diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..e69de29 diff --git a/.idea/.name b/.idea/.name new file mode 100644 index 0000000..e69de29 diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..03d9549 --- /dev/null +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..e69de29 diff --git a/.idea/learn_torch.iml b/.idea/learn_torch.iml new file mode 100644 index 0000000..9bd3226 --- /dev/null +++ b/.idea/learn_torch.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..6268301 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..e69de29 diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..9034421 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,7 @@ + + + + + + + \ No newline at end of file diff --git a/.idea/workspace.xml b/.idea/workspace.xml new file mode 100644 index 0000000..d0d55ae --- /dev/null +++ b/.idea/workspace.xml @@ -0,0 +1,228 @@ + + + + + + + + + + + + + + + + + + + + + { + "customColor": "", + "associatedIndex": -1 +} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1690473289898 + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/dataloader.py b/dataloader.py new file mode 100644 index 0000000..c1b314e --- /dev/null +++ b/dataloader.py @@ -0,0 +1,26 @@ +import torchvision +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter + +# 准备的测试数据集 +test_data = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor()) + +test_loader = DataLoader(dataset=test_data, batch_size=64, shuffle=True, num_workers=0, drop_last=True) + +# 测试数据集中第一张图片以及target +img, target = test_data[0] +print(test_data.classes) +print(img.shape) +print(target) + +writer = SummaryWriter("dataloader") +for epoch in range(2): + step = 0 + for data in test_loader: + imgs, targets = data + # print(imgs.shape) + # print(targets) + writer.add_images("Epoch: {}".format(epoch), imgs, step) + step = step + 1 + +writer.close() \ No newline at end of file diff --git a/dataloader/events.out.tfevents.1690783629.DESKTOP-NPRDBA4.18472.0 b/dataloader/events.out.tfevents.1690783629.DESKTOP-NPRDBA4.18472.0 new file mode 100644 index 0000000..e69de29 diff --git a/dataloader/events.out.tfevents.1690783658.DESKTOP-NPRDBA4.20520.0 b/dataloader/events.out.tfevents.1690783658.DESKTOP-NPRDBA4.20520.0 new file mode 100644 index 0000000..e69de29 diff --git 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b/dataset/val/bees/759745145_e8bc776ec8.jpg new file mode 100644 index 0000000..e69de29 diff --git a/dataset/val/bees/936182217_c4caa5222d.jpg b/dataset/val/bees/936182217_c4caa5222d.jpg new file mode 100644 index 0000000..e69de29 diff --git a/dataset/val/bees/abeja.jpg b/dataset/val/bees/abeja.jpg new file mode 100644 index 0000000..e69de29 diff --git a/dataset_transform.py b/dataset_transform.py new file mode 100644 index 0000000..e0ef2ed --- /dev/null +++ b/dataset_transform.py @@ -0,0 +1,25 @@ +import torchvision +from torch.utils.tensorboard import SummaryWriter + +dataset_transform = torchvision.transforms.Compose([ + torchvision.transforms.ToTensor(), + +]) +train_set = torchvision.datasets.CIFAR10(root='./dataset', train=True, transform=dataset_transform ,download=True) +test_set = torchvision.datasets.CIFAR10(root='./dataset', train=False, transform=dataset_transform ,download=True) + +# print(test_set[0]) +# print(test_set.classes) +# img, target = test_set[0] +# print(img) +# print(target) +# print(test_set.classes[target]) +# img.show() +# print(test_set[0]) + +writer = SummaryWriter("p10") +for i in range(10): + img, target = test_set[i] + writer.add_image("test_set", img, i) + +writer.close() \ No newline at end of file diff --git a/images/7759525_1363d24e88.jpg b/images/7759525_1363d24e88.jpg new file mode 100644 index 0000000..e69de29 diff --git a/logs/events.out.tfevents.1690790854.DESKTOP-NPRDBA4.11540.0 b/logs/events.out.tfevents.1690790854.DESKTOP-NPRDBA4.11540.0 new file mode 100644 index 0000000..e69de29 diff --git a/logs_maxpool/events.out.tfevents.1690793100.DESKTOP-NPRDBA4.20196.0 b/logs_maxpool/events.out.tfevents.1690793100.DESKTOP-NPRDBA4.20196.0 new file mode 100644 index 0000000..e69de29 diff --git a/logs_relu/events.out.tfevents.1690795272.DESKTOP-NPRDBA4.12024.0 b/logs_relu/events.out.tfevents.1690795272.DESKTOP-NPRDBA4.12024.0 new file mode 100644 index 0000000..e69de29 diff --git a/read_data.py b/read_data.py new file mode 100644 index 0000000..f3dc554 --- /dev/null +++ b/read_data.py @@ -0,0 +1,33 @@ +from torch.utils.data import Dataset +import cv2 +from PIL import Image +import os + + +class MyDataset(Dataset): + + def __init__(self, root_dir, label_dir): + self.root_dir = root_dir + self.label_dir = label_dir + self.path = os.path.join(self.root_dir, self.label_dir) + self.img_path = os.listdir(self.path) + + # __getitem__ 是一个魔法方法，当使用dataset[i]获取第i个样本时，就会调用该方法。 + def __getitem__(self, idx): + img_name = self.img_path[idx] + img_item_path = os.path.join(self.root_dir, self.label_dir, img_name) + img = Image.open(img_item_path) + label = self.label_dir + + return img, label + + def __len__(self): + return len(self.img_path) + + +root_dir = 'dataset/train' +ants_label_dir = 'ants' +bees_label_dir = 'bees' +ants_dataset = MyDataset(root_dir, ants_label_dir) +bees_dataset = MyDataset(root_dir, bees_label_dir) +print(ants_dataset[0]) \ No newline at end of file diff --git a/src/nn_conv.py b/src/nn_conv.py new file mode 100644 index 0000000..46238f9 --- /dev/null +++ b/src/nn_conv.py @@ -0,0 +1,31 @@ +import torch +import torch.nn.functional as F + +input = torch.tensor([[1, 2, 0, 3, 1], + [0, 1, 2, 3, 2], + [1, 2, 1, 0, 0], + [5, 2, 3, 1, 1], + [2, 1, 0, 1, 1]]) + +kernel = torch.tensor([[1, 2, 1], + [0, 1, 0], + [2, 1, 0]]) + +input = torch.reshape(input, (1, 1, 5, 5)) +kernel = torch.reshape(kernel, (1, 1, 3, 3)) + +print(input.shape) +print(kernel.shape) + +output = F.conv2d(input, kernel, stride=1) +print(output) + +output2 = F.conv2d(input, kernel, stride=2) +print(output2) + +# stride步进 kernel我们的卷积核 padding是填充四周 +output3 = F.conv2d(input, kernel, stride=1, padding=1) +print(output3) + + + diff --git a/src/nn_conv2d.py b/src/nn_conv2d.py new file mode 100644 index 0000000..f3617d5 --- /dev/null +++ b/src/nn_conv2d.py @@ -0,0 +1,38 @@ +import torch +import torchvision +import torch.nn as nn +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter + +dataset = torchvision.datasets.CIFAR10(root='../dataset', train=False, transform=torchvision.transforms.ToTensor(), + download=True) +dataloader = DataLoader(dataset, batch_size=64) + +class Tudui(nn.Module): + def __init__(self): + super(Tudui, self).__init__() + self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=0) + + def forward(self, x): + x = self.conv1(x) + return x + +tudui = Tudui() +# print(tudui) + +writer = SummaryWriter('../logs') +step = 0 +for data in dataloader: + imgs, targets = data + output = tudui(imgs) + print(imgs.shape) + print(output.shape) + # torch.Size([64, 3, 32, 32]) + writer.add_images('input', imgs, step) + # torch.Size([64, 6, 30, 30]) => torch.Size([64, 3, 30, 30]) + output = torch.reshape(output, (-1, 3, 30, 30)) + writer.add_images('output', output, step) + step += 1 + +writer.close() + diff --git a/src/nn_liner.py b/src/nn_liner.py new file mode 100644 index 0000000..c6a6d62 --- /dev/null +++ b/src/nn_liner.py @@ -0,0 +1,30 @@ +import torch +import torchvision +from torch import nn +from torch.nn import Linear +from torch.utils.data import DataLoader + +datasset = torchvision.datasets.CIFAR10(root='../dataset', train=False, download=True, + transform=torchvision.transforms.ToTensor()) +dataloader = DataLoader(datasset, batch_size=64) + + +class Tudui(nn.Module): + def __init__(self): + super(Tudui, self).__init__() + self.Linera1 = Linear(196608, 10) + + def forward(self, input): + output = self.Linera1(input) + return output + + +tudui = Tudui() + +for data in dataloader: + imgs, targets = data + output = torch.reshape(imgs, (1, 1, 1, -1)) + output = torch.flatten(imgs) + print(output.shape) + output = tudui(output) + print(output.shape) diff --git a/src/nn_maxpool.py b/src/nn_maxpool.py new file mode 100644 index 0000000..c71f71d --- /dev/null +++ b/src/nn_maxpool.py @@ -0,0 +1,50 @@ +import torch +import torchvision +from torch import nn +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter + +dataset = torchvision.datasets.CIFAR10(root='../dataset', train=False, + transform=torchvision.transforms.ToTensor(),) + +dataloader = DataLoader(dataset, batch_size=64) + +# input = torch.tensor([[1, 2, 0, 3, 1], +# [0, 1, 2, 3, 1], +# [1, 2, 1, 0, 0], +# [5, 2, 3, 1, 1], +# [2, 1, 0, 1, 1]], dtype=torch.float32) +# +# # 转换为(C,H ,W)格式 unsqueeze是增加维度 0是在第0维增加 +# # input = input.unsqueeze(0).unsqueeze(0) +# input = torch.reshape(input, (-1, 1, 5, 5)) +# print(input.shape) + +class Tudui(nn.Module): + def __init__(self): + super(Tudui, self).__init__() + # ceil_mode=True表示向上取整 + # self.maxpool1 = nn.MaxPool2d(kernel_size=3, ceil_mode=True) + self.maxpool1 = nn.MaxPool2d(kernel_size=3, ceil_mode=False) + + + def forward(self, input): + output = self.maxpool1(input) + return output + +# 最大池化的感觉就是把一个区域内的最大值取出来 +# 文件尺寸会变小 +tudui = Tudui() +step = 0 +writer = SummaryWriter("../logs_maxpool") +for data in dataloader: + imgs, targets = data + output = tudui(imgs) + writer.add_images("input", imgs, step) + writer.add_images("output", output, step) + step += 1 + +writer.close() + + + diff --git a/src/nn_module.py b/src/nn_module.py new file mode 100644 index 0000000..304d96b --- /dev/null +++ b/src/nn_module.py @@ -0,0 +1,17 @@ +import torch +from torch import nn + + +class Tudui(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, input): + output = input + 1 + return output + + +tudui = Tudui() +x = torch.tensor(1.0) +output = tudui(x) +print(output) diff --git a/src/nn_relu.py b/src/nn_relu.py new file mode 100644 index 0000000..83aa8bf --- /dev/null +++ b/src/nn_relu.py @@ -0,0 +1,40 @@ +import torch +import torchvision +from torch.nn import ReLU, Sigmoid +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter + +# 检查GPU是否可用，若可用，则使用GPU，否则使用CPU +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +dataset = torchvision.datasets.CIFAR10(root='../dataset', train=False, download=True, + transform=torchvision.transforms.ToTensor()) +dataloader = DataLoader(dataset, batch_size=64) + + +class Tudui(torch.nn.Module): + def __init__(self): + super(Tudui, self).