def efficientnet_edgetpu(width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
survival_prob=0.8):
"""Creates an efficientnet-edgetpu model."""
blocks_args = [
'r1_k3_s11_e4_i24_o24_c1_noskip',
'r2_k3_s22_e8_i24_o32_c1',
'r4_k3_s22_e8_i32_o48_c1',
'r5_k5_s22_e8_i48_o96',
'r4_k5_s11_e8_i96_o144',
'r2_k5_s22_e8_i144_o192',
]
global_params = efficientnet_model.GlobalParams(
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
survival_prob=survival_prob,
data_format='channels_last',
num_classes=1001,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None,
relu_fn=tf.nn.relu,
# The default is TPU-specific batch norm.
# The alternative is tf.layers.BatchNormalization.
batch_norm=utils.TpuBatchNormalization, # TPU-specific requirement.
use_se=False)
decoder = efficientnet_builder.BlockDecoder()
return decoder.decode(blocks_args), global_params
def efficientnet(width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
drop_connect_rate=0.2):
"""Creates a efficientnet model."""
blocks_args = [
'r1_k3_s11_e1_i32_o16_se0.25',
'r2_k3_s22_e6_i16_o24_se0.25',
'r2_k5_s22_e6_i24_o40_se0.25',
'r3_k3_s22_e6_i40_o80_se0.25',
'r3_k5_s11_e6_i80_o112_se0.25',
'r4_k5_s22_e6_i112_o192_se0.25',
'r1_k3_s11_e6_i192_o320_se0.25',
]
global_params = efficientnet_model.GlobalParams(
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
drop_connect_rate=drop_connect_rate,
data_format='channels_last',
num_classes=1000,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None,
relu_fn=tf.nn.swish,
# The default is TPU-specific batch norm.
# The alternative is tf.layers.BatchNormalization.
batch_norm=utils.TpuBatchNormalization, # TPU-specific requirement.
use_se=True)
decoder = BlockDecoder()
return decoder.decode(blocks_args), global_params
def efficientnet_edge(width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
survival_prob=0.8):
"""Creates a efficientnet model."""
global_params = efficientnet_model.GlobalParams(
blocks_args=_DEFAULT_BLOCKS_ARGS,
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
survival_prob=survival_prob,
data_format='channels_last',
num_classes=1000,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None,
relu_fn=tf.nn.relu6, # Relu6 is for easier quantization.
# The default is TPU-specific batch norm.
# The alternative is tf.layers.BatchNormalization.
batch_norm=utils.TpuBatchNormalization, # TPU-specific requirement.
clip_projection_output=False,
local_pooling=True, # special cases for tflite issues.
use_se=False) # SE is not well supported on many edge devices.
return global_params
def efficientnet(width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
drop_connect_rate=0.2):
"""Creates a efficientnet model."""
blocks_args = [
'r1_k3_s11_e1_i32_o16_se0.25',
'r2_k3_s22_e6_i16_o24_se0.25',
'r2_k5_s22_e6_i24_o40_se0.25',
'r3_k3_s22_e6_i40_o80_se0.25',
'r3_k5_s11_e6_i80_o112_se0.25',
'r4_k5_s22_e6_i112_o192_se0.25',
'r1_k3_s11_e6_i192_o320_se0.25',
]
global_params = efficientnet_model.GlobalParams(
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
drop_connect_rate=drop_connect_rate,
data_format='channels_last',
num_classes=1000,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None)
decoder = BlockDecoder()
return decoder.decode(blocks_args), global_params
def efficientnet_edgetpu(width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
drop_connect_rate=0.2):
"""Creates an efficientnet-edgetpu model."""
blocks_args = [
'r1_k3_s11_e4_i24_o24_c1_noskip',
'r2_k3_s22_e8_i24_o32_c1',
'r4_k3_s22_e8_i32_o48_c1',
'r5_k5_s22_e8_i48_o96',
'r4_k5_s11_e8_i96_o144',
'r2_k5_s22_e8_i144_o192',
]
global_params = efficientnet_model.GlobalParams(
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
drop_connect_rate=drop_connect_rate,
data_format='channels_last',
num_classes=1001,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None,
relu_fn=tf.nn.relu)
decoder = efficientnet_builder.BlockDecoder()
return decoder.decode(blocks_args), global_params
def efficientnet(width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
stochastic_depth_rate=0.2):
'''Creates a efficientnet model.'''
blocks_args = [
'r1_k3_s11_e1_i32_o16_se0.25',
'r2_k3_s22_e6_i16_o24_se0.25',
'r2_k5_s22_e6_i24_o40_se0.25',
'r3_k3_s22_e6_i40_o80_se0.25',
'r3_k5_s11_e6_i80_o112_se0.25',
'r4_k5_s22_e6_i112_o192_se0.25',
'r1_k3_s11_e6_i192_o320_se0.25',
]
if FLAGS.small_image_model:
blocks_args[1] = 'r2_k3_s11_e6_i16_o24_se0.25'
blocks_args[2] = 'r2_k5_s11_e6_i24_o40_se0.25'
global_params = efficientnet_model.GlobalParams(
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
stochastic_depth_rate=stochastic_depth_rate,
data_format='channels_last',
num_classes=1000,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None,
relu_fn=tf.nn.swish)
decoder = BlockDecoder()
return decoder.decode(blocks_args), global_params
def efficientnet(width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
survival_prob=0.8):
"""Creates a efficientnet model."""
global_params = efficientnet_model.GlobalParams(
blocks_args=_DEFAULT_BLOCKS_ARGS,
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
survival_prob=survival_prob,
data_format='channels_last',
num_classes=1000,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None,
relu_fn=tf.nn.swish,
# The default is TPU-specific batch norm.
# The alternative is tf.layers.BatchNormalization.
batch_norm=utils.TpuBatchNormalization, # TPU-specific requirement.
use_se=True,
clip_projection_output=False)
return global_params
