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Commit 8f33972c authored by Vishnu Banna's avatar Vishnu Banna
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backbone update

parent 9db38a15
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Showing with 29 additions and 27 deletions
official/vision/beta/projects/yolo/modeling/backbones/darknet.py
View file @ 8f33972c
......@@ -103,11 +103,11 @@ class LayerBuilder:
def __init__(self):
self._layer_dict = {
'ConvBN': (nn_blocks.ConvBN, self.conv_bn_config_todict),
'ConvBN': (nn_blocks.ConvBN, self.ConvBN_config_todict),
'MaxPool': (tf.keras.layers.MaxPool2D, self.maxpool_config_todict)
}
def conv_bn_config_todict(self, config, kwargs):
def ConvBN_config_todict(self, config, kwargs):
dictvals = {
'filters': config.filters,
'kernel_size': config.kernel_size,
......@@ -140,7 +140,7 @@ class LayerBuilder:
LISTNAMES = [
'default_layer_name', 'level_type', 'number_of_layers_in_level',
'bottleneck', 'filters', 'kernal_size', 'pool_size', 'strides', 'padding',
'default_activation', 'route', 'dilation', 'level/name', 'is_output'
'default_activation', 'route', 'dialation', 'level/name', 'is_output'
]
CSPDARKNET53 = {
......@@ -384,13 +384,13 @@ class Darknet(tf.keras.Model):
max_level=5,
width_scale=1.0,
depth_scale=1.0,
csp_level_mod=(),
csp_level_mod=[],
activation=None,
use_sync_bn=False,
norm_momentum=0.99,
norm_epsilon=0.001,
dilate=False,
kernel_initializer='glorot_uniform',
kernel_initializer='VarianceScaling',
kernel_regularizer=None,
bias_regularizer=None,
**kwargs):
......@@ -461,23 +461,23 @@ class Darknet(tf.keras.Model):
if config.stack is None:
x = self._build_block(
stack_outputs[config.route], config, name=f'{config.layer}_{i}')
stack_outputs[config.route], config, name=f"{config.layer}_{i}")
stack_outputs.append(x)
elif config.stack == 'residual':
x = self._residual_stack(
stack_outputs[config.route], config, name=f'{config.layer}_{i}')
stack_outputs[config.route], config, name=f"{config.layer}_{i}")
stack_outputs.append(x)
elif config.stack == 'csp':
x = self._csp_stack(
stack_outputs[config.route], config, name=f'{config.layer}_{i}')
stack_outputs[config.route], config, name=f"{config.layer}_{i}")
stack_outputs.append(x)
elif config.stack == 'csp_tiny':
x_pass, x = self._csp_tiny_stack(
stack_outputs[config.route], config, name=f'{config.layer}_{i}')
stack_outputs[config.route], config, name=f"{config.layer}_{i}")
stack_outputs.append(x_pass)
elif config.stack == 'tiny':
x = self._tiny_stack(
stack_outputs[config.route], config, name=f'{config.layer}_{i}')
stack_outputs[config.route], config, name=f"{config.layer}_{i}")
stack_outputs.append(x)
if (config.is_output and self._min_size is None):
endpoints[str(config.output_name)] = x
......@@ -504,13 +504,15 @@ class Darknet(tf.keras.Model):
residual_filter_scale = 1
scale_filters = 2
self._default_dict['activation'] = self._get_activation(config.activation)
self._default_dict['name'] = f'{name}_csp_down'
self._default_dict['name'] = f"{name}_csp_down"
if self._dilate:
self._default_dict['dilation_rate'] = config.dilation_rate
degrid = int(tf.math.log(float(config.dilation_rate)) / tf.math.log(2.))
