def eval(logdir):
# Load graph
model = Net1()
# dataflow
df = Net1DataFlow(hp.Test1.data_path, hp.Test1.batch_size)
ckpt = tf.train.latest_checkpoint(logdir)
pred_conf = PredictConfig(model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names())
if ckpt:
pred_conf.session_init = SaverRestore(ckpt)
predictor = OfflinePredictor(pred_conf)
x_mfccs, y_ppgs = next(df().get_data())
y_ppg_1d, pred_ppg_1d, summ_loss, summ_acc = predictor(x_mfccs, y_ppgs)
# plot confusion matrix
_, idx2phn = load_vocab()
y_ppg_1d = [idx2phn[i] for i in y_ppg_1d]
pred_ppg_1d = [idx2phn[i] for i in pred_ppg_1d]
summ_cm = plot_confusion_matrix(y_ppg_1d, pred_ppg_1d, phns)
writer = tf.summary.FileWriter(logdir)
writer.add_summary(summ_loss)
writer.add_summary(summ_acc)
writer.add_summary(summ_cm)
writer.close()
def __init__(self, config):
"""Create a quality that uses `nomagic_net` as a quality function."""
from nomagic_submission import ConvNetModel
from tensorpack import SaverRestore
from tensorpack.predict import OfflinePredictor
from tensorpack.predict.config import PredictConfig
GraspQualityFunction.__init(self)
# Store parameters.
self._model_path = config["gqcnn_model"]
self._batch_size = config["batch_size"]
self._crop_height = config["crop_height"]
self._crop_width = config["crop_width"]
self._im_height = config["im_height"]
self._im_width = config["im_width"]
self._num_channels = config["num_channels"]
self._pose_dim = config["pose_dim"]
self._gripper_mode = config["gripper_mode"]
self._data_mean = config["data_mean"]
self._data_std = config["data_std"]
# Init config.
model = ConvNetModel()
self._config = PredictConfig(model=model,
session_init=SaverRestore(
self._model_path),
output_names=["prob"])
self._predictor = OfflinePredictor(self._config)
def eval(logdir1, logdir2):
# Load graph
model = Net2()
# dataflow
df = Net2DataFlow(hp.test2.data_path, hp.test2.batch_size)
ckpt1 = tf.train.latest_checkpoint(logdir1)
ckpt2 = tf.train.latest_checkpoint(logdir2)
session_inits = []
if ckpt2:
session_inits.append(SaverRestore(ckpt2))
if ckpt1:
session_inits.append(SaverRestore(ckpt1, ignore=['global_step']))
pred_conf = PredictConfig(model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=ChainInit(session_inits))
predictor = OfflinePredictor(pred_conf)
# x_mfccs, y_spec, _ = next(df().get_data())
summ_loss, = predictor(next(df().get_data()))
writer = tf.summary.FileWriter(logdir2)
writer.add_summary(summ_loss)
writer.close()
def set_enviroment(args, logdir1, logdir2):
# Load graph
set_env_s = datetime.datetime.now()
ckpt1 = tf.train.latest_checkpoint(logdir1)
ckpt2 = '{}/{}'.format(logdir2, args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir2)
model = Net2ForConvert()
session_inits = []
if ckpt2:
session_inits.append(SaverRestore(ckpt2))
if ckpt1:
session_inits.append(SaverRestore(ckpt1, ignore=['global_step']))
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
else:
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
print("PredictConfig")
pred_conf = PredictConfig(
model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=ChainInit(session_inits)
)
print("OfflinePredictor")
predictor = OfflinePredictor(pred_conf)
set_env_e = datetime.datetime.now()
set_env_t = set_env_e - set_env_s
print("Setting Environment time:{}s".format(set_env_t.seconds))
return predictor
def __init__(self, config):
"""Create a quality that uses nomagic_net as a quality function. """
from nomagic_submission import ConvNetModel
from tensorpack.predict.config import PredictConfig
from tensorpack import SaverRestore
from tensorpack.predict import OfflinePredictor
# store parameters
self._model_path = config['gqcnn_model']
self._batch_size = config['batch_size']
self._crop_height = config['crop_height']
self._crop_width = config['crop_width']
