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import os
import torch
import numpy as np
import pandas as pd
import redis
from omegaconf import OmegaConf
import time
from tqdm import tqdm
import editdistance
import argparse
from rnn_model import GRUDecoder
from evaluate_model_helpers import *
# argument parser for command line arguments
parser = argparse.ArgumentParser(description='Evaluate a pretrained RNN model on the copy task dataset.')
parser.add_argument('--model_path', type=str, default='../data/t15_pretrained_rnn_baseline',
help='Path to the pretrained model directory (relative to the current working directory).')
parser.add_argument('--data_dir', type=str, default='../data/hdf5_data_final',
help='Path to the dataset directory (relative to the current working directory).')
parser.add_argument('--eval_type', type=str, default='test', choices=['val', 'test'],
help='Evaluation type: "val" for validation set, "test" for test set. '
'If "test", ground truth is not available.')
parser.add_argument('--csv_path', type=str, default='../data/t15_copyTaskData_description.csv',
help='Path to the CSV file with metadata about the dataset (relative to the current working directory).')
parser.add_argument('--gpu_number', type=int, default=-1,
help='GPU number to use for RNN model inference. Set to -1 to use CPU.')
args = parser.parse_args()
# paths to model and data directories
# Note: these paths are relative to the current working directory
model_path = args.model_path
data_dir = args.data_dir
# define evaluation type
eval_type = args.eval_type # can be 'val' or 'test'. if 'test', ground truth is not available
# load csv file
b2txt_csv_df = pd.read_csv(args.csv_path)
# load model args
model_args = OmegaConf.load(os.path.join(model_path, 'checkpoint/args.yaml'))
# set up gpu device
gpu_number = args.gpu_number
if torch.cuda.is_available() and gpu_number >= 0:
if gpu_number >= torch.cuda.device_count():
raise ValueError(f'GPU number {gpu_number} is out of range. Available GPUs: {torch.cuda.device_count()}')
device = f'cuda:{gpu_number}'
device = torch.device(device)
print(f'Using {device} for model inference.')
else:
if gpu_number >= 0:
print(f'GPU number {gpu_number} requested but not available.')
print('Using CPU for model inference.')
device = torch.device('cpu')
# define model
model = GRUDecoder(
neural_dim = model_args['model']['n_input_features'],
n_units = model_args['model']['n_units'],
n_days = len(model_args['dataset']['sessions']),
n_classes = model_args['dataset']['n_classes'],
rnn_dropout = model_args['model']['rnn_dropout'],
input_dropout = model_args['model']['input_network']['input_layer_dropout'],
n_layers = model_args['model']['n_layers'],
patch_size = model_args['model']['patch_size'],
patch_stride = model_args['model']['patch_stride'],
)
# load model weights
checkpoint = torch.load(
os.path.join(model_path, 'checkpoint/best_checkpoint'),
map_location=device,
weights_only=False,
)
# rename keys to not start with "module." (happens if model was saved with DataParallel)
for key in list(checkpoint['model_state_dict'].keys()):
checkpoint['model_state_dict'][key.replace("module.", "")] = checkpoint['model_state_dict'].pop(key)
checkpoint['model_state_dict'][key.replace("_orig_mod.", "")] = checkpoint['model_state_dict'].pop(key)
model.load_state_dict(checkpoint['model_state_dict'])
# add model to device
model.to(device)
# set model to eval mode
model.eval()
# load data for each session
test_data = {}
total_test_trials = 0
for session in model_args['dataset']['sessions']:
files = [f for f in os.listdir(os.path.join(data_dir, session)) if f.endswith('.hdf5')]
if f'data_{eval_type}.hdf5' in files:
eval_file = os.path.join(data_dir, session, f'data_{eval_type}.hdf5')
data = load_h5py_file(eval_file, b2txt_csv_df)
test_data[session] = data
total_test_trials += len(test_data[session]["neural_features"])
print(f'Loaded {len(test_data[session]["neural_features"])} {eval_type} trials for session {session}.')
