70 lines
2.1 KiB
Python
70 lines
2.1 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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from . import measure
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from ..p_utils import get_layer_metric_array
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@measure('synflow', bn=False, mode='param')
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@measure('synflow_bn', bn=True, mode='param')
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def compute_synflow_per_weight(net, inputs, targets, mode, split_data=1, loss_fn=None):
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device = inputs.device
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#convert params to their abs. Keep sign for converting it back.
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@torch.no_grad()
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def linearize(net):
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signs = {}
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for name, param in net.state_dict().items():
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signs[name] = torch.sign(param)
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param.abs_()
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return signs
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#convert to orig values
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@torch.no_grad()
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def nonlinearize(net, signs):
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for name, param in net.state_dict().items():
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if 'weight_mask' not in name:
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param.mul_(signs[name])
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# keep signs of all params
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signs = linearize(net)
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# Compute gradients with input of 1s
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net.zero_grad()
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net.double()
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input_dim = list(inputs[0,:].shape)
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inputs = torch.ones([1] + input_dim).double().to(device)
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output = net.forward(inputs)
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torch.sum(output).backward()
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# select the gradients that we want to use for search/prune
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def synflow(layer):
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if layer.weight.grad is not None:
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return torch.abs(layer.weight * layer.weight.grad)
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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, synflow, mode)
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# apply signs of all params
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nonlinearize(net, signs)
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return grads_abs
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