def __init__(self, model_config: Model_Config, train_config: Model_Train_Config):
self.input_shape = model_config.input_shape
self.conv0 = Layer_conv_bn_relu((7, 7, self.input_shape[-1], 64), 2)
Ns = [3, 4, 6, 3]
blocks_output_depths = [64, 128, 256, 512]
se_reduction = 16
block = SEResidual_block_compact_v3
self.stack_of_stacks_of_blocks = []
input_input_depth = self.conv0.OUTPUT_CHANNELS
for i, N, output_depth in zip(range(len(Ns)), Ns, blocks_output_depths):
stack_of_blocks = Stack_of_blocks(N=N, block=block, input_input_depth=input_input_depth, block_output_depth=output_depth, reduction=se_reduction)
self.stack_of_stacks_of_blocks.append(stack_of_blocks)
input_input_depth = stack_of_blocks.OUTPUT_CHANNELS
self.fc_logits = FC_logits(output_depth, self.OUTPUT_CHANNELS)
self.dropout_drop_prob = train_config.dropout_drop_prob
self.__call__ = tf.function(
self.__call__,
input_signature=[
tf.TensorSpec(self.bs + self.input_shape, tf.float32),
])
self.get_logits = tf.function(
self.get_logits,
input_signature=[
tf.TensorSpec(self.bs + self.input_shape, tf.float32), tf.TensorSpec([], tf.bool),
])
self.reg_loss = tf.function(
self.reg_loss,
input_signature=[])
def __init__(self, model_config: Model_Config, train_config: Model_Train_Config):
super(ResNet18_v2, self).__init__(model_config, train_config)
self.input_shape = model_config.input_shape
self.conv0 = Layer_conv_bn_relu((7, 7, self.input_shape[-1], 64), 2)
Ns = [2, 2, 2, 2]
blocks_output_depths = [64, 128, 256, 512]
block = Residual_block_compact_v3
self.stack_of_stacks_of_blocks = []
input_input_depth = self.conv0.OUTPUT_CHANNELS
for i, N, output_depth in zip(range(len(Ns)), Ns, blocks_output_depths):
stack_of_blocks = Stack_of_blocks(N=N, block=block, input_input_depth=input_input_depth, block_output_depth=output_depth)
self.stack_of_stacks_of_blocks.append(stack_of_blocks)
input_input_depth = stack_of_blocks.OUTPUT_CHANNELS
self.fc_logits = FC_logits(output_depth, self.OUTPUT_CHANNELS)
self.optimizer = train_config.optimizer
self.reg_l2_beta = train_config.reg_l2_beta
self.dropout_drop_prob = train_config.dropout_drop_prob
self.__call__ = tf.function(
self.__call__,
input_signature=[
tf.TensorSpec(self.bs + self.input_shape, tf.float32),
])
self.get_logits = tf.function(
self.get_logits,
input_signature=[
tf.TensorSpec(self.bs + self.input_shape, tf.float32), tf.TensorSpec([], tf.bool),
])
self.train_step = tf.function(
self.train_step,
input_signature=[
tf.TensorSpec(self.bs + self.input_shape, tf.float32),
tf.TensorSpec(self.bs + (self.OUTPUT_CHANNELS,), tf.float32),
])
self.loss = tf.function(
loss,
input_signature=[
tf.TensorSpec(self.bs + (self.OUTPUT_CHANNELS,), tf.float32),
tf.TensorSpec(self.bs + (self.OUTPUT_CHANNELS,), tf.float32),
])
self.acc = tf.function(
acc,
input_signature=[
tf.TensorSpec(self.bs + (self.OUTPUT_CHANNELS,), tf.float32),
tf.TensorSpec(self.bs + (self.OUTPUT_CHANNELS,), tf.float32),
])
class ResNet34_v2_128out(tf.Module):
OUTPUT_CHANNELS = 2
bs = (None, )
l2_loss = tf.nn.l2_loss(0.0)
NAME = 'ResNet34_v2_128out'
def __init__(self, model_config: Model_Config,
train_config: Model_Train_Config):
self.input_shape = model_config.input_shape
self.conv0 = Layer_conv_bn_relu((7, 7, self.input_shape[-1], 64), 2)
Ns = [3, 4, 6, 3]
blocks_output_depths = [64, 128, 256, 128]
block = Residual_block_compact_v3
self.stack_of_stacks_of_blocks = []
input_input_depth = self.conv0.OUTPUT_CHANNELS
for i, N, output_depth in zip(range(len(Ns)), Ns,
blocks_output_depths):
stack_of_blocks = Stack_of_blocks(
N=N,
block=block,
input_input_depth=input_input_depth,
block_output_depth=output_depth)
self.stack_of_stacks_of_blocks.append(stack_of_blocks)
input_input_depth = stack_of_blocks.OUTPUT_CHANNELS
self.fc_logits = FC_logits(output_depth, self.OUTPUT_CHANNELS)
self.dropout_drop_prob = train_config.dropout_drop_prob
self.__call__ = tf.function(self.__call__,
input_signature=[
tf.TensorSpec(
self.bs + self.input_shape,
tf.float32),
])