print(f'Total number of {eval_type} trials: {total_test_trials}')
print()
# put neural data through the pretrained model to get phoneme predictions (logits)
with tqdm(total=total_test_trials, desc='Predicting phoneme sequences', unit='trial') as pbar:
for session, data in test_data.items():
data['logits'] = []
data['pred_seq'] = []
input_layer = model_args['dataset']['sessions'].index(session)
for trial in range(len(data['neural_features'])):
# get neural input for the trial
neural_input = data['neural_features'][trial]
# add batch dimension
neural_input = np.expand_dims(neural_input, axis=0)
# convert to torch tensor
neural_input = torch.tensor(neural_input, device=device, dtype=torch.bfloat16)
# run decoding step
logits = runSingleDecodingStep(neural_input, input_layer, model, model_args, device)
data['logits'].append(logits)
pbar.update(1)
pbar.close()
# convert logits to phoneme sequences and print them out
for session, data in test_data.items():
data['pred_seq'] = []
for trial in range(len(data['logits'])):
logits = data['logits'][trial][0]
pred_seq = np.argmax(logits, axis=-1)
# remove blanks (0)
pred_seq = [int(p) for p in pred_seq if p != 0]
# remove consecutive duplicates
pred_seq = [pred_seq[i] for i in range(len(pred_seq)) if i == 0 or pred_seq[i] != pred_seq[i-1]]
# convert to phonemes
pred_seq = [LOGIT_TO_PHONEME[p] for p in pred_seq]
# add to data
data['pred_seq'].append(pred_seq)
# print out the predicted sequences
block_num = data['block_num'][trial]
trial_num = data['trial_num'][trial]
print(f'Session: {session}, Block: {block_num}, Trial: {trial_num}')
if eval_type == 'val':
sentence_label = data['sentence_label'][trial]
true_seq = data['seq_class_ids'][trial][0:data['seq_len'][trial]]
true_seq = [LOGIT_TO_PHONEME[p] for p in true_seq]
print(f'Sentence label: {sentence_label}')
print(f'True sequence: {" ".join(true_seq)}')
print(f'Predicted Sequence: {" ".join(pred_seq)}')
print()
# language model inference via redis
# make sure that the standalone language model is running on the localhost redis ip
# see README.md for instructions on how to run the language model
def connect_to_redis_with_retry(host, port, password, db=0, max_retries=10, retry_delay=3):
"""Connect to Redis with retry logic"""
for attempt in range(max_retries):
try:
print(f"Attempting to connect to Redis at {host}:{port} (attempt {attempt + 1}/{max_retries})...")
r = redis.Redis(host=host, port=port, db=db, password=password)
r.ping() # Test the connection
print(f"Successfully connected to Redis at {host}:{port}")
return r
except redis.exceptions.ConnectionError as e:
print(f"Redis connection failed (attempt {attempt + 1}/{max_retries}): {e}")
if attempt < max_retries - 1:
print(f"Retrying in {retry_delay} seconds...")
time.sleep(retry_delay)
else:
print("Max retries reached. Could not connect to Redis.")
raise e
except Exception as e:
print(f"Unexpected error connecting to Redis: {e}")
if attempt < max_retries - 1:
print(f"Retrying in {retry_delay} seconds...")
