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catdog_classification.py
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catdog_classification.py
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import os
import cv2
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from sklearn.model_selection import train_test_split
""" To do - Transfer learning to use this to identify some other image features"""
REBUILD_DATA = True
REBUILD_MODEL = True
if torch.cuda.is_available():
device = torch.device("cuda:0") # you can continue going on here, like cuda:1 cuda:2....etc.
print("Running on the GPU")
else:
device = torch.device("cpu")
print("Running on the CPU")
# Preprocessing step can take a lot of time - you want to run it as few as possible
class DogsvCats():
""" Repeated steps basically means that encapsulating it is helpful """
# Images needs to be of the same shape
img_size = 50
cats = "/home/parvfect/Documents/AI/catdog/PetImages/Cat"
dogs = "/home/parvfect/Documents/AI/catdog/PetImages/Dog"
Labels = {cats:0, dogs:1}
training_data = []
cat_count = 0
dog_count = 0
# Balance is very important in the dataset
def make_training_data(self):
for label in self.Labels:
print(label)
# tqdm is a progress bar
for f in tqdm(os.listdir(label)):
try:
path = os.path.join(label, f)
# You don't have to convert to grayscale - is color a relevant feature?
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (self.img_size, self.img_size))
""" One hot encoding - what index is hot?
Cat = [0 1]
Dog [ 1 0]
np.eye(x)[y]
"""
self.training_data.append([np.array(img), np.eye(2)[self.Labels[label]]])
if label == self.cats:
self.cat_count += 1
elif label == self.dogs:
self.dog_count += 1
except Exception as e:
print((e))
pass
np.random.shuffle(self.training_data)
np.save("training_data.npy", self.training_data)
print("Cats ",self.cat_count)
print("Dogs", self.dog_count)
if REBUILD_DATA:
dogsvcats = DogsvCats()
dogsvcats.make_training_data()
training_data = np.load("training_data.npy", allow_pickle=True)
#plt.imshow(training_data[10000][0], cmap = 'gray')
#plt.show()
# Take batches of data and classify it using a convolutional neural network to classify
class Net(nn.Module):
def __init__(self):
super().__init__() # just run the init of parent class (nn.Module)
self.conv1 = nn.Conv2d(1, 32, 5) # input is 1 image, 32 output channels, 5x5 kernel / window
self.conv2 = nn.Conv2d(32, 64, 5) # input is 32, bc the first layer output 32. Then we say the output will be 64 channels, 5x5 kernel / window
self.conv3 = nn.Conv2d(64, 128, 5)
x = torch.randn(50,50).view(-1,1,50,50)
self._to_linear = None
self.convs(x)
self.fc1 = nn.Linear(self._to_linear, 512)
self.fc2 = nn.Linear(512, 2)
def convs(self, x):
# max pooling over 2x2
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv3(x)), (2, 2))
if self._to_linear is None:
self._to_linear = x[0].shape[0]*x[0].shape[1]*x[0].shape[2]
return x
def forward(self, x):
x = self.convs(x)
x = x.view(-1, self._to_linear) # .view is reshape ... this flattens X before
x = F.relu(self.fc1(x))
x = self.fc2(x) # bc this is our output layer. No activation here.
return F.softmax(x, dim=1)
net = Net()
optimizer = optim.Adam(net.parameters(), lr = 0.001)
loss_function = nn.MSELoss()
# Making sure the data is torch acceptable and seperating into samples and labels
X = torch.Tensor([i[0] for i in training_data]).view(-1,50,50)
X = X/255.0
y = torch.Tensor([i[1] for i in training_data])
VAL_PCT = 0.1 # lets reserve 10% of our data for validation
val_size = int(len(X)*VAL_PCT)
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.1)
batch_size = 100
epochs = 2
EPOCHS = 3
def train(net):
optimizer = optim.Adam(net.parameters(), lr=0.001)
BATCH_SIZE = 100
EPOCHS = 3
for epoch in range(EPOCHS):
for i in range(0, len(X_train), BATCH_SIZE): # from 0, to the len of x, stepping BATCH_SIZE at a time. [:50] ..for now just to dev
#print(f"{i}:{i+BATCH_SIZE}")
batch_X = X_train[i:i+BATCH_SIZE].view(-1, 1, 50, 50)
batch_y = y_train[i:i+BATCH_SIZE]
batch_X, batch_y = batch_X.to(device), batch_y.to(device)
net.zero_grad()
optimizer.zero_grad() # zero the gradient buffers
outputs = net(batch_X)
loss = loss_function(outputs, batch_y)
loss.backward()
optimizer.step() # Does the update
print(f"Epoch: {epoch}. Loss: {loss}")
torch.save(net, "/home/parvfect/Documents/machine_learning/models/catdog")
def test(net):
correct = 0
total = 0
for i in tqdm(range(0, len(X_test), BATCH_SIZE)):
batch_X = X_test[i:i+BATCH_SIZE].view(-1, 1, 50, 50).to(device)
batch_y = y_test[i:i+BATCH_SIZE].to(device)
batch_out = net(batch_X)
out_maxes = [torch.argmax(i) for i in batch_out]
target_maxes = [torch.argmax(i) for i in batch_y]
for i,j in zip(out_maxes, target_maxes):
if i == j:
correct += 1
total += 1
print("Accuracy: ", round(correct/total, 3))
if REBUILD_MODEL:
train(net)
model = net
else:
model = torch.load("/home/parvfect/Documents/machine_learning/models/catdog")
test(model)