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import os
import torch
import matplotlib.pyplot as plt
import numpy as np
from typing import Optional
class DebugUtils:
def __init__(self, pic_dir: str):
self.pic_dir = pic_dir
if not os.path.exists(self.pic_dir):
os.makedirs(self.pic_dir)
def check_orthogonal(self, tensor: torch.Tensor) -> float:
if tensor.dim() > 2:
raise ValueError("Tensor dimension must be 1 or 2.")
if tensor.dim() == 1:
return 0.0 # 1D tensor is not applicable for orthogonality
if tensor.dim() == 2:
num_rows, num_dims = tensor.shape
dot_products = torch.mm(tensor, tensor.t())
norms_squared = torch.diag(dot_products)
sum_abs_dot_products = torch.sum(torch.abs(dot_products - torch.diag(norms_squared)))
max_sum = (num_rows * (num_rows - 1)) / 2
orthogonality_score = 1.0 - (sum_abs_dot_products / max_sum)
return orthogonality_score.item()
def plot_tensor(self, tensor: torch.Tensor, pic_name: str, x_axis: Optional[str], y_axis: Optional[str], plot_number: bool = False) -> None:
if tensor.dim() > 2:
raise ValueError("Tensor dimensions must be 2 or lower for plotting.")
fig, ax = plt.subplots()
if tensor.dim() == 1:
tensor_shape = tensor.shape[0]
ax.set_yticks([])
ax.set_xticks(np.arange(tensor_shape))
color_matrix = np.ones((1, tensor_shape, 3)) * [0.7, 0.7, 1.0]
ax.imshow(color_matrix, aspect='equal')
for i in range(1, tensor_shape):
ax.axvline(x=i - 0.5, color='black', linewidth=1)
if x_axis:
ax.set_xlabel(x_axis)
else:
heatmap = ax.imshow(tensor, cmap='YlOrRd', aspect='auto')
if plot_number:
for i in range(tensor.shape[0]):
for j in range(tensor.shape[1]):
ax.text(j, i, f'{tensor[i, j]:.3f}', ha='center', va='center', color='black')
if y_axis:
ax.set_ylabel(y_axis)
if x_axis:
ax.set_xlabel(x_axis)
plt.colorbar(heatmap, ax=ax, orientation='vertical')
plt.savefig(os.path.join(self.pic_dir, pic_name))
plt.close()
debuge_utils = DebugUtils("./picture") |