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f1_score.py
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f1_score.py
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from loader import ODIR5K
from torch.utils.data import DataLoader
import numpy as np
import torch
import random
import os
from loguru import logger as printer
from model import get_model
import argparse
class Evaluater:
def __init__(self, params):
self.params = params
self.set_seed(42)
self.device = self.params.get("device")
test_dataset = ODIR5K(self.params.get("img_dir"),
self.params.get("label_dir"),
train_test_size=self.params.get("train_test_size"),
is_train=False, augment=self.params.get("augment"))
self.test_loader = DataLoader(dataset=test_dataset,
batch_size=self.params["batch_size"],
shuffle=self.params["shuffle"],
num_workers=self.params["num_workers"])
def set_seed(self, seed: int = 42) -> None:
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
# When running on the CuDNN backend, two further options must be set
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Set a fixed value for the hash seed
os.environ["PYTHONHASHSEED"] = str(seed)
printer.info(f"Random seed set as {seed}")
def run(self):
model = get_model(self.params.get("model_name"), self.device, {})
model.load_state_dict(torch.load(self.params.get("load_model_path")))
model.eval()
true_class = []
pred_class = []
with torch.no_grad():
for batch_idx, batch in enumerate(self.test_loader):
data, label = batch["data"].to(self.device), batch["label"].to(self.device)
output = model(data)
pred = output.argmax(dim=1, keepdim=True)
true_class += list(label)
pred_class += list(pred)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='F1')
parser.add_argument('--model_name', default="EfficientNetB1Pretrained", help='You can use all models in model.py')
parser.add_argument('--img_dir', default="data/preprocessed_images", help='data_dir')
parser.add_argument('--label_dir', default="data/full_df.csv", help='labels')
parser.add_argument('--batch_size', default=15, help='bs')
parser.add_argument('--shuffle', default=True, help='shuffle images')
parser.add_argument('--train_test_size', default=0.8, help='iter_count')
parser.add_argument('--load_model', default=True, help='device')
parser.add_argument('--load_model_path', default="logs/EDD_Seed_and_LRSchedular_Exp/tb_2023_05_29-12:59:34_AM/models/net_best_epoch_9__iter_133__loss_0.4826__acc_0.8796875000000001.pth", help='warmup')
parser.add_argument('--num_workers', default=4, help='iter_count')
parser.add_argument('--device', default="cuda", help='device')
args = parser.parse_args()
params = vars(args)
print(args)
trainer = Evaluater(params)
trainer.run()