# Copyright 2021 Samsung Electronics Co., Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================= import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import copy import types from . import measure from ..p_utils import get_layer_metric_array def snip_forward_conv2d(self, x): return F.conv2d(x, self.weight * self.weight_mask, self.bias, self.stride, self.padding, self.dilation, self.groups) def snip_forward_linear(self, x): return F.linear(x, self.weight * self.weight_mask, self.bias) @measure('snip', bn=True, mode='param') def compute_snip_per_weight(net, inputs, targets, mode, loss_fn, split_data=1): for layer in net.modules(): if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear): layer.weight_mask = nn.Parameter(torch.ones_like(layer.weight)) layer.weight.requires_grad = False # Override the forward methods: if isinstance(layer, nn.Conv2d): layer.forward = types.MethodType(snip_forward_conv2d, layer) if isinstance(layer, nn.Linear): layer.forward = types.MethodType(snip_forward_linear, layer) # Compute gradients (but don't apply them) net.zero_grad() N = inputs.shape[0] for sp in range(split_data): st=sp*N//split_data en=(sp+1)*N//split_data outputs = net.forward(inputs[st:en]) loss = loss_fn(outputs, targets[st:en]) loss.backward() # select the gradients that we want to use for search/prune def snip(layer): if layer.weight_mask.grad is not None: return torch.abs(layer.weight_mask.grad) else: return torch.zeros_like(layer.weight) grads_abs = get_layer_metric_array(net, snip, mode) return grads_abs