__init__() + self.relu1 = ReLU(inplace=True) + self.sigmoid1 = Sigmoid() + + def forward(self, input): + output = self.sigmoid1(input) + return output + + +tudui = Tudui().to(device) + +writer = SummaryWriter('../logs_relu') +step = 0 +for data in dataloader: + imgs, targets = data + # 将数据移动到GPU上 + imgs = imgs.to(device) + targets = targets.to(device) + + writer.add_images('input', imgs, step) + output = tudui(imgs) + writer.add_images('output', output, step) + step += 1 +writer.close() diff --git a/test/call_test.py b/test/call_test.py new file mode 100644 index 0000000..4bc3ed8 --- /dev/null +++ b/test/call_test.py @@ -0,0 +1,11 @@ +class Person: + def __call__(self, name): + print("__call__"+"hello", name) + + def hello(self, name): + print("hello", name) + + +person = Person() +person("world") +person.hello("world") \ No newline at end of file diff --git a/test_tb.py b/test_tb.py new file mode 100644 index 0000000..068db2c --- /dev/null +++ b/test_tb.py @@ -0,0 +1,18 @@ +from torch.utils.tensorboard import SummaryWriter +import numpy as np +from PIL import Image + +writer = SummaryWriter('logs') +# image_path = r"C:\Users\12642\Desktop\Picture\dataset\train\ants\0013035.jpg" +image_path = r"C:\Users\12642\Desktop\Picture\dataset\train\ants\6240338_93729615ec.jpg" +img_PIL = Image.open(image_path) +img_array = np.array(img_PIL) +print(type(img_array)) +print(img_array.shape) + + +writer.add_image("train", img_array, 1, dataformats='HWC') +for i in range(100): + writer.add_scalar("y=2x", 2*i, i) + +writer.close() diff --git a/transforms_test.py b/transforms_test.py new file mode 100644 index 0000000..d4d9713 --- /dev/null +++ b/transforms_test.py @@ -0,0 +1,15 @@ +from PIL import Image +from torch.utils.tensorboard import SummaryWriter +from torchvision import transforms + +img_path = r"C:\Users\12642\Desktop\Picture\dataset\train\ants\6240338_93729615ec.jpg" +img = Image.open(img_path) + +writer = SummaryWriter("logs") + +# transforms.ToTensor() 将 PIL.Image 或者 numpy.ndarray 转化为 tensor +tensor_trans = transforms.ToTensor() +tensor_img = transforms.ToTensor()(img) + +writer.add_image("tensor_img", tensor_img) # 这里的tensor_img是一个三维的tensor，所以可以直接显示 +writer.close() \ No newline at end of file diff --git a/userful_transforms.py b/userful_transforms.py new file mode 100644 index 0000000..0cab2b7 --- /dev/null +++ b/userful_transforms.py @@ -0,0 +1,44 @@ +from PIL import Image +from torch.utils.tensorboard import SummaryWriter +from torchvision import transforms + +writer = SummaryWriter("logs") +img = Image.open(r"images/7759525_1363d24e88.jpg") + +# TeTensor的使用 +trans_totensor = transforms.ToTensor() +img_tensor = trans_totensor(img) +writer.add_image("ToTensor", img_tensor) + +# Normalize的使用 +print(img_tensor[0][0][0]) +trans_norm = transforms.Normalize([1, 3, 5], [3, 2, 1]) +img_norm = trans_norm(img_tensor) +print(img_norm[0][0][0]) +writer.add_image("Normalize", img_norm,2) + +# Resize的使用 +print(img.size) +trans_resize = transforms.Resize((512, 512)) +# img PIL -> resize -> img_resize PIL +img_resize = trans_resize(img) # 返回的是PIL +# img_resize PIL -> ToTensor -> img_resize Tensor +img_resize = trans_totensor(img_resize) # PIL转换为Tensor +writer.add_image("Resize", img_resize, 0) + +# Compose的使用 compose = ToTensor + Normalize +trans_resize_2 = transforms.Resize(512) +# transforms.Compose([trans_resize_2, trans_totensor])是一个类，可以直接调用 +# 用于将多个transforms组合起来使用 达到 ToTensor + Normalize 的效果 +trans_compose = transforms.Compose([trans_resize_2, trans_totensor]) +img_resize_2 = trans_compose(img) +writer.add_image("Raszie", img_resize_2, 1) + +# RandomCrop的使用 +trans_random = transforms.RandomCrop(256, 128) +trans_compose2 = transforms.Compose([trans_random, trans_totensor]) +for i in range(10): + img_crop = trans_compose2(img) + writer.add_image("RandomCrop", img_crop, i) + +writer.close() \ No newline at end of file