def validate_config(self, model, config_list):
schema = PrunerSchema([{
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)
def validate_config(self, model, config_list):
schema = PrunerSchema([{
Optional('sparsity'): And(float, lambda n: 0 < n < 1),
'op_types': ['BatchNorm2d'],
Optional('op_names'): [str],
Optional('exclude'): bool
}], 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):
"""
Parameters
----------
model : torch.nn.Module
Model to be pruned
config_list : list
List on pruning configs
"""
schema = PrunerSchema([{
Optional('sparsity'): And(float, lambda n: 0 <= n <= 1),
Optional('op_types'): [str],
Optional('op_names'): [str],
Optional('exclude'): bool
}], 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 = PrunerSchema(
[{
Optional('sparsity'): And(float, lambda n: 0 < n < 1),
Optional('op_types'): [str],
Optional('op_names'): [str],
Optional('exclude'): bool
}], model, _logger)
elif self.base_algo in ['l1', 'l2', 'fpgm']:
schema = PrunerSchema(
[{
Optional('sparsity'): And(float, lambda n: 0 < n < 1),
'op_types': ['Conv2d'],
Optional('op_names'): [str],
Optional('exclude'): bool
}], 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 = PrunerSchema(
[{
Optional('sparsity'): And(float, lambda n: 0 < n < 1),
'prune_iterations': And(int, lambda n: n > 0),
Optional('op_types'): [str],
Optional('op_names'): [str],
Optional('exclude'): bool
}], 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'
