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VGG.py
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VGG.py
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import torch
from torch import nn
import torch.nn.functional as F
import torchvision
import numpy as np
import cv2
from glob import glob
import os
import tqdm
device='cpu'
# Training Loop Embedded Into Model
'''
Training Modules
'''
class ClassificationBase(nn.Module):
def training_step(self, batch):
images, labels = batch
images = images.to(device)
labels = labels.to(device)
out = self(images)
loss = F.cross_entropy(out, labels)
acc = accuracy(out, labels)
return loss, acc
def validation_step(self, batch):
images, labels = batch
images = images.to(device)
labels = labels.to(device)
out = self(images)
loss = F.cross_entropy(out, labels)
acc = accuracy(out, labels)
return {'val_loss': loss.detach(), 'val_acc': acc}
def validation_epoch_end(self, outputs):
batch_losses = [x['val_loss'] for x in outputs]
epoch_loss = torch.stack(batch_losses).mean()
batch_accs = [x['val_acc'] for x in outputs]
epoch_acc = torch.stack(batch_accs).mean()
return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}
def epoch_end(self, epoch, result):
print("Epoch [{}], val_loss: {:.4f}, val_acc: {:.4f}".format(epoch, result['val_loss'], result['val_acc']))
# Accuracy & Validation Functions
def accuracy(outputs, labels):
_, preds = torch.max(outputs, dim=1)
return torch.tensor(torch.sum(preds == labels).item() / len(preds))
def evaluate(model, val_loader):
outputs = [model.validation_step(batch) for batch in val_loader]
return model.validation_epoch_end(outputs)
'''
Model Class
'''
class VGG(ClassificationBase):
'''
Accepts Only RGB Images Of Height, Width: (224, 224)
Input Size : 3, 224, 224
Output Size : output_classes
'''
def __init__(self, output_classes=None):
super(ClassificationBase, self).__init__()
self.relu = nn.ReLU()
self.pool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(3, 3), padding=(1, 1)),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), padding=(1, 1)),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.conv3 = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), padding=(1, 1)),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.conv4 = nn.Sequential(
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=(3, 3), padding=(1, 1)),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.conv5 = nn.Sequential(
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3, 3), padding=(1, 1)),
nn.BatchNorm2d(256),
nn.ReLU(),
)
self.conv6 = nn.Sequential(
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3, 3), padding=(1, 1)),
nn.BatchNorm2d(256),
nn.ReLU(),
)
self.conv7 = nn.Sequential(
nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(3, 3), padding=(1, 1)),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.conv8 = nn.Sequential(
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), padding=(1, 1)),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.l1 = nn.Linear(512*7*7, 4096)
self.l2 = nn.Linear(4096, 4096)
self.l3 = nn.Linear(4096, 1000)
self.l4 = nn.Linear(1000, output_classes)
def forward(self, x):
x = self.conv1(x)
x = self.pool(self.conv2(x))
x = self.conv3(x)
x = self.pool(self.conv4(x))
x = self.conv5(x)
x = self.pool(self.conv6(self.conv6(x)))
x = self.conv7(x)
x = self.pool(self.conv8(self.conv8(x)))
x = self.conv8(x)
x = self.pool(self.conv8(self.conv8(x)))
x = x.reshape(x.shape[0], -1)
x = self.l1(x)
x = self.l2(x)
x = self.l3(x)
x = self.l4(x)
return x