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| import torch | |
| from torch import nn | |
| import numpy as np | |
| # from datas.dataset_3d import * | |
| from torch.nn import functional as F | |
| class Normalize(nn.Module): | |
| def __init__(self, dim: int) -> None: | |
| super().__init__() | |
| self.dim = dim | |
| def forward(self, x): | |
| return torch.nn.functional.normalize(x, dim=self.dim, p=2) | |
| class LinearLayer(nn.Module): | |
| def __init__(self, dim_in, dim_out, k): | |
| super(LinearLayer, self).__init__() | |
| self.fc = nn.ModuleList([nn.Linear(dim_in, dim_out) for i in range(k)]) | |
| def forward(self, tokens): | |
| for i in range(len(tokens)): | |
| if len(tokens[i].shape) == 3: | |
| tokens[i] = tokens[i].transpose(0,1) | |
| tokens[i] = self.fc[i](tokens[i][:, 1:, :]) | |
| else: | |
| B, C, H, W = tokens[i].shape | |
| tokens[i] = self.fc[i](tokens[i].view(B, C, -1).permute(0, 2, 1).contiguous()) | |
| return tokens | |
| class PromptLearner(nn.Module): | |
| def __init__(self, dim_in, dim_out) -> None: | |
| super().__init__() | |
| self.meta_net = nn.Sequential( | |
| nn.Conv2d(dim_in, dim_in * 4, kernel_size=3, padding=1), | |
| # nn.BatchNorm2d(dim_in * 4), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(2), # 112 * 112 | |
| nn.Conv2d(dim_in * 4, dim_in * 16, kernel_size=3, padding=1), | |
| # nn.BatchNorm2d(dim_in * 16), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(2), # 56 * 56 | |
| nn.Conv2d(dim_in * 16, dim_in * 64, kernel_size=3, padding=1), | |
| # nn.BatchNorm2d(dim_in * 64), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(2), # 28 * 28 | |
| nn.Conv2d(dim_in * 64, dim_in * 256, kernel_size=3, padding=1), | |
| # nn.BatchNorm2d(dim_in * 256), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(2), # 14 * 14 | |
| nn.Conv2d(dim_in * 256, dim_in * 1024, kernel_size=3, padding=1), | |
| # nn.BatchNorm2d(dim_in * 1024), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(2), # 7 * 7 | |
| nn.Conv2d(dim_in * 1024, dim_out, kernel_size=5, padding=0), | |
| # nn.BatchNorm2d(dim_out), | |
| # nn.ReLU(inplace=True), | |
| ) | |
| self.base_prompts = nn.Parameter(torch.randn((9, dim_out)),requires_grad=True) | |
| def forward(self, input): | |
| B,C,H,W = input.shape | |
| img_prompts = self.meta_net(input) | |
| # print(input.shape, img_prompts.shape) | |
| img_prompts = img_prompts.reshape(B,4096,9).transpose(-2,-1) | |
| output = torch.cat([self.base_prompts.expand(B,-1,-1), img_prompts], dim=1) | |
| return output |