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Running on Zero
Running on Zero
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7826761 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels, eps=1e-5)
def forward(self, x):
return F.relu(self.bn(self.conv(x)), inplace=True)
class Inception(nn.Module):
def __init__(self):
super().__init__()
self.branch1x1 = BasicConv2d(128, 32, kernel_size=1, padding=0)
self.branch1x1_2 = BasicConv2d(128, 32, kernel_size=1, padding=0)
self.branch3x3_reduce = BasicConv2d(128, 24, kernel_size=1, padding=0)
self.branch3x3 = BasicConv2d(24, 32, kernel_size=3, padding=1)
self.branch3x3_reduce_2 = BasicConv2d(128, 24, kernel_size=1, padding=0)
self.branch3x3_2 = BasicConv2d(24, 32, kernel_size=3, padding=1)
self.branch3x3_3 = BasicConv2d(32, 32, kernel_size=3, padding=1)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch1x1_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch1x1_2 = self.branch1x1_2(branch1x1_pool)
branch3x3_reduce = self.branch3x3_reduce(x)
branch3x3 = self.branch3x3(branch3x3_reduce)
branch3x3_reduce_2 = self.branch3x3_reduce_2(x)
branch3x3_2 = self.branch3x3_2(branch3x3_reduce_2)
branch3x3_3 = self.branch3x3_3(branch3x3_2)
return torch.cat([branch1x1, branch1x1_2, branch3x3, branch3x3_3], 1)
class FaceBoxes(nn.Module):
def __init__(self, num_classes: int):
super().__init__()
self.num_classes = num_classes
self.conv1 = BasicConv2d(3, 8, kernel_size=3, stride=2, padding=1)
self.conv2 = BasicConv2d(8, 16, kernel_size=3, stride=2, padding=1)
self.conv3 = BasicConv2d(16, 32, kernel_size=3, stride=2, padding=1)
self.conv4 = BasicConv2d(32, 64, kernel_size=3, stride=2, padding=1)
self.conv5 = BasicConv2d(64, 128, kernel_size=3, stride=2, padding=1)
self.inception1 = Inception()
self.inception2 = Inception()
self.inception3 = Inception()
self.conv6_1 = BasicConv2d(128, 128, kernel_size=1, stride=1, padding=0)
self.conv6_2 = BasicConv2d(128, 256, kernel_size=3, stride=2, padding=1)
self.conv7_1 = BasicConv2d(256, 128, kernel_size=1, stride=1, padding=0)
self.conv7_2 = BasicConv2d(128, 256, kernel_size=3, stride=2, padding=1)
self.loc, self.conf = self.multibox(num_classes)
self.softmax = nn.Softmax(dim=-1)
def multibox(self, num_classes: int):
loc_layers = [
nn.Conv2d(128, 21 * 4, kernel_size=3, padding=1),
nn.Conv2d(256, 1 * 4, kernel_size=3, padding=1),
nn.Conv2d(256, 1 * 4, kernel_size=3, padding=1),
]
conf_layers = [
nn.Conv2d(128, 21 * num_classes, kernel_size=3, padding=1),
nn.Conv2d(256, 1 * num_classes, kernel_size=3, padding=1),
nn.Conv2d(256, 1 * num_classes, kernel_size=3, padding=1),
]
return nn.Sequential(*loc_layers), nn.Sequential(*conf_layers)
def forward(self, x):
sources = []
loc = []
conf = []
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.inception1(x)
x = self.inception2(x)
x = self.inception3(x)
sources.append(x)
x = self.conv6_1(x)
x = self.conv6_2(x)
sources.append(x)
x = self.conv7_1(x)
x = self.conv7_2(x)
sources.append(x)
for feat, l, c in zip(sources, self.loc, self.conf):
loc.append(l(feat).permute(0, 2, 3, 1))
conf.append(c(feat).permute(0, 2, 3, 1))
loc = torch.cat([o.reshape(o.size(0), -1) for o in loc], 1)
conf = torch.cat([o.reshape(o.size(0), -1) for o in conf], 1)
return loc.view(loc.size(0), -1, 4), self.softmax(conf.view(conf.size(0), -1, self.num_classes))
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