77 lines
2.4 KiB
Python
77 lines
2.4 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 torch import nn
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import numpy as np
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from . import measure
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def get_flattened_metric(net, metric):
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grad_list = []
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for layer in net.modules():
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if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear):
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grad_list.append(metric(layer).flatten())
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flattened_grad = np.concatenate(grad_list)
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return flattened_grad
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def get_grad_conflict(net, inputs, targets, loss_fn):
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N = inputs.shape[0]
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batch_grad = []
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for i in range(N):
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net.zero_grad()
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outputs = net.forward(inputs[[i]])
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loss = loss_fn(outputs, targets[[i]])
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loss.backward()
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flattened_grad = get_flattened_metric(net, lambda
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l: l.weight.grad.data.clone().cpu().numpy() if l.weight.grad is not None else torch.zeros_like(
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l.weight).clone().cpu().numpy())
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batch_grad.append(flattened_grad)
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batch_grad = np.stack(batch_grad)
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direction_code = np.sign(batch_grad)
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direction_code = abs(direction_code.sum(axis=0))
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score = np.nansum(direction_code)
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return score
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def get_gradsign(input, target, net, device, loss_fn):
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s = []
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net = net.to(device)
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x, target = input, target
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# x2 = torch.clone(x)
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# x2 = x2.to(device)
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x, target = x.to(device), target.to(device)
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s.append(get_grad_conflict(net=net, inputs=x, targets=target, loss_fn=loss_fn))
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s = np.mean(s)
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return s
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@measure('gradsign', bn=True)
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def compute_gradsign(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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gradsign = get_gradsign(inputs, targets, net, device, loss_fn)
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except Exception as e:
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print(e)
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gradsign= np.nan
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return gradsign
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