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用python搭建一个花卉识别系统

来源：花匠小妙招时间：2024-10-31 02:23

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import torch

import torch.nn as nn

from torchvision import transforms, datasets, utils

import matplotlib.pyplot as plt

import numpy as np

import torch.optim as optim

from model import AlexNet

import os

import json

import time

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

with open(os.path.join("train.log"), "a") as log:

log.write(str(device)+"n")

data_transform = {

"train": transforms.Compose([transforms.RandomResizedCrop(224),

transforms.RandomHorizontalFlip(p=0.5),

transforms.ToTensor(),

transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),

"val": transforms.Compose([transforms.Resize((224, 224)),

transforms.ToTensor(),

transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])}

data_root = os.path.abspath(os.path.join(os.getcwd(), "../.."))

image_path = data_root + "/jqsj/data_set/flower_data/"

train_dataset = datasets.ImageFolder(root=image_path + "/train",

transform=data_transform["train"])

train_num = len(train_dataset)

train_loader = torch.utils.data.DataLoader(train_dataset,

batch_size=32,

shuffle=True,

num_workers=0)

validate_dataset = datasets.ImageFolder(root=image_path + "/val",

transform=data_transform["val"])

val_num = len(validate_dataset)

validate_loader = torch.utils.data.DataLoader(validate_dataset,

batch_size=32,

shuffle=True,

num_workers=0)

flower_list = train_dataset.class_to_idx

cla_dict = dict((val, key) for key, val in flower_list.items())

json_str = json.dumps(cla_dict, indent=4)

with open('class_indices.json', 'w') as json_file:

json_file.write(json_str)

net = AlexNet(num_classes=5, init_weights=True)

net.to(device)

loss_function = nn.CrossEntropyLoss()

optimizer = optim.Adam(net.parameters(), lr=0.0002)

save_path = './AlexNet.pth'

best_acc = 0.0

for epoch in range(150):

net.train()

running_loss = 0.0

time_start = time.perf_counter()

for step, data in enumerate(train_loader, start=0):

images, labels = data

optimizer.zero_grad()

outputs = net(images.to(device))

loss = loss_function(outputs, labels.to(device))

loss.backward()

optimizer.step()

running_loss += loss.item()

rate = (step + 1) / len(train_loader)

a = "*" * int(rate * 50)

b = "." * int((1 - rate) * 50)

with open(os.path.join("train.log"), "a") as log:

log.write(str("rtrain loss: {:^3.0f}%[{}->{}]{:.3f}".format(int(rate * 100), a, b, loss))+"n")

print("rtrain loss: {:^3.0f}%[{}->{}]{:.3f}".format(int(rate * 100), a, b, loss), end="")

print()

with open(os.path.join("train.log"), "a") as log:

log.write(str('%f s' % (time.perf_counter()-time_start))+"n")

print('%f s' % (time.perf_counter()-time_start))

net.eval()

acc = 0.0

with torch.no_grad():

for val_data in validate_loader:

val_images, val_labels = val_data

outputs = net(val_images.to(device))

predict_y = torch.max(outputs, dim=1)[1]

acc += (predict_y == val_labels.to(device)).sum().item()

val_accurate = acc / val_num

if val_accurate > best_acc:

best_acc = val_accurate

torch.save(net.state_dict(), save_path)

with open(os.path.join("train.log"), "a") as log:

log.write(str('[epoch %d] train_loss: %.3f test_accuracy: %.3f n' %

(epoch + 1, running_loss / step, val_accurate))+"n")

print('[epoch %d] train_loss: %.3f test_accuracy: %.3f n' %

(epoch + 1, running_loss / step, val_accurate))

with open(os.path.join("train.log"), "a") as log:

log.write(str('Finished Training')+"n")

print('Finished Training')

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