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getT_SNEUtil.py
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from models.getModel import get_encoder
from EvaluateMetrics.EvaluateMetricUtils import get_feature, getT_SNE
from typing import List
import argparse
import torch
from tqdm import tqdm
import torch.utils.data
from torch.autograd import Variable
import numpy as np
from os.path import join
import os
class getTSNEUtil:
def __init__(self, testDataRoot, resultRootPath, classNumber: int,
modelEncoder: str, labelList: List, getDataFunc,
imgSize: int, modelParamPath: str, dataSetFlag=None,
target_layers=['avgpool'], gpuNumber="cuda:3", batchSize=1,
kFold=1, imgDataRoot=None, readExcelFileFlag=False,
excelFilePath=None, savePath=None, otherFlag=None):
self.testDataRoot = testDataRoot
self.resultRootPath = resultRootPath
self.classNumber = classNumber
self.modelEncoder = modelEncoder
self.labelList = labelList
self.gpuNumber = gpuNumber
self.getDataFunc = getDataFunc
self.imgSize = imgSize
self.dataSetFlag = dataSetFlag
self.modelParamPath = modelParamPath
self.batchSize = batchSize
self.target_layers = target_layers
self.device = torch.device(gpuNumber if torch.cuda.is_available() else "cpu")
self.kFold = kFold
self.imgDataRoot = imgDataRoot
self.readExcelFileFlag = readExcelFileFlag
self.excelFilePath = excelFilePath
self.savePath = savePath
self.otherFlag = otherFlag
def getParser(self):
parser = argparse.ArgumentParser(description='Generate t-SNE!')
parser.add_argument('--batchSize', default=self.batchSize, type=int)
parser.add_argument('--num_worker', default=8, type=int, help='data loader worker number.')
parser.add_argument('--testDataRoot', default=self.testDataRoot, type=str)
parser.add_argument('--resultRootPath', default=self.resultRootPath, type=str)
parser.add_argument('--classNumber', default=self.classNumber, type=int)
parser.add_argument('--encoder', default=self.modelEncoder, type=str)
parser.add_argument('--imgSize', default=self.imgSize, type=int)
parser.add_argument('--modelParamPath', default=self.modelParamPath, type=str, help="model param Path.")
args = parser.parse_args()
args.device = self.device
args.tSNE_fileSavePath = join(self.resultRootPath, 'tSNE_filePath')
args.tSNE_figSavePath = join(self.resultRootPath, 'tSNE_figPath')
args.figName = join(args.tSNE_figSavePath, args.encoder + '_tSEN.png')
args.save_name = join(args.tSNE_fileSavePath, args.encoder + '_tSEN.npz')
args.kFold = self.kFold
args.imgDataRoot = self.imgDataRoot
args.readExcelFileFlag = self.readExcelFileFlag
args.excelFilePath = self.excelFilePath
args.preWeight = None
return args
def get_tSNEFile(self):
config = self.getParser()
model = get_encoder(config, pretrained=False)
if config.kFold == 1:
testData = self.getDataFunc(txtFilePath=config.testDataRoot, dataSetFlag=self.dataSetFlag,
imgSize=self.imgSize, test=True)
testLoader = torch.utils.data.DataLoader(testData, batch_size=config.batchSize,
shuffle=False, num_workers=config.num_worker)
model.eval()
if config.modelParamPath:
print("Loading model param!")
model.load_state_dict(
# torch.load(config.modelParamPath)
torch.load(config.modelParamPath,
map_location=lambda storage, loc: storage.cuda(int(self.gpuNumber.split(':')[-1])))
)
else:
Exception("No model Param!")
model.to(config.device)
predLabels = []
features = []
for i_val, (img, real_label, _) in tqdm(enumerate(testLoader)):
with torch.no_grad():
target = real_label
img = img.to(config.device)
target = target.to(config.device)
batch_size = img.size(0)
score = model(img)
m = torch.nn.Softmax(dim=1)
predict_label = score.data.max(1)[1].cpu().numpy()
predLabels.extend(predict_label)
feature = get_feature(model, img, target_layers=self.target_layers)
feature_avg = np.average(feature, axis=0)
print('feature shape:{}, avg feature shape:{}'.format(feature.shape, feature_avg.shape))
features.append(feature_avg)
feature = np.array(features)
featureLabel = np.array(predLabels)
save_name = join(config.tSNE_fileSavePath, config.encoder + '_tSEN.npz')
np.savez(save_name, data=feature, label=featureLabel)
else:
predLabels = []
features = []
for modelParamFoldName in config.testDataRoot:
modelFold = modelParamFoldName.split(".pth")[0].split('_')[0]
validExcelSheetName = 'valid_{}'.format(modelFold)
print(f"Test {config.encoder}, {validExcelSheetName}")
testData = self.getDataFunc(excelFilePath=config.excelFilePath, imgRootPath=config.imgDataRoot,
excelSheetName=validExcelSheetName, trainFlag=False)
testLoader = torch.utils.data.DataLoader(testData, batch_size=config.batchSize, shuffle=False,
num_workers=config.num_worker)
model.eval()
modelPath = join(config.modelParamPath, modelParamFoldName)
print("Loading model param!")
model.load_state_dict(
# torch.load(config.modelParamPath)
torch.load(modelPath,
map_location=lambda storage, loc: storage.cuda(int(self.gpuNumber.split(':')[-1])))
)
model.to(config.device)
if self.otherFlag is not None:
for i_val, dataDict in tqdm(enumerate(testLoader)):
with torch.no_grad():
img = dataDict["A"]
target = dataDict["label"]
img = img.to(config.device)
target = target.to(config.device)
batch_size = img.size(0)
score, _, _, _ = model(img)
m = torch.nn.Softmax(dim=1)
predict_label = score.data.max(1)[1].cpu().numpy()
predLabels.extend(predict_label)
feature = get_feature(model, img, target_layers=self.target_layers, modelName=config.encoder)
feature_avg = np.average(feature, axis=0)
print('feature shape:{}, avg feature shape:{}'.format(feature.shape, feature_avg.shape))
features.append(feature_avg)
else:
for i_val, (img, real_label, _) in tqdm(enumerate(testLoader)):
with torch.no_grad():
target = real_label
img = img.to(config.device)
target = target.to(config.device)
batch_size = img.size(0)
score = model(img)
m = torch.nn.Softmax(dim=1)
predict_label = score.data.max(1)[1].cpu().numpy()
predLabels.extend(predict_label)
feature = get_feature(model, img, target_layers=self.target_layers, modelName=config.encoder)
feature_avg = np.average(feature, axis=0)
print('feature shape:{}, avg feature shape:{}'.format(feature.shape, feature_avg.shape))
features.append(feature_avg)
feature = np.array(features)
featureLabel = np.array(predLabels)
savePath = join(self.savePath, 'tSNE_filePath')
if not os.path.isdir(savePath):
os.makedirs(savePath)
save_name = join(savePath, config.encoder + '_tSEN.npz')
np.savez(save_name, data=feature, label=featureLabel)
def create_tSNE_fig(self):
config = self.getParser()
if config.kFold != 1:
save_name = join(self.savePath, 'tSNE_filePath', config.encoder + '_tSEN.npz')
figSavePath = join(self.savePath, 'tSNE_figPath')
if not os.path.isdir(figSavePath):
os.makedirs(figSavePath)
figName = join(figSavePath, config.encoder + '_tSEN.png')
getT_SNE_obj = getT_SNE(save_name, config.classNumber, self.labelList, figName)
else:
getT_SNE_obj = getT_SNE(config.save_name, config.classNumber, self.labelList, config.figName)
getT_SNE_obj.main()