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_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_condconv(width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
drop_connect_rate=0.2,
condconv_num_experts=None):
"""Creates an efficientnet-condconv 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_cc',
'r4_k5_s22_e6_i112_o192_se0.25_cc',
'r1_k3_s11_e6_i192_o320_se0.25_cc',
]
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,
condconv_num_experts=condconv_num_experts)
decoder = efficientnet_builder.BlockDecoder()
return decoder.decode(blocks_args), global_params
def get_model_params(model_name, override_params):
"""Get the block args and global params for a given model."""
if model_name.startswith('efficientnet-lite'):
width_coefficient, depth_coefficient, _, dropout_rate = (
efficientnet_lite_params(model_name))
global_params = efficientnet_lite(
width_coefficient, depth_coefficient, dropout_rate)
else:
raise NotImplementedError('model name is not pre-defined: %s' % model_name)
if override_params:
# ValueError will be raised here if override_params has fields not included
# in global_params.
global_params = global_params._replace(**override_params)
decoder = efficientnet_builder.BlockDecoder()
blocks_args = decoder.decode(global_params.blocks_args)
logging.info('global_params= %s', global_params)
return blocks_args, global_params
