Python Pytorch GPU内存分配

我有自我修改的汽车识别模型，取自。我使用

Cuda

和

Pytorch:1.4.0

。但是，即使我尝试使用上一个问题的答案，它仍然不想使用

BATCH\u SIZE

>=7。同样的情况也发生了：

CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 4.00 GiB total capacity; 2.74 GiB already allocated; 7.80 MiB free; 2.96 GiB reserved in total by PyTorch)

如果我增加批处理大小，pytorch会给我更多，但还不够： 批量大小=256

CUDA out of memory. Tried to allocate 196.00 MiB (GPU 0; 4.00 GiB total capacity; 2.85 GiB already allocated; 93.80 MiB free; 2.87 GiB reserved in total by PyTorch)

批量大小=512

CUDA out of memory. Tried to allocate 1.53 GiB (GPU 0; 4.00 GiB total capacity; 2.04 GiB already allocated; 927.80 MiB free; 2.06 GiB reserved in total by PyTorch)

我的代码如下： main.py

dataset.py

import numpy as np
import torch
import os
import cv2 as cv2

from torch.utils.data.dataset import Dataset
from torch.utils.data import DataLoader
from torchvision import transforms

from PIL import Image
Image.LOAD_TRUNCATED_IMAGES = True
from os import path
from glob import glob
import random

from config import *

num_classes = { 'ford_explorer': 0, 'nissan_altima': 1, 'volkswagen_jetta': 2 }
# read mean and dev. standard pre-computed
m = 0
s = 0
def dataset_main():
    # dirs = glob(IMAGES_PATH + "/*/")
    # i = 0
    # for d in dirs:
    #     d = d.replace(IMAGES_PATH, "")
    #     d = d.replace("/", "")
    #     if " " in d:
    #         d = d.replace(" ", "_")
    #     num_classes[d] = i
    #     i+=1

    print ("Classes: ")
    print (num_classes)
    print ("")


    if os.path.isfile('./mean_devstd.txt'):
        m_s = open("mean_devstd.txt", "r").read()
        if "," in m_s:
            m_s = m_s.replace("\n", "")
            m_s = m_s.replace("tensor", "")
            m_s = m_s.replace("(", "")
            m_s = m_s.replace(")", "")
            m_s = m_s.split(",")
            m = torch.Tensor( [float(m_s[0]), float(m_s[1]), float(m_s[2])] )
            s = torch.Tensor( [float(m_s[3]), float(m_s[4]), float(m_s[5])] )
        return m, s, num_classes
def get_class(idx):
    #print (num_classes)
    for key in num_classes:
        if idx == num_classes[key]:
            return key

def preprocessing():
    train_csv = ""
    test_csv  = ""
    train_csv_supp = []
    test_csv_supp = []
    class_files_training = []
    class_files_testing  = []

    for key in num_classes:
        if " " in key:
            os.rename(IMAGES_PATH+"/"+key, IMAGES_PATH+"/"+key.replace(" ", "_"))
            key = key.replace(" ", "_")

        class_files = glob(IMAGES_PATH+"/"+str(key)+"/*")
        class_files = [w.replace(IMAGES_PATH+"/"+str(key)+"/", "") for w in class_files]
        class_files.sort()

        class_files_training = class_files[: int(len(class_files)*.66)] # get 66% class images fo training
        class_files_testing = class_files[int(len(class_files)*.66)+1 :] # get 33% class images fo testing

        for f in class_files_training:
            if "," in f or "#" in f or " " in f:
                tmp_f = f.replace(",", "")
                tmp_f = tmp_f.replace("#", "")
                tmp_f = tmp_f.replace(" ", "_")
                os.rename(IMAGES_PATH+"/"+key+"/"+f, IMAGES_PATH+"/"+key+"/"+tmp_f)
                f = tmp_f
            train_csv_supp.append(f + ","+str(key))

