def __init__(self, pretrained=True):
super(FurnitureInceptionResNet299, self).__init__()
self.model = inceptionresnetv2(num_classes=1000, pretrained=pretrained)
self.model.last_linear = Linear(1536, 128)
for m in self.model.last_linear.modules():
if isinstance(m, Linear):
normal_(m.weight, 0, 0.01)
constant_(m.bias, 0.0)
# create aliases:
self.stem = ModuleList([
self.model.conv2d_1a,
self.model.conv2d_2a,
self.model.conv2d_2b,
self.model.conv2d_3b,
self.model.conv2d_4a,
])
self.features = ModuleList([
self.model.mixed_5b, self.model.repeat, self.model.mixed_6a,
self.model.repeat_1, self.model.mixed_7a, self.model.repeat_2,
self.model.block8, self.model.conv2d_7b
])
self.classifier = self.model.last_linear
def __init__(self, pretrained):
super(FPN350, self).__init__()
model = inceptionresnetv2(pretrained=pretrained)
self.low_features = nn.Sequential(model.conv2d_1a, model.conv2d_2a,
model.conv2d_2b, model.maxpool_3a,
model.conv2d_3b, model.conv2d_4a,
model.maxpool_5a, model.mixed_5b,
model.repeat, model.mixed_6a,
model.repeat_1)
self.mid_features = nn.Sequential(model.mixed_7a, model.repeat_2,
model.block8)
self.top_features = nn.Sequential(
model.conv2d_7b,
model.avgpool_1a,
)
self.smooth1 = nn.Conv2d(1088, 256, kernel_size=3, stride=1, padding=1)
self.smooth2 = nn.Conv2d(2080, 320, kernel_size=3, stride=1, padding=1)
self.top_smooth = nn.Conv2d(1536,
256,
kernel_size=1,
stride=1,
padding=0)
# Alias
self.smooth_layers = nn.ModuleList([
self.smooth1,
self.smooth2,
self.top_smooth,
])
def __init__(self, pretrained=True, n_cls_layers=1024):
super(FurnitureInceptionResNetOnFiveCrops, self).__init__()
self.model = inceptionresnetv2(num_classes=1000, pretrained=pretrained)
self.model.avgpool_1a = AdaptiveAvgPool2d(1)
n_crops = 5
self.crop_classifiers = []
for i in range(n_crops):
self.crop_classifiers.append(Linear(1536, n_cls_layers))
for m in self.crop_classifiers[-1].modules():
if isinstance(m, Linear):
normal_(m.weight, 0, 0.01)
constant_(m.bias, 0.0)
# create aliases:
self.stem = ModuleList([
self.model.conv2d_1a,
self.model.conv2d_2a,
self.model.conv2d_2b,
])
self.low_features = ModuleList([
self.model.mixed_5b, self.model.repeat, self.model.mixed_6a,
self.model.repeat_1
])
self.features = ModuleList([
self.model.mixed_7a, self.model.repeat_2, self.model.block8,
self.model.conv2d_7b
])
self.crop_classifiers = ModuleList(self.crop_classifiers)
self.drop = Dropout(p=0.45)
self.relu = ReLU(inplace=True)
self.final_classifier = Linear(n_cls_layers, 128)
for m in self.final_classifier.modules():
normal_(m.weight, mean=0.0, std=0.01)
if m.bias is not None:
constant_(m.bias, 0.0)
def __init__(self, pretrained):
super(InceptionResnetFPN350, self).__init__()
model = inceptionresnetv2(pretrained=pretrained)
self.stem = nn.Sequential(
model.conv2d_1a,
model.conv2d_2a,
model.conv2d_2b,
model.maxpool_3a,
model.conv2d_3b,
model.conv2d_4a,
model.maxpool_5a,
)
self.low_features = nn.Sequential(
model.mixed_5b,
model.repeat,
)
self.layer3 = nn.Sequential(model.mixed_6a, model.repeat_1)
self.layer4 = nn.Sequential(
model.mixed_7a,
model.repeat_2,
model.block8,
model.conv2d_7b,
)
self.conv6 = nn.Conv2d(1536, 256, kernel_size=3, stride=2, padding=1)
self.conv7 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1)
# Top-down layers
self.toplayer = nn.Conv2d(1536,
256,
kernel_size=1,
stride=1,
padding=0)
# Lateral layers
self.latlayer1 = nn.Conv2d(1088,
256,
kernel_size=1,
stride=1,
padding=0)
self.latlayer2 = nn.Conv2d(320,
256,
kernel_size=1,
stride=1,
padding=0)
# Smooth layers
self.smooth1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.smooth2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
# Aliases
self.mid_features = nn.ModuleList([
self.layer3,
self.layer4,
])
self.top_features = nn.ModuleList([
self.conv6, self.conv7, self.toplayer, self.latlayer1,
self.latlayer2, self.smooth1, self.smooth2
])
