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demo.py
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from ast import parse
import torch
from torch.nn import functional as F
import torchvision
import torchaudio
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint
import os
import argparse
import yaml
import pickle
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pyaudio
import wave
import sounddevice as sd
import time
from data.parser import parse_waveforms, parse_waveform
#.datasets import MNISTDataModule, EndToEndDataModule, EndToEndNoTestDataModule, ReasoningDataModule, fetch_perception_data
from training import models, basic_models#BasicLSTM, MNISTModel, Neuroplytorch, ReasoningModel, MNISTWindow
from data import data, datasets
def get_complex_parameters(complex_events_dict) -> tuple:
ce_fsm_list, ce_time_list = [], []
for k in complex_events_dict.keys():
complex_event = complex_events_dict[k]
ce_fsm_list.append(torch.tensor(complex_event['PATTERN']))
max_time = [float('inf') if a=='INF' else a for a in complex_event['MAX_TIME']]
ce_time_list.append(torch.tensor([max_time, complex_event['EVENTS_BETWEEN']]))
return ce_fsm_list, ce_time_list
def moving_average(a, n=3) :
ret = np.cumsum(a, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return ret[n - 1:] / n
if __name__=="__main__":
parser = argparse.ArgumentParser()
# This distinguishes between problems, i.e. the different scenarios, pattern parameters etc.
parser.add_argument('--name', dest='config_name', type=str, default='basic_neuro_experiment')
parser.add_argument('--file', dest='file_loc', type=str, default='datasets/UrbanSound8K/demo_audio_base.wav')
parser.add_argument('--mic', dest='use_mic', type=int, default=0)
args = vars(parser.parse_args())
# TODO: on run, save the config file as hyperparameters for the logger
with open(f'./configs/{args["config_name"]}.yaml') as file:
x = yaml.load(file, Loader=yaml.FullLoader)
training = x['TRAINING']
complex_events = x['COMPLEX EVENTS']
ce_fsm_list, ce_time_list = get_complex_parameters(complex_events)
assert(data.check_complex_parameters(ce_fsm_list, ce_time_list), "Pattern and temporal metadata don't match, check the config file")
MODULE_NAME = args['config_name']
perception_model_args = training['PERCEPTION']['PARAMETERS'].get('MODEL', {})
reasoning_model_args = training['REASONING']['PARAMETERS'].get('MODEL', {})
end_to_end_model_args = training['NEUROPLYTORCH']['PARAMETERS'].get('MODEL', {})
perception_dataset_args = training['PERCEPTION']['PARAMETERS'].get('DATASET', {})
reasoning_dataset_args = training['REASONING']['PARAMETERS'].get('DATASET', {})
end_to_end_dataset_args = training['NEUROPLYTORCH']['PARAMETERS'].get('DATASET', {})
perception_loss_str = training['PERCEPTION'].get('PRETRAIN', {}).get('LOSS_FUNCTION', 'MSELoss')
reasoning_loss_str = training['REASONING'].get('LOSS_FUNCTION', 'MSELoss')
pretrain_perception = training['PERCEPTION'].get('PRETRAIN', {}).get('PRETRAIN_PERCEPTION', False)
pretrain_num_epochs = training['PERCEPTION'].get('PRETRAIN', {}).get('PRETRAIN_EPOCHS', 10)
pretrain_lr = training['PERCEPTION'].get('PRETRAIN', {}).get('LEARNING_RATE', 0.001)
reasoning_lr = training['REASONING'].get('LEARNING_RATE', 0.001)
reasoning_epochs = training['REASONING']['EPOCHS']
reasoning_num_data = training['REASONING']['EPOCHS']
end_to_end_lr = training['NEUROPLYTORCH'].get('LEARNING_RATE', 0.001)
end_to_end_loss_str = training['NEUROPLYTORCH'].get('LOSS_FUNCTION', 'MSELoss')
end_to_end_epochs = training['NEUROPLYTORCH']['EPOCHS']
no_test = end_to_end_dataset_args.get('no_test', True)
window_size = training.get('WINDOW_SIZE', 10)
num_primitive_events = training.get('NUM_PRIMITIVE_EVENTS', 10)
input_size = training['PERCEPTION']['INPUT_SIZE']
new_reasoning = basic_models.BasicLSTM(input_size=num_primitive_events, output_size=len(ce_fsm_list), loss_str=reasoning_loss_str, **reasoning_model_args)
new_perception = basic_models.get_model(training['PERCEPTION']['MODEL'])(input_size=input_size, output_size=num_primitive_events, **perception_model_args)
new_reasoning.load_state_dict(torch.load(f'models/neuroplytorch/{reasoning_loss_str}_{MODULE_NAME}/reasoning_model.pt'))
new_perception.load_state_dict(torch.load(f'models/neuroplytorch/{reasoning_loss_str}_{MODULE_NAME}/perception_model.pt'))