        for f in class_files_testing:
            if "," in f or "#" in f or " " in f:
                tmp_f = f.replace(",", "")
                tmp_f = tmp_f.replace("#", "")
                tmp_f = tmp_f.replace(" ", "_")
                os.rename(IMAGES_PATH+"/"+key+"/"+f, IMAGES_PATH+"/"+key+"/"+tmp_f)
                f = tmp_f
            test_csv_supp.append(f + ","+str(key))

    random.shuffle(train_csv_supp)
    random.shuffle(test_csv_supp)

    for t in train_csv_supp:
        train_csv += t + "\n"

    for t in test_csv_supp:
        test_csv += t + "\n"

    train_csv_file = open("train_file.csv", "w+")
    train_csv_file.write(train_csv)
    train_csv_file.close()

    test_csv_file = open("test_file.csv", "w+")
    test_csv_file.write(test_csv)
    test_csv_file.close()

    # Algorithms to calculate mean and standard_deviation
    print("Loading dataset...")
    dataset = LocalDataset(IMAGES_PATH, TRAINING_PATH, transform=transforms.ToTensor())
    print("Calculating mean & dev std...")

    m = torch.zeros(3) # Mean
    s = torch.zeros(3) # Standard Deviation
    for sample in dataset:
        m += sample['image'].sum(1).sum(1)
        s += ((sample['image']-m.view(3,1,1))**2).sum(1).sum(1)
    m /= len(dataset)*256*144    
    s = torch.sqrt(s/(len(dataset)*256*144))

    print("Calculated mean and standard deviation!")
    str_m = str(m[0])+","+str(m[1])+","+str(m[2])
    str_s = str(s[0])+","+str(s[1])+","+str(s[2])
    file = open("mean_devstd.txt", "w+")
    file.write(str(str_m)+","+str(str_s))
    file.close()
#preprocessing()

class LocalDataset(Dataset):

    def __init__(self, base_path, txt_list, transform=None):
        self.base_path=base_path
        self.images = np.loadtxt(txt_list,delimiter=',',dtype='str') # use np.genfrom() instead of np.loadtxt() to skip errors

        self.transform = transform

    def __getitem__(self, index):
        f,c = self.images[index]

        image_path = path.join(self.base_path + "/" + str(c), f)
        # image_path = path.join(str(c), f)
        im = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)

        if self.transform is not None:
            im = self.transform(Image.fromarray(im))
        label = num_classes[c]

        return { 'image' : im, 'label':label, 'img_name': f }

    def __len__(self):
        return len(self.images)

---

我不明白，我的gpu哪里出了问题，因为一切似乎都很好。这只是我的显卡出了问题吗？我有GTX 1050 TI，它应该工作得很好

我认为该项目使用了ResNet-152，这是一个230MB的模型，只是在参数上。这些文档说，您至少有27GB的空间在224x224处训练一批128人@Reactgular，非常感谢。它起作用了

import numpy as np
import torch
import os
import cv2 as cv2

from torch.utils.data.dataset import Dataset
from torch.utils.data import DataLoader
from torchvision import transforms

from PIL import Image
Image.LOAD_TRUNCATED_IMAGES = True
from os import path
from glob import glob
import random

from config import *

num_classes = { 'ford_explorer': 0, 'nissan_altima': 1, 'volkswagen_jetta': 2 }
# read mean and dev. standard pre-computed
m = 0
s = 0
def dataset_main():
    # dirs = glob(IMAGES_PATH + "/*/")
    # i = 0
    # for d in dirs:
    #     d = d.replace(IMAGES_PATH, "")
    #     d = d.replace("/", "")
    #     if " " in d:
    #         d = d.replace(" ", "_")
    #     num_classes[d] = i
    #     i+=1

    print ("Classes: ")
    print (num_classes)
    print ("")


    if os.path.isfile('./mean_devstd.txt'):
        m_s = open("mean_devstd.txt", "r").read()
        if "," in m_s:
            m_s = m_s.replace("\n", "")
            m_s = m_s.replace("tensor", "")
            m_s = m_s.replace("(", "")
            m_s = m_s.replace(")", "")
            m_s = m_s.split(",")
            m = torch.Tensor( [float(m_s[0]), float(m_s[1]), float(m_s[2])] )
            s = torch.Tensor( [float(m_s[3]), float(m_s[4]), float(m_s[5])] )
        return m, s, num_classes
def get_class(idx):
    #print (num_classes)
    for key in num_classes:
        if idx == num_classes[key]:
            return key

