45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
# Copyright 2021 Samsung Electronics Co., Ltd.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =============================================================================
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import torch
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import torch.nn.functional as F
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from . import measure
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from ..p_utils import get_layer_metric_array
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@measure('plain', bn=True, mode='param')
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def compute_plain_per_weight(net, inputs, targets, mode, loss_fn, split_data=1):
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net.zero_grad()
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N = inputs.shape[0]
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for sp in range(split_data):
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st=sp*N//split_data
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en=(sp+1)*N//split_data
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outputs = net.forward(inputs[st:en])
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loss = loss_fn(outputs, targets[st:en])
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loss.backward()
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# select the gradients that we want to use for search/prune
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def plain(layer):
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if layer.weight.grad is not None:
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return layer.weight.grad * layer.weight
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else:
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return torch.zeros_like(layer.weight)
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grads_abs = get_layer_metric_array(net, plain, mode)
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return grads_abs
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