def summary(self):
"""
Print network structure
:return: none
"""
print('Name:' + self.net_name)
if torch.cuda.is_available():
print("GPU: Enabled")
if self.input_shape_chw is not None:
print('Input Shape: ' + str(self.input_shape_chw))
print('Net Structure:')
summary_layers(self, self.input_shape_chw)
self.block6 = nn.Sequential(
drn_module.layer7,
drn_module.layer8
)
self.block7 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True)
)
def forward(self, x):
"""
forward with image & scene feature
:param image: (N, C, H, W)
:return:
"""
x0 = self.block0(x) # 192x256
x1 = self.block1(x0) # 96x128
x2 = self.block2(x1) # 48x64
x3 = self.block3(x2) # 24x32
x4 = self.block4(x3) # 12x16
x5 = self.block5(x4) # 6x8
x6 = self.block6(x5) # 3x4
x7 = self.block7(x6) # 2x2
return x7, x6, x5, x4, x3, x2, x1, x0
if __name__ == '__main__':
from core_dl.module_util import summary_layers
model = CoordNet().cuda()
summary_layers(model, input_size=(3, 192, 256))