| import math | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| def conv7x7(in_planes, out_planes, stride=1, groups=1, dilation=1): | |
| """7x7 convolution with padding""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=7, stride=stride, | |
| padding=3*dilation, groups=groups, bias=False, dilation=dilation) | |
| def conv5x5(in_planes, out_planes, stride=1, groups=1, dilation=1): | |
| """5x5 convolution with padding""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=5, stride=stride, | |
| padding=2*dilation, groups=groups, bias=False, dilation=dilation) | |
| def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): | |
| """3x3 convolution with padding""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, | |
| padding=dilation, groups=groups, bias=False, dilation=dilation) | |
| def conv1x1(in_planes, out_planes, stride=1): | |
| """1x1 convolution""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | |
| def maxpool(**kwargs): | |
| return nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| def avgpool(**kwargs): | |
| return nn.AvgPool2d(kernel_size=3, stride=2, padding=1) | |
| def dropout(prob): | |
| return nn.Dropout(prob) | |
| conv3x3sep = lambda i, o, s=1: conv3x3(i, o, s, groups=i) | |
| conv3x3g2 = lambda i, o, s=1: conv3x3(i, o, s, groups=2) | |
| conv3x3g4 = lambda i, o, s=1: conv3x3(i, o, s, groups=4) | |
| conv3x3g8 = lambda i, o, s=1: conv3x3(i, o, s, groups=8) | |
| conv3x3dw = lambda i, o, s=1: conv3x3(i, o, s, groups=i) | |
| conv3x3d2 = lambda i, o, s=1: conv3x3(i, o, s, dilation=2) | |
| conv3x3d3 = lambda i, o, s=1: conv3x3(i, o, s, dilation=3) | |
| conv3x3d4 = lambda i, o, s=1: conv3x3(i, o, s, dilation=4) | |
| conv5x5sep = lambda i, o, s=1: conv5x5(i, o, s, groups=i) | |
| conv5x5g2 = lambda i, o, s=1: conv5x5(i, o, s, groups=2) | |
| conv5x5g4 = lambda i, o, s=1: conv5x5(i, o, s, groups=4) | |
| conv5x5g8 = lambda i, o, s=1: conv5x5(i, o, s, groups=8) | |
| conv5x5dw = lambda i, o, s=1: conv5x5(i, o, s, groups=i) | |
| conv5x5d2 = lambda i, o, s=1: conv5x5(i, o, s, dilation=2) | |
| conv5x5d3 = lambda i, o, s=1: conv5x5(i, o, s, dilation=3) | |
| conv5x5d4 = lambda i, o, s=1: conv5x5(i, o, s, dilation=4) | |
| conv7x7sep = lambda i, o, s=1: conv7x7(i, o, s, groups=i) | |
| conv7x7g2 = lambda i, o, s=1: conv7x7(i, o, s, groups=2) | |
| conv7x7g4 = lambda i, o, s=1: conv7x7(i, o, s, groups=4) | |
| conv7x7g8 = lambda i, o, s=1: conv7x7(i, o, s, groups=8) | |
| conv7x7dw = lambda i, o, s=1: conv7x7(i, o, s, groups=i) | |
| conv7x7d2 = lambda i, o, s=1: conv7x7(i, o, s, dilation=2) | |
| conv7x7d3 = lambda i, o, s=1: conv7x7(i, o, s, dilation=3) | |
| conv7x7d4 = lambda i, o, s=1: conv7x7(i, o, s, dilation=4) |