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dfd1909 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | from typing import Any,Dict
from torch import Tensor, dtype, device
from numpy import ndarray
import os
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
class UtilTorch:
@staticmethod
def to_np(tensor:Tensor, do_squeeze:bool = True) -> ndarray:
if do_squeeze:
return tensor.squeeze().detach().cpu().numpy()
else:
return tensor.detach().cpu().numpy()
@staticmethod
def to_torch(numpy_array:ndarray, dtype:dtype = torch.float32) -> Tensor:
return torch.tensor(numpy_array, dtype=dtype)
@staticmethod
def register_buffer(model:nn.Module,
variable_name:str,
value:Any,
dtype:dtype = torch.float32) -> Any:
if type(value) != Tensor:
value = torch.tensor(value, dtype=dtype)
model.register_buffer(variable_name, value)
return getattr(model,variable_name)
@staticmethod
def get_param_num(model:nn.Module) -> Dict[str,int]:
num_param : int = sum(param.numel() for param in model.parameters())
trainable_param : int = sum(param.numel() for param in model.parameters() if param.requires_grad)
return {'total':num_param, 'trainable':trainable_param}
@staticmethod
def freeze_param(model:nn.Module) -> nn.Module:
model = model.eval()
model.train = lambda self: self #override train with useless function
for param in model.parameters():
param.requires_grad = False
return model
@staticmethod
def get_model_device(model:nn.Module) -> device:
return next(model.parameters()).device
@staticmethod
def interpolate_2d(input:Tensor, #[width, height] | [batch, width, height] | [batch, channels, width, height]
size_after_interpolation:tuple, #(width, height)
mode:str = 'nearest'
) -> Tensor:
if len(input.shape) == 2:
shape_after_interpolation = size_after_interpolation
input = input.view(1,1,*(input.shape))
elif len(input.shape) == 3:
shape_after_interpolation = (input.shape[0],*(size_after_interpolation))
input = input.unsqueeze(1)
elif len(input.shape) == 4:
shape_after_interpolation = (input.shape[0],input.shape[1],*(size_after_interpolation))
return F.interpolate(input, size = size_after_interpolation, mode=mode).view(shape_after_interpolation)
@staticmethod
def tsne_plot(save_file_path:str,
class_array:ndarray, #[the number of data, 1] data must be integer for class. ex) [[1],[3],...]
embedding_array:ndarray, #[the number of data, channel_size]
figure_size:tuple = (10,10),
legend:str = 'full',
point_size:float = None #s=200
) -> None:
import pandas as pd
import seaborn as sns
assert os.path.splitext(save_file_path)[-1] == '.png', 'save_file_path should be *.png'
print('generating t-SNE plot...')
tsne = TSNE(random_state=0)
tsne_output:ndarray = tsne.fit_transform(embedding_array)
df = pd.DataFrame(tsne_output, columns=['x', 'y'])
df['class'] = class_array
plt.rcParams['figure.figsize'] = figure_size
scatterplot_args:dict = {'x':'x', 'y':'y', 'hue':'class', 'palette':sns.color_palette("hls", 10),
'data':df, 'marker':'o', 'legend':legend, 'alpha':0.5}
if point_size is not None: scatterplot_args['s'] = point_size
sns.scatterplot(**scatterplot_args)
plt.xticks([])
plt.yticks([])
plt.xlabel('')
plt.ylabel('')
plt.savefig(save_file_path, bbox_inches='tight')
@staticmethod
def update_ema(ema_model:nn.Module, model:nn.Module, decay:float=0.9999) -> None:
"""
Step the EMA model towards the current model.
"""
with torch.no_grad():
ema_params = OrderedDict(ema_model.named_parameters())
model_params = OrderedDict(model.named_parameters())
for name, param in model_params.items():
name = name.replace("module.", "")
# TODO: Consider applying only to params that require_grad to avoid small numerical changes of pos_embed
ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
@staticmethod
def mean_flat(tensor):
"""
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
@staticmethod
def kl_div_gaussian(mean1:Tensor, logvar1:Tensor, mean2:Tensor, logvar2:Tensor) -> Tensor:
"""
Compute the KL divergence between two gaussians.
Shapes are automatically broadcasted, so batches can be compared to
scalars, among other use cases.
"""
return 0.5 * ( -1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)) |