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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
from collections import defaultdict
import json
import logging
from logging.handlers import TimedRotatingFileHandler
import os
import platform
from pathlib import Path
import sys
import shutil
from typing import List
pwd = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(pwd, "../../"))
import pandas as pd
from scipy.io import wavfile
import torch
from tqdm import tqdm
from toolbox.torch.utils.data.vocabulary import Vocabulary
from toolbox.torchaudio.models.cnn_audio_classifier.modeling_cnn_audio_classifier import WaveClassifierPretrainedModel
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", default="dataset.xlsx", type=str)
parser.add_argument("--vocabulary_dir", default="vocabulary", type=str)
parser.add_argument("--model_dir", default="best", type=str)
parser.add_argument("--output_file", default="evaluation.xlsx", type=str)
args = parser.parse_args()
return args
def logging_config():
fmt = "%(asctime)s - %(name)s - %(levelname)s %(filename)s:%(lineno)d > %(message)s"
logging.basicConfig(format=fmt,
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.DEBUG)
stream_handler = logging.StreamHandler()
stream_handler.setLevel(logging.INFO)
stream_handler.setFormatter(logging.Formatter(fmt))
logger = logging.getLogger(__name__)
return logger
def main():
args = get_args()
logger = logging_config()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
logger.info("GPU available count: {}; device: {}".format(n_gpu, device))
logger.info("prepare vocabulary, model")
vocabulary = Vocabulary.from_files(args.vocabulary_dir)
model = WaveClassifierPretrainedModel.from_pretrained(
pretrained_model_name_or_path=args.model_dir,
)
model.to(device)
model.eval()
logger.info("read excel")
df = pd.read_excel(args.dataset)
result = list()
total_correct = 0
total_examples = 0
progress_bar = tqdm(total=len(df), desc="Evaluation")
for i, row in df.iterrows():
filename = row["filename"]
ground_true = row["labels"]
sample_rate, waveform = wavfile.read(filename)
waveform = waveform / (1 << 15)
waveform = torch.tensor(waveform, dtype=torch.float32)
waveform = torch.unsqueeze(waveform, dim=0)
waveform = waveform.to(device)
with torch.no_grad():
logits = model.forward(waveform)
probs = torch.nn.functional.softmax(logits, dim=-1)
label_idx = torch.argmax(probs, dim=-1)
label_idx = label_idx.cpu()
probs = probs.cpu()
label_idx = label_idx.numpy()[0]
label_str = vocabulary.get_token_from_index(label_idx, namespace="labels")
prob = probs[0][label_idx].numpy()
correct = 1 if label_str == ground_true else 0
row_ = dict(row)
row_["predict"] = label_str
row_["prob"] = prob
row_["correct"] = correct
result.append(row_)
total_examples += 1
total_correct += correct
accuracy = total_correct / total_examples
progress_bar.update(1)
progress_bar.set_postfix({
"accuracy": accuracy,
})
result = pd.DataFrame(result)
result.to_excel(
args.output_file,
index=False
)
return
if __name__ == '__main__':
main()
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