pytorch lighning addition

This commit is contained in:
XsedoX 2022-05-25 19:47:08 +02:00
parent 4e3e68d4c3
commit 088e90ec5b
16 changed files with 131 additions and 65 deletions

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import torch
import pytorch_lightning as pl
import torch.nn as nn
from torch.optim import SGD, Adam, lr_scheduler
import torch.nn.functional as F
from torch.utils.data import DataLoader
from watersandtreegrass import WaterSandTreeGrass
from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
class NeuralNetwork(nn.Module):
def __init__(self, num_classes=4):
super(NeuralNetwork, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
self.pool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))
self.conv2 = nn.Conv2d(in_channels=10, out_channels=20, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
self.fc1 = nn.Linear(20*9*9, num_classes)
class NeuralNetwork(pl.LightningModule):
def __init__(self, numChannels=3, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, num_classes=4):
super().__init__()
self.layer = nn.Sequential(
nn.Linear(36*36*3, 300),
nn.ReLU(),
nn.Linear(300, 4),
nn.LogSoftmax(dim=-1)
)
self.batch_size = batch_size
self.learning_rate = learning_rate
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool(x)
x = F.relu(self.conv2(x))
x = self.pool(x)
x = x.reshape(x.shape[0], -1)
x = self.fc1(x)
x = self.layer(x)
return x
def configure_optimizers(self):
optimizer = SGD(self.parameters(), lr=self.learning_rate)
return optimizer
def training_step(self, batch, batch_idx):
x, y = batch
scores = self(x)
loss = F.nll_loss(scores, y)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
scores = self(x)
val_loss = F.nll_loss(scores, y)
self.log("val_loss", val_loss, on_step=True, on_epoch=True, sync_dist=True)
def test_step(self, batch, batch_idx):
x, y = batch
scores = self(x)
test_loss = F.nll_loss(scores, y)
self.log("test_loss", test_loss, on_step=True, on_epoch=True, sync_dist=True)

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@ -1,5 +1,6 @@
import torch
from common.constants import device, batch_size, num_epochs, learning_rate, setup_photos, id_to_class
import common.helpers
from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
from watersandtreegrass import WaterSandTreeGrass
from torch.utils.data import DataLoader
from neural_network import NeuralNetwork
@ -7,24 +8,25 @@ from torchvision.io import read_image, ImageReadMode
import torch.nn as nn
from torch.optim import Adam
import matplotlib.pyplot as plt
CNN = NeuralNetwork().to(device)
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping
def train(model):
model = model.to(DEVICE)
model.train()
trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=setup_photos)
testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=setup_photos)
train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(testset, batch_size=batch_size, shuffle=True)
trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = DataLoader(testset, batch_size=BATCH_SIZE, shuffle=True)
criterion = nn.CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=learning_rate)
optimizer = Adam(model.parameters(), lr=LEARNING_RATE)
for epoch in range(num_epochs):
for epoch in range(NUM_EPOCHS):
for batch_idx, (data, targets) in enumerate(train_loader):
data = data.to(device=device)
targets = targets.to(device=device)
data = data.to(device=DEVICE)
targets = targets.to(device=DEVICE)
scores = model(data)
loss = criterion(scores, targets)
@ -34,39 +36,62 @@ def train(model):
optimizer.step()
if epoch % 2 == 0:
if batch_idx % 4 == 0:
print("epoch: %d loss: %.4f" % (epoch, loss.item()))
print("FINISHED TRAINING!")
torch.save(model.state_dict(), "./learnednetwork.pth")
print("Checking accuracy for the train set.")
check_accuracy(train_loader)
print("Checking accuracy for the test set.")
check_accuracy(test_loader)
torch.save(model.state_dict(), "./learnedNetwork.pt")
print("Checking accuracy for the tiles.")
check_accuracy_tiles()
def check_accuracy(loader):
num_correct = 0
num_samples = 0
model = NeuralNetwork()
def check_accuracy_tiles():
answer = 0
for i in range(100):
if what_is_it('../../resources/textures/grass_with_tree.jpg') == 'tree':
answer = answer + 1
print("Accuracy(%) grass_with_tree.jpg", answer)
model.load_state_dict(torch.load("./learnedNetwork.pt", map_location=device))
model = model.to(device)
answer = 0
for i in range(100):
if what_is_it('../../resources/textures/grass2.png') == 'grass':
answer = answer + 1
print("Accuracy(%) grass2.png", answer)
with torch.no_grad():
model.eval()
for x, y in loader:
x = x.to(device=device)
y = y.to(device=device)
answer = 0
for i in range(100):
if what_is_it('../../resources/textures/grass3.png') == 'grass':
answer = answer + 1
print("Accuracy(%) grass3.png", answer)
scores = model(x)
answer = 0
for i in range(100):
if what_is_it('../../resources/textures/grass4.png') == 'grass':
answer = answer + 1
print("Accuracy(%) grass4.png", answer)
_, predictions = scores.max(1)
num_correct += (predictions == y).sum()
num_samples += predictions.size(0)
answer = 0
for i in range(100):
if what_is_it('../../resources/textures/grass1.png') == 'grass':
answer = answer + 1
print("Accuracy(%) grass1.png", answer)
print(f"Got {num_correct}/{num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}%")
answer = 0
for i in range(100):
if what_is_it('../../resources/textures/water.png') == 'water':
answer = answer + 1
print("Accuracy(%) water.png", answer)
answer = 0
for i in range(100):
if what_is_it('../../resources/textures/sand.png') == 'sand':
answer = answer + 1
print("Accuracy(%) sand.png", answer)
def what_is_it(img_path, show_img=False):
@ -74,17 +99,27 @@ def what_is_it(img_path, show_img=False):
if show_img:
plt.imshow(plt.imread(img_path))
plt.show()
image = setup_photos(image).unsqueeze(0)
model = NeuralNetwork()
model.load_state_dict(torch.load("./learnedNetwork.pt", map_location=device))
model = model.to(device)
image = image.to(device)
image = SETUP_PHOTOS(image).unsqueeze(0)
model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_3/checkpoints/epoch=8-step=810.ckpt')
with torch.no_grad():
model.eval()
idx = int(model(image).argmax(dim=1))
return id_to_class[idx]
return ID_TO_CLASS[idx]
train(CNN)
CNN = NeuralNetwork()
trainer = pl.Trainer(accelerator='gpu', devices=1, auto_scale_batch_size=True, callbacks=[EarlyStopping('val_loss')], max_epochs=NUM_EPOCHS)
#trainer = pl.Trainer(accelerator='gpu', devices=1, auto_lr_find=True, max_epochs=NUM_EPOCHS)
trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = DataLoader(testset, batch_size=BATCH_SIZE)
#trainer.fit(CNN, train_loader, test_loader)
#trainer.tune(CNN, train_loader, test_loader)
check_accuracy_tiles()
print(what_is_it('../../resources/textures/sand.png', True))

