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import os
import sys
import time
import torch
import argparse
import torchvision
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from logger_utils import CSVWriter
from model_main import CRNN, STN_CRNN
from utils import ctc_decode, compute_wer_and_cer_for_sample
from dataset import HWRecogIAMDataset, split_dataset, get_dataloader_for_testing
def test(
hw_model,
test_loader,
device,
list_test_files,
which_ctc_decoder="beam_search",
save_prediction_stats=False,
):
"""
---------
Arguments
---------
hw_model : object
handwriting recognition model object
test_loader : object
dataset loader object
device : str
device to be used for running the evaluation
list_test_files : list
list of all the test files
which_ctc_decoder : str
string indicating which ctc decoder to use
save_prediction_stats : bool
whether to save prediction stats
"""
hw_model.eval()
num_test_samples = len(test_loader.dataset)
num_test_batches = len(test_loader)
count = 0
list_test_cers, list_test_wers = [], []
if save_prediction_stats:
csv_writer = CSVWriter(
file_name="pred_stats.csv",
column_names=["file_name", "num_chars", "num_words", "cer", "wer"],
)
with torch.no_grad():
for images, labels, length_labels in test_loader:
count += 1
images = images.to(device, dtype=torch.float)
log_probs = hw_model(images)
pred_labels = ctc_decode(log_probs, which_ctc_decoder=which_ctc_decoder)
labels = labels.cpu().numpy().tolist()
str_label = [HWRecogIAMDataset.LABEL_2_CHAR[i] for i in labels]
str_label = "".join(str_label)
str_pred = [HWRecogIAMDataset.LABEL_2_CHAR[i] for i in pred_labels[0]]
str_pred = "".join(str_pred)
cer_sample, wer_sample = compute_wer_and_cer_for_sample(str_pred, str_label)
list_test_cers.append(cer_sample)
list_test_wers.append(wer_sample)
print(
f"progress: {count}/{num_test_samples}, test file: {list_test_files[count-1]}"
)
print(f"{str_label} - label")
print(f"{str_pred} - prediction")
print(f"cer: {cer_sample:.3f}, wer: {wer_sample:.3f}\n")
if save_prediction_stats:
csv_writer.write_row(
[
list_test_files[count - 1],
len(str_label),
len(str_label.split(" ")),
cer_sample,
wer_sample,
]
)
list_test_cers = np.array(list_test_cers)
list_test_wers = np.array(list_test_wers)
mean_test_cer = np.mean(list_test_cers)
mean_test_wer = np.mean(list_test_wers)
print(f"test set - mean cer: {mean_test_cer:.3f}, mean wer: {mean_test_wer:.3f}\n")
if save_prediction_stats:
csv_writer.close()
return
def test_hw_recognizer(FLAGS):
file_txt_labels = os.path.join(FLAGS.dir_dataset, "iam_lines_gt.txt")
dir_images = os.path.join(FLAGS.dir_dataset, "img")
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
# choose a device for testing
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
# get the internal test set files
test_x, test_y = split_dataset(file_txt_labels, for_train=False)
num_test_samples = len(test_x)
# get the internal test set dataloader
test_loader = get_dataloader_for_testing(
test_x,
test_y,
dir_images=dir_images,
image_height=FLAGS.image_height,
image_width=FLAGS.image_width,
)
num_classes = len(HWRecogIAMDataset.LABEL_2_CHAR) + 1
print(f"task - handwriting recognition")
print(f"model: {FLAGS.which_hw_model}, ctc decoder: {FLAGS.which_ctc_decoder}")
print(f"image height: {FLAGS.image_height}, image width: {FLAGS.image_width}")
print(f"num test samples: {num_test_samples}")
# load the right model
if FLAGS.which_hw_model == "crnn":
hw_model = CRNN(num_classes, FLAGS.image_height)
elif FLAGS.which_hw_model == "stn_crnn":
hw_model = STN_CRNN(num_classes, FLAGS.image_height, FLAGS.image_width)
else:
print(f"unidentified option : {FLAGS.which_hw_model}")
sys.exit(0)
hw_model.to(device)
hw_model.load_state_dict(torch.load(FLAGS.file_model))
# start testing of the model on the internal set
print(f"testing of handwriting recognition model {FLAGS.which_hw_model} started\n")
test(
hw_model,
test_loader,
device,
test_x,
FLAGS.which_ctc_decoder,
bool(FLAGS.save_prediction_stats),
)
print(f"testing handwriting recognition model completed!!!!")
return
def main():
image_height = 32
image_width = 768
which_hw_model = "crnn"
dir_dataset = "/home/abhishek/Desktop/RUG/hw_recognition/IAM-data/"
file_model = "model_crnn/crnn_H_32_W_768_E_177.pth"
which_ctc_decoder = "beam_search"
save_prediction_stats = 0
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--image_height",
default=image_height,
type=int,
help="image height to be used to predict with the model",
)
parser.add_argument(
"--image_width",
default=image_width,
type=int,
help="image width to be used to predict with the model",
)
parser.add_argument(
"--dir_dataset",
default=dir_dataset,
type=str,
help="full directory path to the dataset",
)
parser.add_argument(
"--which_hw_model",
default=which_hw_model,
type=str,
choices=["crnn", "stn_crnn"],
help="which model to be used for prediction",
)
parser.add_argument(
"--which_ctc_decoder",
default=which_ctc_decoder,
type=str,
choices=["beam_search", "greedy"],
help="which ctc decoder to use",
)
parser.add_argument(
"--file_model",
default=file_model,
type=str,
help="full path to trained model file (.pth)",
)
parser.add_argument(
"--save_prediction_stats",
default=save_prediction_stats,
type=int,
choices=[0, 1],
help="save prediction stats (1 - yes, 0 - no)",
)
FLAGS, unparsed = parser.parse_known_args()
test_hw_recognizer(FLAGS)
return
if __name__ == "__main__":
main()
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