def test_get_last_pruned_layers(model, ref_last_module_names):
config = get_basic_pruning_config(input_sample_size=(1, 1, 8, 8))
config['compression']['algorithm'] = 'filter_pruning'
pruned_model, _ = create_compressed_model_and_algo_for_test(model(), config)
first_pruned_modules = get_last_pruned_modules(pruned_model,
FilterPruningBuilder(config).get_types_of_pruned_modules())
ref_last_modules = [getattr(pruned_model, module_name) for module_name in ref_last_module_names]
assert set(first_pruned_modules) == set(ref_last_modules)
def _is_module_prunable(self, target_model: NNCFNetwork, module, module_scope: Scope):
"""
Check whether we should prune module according to algorithm parameters.
:param target_model: model to work with
:param module: module to check
:param module_scope: scope for module
:return: (prune: bool, msg: str)
prune: Whether we should prune module
msg: additional information why we should/shouldn't prune
"""
prune = True
msg = None
pruned_types = self.get_op_types_of_pruned_modules()
input_non_pruned_modules = get_first_pruned_modules(target_model, pruned_types + ['linear'])
output_non_pruned_modules = get_last_pruned_modules(target_model, pruned_types + ['linear'])
module_scope_str = str(module_scope)
if self.ignore_frozen_layers and not module.weight.requires_grad:
msg = "Ignored adding Weight Pruner in scope: {} because"\
" the layer appears to be frozen (requires_grad=False)".format(module_scope_str)
prune = False
elif not self._should_consider_scope(module_scope_str):
msg = "Ignored adding Weight Pruner in scope: {}".format(module_scope_str)
prune = False
elif not self.prune_first and module in input_non_pruned_modules:
msg = "Ignored adding Weight Pruner in scope: {} because"\
" this scope is one of the first convolutions".format(module_scope_str)
prune = False
elif not self.prune_last and module in output_non_pruned_modules:
msg = "Ignored adding Weight Pruner in scope: {} because"\
" this scope is one of the last convolutions".format(module_scope_str)
prune = False
elif is_grouped_conv(module):
if not is_depthwise_conv(module):
msg = "Ignored adding Weight Pruner in scope: {} because" \
" this scope is grouped convolution".format(module_scope_str)
prune = False
elif not self.prune_downsample_convs and is_conv_with_downsampling(module):
msg = "Ignored adding Weight Pruner in scope: {} because"\
" this scope is convolution with downsample".format(module_scope_str)
prune = False
return prune, msg
def _prune_weights(self, target_model: NNCFNetwork):
device = next(target_model.parameters()).device
modules_to_prune = target_model.get_nncf_modules()
insertion_commands = []
bn_for_depthwise = {}
input_non_pruned_modules = get_first_pruned_modules(
target_model,
self.get_types_of_pruned_modules() + ['linear'])
output_non_pruned_modules = get_last_pruned_modules(
target_model,
self.get_types_of_pruned_modules() + ['linear'])
for module_scope, module in modules_to_prune.items():
# Check that we need to prune weights in this op
if not self._is_pruned_module(module):
continue
module_scope_str = str(module_scope)
if self.ignore_frozen_layers and not module.weight.requires_grad:
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" the layer appears to be frozen (requires_grad=False)".
format(module_scope_str))
continue
if not self._should_consider_scope(module_scope_str):
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {}".format(
module_scope_str))
continue
if not self.prune_first and module in input_non_pruned_modules:
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is one of the first convolutions".format(
module_scope_str))
continue
if not self.prune_last and module in output_non_pruned_modules:
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is one of the last convolutions".format(
module_scope_str))
continue
if is_grouped_conv(module):
if is_depthwise_conv(module):
previous_conv = get_previous_conv(target_model, module,
module_scope)
if previous_conv:
depthwise_bn = get_bn_for_module_scope(
target_model, module_scope)
bn_for_depthwise[str(previous_conv.op_exec_context.
scope_in_model)] = depthwise_bn
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is grouped convolution".format(
module_scope_str))
continue
if not self.prune_downsample_convs and is_conv_with_downsampling(
module):
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is convolution with downsample".format(
module_scope_str))
continue
nncf_logger.info(
"Adding Weight Pruner in scope: {}".format(module_scope_str))
operation = self.create_weight_pruning_operation(module)
hook = UpdateWeight(operation).to(device)
insertion_commands.append(
InsertionCommand(
InsertionPoint(
InputAgnosticOperationExecutionContext(
"", module_scope, 0),
InsertionType.NNCF_MODULE_PRE_OP), hook,
OperationPriority.PRUNING_PRIORITY))
related_modules = {}
if self.prune_batch_norms:
related_modules[
PrunedModuleInfo.BN_MODULE_NAME] = get_bn_for_module_scope(
target_model, module_scope)
self._pruned_module_info.append(
PrunedModuleInfo(module_scope_str, module, hook.operand,
related_modules))
if self.prune_batch_norms:
self.update_minfo_with_depthwise_bn(bn_for_depthwise)
return insertion_commands
def _prune_weights(self, target_model: NNCFNetwork):
device = next(target_model.parameters()).device
modules_to_prune = target_model.get_nncf_modules()
insertion_commands = []
input_non_pruned_modules = get_first_pruned_modules(
target_model,
self.get_types_of_pruned_modules() + ['linear'])
output_non_pruned_modules = get_last_pruned_modules(
target_model,
self.get_types_of_pruned_modules() + ['linear'])
for module_scope, module in modules_to_prune.items():
# Check that we need to prune weights in this op
if not self._is_pruned_module(module):
continue
module_scope_str = str(module_scope)
if not self._should_consider_scope(module_scope_str):
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {}".format(
module_scope_str))
continue
if not self.prune_first and module in input_non_pruned_modules:
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is one of the first convolutions".format(
module_scope_str))
continue
if not self.prune_last and module in output_non_pruned_modules:
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is one of the last convolutions".format(
module_scope_str))
continue
if not self.prune_downsample_convs and is_conv_with_downsampling(
module):
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is convolution with downsample".format(
module_scope_str))
continue
nncf_logger.info(
"Adding Weight Pruner in scope: {}".format(module_scope_str))
operation = self.create_weight_pruning_operation(module)
hook = UpdateWeight(operation).to(device)
insertion_commands.append(
InsertionCommand(
InsertionPoint(
InputAgnosticOperationExecutionContext(
"", module_scope, 0),
InsertionType.NNCF_MODULE_PRE_OP), hook,
OperationPriority.PRUNING_PRIORITY))
related_modules = {}
if self.prune_batch_norms:
related_modules['bn_module'] = get_bn_for_module_scope(
target_model, module_scope)
self._pruned_module_info.append(
PrunedModuleInfo(module_scope_str, module, hook.operand,
related_modules))
return insertion_commands