74 lines
2.1 KiB
Python
74 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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import copy
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import time
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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 numpy as np
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import torch
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from torch import nn
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from . import measure
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def get_score(net, x, target, device, split_data):
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result_list = []
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x = torch.randn(size=(1, 3, 64, 64)).to(device)
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net.to(device)
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def forward_hook(module, data_input, data_output):
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fea = data_output[0].detach()
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fea = fea.reshape(fea.shape[0], -1)
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n = fea.shape[0]
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corr = torch.corrcoef(fea)
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corr[torch.isnan(corr)] = 0
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corr[torch.isinf(corr)] = 0
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values = torch.linalg.eig(corr)[0]
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# result = np.real(np.min(values)) / np.real(np.max(values))
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result = torch.min(torch.real(values))
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result_list.append(result)
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for name, modules in net.named_modules():
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modules.register_forward_hook(forward_hook)
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N = x.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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y = net(x[st:en])
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# break
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results = torch.tensor(result_list)
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results = results[torch.logical_not(torch.isnan(results))]
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v = torch.sum(results)
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result_list.clear()
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return v.item()
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@measure('meco', bn=True)
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def compute_meco(net, inputs, targets, split_data=1, loss_fn=None):
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device = inputs.device
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# Compute gradients (but don't apply them)
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net.zero_grad()
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try:
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meco = get_score(net, inputs, targets, device, split_data=split_data)
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except Exception as e:
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print(e)
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meco = np.nan, None
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return meco
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