self.get_logits = tf.function(self.get_logits,
input_signature=[
tf.TensorSpec(
self.bs + self.input_shape,
tf.float32),
tf.TensorSpec([], tf.bool),
])
self.reg_loss = tf.function(self.reg_loss, input_signature=[])
@tf.function
def __call__(self, x):
out = self.get_logits(x, False)
out = tf.nn.softmax(out)
return out
@tf.function
def get_logits(self, x, training):
conv0 = self.conv0(x, training=training)
pool0 = tf.nn.max_pool(conv0,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME')
for block in self.stack_of_stacks_of_blocks:
pool0 = block(pool0, training)
pool5 = tf.reduce_mean(pool0, axis=[1, 2])
if training:
pool5 = tf.nn.dropout(pool5, rate=self.dropout_drop_prob)
fc_output = self.fc_logits(pool5, self.OUTPUT_CHANNELS)
return fc_output
@tf.function
def reg_loss(self):
l2_loss = tf.nn.l2_loss(0.0)
l2_loss += self.conv0.get_reg_loss_l2()
for block in self.stack_of_stacks_of_blocks:
l2_loss += block.get_reg_loss_l2()
l2_loss += self.fc_logits.get_reg_loss_l2()
return l2_loss
class ResNet14_v2(Base):
OUTPUT_CHANNELS = 2
bs = (None, )
l2_loss = tf.nn.l2_loss(0.0)
NAME = 'ResNet14_v2' # TODO
def __init__(self, model_config: Model_Config,
train_config: Model_Train_Config):
super(ResNet14_v2, self).__init__(model_config, train_config)
self.input_shape = model_config.input_shape
self.conv0 = Layer_conv_bn_relu((7, 7, self.input_shape[-1], 64), 2)
Ns = [2, 2, 2]
blocks_output_depths = [64, 128, 256]
block = Residual_block_compact_v3
self.stack_of_stacks_of_blocks = []
input_input_depth = self.conv0.OUTPUT_CHANNELS
for i, N, output_depth in zip(range(len(Ns)), Ns,
blocks_output_depths):
stack_of_blocks = Stack_of_blocks(
N=N,
block=block,
input_input_depth=input_input_depth,
block_output_depth=output_depth)
self.stack_of_stacks_of_blocks.append(stack_of_blocks)
input_input_depth = stack_of_blocks.OUTPUT_CHANNELS
self.fc_logits = FC_logits(output_depth, self.OUTPUT_CHANNELS)
self.optimizer = train_config.optimizer
self.reg_l2_beta = train_config.reg_l2_beta
self.dropout_drop_prob = train_config.dropout_drop_prob
self.__call__ = tf.function(self.__call__,
input_signature=[
tf.TensorSpec(
self.bs + self.input_shape,
tf.float32),
])
self.get_logits = tf.function(self.get_logits,
input_signature=[
tf.TensorSpec(
self.bs + self.input_shape,
tf.float32),
tf.TensorSpec([], tf.bool),
])
self.train_step = tf.function(
self.train_step,
input_signature=[
tf.TensorSpec(self.bs + self.input_shape, tf.float32),
tf.TensorSpec(self.bs + (self.OUTPUT_CHANNELS, ), tf.float32),
])
self.loss = tf.function(
loss,
input_signature=[
tf.TensorSpec(self.bs + (self.OUTPUT_CHANNELS, ), tf.float32),
tf.TensorSpec(self.bs + (self.OUTPUT_CHANNELS, ), tf.float32),
])
self.acc = tf.function(
acc,
input_signature=[
tf.TensorSpec(self.bs + (self.OUTPUT_CHANNELS, ), tf.float32),
tf.TensorSpec(self.bs + (self.OUTPUT_CHANNELS, ), tf.float32),
])
@tf.function
def __call__(self, x):
out = self.get_logits(x, False)
out = tf.nn.softmax(out)
return out
@tf.function
def get_logits(self, x, training):
conv0 = self.conv0(x, training=training)
pool0 = tf.nn.max_pool(conv0,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME')
for block in self.stack_of_stacks_of_blocks:
pool0 = block(pool0, training)
pool5 = tf.reduce_mean(pool0, axis=[1, 2])
if training:
pool5 = tf.nn.dropout(pool5, rate=self.dropout_drop_prob)
fc_output = self.fc_logits(pool5, self.OUTPUT_CHANNELS)
return fc_output
@tf.function
def train_step(self, data, gt):
with tf.GradientTape() as tape:
out = self.get_logits(data, True)
loss = self.loss(gt, out) + self.reg_loss() * self.reg_l2_beta
predicts = tf.nn.softmax(out)
acc = self.acc(gt, predicts)
grads = tape.gradient(loss, self.trainable_variables)
self.optimizer.apply_gradients(
list(zip(grads, self.trainable_variables)))
return loss, acc
def reg_loss(self):
l2_loss = tf.nn.l2_loss(0.0)
l2_loss += self.conv0.get_reg_loss_l2()
for block in self.stack_of_stacks_of_blocks:
l2_loss += block.get_reg_loss_l2()
l2_loss += self.fc_logits.get_reg_loss_l2()
return l2_loss