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| Original file line number | Diff line number | Diff line change |
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| ## ShapeNet | ||
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| 1. Shapenet is a model that uses fully connected neural network to estimate shape parameters. Code of ShapeNet are adapted from [bihand](https://github.com/lixiny/bihand). | ||
| 2. The training set is generated by MANO. | ||
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| 1. First sample shape parameters from normal distribution N(0,3) | ||
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| 2. Calculate the relative bone length corresponding to the shape parameter. | ||
| 3. The loss of ShapeNet consists of two parts, one is the error of relative bone length, the other is the regularization loss of shape parameters. | ||
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| $$ | ||
| L = \lambda_1 ||\hat{x} - x||_2^2 + \lambda_2 || \hat{y}||_2^2 \\ | ||
| $$ | ||
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| $x$ is the input relative bone length. | ||
| $\hat{y}$ is the output shape parameters of ShapeNet. | ||
| $\hat{x}$ is the relative bone length corresponding to the shape parameter. | ||
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| In my opinion, shape parameters include not only relative bone length information, but also absolute bone length information. It is impossible to guarantee that a relative bone length only corresponds to one shape parameter, which is necessary for neural networks. Therefore, the loss function does not directly calculate the error between the shape parameter and the label of it. | ||
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| 4. AUC of ShapeNet can refer [README.md](./README.md). You can get higher AUC, if you change " beta = torch.tanh(beta) " which is the line 85 of model/shape_net.py to "beta = 3*torch.tanh(beta) ". This will make the output range of ShapeNet bigger and get higher AUC. According to the experiment, the finger will be thinner. | ||
| 5. I didn't adjust the parameters carefully. Maybe you can get better results if you adjust parameters. | ||
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| Original file line number | Diff line number | Diff line change |
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| import sys | ||
| import pickle | ||
| import torch | ||
| import os | ||
| from torch.utils import data | ||
| from termcolor import colored, cprint | ||
| import numpy as np | ||
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| sik1m_inst = 0 | ||
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| class _SIK1M(data.Dataset): | ||
| """ | ||
| The Loader for joints so3 and quat | ||
| """ | ||
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| def __init__( | ||
| self, | ||
| data_root="data", | ||
| data_source=None | ||
| ): | ||
| print("Initialize _SIK1M instance") | ||
| bone_len_path = os.path.join(data_root, 'data_bone.npy') | ||
| shape_path = os.path.join(data_root, 'data_shape.npy') | ||
| self.bone_len = np.load(bone_len_path) | ||
| self.shape = np.load(shape_path) | ||
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| def __len__(self): | ||
| return self.shape.shape[0] | ||
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| def __getitem__(self, index): | ||
| temp_bone_len = self.bone_len[index] | ||
| temp_shape = self.shape[index] | ||
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| metas = { | ||
| 'rel_bone_len': temp_bone_len, | ||
| 'shape': temp_shape | ||
| } | ||
| return metas | ||
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| class SIK1M(data.Dataset): | ||
| def __init__( | ||
| self, | ||
| data_split="train", | ||
| data_root="data", | ||
| split_ratio=0.8 | ||
| ): | ||
| global sik1m_inst | ||
| if not sik1m_inst: | ||
| sik1m_inst = _SIK1M(data_root=data_root) | ||
| self.sik1m = sik1m_inst | ||
| self.permu = list(range(len(self.sik1m))) | ||
| self.alllen = len(self.sik1m) | ||
| self.data_split = data_split | ||
| # add the 0.1* the std of Relative bone length as noise, you can change it or not add | ||
| self.noise = np.array([0.02906406, 0.02663224, 0.01769793, 0.0274501, 0.02573783, 0.0222863, | ||
| 0., 0.02855567, 0.02330295, 0.0253288, 0.0266308, 0.02495683, 0.03685857, 0.02430637, | ||
| 0.02349446]) | ||
| self.noise = self.noise / 10.0 | ||
| if data_split == "train": | ||
| self.vislen = int(len(self.sik1m) * split_ratio) | ||
| self.sub_permu = self.permu[:self.vislen] | ||
| elif data_split in ["val", "test"]: | ||
| self.vislen = self.alllen - int(len(self.sik1m) * split_ratio) | ||
| self.sub_permu = self.permu[(self.alllen - self.vislen):] | ||
| else: | ||
| self.vislen = len(self.sik1m) | ||
| self.sub_permu = self.permu[:self.vislen] | ||
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| def __len__(self): | ||
| return self.vislen | ||
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| def __getitem__(self, index): | ||
| item = self.sik1m[self.sub_permu[index]] | ||
| temp = np.random.randn(15, ) | ||
| temp = np.multiply(self.noise, temp) | ||
| item['rel_bone_len'] += temp | ||
| return item | ||
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| def main(): | ||
| sik1m_train = SIK1M( | ||
| data_split="train", | ||
| data_root="data" | ||
| ) | ||
| sik1m_test = SIK1M( | ||
| data_split="test" | ||
| ) | ||
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| metas = sik1m_train[2] | ||
| print(metas) | ||
| metas = sik1m_train[2] | ||
| print(metas) | ||
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| if __name__ == "__main__": | ||
| main() |
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