84 lines
2.9 KiB
Python
84 lines
2.9 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 numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ..models import *
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def get_some_data(train_dataloader, num_batches, device):
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traindata = []
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dataloader_iter = iter(train_dataloader)
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for _ in range(num_batches):
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traindata.append(next(dataloader_iter))
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inputs = torch.cat([a for a,_ in traindata])
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targets = torch.cat([b for _,b in traindata])
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inputs = inputs.to(device)
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targets = targets.to(device)
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return inputs, targets
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def get_some_data_grasp(train_dataloader, num_classes, samples_per_class, device):
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datas = [[] for _ in range(num_classes)]
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labels = [[] for _ in range(num_classes)]
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mark = dict()
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dataloader_iter = iter(train_dataloader)
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while True:
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inputs, targets = next(dataloader_iter)
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for idx in range(inputs.shape[0]):
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x, y = inputs[idx:idx+1], targets[idx:idx+1]
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category = y.item()
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if len(datas[category]) == samples_per_class:
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mark[category] = True
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continue
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datas[category].append(x)
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labels[category].append(y)
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if len(mark) == num_classes:
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break
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x = torch.cat([torch.cat(_, 0) for _ in datas]).to(device)
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y = torch.cat([torch.cat(_) for _ in labels]).view(-1).to(device)
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return x, y
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def get_layer_metric_array(net, metric, mode):
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metric_array = []
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for layer in net.modules():
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if mode=='channel' and hasattr(layer,'dont_ch_prune'):
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continue
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if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear):
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metric_array.append(metric(layer))
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return metric_array
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def reshape_elements(elements, shapes, device):
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def broadcast_val(elements, shapes):
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ret_grads = []
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for e,sh in zip(elements, shapes):
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ret_grads.append(torch.stack([torch.Tensor(sh).fill_(v) for v in e], dim=0).to(device))
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return ret_grads
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if type(elements[0]) == list:
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outer = []
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for e,sh in zip(elements, shapes):
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outer.append(broadcast_val(e,sh))
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return outer
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else:
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return broadcast_val(elements, shapes)
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def count_parameters(model):
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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