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6276d4c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
from torchvision.models import efficientnet_b0, EfficientNet_B0_Weights
from tqdm import tqdm
from src.dataset import create_dataloaders
def set_seed(seed=42):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
#training loop
def train_one_epoch(model, dataloader, criterion, optimizer, device):
model.train()
total_loss = 0.0
correct = 0
total = 0
for images, labels, _ in tqdm(dataloader):
images = images.to(device)
labels = labels.to(device).long()
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
total_loss += loss.item()
preds = torch.argmax(outputs, dim=1)
correct += (preds == labels).sum().item()
total += labels.size(0)
avg_loss = total_loss / len(dataloader)
accuracy = 100.0 * correct / total
return avg_loss, accuracy
#validation loop
def validate(model, dataloader, criterion, device):
model.eval()
total_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
for images, labels, _ in dataloader:
images = images.to(device)
labels = labels.to(device).long()
outputs = model(images)
loss = criterion(outputs, labels)
total_loss += loss.item()
preds = torch.argmax(outputs, dim=1)
correct += (preds == labels).sum().item()
total += labels.size(0)
avg_loss = total_loss / len(dataloader)
accuracy = 100.0 * correct / total
return avg_loss, accuracy
def plot_curves(train_losses, val_losses, train_accuracies, val_accuracies):
epochs_done = len(train_losses)
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
plt.plot(range(1, epochs_done + 1), train_losses, marker="o", label="Train Loss")
plt.plot(range(1, epochs_done + 1), val_losses, marker="o", label="Val Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("Loss Curve")
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(range(1, epochs_done + 1), train_accuracies, marker="o", label="Train Accuracy")
plt.plot(range(1, epochs_done + 1), val_accuracies, marker="o", label="Val Accuracy")
plt.xlabel("Epoch")
plt.ylabel("Accuracy (%)")
plt.title("Accuracy Curve")
plt.legend()
plt.show()
set_seed(42)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
train_loader, val_loader, test_loader, class_names = create_dataloaders()
num_classes = len(class_names)
print("Number of classes:", num_classes)
print("Classes:", class_names)
weights = EfficientNet_B0_Weights.DEFAULT
model = efficientnet_b0(weights=weights)
#freezing the feature extractor and modifying the classifier
for param in model.features.parameters():
param.requires_grad = False
in_features = model.classifier[1].in_features
model.classifier = nn.Sequential(
nn.Dropout(p=0.3),
nn.Linear(in_features, num_classes)
)
model = model.to(device)
criterion = nn.CrossEntropyLoss()
train_losses = []
val_losses = []
train_accuracies = []
val_accuracies = []
# Phase 1: train classifier only
optimizer = optim.AdamW(
filter(lambda p: p.requires_grad, model.parameters()),
lr=1e-3,
weight_decay=1e-4
)
epochs_phase1 = 10
for epoch in range(epochs_phase1):
train_loss, train_acc = train_one_epoch(model, train_loader, criterion, optimizer, device)
val_loss, val_acc = validate(model, val_loader, criterion, device)
train_losses.append(train_loss)
val_losses.append(val_loss)
train_accuracies.append(train_acc)
val_accuracies.append(val_acc)
print(
f"[Phase 1] Epoch {epoch + 1}/{epochs_phase1} | "
f"Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.2f}% | "
f"Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.2f}%"
)
# Unfreeze all feature layers for full fine-tuning
for param in model.features.parameters():
param.requires_grad = True
optimizer = optim.AdamW(
model.parameters(),
lr=1e-5,
weight_decay=1e-4
)
epochs_phase2 = 20
for epoch in range(epochs_phase2):
train_loss, train_acc = train_one_epoch(model, train_loader, criterion, optimizer, device)
val_loss, val_acc = validate(model, val_loader, criterion, device)
train_losses.append(train_loss)
val_losses.append(val_loss)
train_accuracies.append(train_acc)
val_accuracies.append(val_acc)
print(
f"[Phase 2] Epoch {epoch + 1}/{epochs_phase2} | "
f"Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.2f}% | "
f"Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.2f}%"
)
plot_curves(train_losses, val_losses, train_accuracies, val_accuracies)
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