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