127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
import plotly_express as px
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import torchvision.transforms.functional as Vision
|
|
import cv2
|
|
import math
|
|
import numpy as np
|
|
import os
|
|
from pathlib import Path
|
|
|
|
|
|
def DumpTensorToImage(tensor, name, forceSquare=False, scale=1.0, Contrast=None, GridValue=None, Value2Log=False):
|
|
if len(tensor.shape) != 2 and len(tensor.shape) != 1 and len(tensor.shape) != 3:
|
|
raise ("Error input dims")
|
|
if ("." not in name) or (name.split(".")[-1] not in {"jpg", "png", "bmp"}):
|
|
raise ("Error input name")
|
|
if Value2Log:
|
|
DumpTensorToLog(tensor, name[:-4] + ".log")
|
|
|
|
if len(tensor.shape) == 3:
|
|
channel = tensor.shape[0]
|
|
x = math.ceil((channel) ** 0.5)
|
|
calc = tensor.reshape((channel, tensor.shape[1] * tensor.shape[2]))
|
|
if not Contrast:
|
|
tensormax = calc.max(1)[0]
|
|
tensormin = calc.min(1)[0]
|
|
else:
|
|
tensormax = Contrast[1]
|
|
tensormin = Contrast[0]
|
|
calc = calc.transpose(1, 0)
|
|
calc = ((calc - tensormin) / (tensormax - tensormin)) * 255.0
|
|
calc = calc.transpose(1, 0)
|
|
calc = calc.reshape((channel, tensor.shape[1], tensor.shape[2]))
|
|
if not GridValue:
|
|
GridValue = 128.0
|
|
calc = F.pad(calc, (0, 0, 0, 0, 0, x * x - channel), mode="constant", value=GridValue)
|
|
calc = calc.reshape((x, x, tensor.shape[1], tensor.shape[2]))
|
|
calc = F.pad(calc, (0, 1, 0, 1, 0, 0), mode="constant", value=GridValue)
|
|
tensor = calc.permute((0, 2, 1, 3))
|
|
tensor = tensor.reshape((x * tensor.shape[1], x * tensor.shape[3]))
|
|
DumpTensorToImage(tensor, name, forceSquare=False, scale=scale, Contrast=[0.0, 255.0], GridValue=GridValue)
|
|
return
|
|
|
|
tensor = tensor.float()
|
|
if not Contrast:
|
|
maxv = torch.max(tensor)
|
|
minv = torch.min(tensor)
|
|
else:
|
|
maxv = Contrast[1]
|
|
minv = Contrast[0]
|
|
tensor = ((tensor - minv) / (maxv - minv)) * 255.0
|
|
img = tensor.detach().cpu().numpy()
|
|
srp = img.shape
|
|
|
|
if len(srp) == 1: # 1D的数据自动折叠成2D图像
|
|
ceiled = math.ceil((srp[0]) ** 0.5)
|
|
img = cv2.copyMakeBorder(img, 0, ceiled * ceiled - srp[0], 0, 0, 0)
|
|
img = img.reshape((ceiled, ceiled))
|
|
srp = img.shape
|
|
if forceSquare: # 拉伸成正方形
|
|
img = cv2.resize(img, [max(srp), max(srp)])
|
|
srp = img.shape
|
|
if scale != 1.0:
|
|
img = cv2.resize(img, [int(srp[0] * scale), int(srp[1] * scale)])
|
|
srp = img.shape
|
|
|
|
img = img * (-1)
|
|
img = img + 255
|
|
img[img < 0] = 0
|
|
img = np.nan_to_num(img, nan=0.0)
|
|
img[img > 255] = 255
|
|
imgs = img.astype(np.uint8)
|
|
imgs = cv2.applyColorMap(imgs, cv2.COLORMAP_JET)
|
|
directory = Path(name).parent
|
|
if not directory.is_dir():
|
|
directory.mkdir(parents=True, exist_ok=True)
|
|
cv2.imwrite(name, imgs)
|
|
|
|
|
|
def DumpTensorToLog(tensor, name="log"):
|
|
tensor_mean = torch.mean(tensor).cpu().detach().numpy()
|
|
tensor_abs_mean = torch.mean(torch.abs(tensor)).cpu().detach().numpy()
|
|
tensor_range = (torch.max(tensor) - torch.min(tensor)).cpu().detach().numpy()
|
|
f = open(name, "w")
|
|
f.writelines("tensor mean: %s" % tensor_mean + os.linesep)
|
|
f.writelines("tensor abs mean: %s" % tensor_abs_mean + os.linesep)
|
|
f.writelines("tensor range: %s" % tensor_range + os.linesep)
|
|
data = tensor.reshape([-1]).float().cpu().detach().numpy().tolist()
|
|
for d in data:
|
|
f.writelines("%s" % d + os.linesep)
|
|
f.close()
|
|
|
|
|
|
def DumpTensorToFile(tensor, name="tensor.pt"):
|
|
torch.save(tensor.cpu(), name)
|
|
|
|
|
|
def LoadTensorToFile(name="tensor.pt"):
|
|
return torch.load(name)
|
|
|
|
|
|
def DumpListToFile(list, name="list"):
|
|
f = open(name, "w")
|
|
for d in list:
|
|
f.writelines("%s" % d + os.linesep)
|
|
f.close()
|
|
|
|
|
|
prob_true = 0
|
|
prob_all = 0
|
|
|
|
|
|
def ProbGE0(tensor: torch.tensor):
|
|
global prob_true
|
|
global prob_all
|
|
m = tensor.ge(0)
|
|
prob_true = prob_true + m.sum().item()
|
|
prob_all = prob_all + math.prod(tensor.shape)
|
|
|
|
|
|
def DumpProb():
|
|
global prob_true
|
|
global prob_all
|
|
print("prob_true : " + str(prob_true))
|
|
print("prob_all : " + str(prob_all))
|
|
print("prob : " + str((prob_true * 100) / prob_all) + "%")
|