Source code for thelper.nn.inceptionresnetv2

# InceptionResNetv2 derived from:
# https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/inceptionresnetv2.py
import torch
import torch.nn

import thelper.nn
import thelper.nn.coordconv


[docs]class BasicConv2d(torch.nn.Module):
[docs] def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): super().__init__() self.conv = torch.nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False) # verify bias false self.bn = torch.nn.BatchNorm2d(out_planes, eps=0.001, # value found in tensorflow momentum=0.1, # default pytorch value affine=True) self.relu = torch.nn.ReLU(inplace=False)
[docs] def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) return x
[docs]class Mixed_5b(torch.nn.Module):
[docs] def __init__(self): super().__init__() self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1) self.branch1 = torch.nn.Sequential( BasicConv2d(192, 48, kernel_size=1, stride=1), BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2) ) self.branch2 = torch.nn.Sequential( BasicConv2d(192, 64, kernel_size=1, stride=1), BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1), BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1) ) self.branch3 = torch.nn.Sequential( torch.nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), BasicConv2d(192, 64, kernel_size=1, stride=1) )
[docs] def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) x3 = self.branch3(x) out = torch.cat((x0, x1, x2, x3), 1) return out
[docs]class Block35(torch.nn.Module):
[docs] def __init__(self, scale=1.0): super().__init__() self.scale = scale self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1) self.branch1 = torch.nn.Sequential( BasicConv2d(320, 32, kernel_size=1, stride=1), BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1) ) self.branch2 = torch.nn.Sequential( BasicConv2d(320, 32, kernel_size=1, stride=1), BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1), BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1) ) self.conv2d = torch.nn.Conv2d(128, 320, kernel_size=1, stride=1) self.relu = torch.nn.ReLU(inplace=False)
[docs] def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) out = self.conv2d(out) out = out * self.scale + x out = self.relu(out) return out
[docs]class Mixed_6a(torch.nn.Module):
[docs] def __init__(self): super().__init__() self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2) self.branch1 = torch.nn.Sequential( BasicConv2d(320, 256, kernel_size=1, stride=1), BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1), BasicConv2d(256, 384, kernel_size=3, stride=2) ) self.branch2 = torch.nn.MaxPool2d(3, stride=2)
[docs] def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) return out
[docs]class Block17(torch.nn.Module):
[docs] def __init__(self, scale=1.0): super().__init__() self.scale = scale self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1) self.branch1 = torch.nn.Sequential( BasicConv2d(1088, 128, kernel_size=1, stride=1), BasicConv2d(128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)), BasicConv2d(160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)) ) self.conv2d = torch.nn.Conv2d(384, 1088, kernel_size=1, stride=1) self.relu = torch.nn.ReLU(inplace=False)
[docs] def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) out = self.conv2d(out) out = out * self.scale + x out = self.relu(out) return out
[docs]class Mixed_7a(torch.nn.Module):
[docs] def __init__(self): super().__init__() self.branch0 = torch.nn.Sequential( BasicConv2d(1088, 256, kernel_size=1, stride=1), BasicConv2d(256, 384, kernel_size=3, stride=2) ) self.branch1 = torch.nn.Sequential( BasicConv2d(1088, 256, kernel_size=1, stride=1), BasicConv2d(256, 288, kernel_size=3, stride=2) ) self.branch2 = torch.nn.Sequential( BasicConv2d(1088, 256, kernel_size=1, stride=1), BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1), BasicConv2d(288, 320, kernel_size=3, stride=2) ) self.branch3 = torch.nn.MaxPool2d(3, stride=2)
[docs] def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) x3 = self.branch3(x) out = torch.cat((x0, x1, x2, x3), 1) return out
[docs]class Block8(torch.nn.Module):
[docs] def __init__(self, scale=1.0, noReLU=False): super(Block8, self).__init__() self.scale = scale self.noReLU = noReLU self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1) self.branch1 = torch.nn.Sequential( BasicConv2d(2080, 192, kernel_size=1, stride=1), BasicConv2d(192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)), BasicConv2d(224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)) ) self.conv2d = torch.nn.Conv2d(448, 2080, kernel_size=1, stride=1) if not self.noReLU: self.relu = torch.nn.ReLU(inplace=False)
[docs] def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) out = self.conv2d(out) out = out * self.scale + x if not self.noReLU: out = self.relu(out) return out
[docs]class InceptionResNetV2(thelper.nn.Module):
[docs] def __init__(self, task, input_channels=3): # note: must always forward args to base class to keep backup super().__init__(task, input_channels=input_channels) self.conv2d_1a = BasicConv2d(input_channels, 32, kernel_size=3, stride=2) self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1) self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1) self.maxpool_3a = torch.nn.MaxPool2d(3, stride=2) self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1) self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1) self.maxpool_5a = torch.nn.MaxPool2d(3, stride=2) self.mixed_5b = Mixed_5b() self.repeat = torch.nn.Sequential( Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17) ) self.mixed_6a = Mixed_6a() self.repeat_1 = torch.nn.Sequential( Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10) ) self.mixed_7a = Mixed_7a() self.repeat_2 = torch.nn.Sequential( Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20) ) self.block8 = Block8(noReLU=True) self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1) self.avgpool_1a = torch.nn.AvgPool2d(8, count_include_pad=False) self.last_linear = torch.nn.Linear(1536, 1000) self.set_task(task)
[docs] def features(self, input): x = self.conv2d_1a(input) x = self.conv2d_2a(x) x = self.conv2d_2b(x) x = self.maxpool_3a(x) x = self.conv2d_3b(x) x = self.conv2d_4a(x) x = self.maxpool_5a(x) x = self.mixed_5b(x) x = self.repeat(x) x = self.mixed_6a(x) x = self.repeat_1(x) x = self.mixed_7a(x) x = self.repeat_2(x) x = self.block8(x) x = self.conv2d_7b(x) return x
[docs] def logits(self, features): x = self.avgpool_1a(features) x = x.view(x.size(0), -1) x = self.last_linear(x) return x
[docs] def forward(self, input): x = self.features(input) x = self.logits(x) return x
[docs] def set_task(self, task): assert isinstance(task, thelper.tasks.Classification), "missing impl for non-classif task type" num_classes = len(task.class_names) if self.last_linear.out_features != num_classes: self.last_linear = torch.nn.Linear(1536, num_classes) self.task = task