111 lines
3.5 KiB
Python
111 lines
3.5 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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from torch import nn
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import numpy as np
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from . import measure
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def network_weight_gaussian_init(net: nn.Module):
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with torch.no_grad():
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for n, m in net.named_modules():
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if isinstance(m, nn.Conv2d):
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nn.init.normal_(m.weight)
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if hasattr(m, 'bias') and m.bias is not None:
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.BatchNorm2d):
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try:
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nn.init.ones_(m.weight)
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nn.init.zeros_(m.bias)
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except:
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pass
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight)
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if hasattr(m, 'bias') and m.bias is not None:
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nn.init.zeros_(m.bias)
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else:
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continue
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return net
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def get_zen(gpu, model, mixup_gamma=1e-2, resolution=32, batch_size=64, repeat=32,
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fp16=False):
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info = {}
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nas_score_list = []
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if gpu is not None:
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device = torch.device(gpu)
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else:
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device = torch.device('cpu')
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if fp16:
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dtype = torch.half
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else:
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dtype = torch.float32
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with torch.no_grad():
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for repeat_count in range(repeat):
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network_weight_gaussian_init(model)
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input = torch.randn(size=[batch_size, 3, resolution, resolution], device=device, dtype=dtype)
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input2 = torch.randn(size=[batch_size, 3, resolution, resolution], device=device, dtype=dtype)
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mixup_input = input + mixup_gamma * input2
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output = model.forward_pre_GAP(input)
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mixup_output = model.forward_pre_GAP(mixup_input)
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nas_score = torch.sum(torch.abs(output - mixup_output), dim=[1, 2, 3])
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nas_score = torch.mean(nas_score)
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# compute BN scaling
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log_bn_scaling_factor = 0.0
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for m in model.modules():
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if isinstance(m, nn.BatchNorm2d):
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try:
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bn_scaling_factor = torch.sqrt(torch.mean(m.running_var))
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log_bn_scaling_factor += torch.log(bn_scaling_factor)
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except:
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pass
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pass
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pass
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nas_score = torch.log(nas_score) + log_bn_scaling_factor
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nas_score_list.append(float(nas_score))
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std_nas_score = np.std(nas_score_list)
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avg_precision = 1.96 * std_nas_score / np.sqrt(len(nas_score_list))
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avg_nas_score = np.mean(nas_score_list)
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info = float(avg_nas_score)
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return info
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@measure('zen', bn=True)
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def compute_zen(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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zen = get_zen(device,net)
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except Exception as e:
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print(e)
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zen= np.nan
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return zen
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