def _set_binary_masks_for_filters(self, pruning_rate):
nncf_logger.debug("Setting new binary masks for pruned modules.")
with torch.no_grad():
for group in self.pruned_module_groups_info.get_all_clusters():
filters_num = torch.tensor(
[get_filters_num(minfo.module) for minfo in group.nodes])
assert torch.all(filters_num == filters_num[0])
device = group.nodes[0].module.weight.device
cumulative_filters_importance = torch.zeros(
filters_num[0]).to(device)
# 1. Calculate cumulative importance for all filters in group
for minfo in group.nodes:
filters_importance = self.filter_importance(
minfo.module.weight,
minfo.module.target_weight_dim_for_compression)
cumulative_filters_importance += filters_importance
# 2. Calculate threshold
num_of_sparse_elems = get_rounded_pruned_element_number(
cumulative_filters_importance.size(0), pruning_rate)
threshold = sorted(cumulative_filters_importance)[min(
num_of_sparse_elems, filters_num[0] - 1)]
mask = calculate_binary_mask(cumulative_filters_importance,
threshold)
# 3. Set binary masks for filter
for minfo in group.nodes:
pruning_module = minfo.operand
pruning_module.binary_filter_pruning_mask = mask
# Calculate actual flops with new masks
self.current_flops = self._calculate_flops_pruned_model_by_masks()
def _set_binary_masks_for_filters(self):
nncf_logger.debug("Setting new binary masks for pruned modules.")
with torch.no_grad():
for minfo in self.pruned_module_info:
pruning_module = minfo.operand
# 1. Calculate importance for all filters in all weights
# 2. Calculate thresholds for every weight
# 3. Set binary masks for filter
filters_importance = self.filter_importance(minfo.module.weight)
num_of_sparse_elems = get_rounded_pruned_element_number(filters_importance.size(0),
self.pruning_rate)
threshold = sorted(filters_importance)[num_of_sparse_elems]
pruning_module.binary_filter_pruning_mask = calculate_binary_mask(filters_importance, threshold)
def _set_binary_masks_for_all_pruned_modules(self):
nncf_logger.debug("Setting new binary masks for all pruned modules together.")
normalized_weights = []
filter_importances = []
for minfo in self.pruned_module_info:
pruning_module = minfo.operand
# 1. Calculate importance for all filters in all weights
# 2. Calculate thresholds for every weight
# 3. Set binary masks for filter
normalized_weight = self.weights_normalizer(minfo.module.weight)
normalized_weights.append(normalized_weight)
filter_importances.append(self.filter_importance(normalized_weight))
importances = torch.cat(filter_importances)
threshold = sorted(importances)[int(self.pruning_rate * importances.size(0))]
for i, minfo in enumerate(self.pruned_module_info):
pruning_module = minfo.operand
pruning_module.binary_filter_pruning_mask = calculate_binary_mask(filter_importances[i], threshold)
def _set_binary_masks_for_all_pruned_modules(self, pruning_rate):
nncf_logger.debug(
"Setting new binary masks for all pruned modules together.")
filter_importances = []
# 1. Calculate importances for all groups of filters
for group in self.pruned_module_groups_info.get_all_clusters():
filters_num = torch.tensor(
[get_filters_num(minfo.module) for minfo in group.nodes])
assert torch.all(filters_num == filters_num[0])
device = group.nodes[0].module.weight.device
cumulative_filters_importance = torch.zeros(
filters_num[0]).to(device)
# Calculate cumulative importance for all filters in this group
for minfo in group.nodes:
normalized_weight = self.weights_normalizer(
minfo.module.weight)
filters_importance = self.filter_importance(
normalized_weight,
minfo.module.target_weight_dim_for_compression)
cumulative_filters_importance += filters_importance
filter_importances.append(cumulative_filters_importance)
# 2. Calculate one threshold for all weights
importances = torch.cat(filter_importances)
threshold = sorted(importances)[int(pruning_rate *
importances.size(0))]
# 3. Set binary masks for filters in grops
for i, group in enumerate(
self.pruned_module_groups_info.get_all_clusters()):
mask = calculate_binary_mask(filter_importances[i], threshold)
for minfo in group.nodes:
pruning_module = minfo.operand
pruning_module.binary_filter_pruning_mask = mask
# Calculate actual flops with new masks
self.current_flops = self._calculate_flops_pruned_model_by_masks()