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architecture.py
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architecture.py
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import torch.nn as nn
import torch.nn.functional as F
# constants
# size of grid
S = 7
# number of bounding boxes per grid cell
B = 2
# no classes
C = 20
class darknet(nn.Module):
def __init__(self, batch_norm=True, pretrain=False):
super(darknet, self).__init__()
self.convolutional_layers = self._make_convolutional_layers(batch_norm)
self.fc_layers = nn.Sequential(
nn.Linear(1024 * S * S, 4096),
nn.Dropout(0.6),
# nn.BatchNorm1d(4096),
nn.LeakyReLU(0.1),
nn.Linear(4096, S * S * (B * 5 + C)),
)
def forward(self, x):
# for loop with print size for debugging
for layer in self.convolutional_layers:
x = layer(x)
# print(x.size())
# x = self.convolutional_layers(x)
x = x.view(x.size(0), -1)
x = self.fc_layers(x)
x = x.view(x.size(0), S, S, B * 5 + C)
return x
def _make_convolutional_layers(self, batch_norm):
if batch_norm:
convolutional_layers = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.1, inplace=True),
nn.MaxPool2d(2, stride=2),
nn.Conv2d(64, 192, 3, padding=1),
nn.BatchNorm2d(192),
nn.LeakyReLU(0.1, inplace=True),
nn.MaxPool2d(2, stride=2),
# [1, 192, 56, 56]
nn.Conv2d(192, 128, 1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(128, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(256, 256, 1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(256, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.1, inplace=True),
nn.MaxPool2d(2, stride=2),
# [1, 256, 28, 28]
nn.Conv2d(512, 256, 1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(256, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 256, 1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(256, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 256, 1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(256, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 256, 1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(256, 512, 3, padding=1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 512, 1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 1024, 3, padding=1),
nn.BatchNorm2d(1024),
nn.LeakyReLU(0.1, inplace=True),
nn.MaxPool2d(2, stride=2),
# [1, 1024, 14, 14]
nn.Conv2d(1024, 512, 1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 1024, 3, padding=1),
nn.BatchNorm2d(1024),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(1024, 512, 1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 1024, 3, padding=1),
nn.BatchNorm2d(1024),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(1024, 1024, 3, padding=1),
nn.BatchNorm2d(1024),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(1024, 1024, 3, stride=2, padding=1),
nn.BatchNorm2d(1024),
nn.LeakyReLU(0.1, inplace=True),
# [1, 1024, 7, 7]
nn.Conv2d(1024, 1024, 3, padding=1),
nn.BatchNorm2d(1024),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(1024, 1024, 3, padding=1),
nn.BatchNorm2d(1024),
nn.LeakyReLU(0.1, inplace=True),
)
else:
convolutional_layers = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3),
nn.LeakyReLU(0.1, inplace=True),
nn.MaxPool2d(2, stride=2),
nn.Conv2d(64, 192, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.MaxPool2d(2, stride=2),
# [1, 192, 56, 56]
nn.Conv2d(192, 128, 1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(128, 256, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(256, 256, 1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(256, 512, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.MaxPool2d(2, stride=2),
# [1, 256, 28, 28]
nn.Conv2d(512, 256, 1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(256, 512, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 256, 1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(256, 512, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 256, 1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(256, 512, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 256, 1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(256, 512, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 512, 1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 1024, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.MaxPool2d(2, stride=2),
# [1, 1024, 14, 14]
nn.Conv2d(1024, 512, 1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 1024, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(1024, 512, 1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(512, 1024, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(1024, 1024, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(1024, 1024, 3, stride=2, padding=1),
nn.LeakyReLU(0.1, inplace=True),
# [1, 1024, 7, 7]
nn.Conv2d(1024, 1024, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(1024, 1024, 3, padding=1),
nn.LeakyReLU(0.1, inplace=True),
)
return convolutional_layers