Update network model structure:

Changed model from FCNN to CNN
This commit is contained in:
Cezary Adamczak 2021-06-20 15:00:34 +02:00
parent 0b2791f961
commit 3898a3bcab

View File

@ -1,18 +1,20 @@
import PIL
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from matplotlib.pyplot import imshow
import os
import PIL
import numpy as np
from matplotlib.pyplot import imshow
def to_negative(img):
img = PIL.ImageOps.invert(img)
return img
class Negative(object):
def __init__(self):
pass
@ -20,41 +22,46 @@ class Negative(object):
def __call__(self, img):
return to_negative(img)
def plotdigit(image):
img = np.reshape(image, (-1, 100))
imshow(img, cmap='Greys')
transform = transforms.Compose([Negative(), transforms.ToTensor()])
train_set = torchvision.datasets.ImageFolder(root='../src/train', transform=transform)
classes = ("apple", "potato")
train_set = torchvision.datasets.ImageFolder(root='train', transform=transform)
classes = ("pepper", "potato", "strawberry", "tomato")
BATCH_SIZE = 2
BATCH_SIZE = 4
train_loader = torch.utils.data.DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.flatten = nn.Flatten()
self.linear_relu_stack = nn.Sequential(
nn.Linear(3 * 100 * 100, 512),
super().__init__()
self.network = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, padding=1), #3 channels to 32 channels
nn.ReLU(),
nn.Linear(512, 512),
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Linear(512, 2),
nn.ReLU()
)
self.linear_relu_stack = self.linear_relu_stack.to(device)
nn.MaxPool2d(2, 2), # output: 64 channels x 50 x 50 image size - decrease
def forward(self, x):
x = self.flatten(x).to(device)
logits = self.linear_relu_stack(x).to(device)
return logits
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1), #increase power of model
nn.ReLU(),
nn.MaxPool2d(2, 2), # output: 128 x 25 x 25
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(5, 5), # output: 256 x 5 x 5
nn.Flatten(), #a single vector 256*5*5,
nn.Linear(256*5*5, 1024),
nn.ReLU(),
nn.Linear(1024, 512),
nn.ReLU(),
nn.Linear(512, 4))
def forward(self, xb):
return self.network(xb)
def training_network():
net = Net()
@ -63,7 +70,7 @@ def training_network():
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(4):
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data[0].to(device), data[1].to(device)
@ -74,7 +81,7 @@ def training_network():
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
if i % 200 == 199:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss))
running_loss = 0.0
@ -84,8 +91,8 @@ def training_network():
def result_from_network(net, loaded_image):
image = PIL.Image.open(loaded_image)
pil_to_tensor = transforms.ToTensor()(image.convert("RGB")).unsqueeze_(0)
outputs = net(pil_to_tensor.to(device))
pil_to_tensor = transforms.Compose([Negative(), transforms.ToTensor()])(image.convert("RGB")).unsqueeze_(0)
outputs = net(pil_to_tensor)
return classes[torch.max(outputs, 1)[1]]
@ -99,7 +106,6 @@ def load_network_from_structure(network):
network.load_state_dict(torch.load('network_model.pth'))
# Create network_model.pth
if __name__ == "__main__":
print(torch.cuda.is_available())
training_network()