self._im_height = config['im_height']
self._im_width = config['im_width']
self._num_channels = config['num_channels']
self._pose_dim = config['pose_dim']
self._gripper_mode = config['gripper_mode']
# init config
model = ConvNetModel()
self._config = PredictConfig(model=model,
session_init=SaverRestore(
self._model_path),
output_names=['prob'])
self._predictor = OfflinePredictor(self._config)
def do_convert(args, logdir1, logdir2):
# Load graph
model = Net2()
df = Net2DataFlow(hp.convert.data_path, hp.convert.batch_size)
ckpt1 = tf.train.latest_checkpoint(logdir1)
ckpt2 = '{}/{}'.format(logdir2, args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir2)
session_inits = []
if ckpt2:
session_inits.append(SaverRestore(ckpt2))
if ckpt1:
session_inits.append(SaverRestore(ckpt1, ignore=['global_step']))
pred_conf = PredictConfig(
model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=ChainInit(session_inits))
predictor = OfflinePredictor(pred_conf)
audio, y_audio, ppgs = convert(predictor, df)
# Write the result
tf.summary.audio('A', y_audio, hp.default.sr, max_outputs=hp.convert.batch_size)
tf.summary.audio('B', audio, hp.default.sr, max_outputs=hp.convert.batch_size)
# Visualize PPGs
heatmap = np.expand_dims(ppgs, 3) # channel=1
tf.summary.image('PPG', heatmap, max_outputs=ppgs.shape[0])
writer = tf.summary.FileWriter(logdir2)
with tf.Session() as sess:
summ = sess.run(tf.summary.merge_all())
writer.add_summary(summ)
writer.close()
def do_convert(args, logdir):
# Load graph
model = Net()
df = NetDataFlow(hp.convert.data_path, hp.convert.batch_size)
ckpt = '{}/{}'.format(logdir, args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir)
session_inits = []
if ckpt:
session_inits.append(SaverRestore(ckpt))
pred_conf = PredictConfig(
model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=ChainInit(session_inits))
predictor = OfflinePredictor(pred_conf)
audio, y_audio = convert(predictor, df)
soundfile.write("a.wav", y_audio[0], 16000, format="wav", subtype="PCM_16")
soundfile.write("b.wav", audio[0], 16000, format="wav", subtype="PCM_16")
# Write the result
tf.summary.audio('A', y_audio, hp.default.sr, max_outputs=hp.convert.batch_size)
tf.summary.audio('B', audio, hp.default.sr, max_outputs=hp.convert.batch_size)
writer = tf.summary.FileWriter(logdir)
with tf.Session() as sess:
summ = sess.run(tf.summary.merge_all())
writer.add_summary(summ)
writer.close()
def do_convert(logdir1, logdir2, input_path, output_path):
# Load graph
model = Net2()
model.actual_duration = librosa.core.get_duration(filename=input_path,
sr=hp.default.sr)
# TODO isolate out logdirs, uhh and how to pre-dl from s3?
assert len(input_path) > 0, "must be non-empty input path"
df = Net2DataFlow(data_path=input_path, batch_size=1)
ckpt1 = tf.train.latest_checkpoint(logdir1)
ckpt2 = tf.train.latest_checkpoint(logdir2)
session_inits = []
session_inits.append(SaverRestore(ckpt2))
session_inits.append(SaverRestore(ckpt1, ignore=['global_step']))
pred_conf = PredictConfig(model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=ChainInit(session_inits))
predictor = OfflinePredictor(pred_conf)
audio, y_audio, ppgs = convert(predictor, df)
write_wav(audio[0], hp.default.sr, output_path)
def get_predictor(cls):
''' load trained model'''
with cls.lock:
# check if model is already loaded
if cls.predictor:
return cls.predictor
os.environ['TENSORPACK_FP16'] = 'true'
# create a mask r-cnn model
mask_rcnn_model = ResNetFPNModel(True)
try:
model_dir = os.environ['SM_MODEL_DIR']
except KeyError:
model_dir = '/opt/ml/model'
try:
cls.pretrained_model = os.environ['PRETRAINED_MODEL']
except KeyError:
pass
# file path to previoulsy trained mask r-cnn model
latest_trained_model = ""
model_search_path = os.path.join(model_dir, "model-*.index" )
