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fusion model
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| mods = { | |
| "resnet50": (torchvision.models.resnet50, 2048), | |
| "resnet152": (torchvision.models.resnet152, 2048), | |
| "densenet121": (torchvision.models.densenet121, 1024*7*7), | |
| "densenet201": (torchvision.models.densenet201, 1920*7*7), | |
| "vgg16": (torchvision.models.vgg16, 512*7*7), | |
| } | |
| MOD = "densenet201" | |
| class Fusion2Model(nn.Module): | |
| def __init__(self, imgs_model): | |
| super().__init__() | |
| self.imgs_model = nn.Sequential(*list(imgs_model.children())[:-1]) | |
| if MOD in("densenet121", "densenet201", "vgg16"): | |
| self.imgs_model.add_module(module = nn.Flatten(), name = "flatten") | |
| input_no = mods[MOD][1] | |
| self.classifier = nn.Sequential( | |
| nn.Linear(input_no, 400), | |
| nn.ReLU(True), | |
| nn.Dropout(p=0.5), # default p=0.5 | |
| nn.Linear(400, 100), | |
| nn.ReLU(True), | |
| nn.Dropout(p=0.2), | |
| nn.Linear(100, 2), | |
| ) | |
| def forward(self, images): | |
| b, n, _, _, _ = images.shape | |
| arr = [] | |
| for i in range(n): | |
| t = self.imgs_model(images[:,i,:,:,:]) # -> [4, 2048, 1, 1] | |
| arr.append(torch.squeeze(t)) # -> [4, 2048] | |
| arr = torch.cat(arr, dim = 0) | |
| brr = [] | |
| for i in range(b): | |
| tr = torch.mean(arr[i::b,:], dim = 0, keepdim=True) | |
| brr.append(tr) | |
| imgs_feat = torch.cat(brr, dim = 0) | |
| return self.classifier(imgs_feat) | |
| imgs_model = mods[MOD][0](pretrained=True).to(device) | |
| fusion2 = Fusion2Model(imgs_model).to(device) |
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