new_perception = models.PerceptionWindow(new_perception, window_size=window_size, num_primitive_events=num_primitive_events)
end_model = models.Neuroplytorch(reasoning_model=new_reasoning, perception_model=new_perception, window_size=window_size, num_primitive_events=num_primitive_events,
loss_str=end_to_end_loss_str, lr=end_to_end_lr)
class_id_to_name = {}
meta = pd.read_csv('datasets/UrbanSound8K/metadata.csv')
for i, v in meta.iterrows():
class_id_to_name[v['classID']] = class_id_to_name.get(v['classID'], v['class'])
if args['use_mic']==1:
#AUDIO INPUT
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
CHUNK = 1024
RECORD_SECONDS = 1
WAVE_OUTPUT_FILENAME = "output.wav"
audio = pyaudio.PyAudio()
vggish_net = torch.hub.load('harritaylor/torchvggish', 'vggish')
# start Recording
stream = audio.open(format=FORMAT, channels=CHANNELS,
rate=RATE, input=True,
frames_per_buffer=CHUNK)
past_xs = []
print("recording")
while(1):
frames = []
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
data = stream.read(CHUNK)
frames.append(data)
waveFile = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
waveFile.setnchannels(CHANNELS)
waveFile.setsampwidth(audio.get_sample_size(FORMAT))
waveFile.setframerate(RATE)
waveFile.writeframes(b''.join(frames))
waveFile.close()
spf = wave.open(WAVE_OUTPUT_FILENAME,'r')
#Extract Raw Audio from Wav File
signal = spf.readframes(-1)
signal = np.fromstring(signal, dtype=np.int16)
copy= signal.copy()
x = parse_waveform('output.wav', vggish_net=vggish_net)
xs = torch.tensor(x)
past_xs.append(xs)
past_xs = past_xs[-10:-1:1] + [past_xs[-1]]
if len(past_xs)==10:
xs = torch.stack(past_xs)
o_i, o_r = end_model(xs[None, :])
for i in range(len(class_id_to_name.keys())):
print(f"{class_id_to_name[i]}:", end='')
for i in range(int(o_r[0][i].item()/0.1)): print("=", end='')
print()
print("\n\n\n\n\n\n\n\n\n\n\n\n\n\n")
elif args['use_mic']==2:
#AUDIO INPUT
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
CHUNK = 1024
RECORD_SECONDS = 1
WAVE_OUTPUT_FILENAME = "output.wav"
INPUT_FILENAME = args['file_loc']
INPUT_WAV, RATE = torchaudio.load(INPUT_FILENAME)
audio = pyaudio.PyAudio()
vggish_net = torch.hub.load('harritaylor/torchvggish', 'vggish')
past_xs = []
print("recording")
time.sleep(10)
for i in range(120):
frames = INPUT_WAV[:,i*RATE:(i+1)*RATE]
torchaudio.save('output.wav', frames, RATE, encoding="PCM_S", bits_per_sample=16)
spf = wave.open(WAVE_OUTPUT_FILENAME,'r')
#Extract Raw Audio from Wav File
signal = spf.readframes(-1)
signal = np.fromstring(signal, dtype=np.int16)
copy= signal.copy()
x = parse_waveform('output.wav', vggish_net=vggish_net)
xs = torch.tensor(x)
past_xs.append(xs)
past_xs = past_xs[-10:-1:1] + [past_xs[-1]]
if len(past_xs)==10:
xs = torch.stack(past_xs)
o_i, o_r = end_model(xs[None, :])
for i in range(len(class_id_to_name.keys())):
print(f"{class_id_to_name[i]}:", end='')
for i in range(int(o_r[0][i].item()/0.1)): print("=", end='')
print()
print("\n\n\n\n\n\n\n\n\n\n\n\n\n\n")
sd.play(torch.mean(frames, axis=0).detach().numpy(), RATE)
time.sleep(0.9)
else:
#x = parse_waveform('datasets/UrbanSound8K/demo_audio_base.wav')
x = parse_waveform(args['file_loc'])
xw, sr = torchaudio.load(args['file_loc'])
xs = torch.tensor(x)
duration = xw.size()[1]/sr
duration_per_clip = duration/xs.size()[0]
confs = []
for i in tqdm(range(0, len(xs)-window_size+1), total=len(xs)-window_size+1):
curr_window = xs[i:i+window_size]
if curr_window.size()[0]<10: break
o_i, o_r = end_model(curr_window[None, :])
confs.append(o_r.cpu().detach().numpy())
confs = np.array(confs)
confs = np.squeeze(confs)
confs = np.swapaxes(confs, 0, 1)
legs = []
ma = 1
times = range((10)+(ma-1), xs.size()[0]+1)
actual_times = [duration_per_clip * i for i in times]
print(actual_times)
print(duration_per_clip)
demo_outs = {}
for i in range(confs.shape[0]):
class_name = class_id_to_name[i]
if class_name not in ['street_music', 'siren', 'children_playing', 'gun_shot']: continue
ma_confs = moving_average(confs[i,:], ma)
plt.plot(times, ma_confs)
legs.append(class_id_to_name[i])
demo_outs[i] = {
'class': class_name,
'times': actual_times,
'confs': ma_confs
}
pickle.dump(demo_outs, open('base.p', 'wb'))
print(class_id_to_name)
plt.legend(legs)
plt.ylabel('Confidence')
plt.xlabel('Time /s')
plt.show()