for model_file in glob.glob(model_search_path):
if model_file > latest_trained_model:
latest_trained_model = model_file
trained_model = latest_trained_model
print(f'Using model: {trained_model}')
# fixed resnet50 backbone weights
cfg.BACKBONE.WEIGHTS = os.path.join(cls.pretrained_model)
cfg.MODE_FPN = True
cfg.MODE_MASK = True
# calling detection dataset gets the number of coco categories
# and saves in the configuration
DetectionDataset()
finalize_configs(is_training=False)
# Create an inference model
# PredictConfig takes a model, input tensors and output tensors
cls.predictor = OfflinePredictor(PredictConfig(
model=mask_rcnn_model,
session_init=get_model_loader(trained_model),
input_names=['images', 'orig_image_dims'],
output_names=[
'generate_{}_proposals_topk_per_image/boxes'.format('fpn' if cfg.MODE_FPN else 'rpn'),
'generate_{}_proposals_topk_per_image/scores'.format('fpn' if cfg.MODE_FPN else 'rpn'),
'fastrcnn_all_scores',
'output/boxes',
'output/scores',
'output/labels',
'output/masks'
]))
return cls.predictor
def compute_accuracy(model, mel_spec, speaker_id, ckpt=None):
pred_conf = PredictConfig(
model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=SaverRestore(ckpt) if ckpt else None)
accuracy_pred = OfflinePredictor(pred_conf)
acc, = accuracy_pred(mel_spec, speaker_id)
return acc
def get_model(game):
""" J.Madge 23.04.2018, 'get_model'.
:param game: Instance of the 'Game' class containing information about
the actions in the game and the location of the associated model.
:return: A model representing the Atari agents pre-trained to play the
specified game.
"""
return OfflinePredictor(
PredictConfig(model=Model(game.actions),
session_init=get_model_loader(game.model),
input_names=['state'],
output_names=['policy']))
def do_convert(args, logdir1, logdir2):
# Load graph
model = Net2()
data = get_mfccs_and_spectrogram(args.file)
ckpt1 = '{}/{}'.format(
logdir1,
args.net1) if args.net1 else tf.train.latest_checkpoint(logdir1)
ckpt2 = '{}/{}'.format(
logdir2,
args.net2) if args.net2 else tf.train.latest_checkpoint(logdir2)
session_inits = []
if ckpt2:
session_inits.append(SaverRestore(ckpt2))
if ckpt1:
session_inits.append(SaverRestore(ckpt1, ignore=['global_step']))
pred_conf = PredictConfig(model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=ChainInit(session_inits))
predictor = OfflinePredictor(pred_conf)
audio, y_audio, ppgs = convert(predictor, data)
target_file = args.file.split('/')[-1]
portion = os.path.splitext(target_file)
# converted_file = target_file.split('.')[0] + '_converted.wav'
converted_file = portion[0] + '.wav'
write_wav(audio[0], hp.Default.sr, args.savepath + converted_file)
# Write the result
tf.summary.audio('A',
y_audio,
hp.Default.sr,
max_outputs=hp.Convert.batch_size)
tf.summary.audio('B',
audio,
hp.Default.sr,
max_outputs=hp.Convert.batch_size)
# Visualize PPGs
heatmap = np.expand_dims(ppgs, 3) # channel=1
tf.summary.image('PPG', heatmap, max_outputs=ppgs.shape[0])
writer = tf.summary.FileWriter(args.savepath)
with tf.Session() as sess:
summ = sess.run(tf.summary.merge_all())
writer.add_summary(summ)
writer.close()
def get_predictor(cls):
''' load trained model'''
with cls.lock:
# check if model is already loaded
if cls.predictor:
return cls.predictor
# create a mask r-cnn model
mask_rcnn_model = ResNetFPNModel()
try:
model_dir = os.environ['SM_MODEL_DIR']
except KeyError:
model_dir = '/opt/ml/model'
try:
cls.pretrained_model = os.environ['PRETRAINED_MODEL']
except KeyError:
pass
# file path to previoulsy trained mask r-cnn model
latest_trained_model = ""
model_search_path = os.path.join(model_dir, "model-*.index")
for model_file in glob.glob(model_search_path):
if model_file > latest_trained_model:
latest_trained_model = model_file
trained_model = latest_trained_model[:-6]