def preprocessing():
    train_csv = ""
    test_csv  = ""
    train_csv_supp = []
    test_csv_supp = []
    class_files_training = []
    class_files_testing  = []

    for key in num_classes:
        if " " in key:
            os.rename(IMAGES_PATH+"/"+key, IMAGES_PATH+"/"+key.replace(" ", "_"))
            key = key.replace(" ", "_")

        class_files = glob(IMAGES_PATH+"/"+str(key)+"/*")
        class_files = [w.replace(IMAGES_PATH+"/"+str(key)+"/", "") for w in class_files]
        class_files.sort()

        class_files_training = class_files[: int(len(class_files)*.66)] # get 66% class images fo training
        class_files_testing = class_files[int(len(class_files)*.66)+1 :] # get 33% class images fo testing

        for f in class_files_training:
            if "," in f or "#" in f or " " in f:
                tmp_f = f.replace(",", "")
                tmp_f = tmp_f.replace("#", "")
                tmp_f = tmp_f.replace(" ", "_")
                os.rename(IMAGES_PATH+"/"+key+"/"+f, IMAGES_PATH+"/"+key+"/"+tmp_f)
                f = tmp_f
            train_csv_supp.append(f + ","+str(key))

        for f in class_files_testing:
            if "," in f or "#" in f or " " in f:
                tmp_f = f.replace(",", "")
                tmp_f = tmp_f.replace("#", "")
                tmp_f = tmp_f.replace(" ", "_")
                os.rename(IMAGES_PATH+"/"+key+"/"+f, IMAGES_PATH+"/"+key+"/"+tmp_f)
                f = tmp_f
            test_csv_supp.append(f + ","+str(key))

    random.shuffle(train_csv_supp)
    random.shuffle(test_csv_supp)

    for t in train_csv_supp:
        train_csv += t + "\n"

    for t in test_csv_supp:
        test_csv += t + "\n"

    train_csv_file = open("train_file.csv", "w+")
    train_csv_file.write(train_csv)
    train_csv_file.close()

    test_csv_file = open("test_file.csv", "w+")
    test_csv_file.write(test_csv)
    test_csv_file.close()

    # Algorithms to calculate mean and standard_deviation
    print("Loading dataset...")
    dataset = LocalDataset(IMAGES_PATH, TRAINING_PATH, transform=transforms.ToTensor())
    print("Calculating mean & dev std...")

    m = torch.zeros(3) # Mean
    s = torch.zeros(3) # Standard Deviation
    for sample in dataset:
        m += sample['image'].sum(1).sum(1)
        s += ((sample['image']-m.view(3,1,1))**2).sum(1).sum(1)
    m /= len(dataset)*256*144    
    s = torch.sqrt(s/(len(dataset)*256*144))

    print("Calculated mean and standard deviation!")
    str_m = str(m[0])+","+str(m[1])+","+str(m[2])
    str_s = str(s[0])+","+str(s[1])+","+str(s[2])
    file = open("mean_devstd.txt", "w+")
    file.write(str(str_m)+","+str(str_s))
    file.close()
#preprocessing()

class LocalDataset(Dataset):

    def __init__(self, base_path, txt_list, transform=None):
        self.base_path=base_path
        self.images = np.loadtxt(txt_list,delimiter=',',dtype='str') # use np.genfrom() instead of np.loadtxt() to skip errors

        self.transform = transform

    def __getitem__(self, index):
        f,c = self.images[index]

        image_path = path.join(self.base_path + "/" + str(c), f)
        # image_path = path.join(str(c), f)
        im = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)

        if self.transform is not None:
            im = self.transform(Image.fromarray(im))
        label = num_classes[c]

        return { 'image' : im, 'label':label, 'img_name': f }

    def __len__(self):
        return len(self.images)