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@ -72,14 +72,15 @@ BAR_HEIGHT_MULTIPLIER = 0.1
#NEURAL_NETWORK
learning_rate = 0.001
batch_size = 7
num_epochs = 100
LEARNING_RATE = 0.13182567385564073
BATCH_SIZE = 64
NUM_EPOCHS = 50
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
classes = ['grass', 'sand', 'tree', 'water']
DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
print("Using ", DEVICE)
CLASSES = ['grass', 'sand', 'tree', 'water']
setup_photos = transforms.Compose([
SETUP_PHOTOS = transforms.Compose([
transforms.Resize(36),
transforms.CenterCrop(36),
transforms.ToPILImage(),
@ -87,5 +88,5 @@ setup_photos = transforms.Compose([
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
id_to_class = {i: j for i, j in enumerate(classes)}
class_to_id = {value: key for key, value in id_to_class.items()}
ID_TO_CLASS = {i: j for i, j in enumerate(CLASSES)}
CLASS_TO_ID = {value: key for key, value in ID_TO_CLASS.items()}

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@ -1,5 +1,5 @@
import pygame
from common.constants import GRID_CELL_PADDING, GRID_CELL_SIZE, COLUMNS, ROWS, classes, class_to_id
from common.constants import GRID_CELL_PADDING, GRID_CELL_SIZE, COLUMNS, ROWS, CLASSES, CLASS_TO_ID
import csv
import os
@ -24,29 +24,29 @@ def createCSV():
writer = csv.writer(train_csvfile)
writer.writerow(["filepath", "type"])
for class_name in classes:
for class_name in CLASSES:
class_dir = train_data_path + "/" + class_name
for filename in os.listdir(class_dir):
f = os.path.join(class_dir, filename)
if os.path.isfile(f):
writer.writerow([f, class_to_id[class_name]])
writer.writerow([f, CLASS_TO_ID[class_name]])
train_csvfile.close()
else:
print("Brak plików do uczenia")
if os.path.exists(train_data_path):
if os.path.exists(test_data_path):
test_csvfile = open('./data/test_csv_file.csv', 'w', newline="")
writer = csv.writer(test_csvfile)
writer.writerow(["filepath", "type"])
for class_name in classes:
for class_name in CLASSES:
class_dir = test_data_path + "/" + class_name
for filename in os.listdir(class_dir):
f = os.path.join(class_dir, filename)
if os.path.isfile(f):
writer.writerow([f, class_to_id[class_name]])
writer.writerow([f, CLASS_TO_ID[class_name]])
test_csvfile.close()
else: