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dfafaa4 | 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 | import os
from pathlib import Path
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import models as tvm
from torchvision.datasets import ImageFolder
from lightly.loss import NTXentLoss
from lightly.models.modules import SimCLRProjectionHead
from lightly.transforms.simclr_transform import SimCLRTransform
from torch.optim.lr_scheduler import LambdaLR
# ----------------------------
# Config
# ----------------------------
DATA_ROOT = "data/eurosat_custom/train" # prepared split (train only, unlabeled)
BATCH_SIZE = 256
EPOCHS = 150
LR = 0.06
NUM_WORKERS = 8
IMG_SIZE = 224 # resize inside transform
OUT_DIR = Path("checkpoints_ssl")
OUT_DIR.mkdir(parents=True, exist_ok=True)
warmup_epochs = 10
total_epochs = EPOCHS
def lr_lambda(epoch):
if epoch < warmup_epochs:
return float(epoch + 1) / warmup_epochs
progress = (epoch - warmup_epochs) / float(total_epochs - warmup_epochs)
return 0.5 * (1.0 + torch.cos(torch.pi * progress))
scheduler = LambdaLR(optimizer, lr_lambda=lr_lambda)
device = "cuda" if torch.cuda.is_available() else "cpu"
use_amp = torch.cuda.is_available() # mixed precision if GPU
# ----------------------------
# Model: ResNet18 encoder + SimCLR projection head
# ----------------------------
class SimCLR(nn.Module):
def __init__(self, backbone, in_dim=512, proj_hidden=512, proj_out=128):
super().__init__()
self.backbone = backbone
self.projection_head = SimCLRProjectionHead(in_dim, proj_hidden, proj_out)
def forward(self, x):
# backbone assumed to output [N, C, 1, 1] after global pooling
x = self.backbone(x).flatten(start_dim=1)
z = self.projection_head(x)
return z
# Build a torchvision resnet18 backbone without the FC layer
resnet = tvm.resnet18(weights=None)
# replace avgpool+fc stack with Identity + keep global avgpool:
# torchvision resnet18 returns features after avgpool as 512-d before fc.
backbone = nn.Sequential(*list(resnet.children())[:-1]) # until avgpool, outputs [N,512,1,1]
model = SimCLR(backbone, in_dim=512, proj_hidden=512, proj_out=128).to(device)
# ----------------------------
# Data: EuroSAT train images as unlabeled pairs of views
# ----------------------------
# SimCLR default normalization in Lightly is ImageNet stats; perfect for ResNet18 at 224.
transform = SimCLRTransform(
input_size=IMG_SIZE,
gaussian_blur=0.1, # EuroSAT is small; mild blur helps but keep modest
cj_strength=0.5, # color jitter strength
)
dataset = ImageFolder(DATA_ROOT, transform=transform)
# ImageFolder returns ( (v1, v2), label ) because transform yields two views.
# We'll ignore labels during pretraining.
loader = DataLoader(
dataset,
batch_size=BATCH_SIZE,
shuffle=True,
drop_last=True,
num_workers=NUM_WORKERS,
pin_memory=torch.cuda.is_available(),
)
# ----------------------------
# Objective & Optimizer
# ----------------------------
criterion = NTXentLoss(temperature=0.5) # standard SimCLR temperature
optimizer = torch.optim.SGD(model.parameters(), lr=LR, momentum=0.9, weight_decay=1e-4)
scaler = torch.cuda.amp.GradScaler(enabled=use_amp)
# ----------------------------
# Training loop
# ----------------------------
print(f"Starting SimCLR pretraining on {device} for {EPOCHS} epochs…")
model.train()
for epoch in range(1, EPOCHS + 1):
total_loss = 0.0
for (v1, v2), _ in loader: # labels are unused
v1 = v1.to(device, non_blocking=True)
v2 = v2.to(device, non_blocking=True)
optimizer.zero_grad(set_to_none=True)
with torch.cuda.amp.autocast(enabled=use_amp):
z1 = model(v1)
z2 = model(v2)
loss = criterion(z1, z2)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
total_loss += loss.detach().item()
avg_loss = total_loss / len(loader)
current_lr = scheduler.get_last_lr()[0]
print(f"epoch {epoch:03d} | loss {avg_loss:.5f} | lr {current_lr:.5f}")
scheduler.step()
# (optional) save checkpoints every N epochs
if epoch % 25 == 0 or epoch == EPOCHS:
# save only the encoder (backbone) weights for fine-tuning
enc_state = {k.replace("backbone.", "", 1): v
for k, v in model.state_dict().items()
if k.startswith("backbone.")}
torch.save(enc_state, OUT_DIR / f"simclr_resnet18_eurosat_epoch{epoch}.pt")
print("Done.") |