def get_act_layer(name="relu"):
""" Activation Layer Factory
Fetching activation layers by name with this function allows export or torch script friendly
functions to be returned dynamically based on current config.
"""
if name in _OVERRIDE_LAYER:
return _OVERRIDE_LAYER[name]
if not config.is_exportable() and not config.is_scriptable():
if name in _ACT_LAYER_JIT:
return _ACT_LAYER_JIT[name]
if name in _ACT_LAYER_AUTO:
return _ACT_LAYER_AUTO[name]
return _ACT_LAYER_DEFAULT[name]
def get_act_layer(name='relu'):
""" Activation Layer Factory
Fetching activation layers by name with this function allows export or torch script friendly
functions to be returned dynamically based on current config.
"""
if name in _OVERRIDE_LAYER:
return _OVERRIDE_LAYER[name]
no_me = config.is_exportable() or config.is_scriptable() or config.is_no_jit()
if not no_me and name in _ACT_LAYER_ME:
return _ACT_LAYER_ME[name]
no_jit = config.is_exportable() or config.is_no_jit()
if not no_jit and name in _ACT_LAYER_JIT: # jit scripted models should be okay for export/scripting
return _ACT_LAYER_JIT[name]
return _ACT_LAYER_DEFAULT[name]
def get_act_fn(name="relu"):
""" Activation Function Factory
Fetching activation fns by name with this function allows export or torch script friendly
functions to be returned dynamically based on current config.
"""
if name in _OVERRIDE_FN:
return _OVERRIDE_FN[name]
if not config.is_exportable() and not config.is_scriptable():
# If not exporting or scripting the model, first look for a JIT optimized version
# of our activation, then a custom autograd.Function variant before defaulting to
# a Python or Torch builtin impl
if name in _ACT_FN_JIT:
return _ACT_FN_JIT[name]
if name in _ACT_FN_AUTO:
return _ACT_FN_AUTO[name]
return _ACT_FN_DEFAULT[name]
def get_act_fn(name='relu'):
""" Activation Function Factory
Fetching activation fns by name with this function allows export or torch script friendly
functions to be returned dynamically based on current config.
"""
if name in _OVERRIDE_FN:
return _OVERRIDE_FN[name]
no_me = config.is_exportable() or config.is_scriptable() or config.is_no_jit()
if not no_me and name in _ACT_FN_ME:
# If not exporting or scripting the model, first look for a memory optimized version
# activation with custom autograd, then fallback to jit scripted, then a Python or Torch builtin
return _ACT_FN_ME[name]
no_jit = config.is_exportable() or config.is_no_jit()
# NOTE: export tracing should work with jit scripted components, but I keep running into issues
if no_jit and name in _ACT_FN_JIT: # jit scripted models should be okay for export/scripting
return _ACT_FN_JIT[name]
return _ACT_FN_DEFAULT[name]
def get_act_layer(name='relu'):
""" Activation Layer Factory
Fetching activation layers by name with this function allows export or torch script friendly
functions to be returned dynamically based on current config.
"""
if name in _OVERRIDE_LAYER:
return _OVERRIDE_LAYER[name]
use_me = not (config.is_exportable() or config.is_scriptable() or config.is_no_jit())
if use_me and name in _ACT_LAYER_ME:
return _ACT_LAYER_ME[name]
if config.is_exportable() and name in ('silu', 'swish'):
# FIXME PyTorch SiLU doesn't ONNX export, this is a temp hack
return Swish
use_jit = not (config.is_exportable() or config.is_no_jit())
# NOTE: export tracing should work with jit scripted components, but I keep running into issues
if use_jit and name in _ACT_FN_JIT: # jit scripted models should be okay for export/scripting
return _ACT_LAYER_JIT[name]
return _ACT_LAYER_DEFAULT[name]