def validate_config(self, model, config_list):
"""
Parameters
----------
model : torch.nn.Module
Model to be pruned
config_list : list of dict
List of configurations
"""
schema = CompressorSchema([{
Optional('quant_types'):
Schema([lambda x: x in ['weight', 'output']]),
Optional('quant_bits'):
Or(
And(int, lambda n: 0 < n < 32),
Schema({
Optional('weight'): And(int, lambda n: 0 < n < 32),
Optional('output'): And(int, lambda n: 0 < n < 32),
})),
Optional('quant_start_step'):
And(int, lambda n: n >= 0),
Optional('op_types'): [str],
Optional('op_names'): [str]
}], model, logger)
schema.validate(config_list)
def validate_config(self, model, config_list):
"""
Parameters
----------
model : torch.nn.Module
Model to be pruned
config_list : list
List on pruning configs
"""
if self._base_algo == 'level':
schema = CompressorSchema(
[{
'sparsity': And(float, lambda n: 0 < n < 1),
Optional('op_types'): [str],
Optional('op_names'): [str],
}], model, _logger)
elif self._base_algo in ['l1', 'l2', 'fpgm']:
schema = CompressorSchema(
[{
'sparsity': And(float, lambda n: 0 < n < 1),
'op_types': ['Conv2d'],
Optional('op_names'): [str]
}], model, _logger)
schema.validate(config_list)
def validate_config(self, model, config_list):
schema = CompressorSchema([{
Optional('quant_types'): ['weight'],
Optional('quant_bits'): Or(8, {'weight': 8}),
Optional('op_types'): [str],
Optional('op_names'): [str]
}], model, logger)
schema.validate(config_list)
def validate_config(self, model, config_list):
schema = CompressorSchema([{
'sparsity': And(float, lambda n: 0 < n < 1),
'op_types': ['BatchNorm2d'],
Optional('op_names'): [str]
}], model, logger)
schema.validate(config_list)
if len(config_list) > 1:
logger.warning('Slim pruner only supports 1 configuration')
def validate_config(self, model, config_list):
schema = CompressorSchema(
[{
Optional('sparsity'): And(float, lambda n: 0 < n < 1),
Optional('op_types'): ['Conv2d'],
Optional('op_names'): [str],
Optional('exclude'): bool
}], model, logger)
schema.validate(config_list)
for config in config_list:
if 'exclude' not in config and 'sparsity' not in config:
raise SchemaError(
'Either sparisty or exclude must be specified!')
def validate_config(self, model, config_list):
"""
Parameters
----------
model : torch.nn.Module
Model to be pruned
config_list : list
List on pruning configs
"""
schema = CompressorSchema([{
'sparsity': And(float, lambda n: 0 < n < 1),
Optional('op_types'): [str],
Optional('op_names'): [str]
}], model, logger)
schema.validate(config_list)
def validate_config(self, model, config_list):
"""
Parameters
----------
model : torch.nn.Module
Model to be pruned
config_list : list
Supported keys:
- prune_iterations : The number of rounds for the iterative pruning.
- sparsity : The final sparsity when the compression is done.
"""
schema = CompressorSchema([{
'sparsity': And(float, lambda n: 0 < n < 1),
'prune_iterations': And(int, lambda n: n > 0),
Optional('op_types'): [str],
Optional('op_names'): [str]
}], model, logger)
schema.validate(config_list)
assert len(set([x['prune_iterations'] for x in config_list])) == 1, 'The values of prune_iterations must be equal in your config'
def validate_config(self, model, config_list):
"""
Parameters
----------
model : torch.nn.Module
Model to be pruned
config_list : list
List on pruning configs
"""
schema = CompressorSchema(
[{
'initial_sparsity': And(float, lambda n: 0 <= n <= 1),
'final_sparsity': And(float, lambda n: 0 <= n <= 1),
'start_epoch': And(int, lambda n: n >= 0),
'end_epoch': And(int, lambda n: n >= 0),
'frequency': And(int, lambda n: n > 0),
Optional('op_types'): [str],
Optional('op_names'): [str]
}], model, logger)
schema.validate(config_list)