tensor)
else:
outputs = self._natural_input(input_tensor)
super_resolution_image = self._super_resolution(outputs)
embeddings = self._face_recognition(super_resolution_image)
return super_resolution_image, embeddings
def _calculate_normalized_embeddings(self, embeddings,
net_type: str = 'syn'):
fc_weights = self.get_weights(net_type)
normalized_weights = tf.Variable(
normalize(fc_weights, name='weights_normalization'),
aggregation=tf.VariableAggregation.NONE,
)
normalized_embeddings = normalize(
embeddings, axis=1, name='embeddings_normalization') * self.scale
replica = tf.distribute.get_replica_context()
replica.merge_call(self.set_weights,
args=(normalized_weights, net_type))
return self.call_fc_classification(normalized_embeddings, net_type)
def _call_training(self, synthetic_images, natural_images=None):
synthetic_outputs = self._synthetic_input(synthetic_images)
synthetic_sr_images = self._super_resolution(synthetic_outputs)
synthetic_embeddings = self._face_recognition(synthetic_sr_images)
synthetic_embeddings = self._calculate_normalized_embeddings(
synthetic_embeddings
)
if natural_images:
natural_outputs = self._natural_input(natural_images)
natural_sr_images = self._super_resolution(natural_outputs)
natural_embeddings = self._face_recognition(natural_sr_images)
natural_embeddings = self._calculate_normalized_embeddings(
natural_embeddings
)
return (
synthetic_sr_images,
synthetic_embeddings,
natural_sr_images,
natural_embeddings,
)
return synthetic_sr_images, synthetic_embeddings
def call(self, input_tensor_01, input_tensor_02=None,
training: bool = True, input_type: str = 'nat'):
if training:
return self._call_training(input_tensor_01, input_tensor_02)
return self._call_evaluating(input_tensor_01, input_type)
def get_weights(self, net_type: str = 'syn'):
if net_type == 'nat':
return self._fc_classification_nat.get_weights()
return self._fc_classification_syn.get_weights()
def set_weights(self, _, weights, net_type: str = 'syn') -> None:
if net_type == 'nat':
self._fc_classification_nat.set_weights([weights.read_value()])
else:
self._fc_classification_syn.set_weights([weights.read_value()])
def call_fc_classification(self, input, net_type: str = 'syn'):
if net_type == 'nat':
return self._fc_classification_nat(input)
return self._fc_classification_syn(input)
class MyModel(Model):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = Conv2D(32,
3,
activation='relu',
autocast=False,
dtype=tf.float32)
self.flatten = Flatten(autocast=False)
self.d1 = Dense(128,
activation='relu',
autocast=False,
dtype=tf.float32)
self.d2 = Dense(10,
activation='softmax',
autocast=False,
dtype=tf.float32)
self.loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
#自定义优化器
self.optimizer = tf.keras.optimizers.SGD()
self.train_loss = tf.keras.metrics.Mean(name='train_loss')
self.train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
name='train_accuracy')
self.test_loss = tf.keras.metrics.Mean(name='test_loss')
self.test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
name='test_accuracy')
# 训练步骤
@tf.function
def train_step_(self, images, labels):
with tf.GradientTape() as tape:
predictions = self(images)
loss = self.loss_object(labels, predictions)
gradients = tape.gradient(loss, self.trainable_variables)
self.optimizer.apply_gradients(zip(gradients,
self.trainable_variables))
self.train_loss(loss)
self.train_accuracy(labels, predictions)
# 测试步骤
@tf.function
def test_step_(self, images, labels):
predictions = self(images)
t_loss = self.loss_object(labels, predictions)
self.test_loss(t_loss)
self.test_accuracy(labels, predictions)
def test(self):
self.test_loss.reset_states()
self.test_accuracy.reset_states()
for test_images, test_labels in self.test_ds:
self.test_step_(test_images, test_labels)
template = 'Test Loss: {}, Test Accuracy: {}'
print(
template.format(self.test_loss.result(),
self.test_accuracy.result() * 100))
return self.test_accuracy.result() * 100
# 具体的计算call
def call(self, x):
x = self.conv1(x)
x = self.flatten(x)
x = self.d1(x)
return self.d2(x)
# 训练
def train_test(self, epoch_data_weight, index):
for epoch in range(epoch_data_weight):
# 在下一个epoch开始时，重置评估指标
self.train_loss.reset_states()
self.train_accuracy.reset_states()
# self.test_loss.reset_states()
# self.test_accuracy.reset_states()
for images, labels in self.train_ds:
self.train_step_(images, labels)
# 先去掉测试集
# for test_images, test_labels in self.test_ds:
# self.test_step_(test_images, test_labels)
template = 'index {} Epoch {}, Loss: {}, Accuracy: {}'
print(
template.format(index, epoch + 1, self.train_loss.result(),
self.train_accuracy.result() * 100))
def get_weights(self):
'''
:return: data_listq
'''
weight = []
weight.append(self.conv1.get_weights())
weight.append(self.d1.get_weights())
weight.append(self.d2.get_weights())
return weight
# 对应每层的权重设置
def set_weights(self, weight):
self.conv1.set_weights(weights=weight[0])
self.d1.set_weights(weights=weight[1])
self.d2.set_weights(weights=weight[2])
# 为每个模型设置数据集
def set_data(self, train_ds, test_ds=None):
self.train_ds = train_ds
self.test_ds = test_ds