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CNN6.py
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CNN6.py
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import torch.nn as nn
from collections import OrderedDict
class CNN6(nn.Module):
def __init__(self):
super(CNN6, self).__init__()
act = nn.LeakyReLU(negative_slope=0.2)
self.body = nn.ModuleList([
nn.Sequential(OrderedDict([
('layer', nn.Conv2d(3, 12, kernel_size=4, padding=2, stride=2, bias=False)),
('act', act)
])),
nn.Sequential(OrderedDict([
('layer', nn.Conv2d(12, 36, kernel_size=3, padding=1, stride=2, bias=False)),
('act', act)
])),
nn.Sequential(OrderedDict([
('layer', nn.Conv2d(36, 36, kernel_size=3, padding=1, stride=1, bias=False)),
('act', act)
])),
nn.Sequential(OrderedDict([
('layer', nn.Conv2d(36, 36, kernel_size=3, padding=1, stride=1, bias=False)),
('act', act)
])),
nn.Sequential(OrderedDict([
('layer', nn.Conv2d(36, 64, kernel_size=3, padding=1, stride=2, bias=False)),
('act', act)
])),
nn.Sequential(OrderedDict([
('layer', nn.Conv2d(64, 128, kernel_size=3, padding=1, stride=1, bias=False)),
('act', act)
])),
nn.Sequential(OrderedDict([
('layer', nn.Linear(3200, 1, bias=False)),
('act', nn.Identity())
]))
])
def forward(self, x):
x_shape = []
for layer in self.body:
if isinstance(layer.layer, nn.Linear):
x = x.flatten(1)
x_shape.append(x.shape)
x = layer(x)
return x, x_shape
@staticmethod
def name():
return 'CNN6'