time.sleep(retry_delay)
else:
raise e
r = connect_to_redis_with_retry('hs.zchens.cn', 6379, 'admin01')
r.flushall() # clear all streams in redis
# define redis streams for the remote language model
remote_lm_input_stream = 'remote_lm_input'
remote_lm_output_partial_stream = 'remote_lm_output_partial'
remote_lm_output_final_stream = 'remote_lm_output_final'
# set timestamps for last entries seen in the redis streams
remote_lm_output_partial_lastEntrySeen = get_current_redis_time_ms(r)
remote_lm_output_final_lastEntrySeen = get_current_redis_time_ms(r)
remote_lm_done_resetting_lastEntrySeen = get_current_redis_time_ms(r)
remote_lm_done_finalizing_lastEntrySeen = get_current_redis_time_ms(r)
remote_lm_done_updating_lastEntrySeen = get_current_redis_time_ms(r)
lm_results = {
'session': [],
'block': [],
'trial': [],
'true_sentence': [],
'pred_sentence': [],
}
# loop through all trials and put logits into the remote language model to get text predictions
# note: this takes ~15-20 minutes to run on the entire test split with the 5-gram LM + OPT rescoring (RTX 4090)
with tqdm(total=total_test_trials, desc='Running remote language model', unit='trial') as pbar:
for session in test_data.keys():
for trial in range(len(test_data[session]['logits'])):
# get trial logits and rearrange them for the LM
logits = rearrange_speech_logits_pt(test_data[session]['logits'][trial])[0]
# reset language model
remote_lm_done_resetting_lastEntrySeen = reset_remote_language_model(r, remote_lm_done_resetting_lastEntrySeen)
'''
# update language model parameters
remote_lm_done_updating_lastEntrySeen = update_remote_lm_params(
r,
remote_lm_done_updating_lastEntrySeen,
acoustic_scale=0.35,
blank_penalty=90.0,
alpha=0.55,
)
'''
# put logits into LM
remote_lm_output_partial_lastEntrySeen, decoded = send_logits_to_remote_lm(
r,
remote_lm_input_stream,
remote_lm_output_partial_stream,
remote_lm_output_partial_lastEntrySeen,
logits,
)
# finalize remote LM
remote_lm_output_final_lastEntrySeen, lm_out = finalize_remote_lm(
r,
remote_lm_output_final_stream,
remote_lm_output_final_lastEntrySeen,
)
# get the best candidate sentence
best_candidate_sentence = lm_out['candidate_sentences'][0]
# store results
lm_results['session'].append(session)
lm_results['block'].append(test_data[session]['block_num'][trial])
lm_results['trial'].append(test_data[session]['trial_num'][trial])
if eval_type == 'val':
lm_results['true_sentence'].append(test_data[session]['sentence_label'][trial])
else:
lm_results['true_sentence'].append(None)
lm_results['pred_sentence'].append(best_candidate_sentence)
# update progress bar
pbar.update(1)
pbar.close()
# if using the validation set, lets calculate the aggregate word error rate (WER)
if eval_type == 'val':
total_true_length = 0
total_edit_distance = 0
lm_results['edit_distance'] = []
lm_results['num_words'] = []
for i in range(len(lm_results['pred_sentence'])):
true_sentence = remove_punctuation(lm_results['true_sentence'][i]).strip()
pred_sentence = remove_punctuation(lm_results['pred_sentence'][i]).strip()
ed = editdistance.eval(true_sentence.split(), pred_sentence.split())
total_true_length += len(true_sentence.split())
total_edit_distance += ed
lm_results['edit_distance'].append(ed)
lm_results['num_words'].append(len(true_sentence.split()))
print(f'{lm_results["session"][i]} - Block {lm_results["block"][i]}, Trial {lm_results["trial"][i]}')
print(f'True sentence: {true_sentence}')
print(f'Predicted sentence: {pred_sentence}')
print(f'WER: {ed} / {100 * len(true_sentence.split())} = {ed / len(true_sentence.split()):.2f}%')
print()
print(f'Total true sentence length: {total_true_length}')
print(f'Total edit distance: {total_edit_distance}')
print(f'Aggregate Word Error Rate (WER): {100 * total_edit_distance / total_true_length:.2f}%')
# write predicted sentences to a csv file. put a timestamp in the filename (YYYYMMDD_HHMMSS)
output_file = os.path.join(model_path, f'baseline_rnn_{eval_type}_predicted_sentences_{time.strftime("%Y%m%d_%H%M%S")}.csv')
ids = [i for i in range(len(lm_results['pred_sentence']))]
df_out = pd.DataFrame({'id': ids, 'text': lm_results['pred_sentence']})
df_out.to_csv(output_file, index=False)