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import os
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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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class Encoder(nn.Module):
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def __init__(self):
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super(Encoder, self).__init__()
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basemodel_name = 'tf_efficientnet_b5_ap'
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print('Loading base model ()...'.format(basemodel_name), end='')
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repo_path = os.path.join(os.path.dirname(__file__), 'efficientnet_repo')
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basemodel = torch.hub.load(repo_path, basemodel_name, pretrained=False, source='local')
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print('Done.')
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print('Removing last two layers (global_pool & classifier).')
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basemodel.global_pool = nn.Identity()
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basemodel.classifier = nn.Identity()
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self.original_model = basemodel
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def forward(self, x):
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features = [x]
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for k, v in self.original_model._modules.items():
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if (k == 'blocks'):
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for ki, vi in v._modules.items():
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features.append(vi(features[-1]))
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else:
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features.append(v(features[-1]))
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return features
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