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evaluate_semi_supervised.py
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evaluate_semi_supervised.py
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import argparse
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torchmetrics
from src.model import ResnetMultiProj
from src.data import get_dataset
from src.transform import ValTransform
from src.utils import get_config, get_device
def evaluate(args):
"""Evaluate Semi-Supervised model on validation set"""
path_config = args.config
config = get_config(path_config)
device = get_device()
# load checkpoint
path_ckpt = args.ckpt
ckpt = torch.load(path_ckpt, map_location=device)
# load encoder
encoder = ResnetMultiProj(**config['encoder'])
num_features = encoder.num_features
encoder = encoder.backbone.to(device)
encoder.load_state_dict(ckpt['encoder'])
encoder.eval()
# load classifier
classifier = nn.Linear(num_features, config['dataset']['n_classes'])
classifier = classifier.to(device)
classifier.load_state_dict(ckpt['classifier'])
classifier.eval()
# get dataset and dataloader
ds_name = config['dataset']['name']
ds_path = config['dataset']['path']
img_size = config['dataset']['size']
trans = ValTransform(ds_name, img_size)
ds = get_dataset(ds_name, train=False, transform=trans, path=ds_path)
dl = DataLoader(ds, batch_size=config['batch_size'], shuffle=False, num_workers=config['n_workers'])
acc = torchmetrics.Accuracy().to(device)
acc_top5 = torchmetrics.Accuracy(top_k=5).to(device)
for batch_x, batch_y in tqdm(dl):
batch_x, batch_y = batch_x.to(device), batch_y.to(device)
with torch.no_grad():
logits = classifier(encoder(batch_x))
curr_acc = acc(logits, batch_y)
curr_acc_top5 = acc_top5(logits, batch_y)
print(f'Acc Top 1: {acc.compute()}')
print(f'Acc Top 5: {acc_top5.compute()}')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config',
help='Path to the config',
required=True, type=str)
parser.add_argument('--ckpt',
help='Path to the checkpoint',
required=True, type=str)
args = parser.parse_args()
evaluate(args)