else:
self._default_dict['dilation_rate'] = 1
degrid = 0
# swap/add dilation
# swap/add dialation
x, x_route = nn_blocks.CSPRoute(
filters=config.filters,
filter_scale=csp_filter_scale,
......@@ -518,9 +520,9 @@ class Darknet(tf.keras.Model):
**self._default_dict)(
inputs)
dilated_reps = config.repetitions - self._default_dict['dilation_rate'] // 2
dilated_reps = config.repetitions - degrid
for i in range(dilated_reps):
self._default_dict['name'] = f'{name}_{i}'
self._default_dict['name'] = f"{name}_{i}"
x = nn_blocks.DarkResidual(
filters=config.filters // scale_filters,
filter_scale=residual_filter_scale,
......@@ -528,17 +530,17 @@ class Darknet(tf.keras.Model):
x)
for i in range(dilated_reps, config.repetitions):
self._default_dict['dilation_rate'] = max(
1, self._default_dict['dilation_rate'] // 2)
self._default_dict[
'dilation_rate'] = self._default_dict['dilation_rate'] // 2
self._default_dict[
'name'] = f"{name}_{i}_degridded_{self._default_dict['dilation_rate']}"
'name'] = f"{name}_{i}_degrided_{self._default_dict['dilation_rate']}"
x = nn_blocks.DarkResidual(
filters=config.filters // scale_filters,
filter_scale=residual_filter_scale,
**self._default_dict)(
x)
self._default_dict['name'] = f'{name}_csp_connect'
self._default_dict['name'] = f"{name}_csp_connect"
output = nn_blocks.CSPConnect(
filters=config.filters,
filter_scale=csp_filter_scale,
......@@ -549,7 +551,7 @@ class Darknet(tf.keras.Model):
def _csp_tiny_stack(self, inputs, config, name):
self._default_dict['activation'] = self._get_activation(config.activation)
self._default_dict['name'] = f'{name}_csp_tiny'
self._default_dict['name'] = f"{name}_csp_tiny"
x, x_route = nn_blocks.CSPTiny(
filters=config.filters, **self._default_dict)(
inputs)
......@@ -563,10 +565,10 @@ class Darknet(tf.keras.Model):
strides=config.strides,
padding='same',
data_format=None,
name=f'{name}_tiny/pool')(
name=f"{name}_tiny/pool")(
inputs)
self._default_dict['activation'] = self._get_activation(config.activation)
self._default_dict['name'] = f'{name}_tiny/conv'
self._default_dict['name'] = f"{name}_tiny/conv"
x = nn_blocks.ConvBN(
filters=config.filters,
kernel_size=(3, 3),
......@@ -580,7 +582,7 @@ class Darknet(tf.keras.Model):
def _residual_stack(self, inputs, config, name):
self._default_dict['activation'] = self._get_activation(config.activation)
self._default_dict['name'] = f'{name}_residual_down'
self._default_dict['name'] = f"{name}_residual_down"
if self._dilate:
self._default_dict['dilation_rate'] = config.dilation_rate
if config.repetitions < 8:
......@@ -592,10 +594,10 @@ class Darknet(tf.keras.Model):
filters=config.filters, downsample=True, **self._default_dict)(
inputs)
dilated_reps = config.repetitions - (
self._default_dict['dilation_rate'] // 2) - 1
dilated_reps = config.repetitions - self._default_dict[
'dilation_rate'] // 2 - 1
for i in range(dilated_reps):
self._default_dict['name'] = f'{name}_{i}'
self._default_dict['name'] = f"{name}_{i}"
x = nn_blocks.DarkResidual(
filters=config.filters, **self._default_dict)(
x)
......@@ -604,7 +606,7 @@ class Darknet(tf.keras.Model):
self._default_dict[
'dilation_rate'] = self._default_dict['dilation_rate'] // 2
self._default_dict[
'name'] = f"{name}_{i}_degridded_{self._default_dict['dilation_rate']}"
'name'] = f"{name}_{i}_degrided_{self._default_dict['dilation_rate']}"
x = nn_blocks.DarkResidual(
filters=config.filters, **self._default_dict)(
x)
......@@ -619,7 +621,7 @@ class Darknet(tf.keras.Model):
i = 0
self._default_dict['activation'] = self._get_activation(config.activation)
while i < config.repetitions:
self._default_dict['name'] = f'{name}_{i}'
self._default_dict['name'] = f"{name}_{i}"
layer = self._registry(config, self._default_dict)
x = layer(x)
i += 1
......
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