print(f'Using model: {trained_model}')
# fixed resnet50 backbone weights
cfg.BACKBONE.WEIGHTS = os.path.join(cls.pretrained_model)
cfg.MODE_FPN = True
cfg.MODE_MASK = True
cfg.TEST.RESULT_SCORE_THRESH = cfg.TEST.RESULT_SCORE_THRESH_VIS
finalize_configs(is_training=False)
# Create an inference model
# PredictConfig takes a model, input tensors and output tensors
input_tensors = mask_rcnn_model.get_inference_tensor_names()[0]
output_tensors = mask_rcnn_model.get_inference_tensor_names()[1]
cls.predictor = OfflinePredictor(
PredictConfig(model=mask_rcnn_model,
session_init=get_model_loader(trained_model),
input_names=input_tensors,
output_names=output_tensors))
return cls.predictor
def inference(input_data, hps, predictor=None):
# input data is numpy has shape: [M, T, D]
# return softmax with C classes
if predictor is None:
M = ModelDesc(hps)
pred_config = PredictConfig(session_init=SmartInit(hps.checkpoint_path),
model=M,
input_names=['x'],
output_names=['predict/y_pred']
)
predictor = OfflinePredictor(pred_config)
outputs = predictor(input_data)
rs = softmax(outputs[0])
return rs
def init_predictor(ckpt_dir):
""" Initializes an OfflinePredictor for the 'Net1' Phoneme classifier, given a directory of tf-checkpoints.
:param ckpt_dir: Checkpoint directory.
:return: OfflinePredictor
"""
ckpt1 = tf.train.latest_checkpoint(ckpt_dir)
assert ckpt1 is not None, "Failed to load checkpoint in '{}'".format(
ckpt_dir)
net1 = Net1()
pred_conf = PredictConfig(
model=net1,
input_names=['x_mfccs'],
output_names=['net1/ppgs'],
session_init=ChainInit([SaverRestore(ckpt1, ignore=['global_step'])]))
predictor = OfflinePredictor(pred_conf)
return predictor
def do_convert(args, logdir1, logdir2, input_dir):
# Load graph
model = Net2()
# input_dir = hp.convert.data_base_dir_original + hp.convert.data_path
df = Net2DataFlow(input_dir, hp.convert.batch_size)
ckpt1 = tf.train.latest_checkpoint(logdir1)
ckpt2 = '{}/{}'.format(
logdir2,
args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir2)
session_inits = []
if ckpt2:
session_inits.append(SaverRestore(ckpt2))
if ckpt1:
session_inits.append(SaverRestore(ckpt1, ignore=['global_step']))
pred_conf = PredictConfig(model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=ChainInit(session_inits))
predictor = OfflinePredictor(pred_conf)
# loop over all the audio files
for wav_file in df.wav_files:
# check if file is present audio
out_path = wav_file.replace(hp.convert.data_base_dir_original,
hp.convert.data_base_dir_convert)
# change file extension from wv1/wv2 to wav
out_path = out_path[:-2] + 'av'
if os.path.isfile(out_path):
# file is already present, move on to the next one.
print("skipping " + wav_file)
continue
print("converting " + wav_file)
# convert audio
audio_len, feats = df.get_features(wav_file)
audio_full = []
for feat in feats:
input_arr = ([feat[0]], [feat[1]], [feat[2]])
audio, ppgs = convert(predictor, input_arr)
audio_full.append(
(audio[0] * hp.convert.amplitude_multiplier).astype(np.int16))
scipy.io.wavfile.write(out_path, hp.default.sr,
np.concatenate(audio_full)[:audio_len])
def eval(logdir):
# Load graph
model = Net()
# dataflow
df = NetDataFlow(hp.test.data_path, hp.test.batch_size)
ckpt = tf.train.latest_checkpoint(logdir)
session_inits = []
if ckpt:
session_inits.append(SaverRestore(ckpt))
pred_conf = PredictConfig(
model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=ChainInit(session_inits))
predictor = OfflinePredictor(pred_conf)
r_mel, t_spec, _ = next(df().get_data())
summ_loss, = predictor(r_mel, t_spec)
writer = tf.summary.FileWriter(logdir)
writer.add_summary(summ_loss)
writer.close()
def get_predictor(cls):
''' load trained model'''
with cls.lock:
# check if model is already loaded
if cls.predictor:
return cls.predictor
os.environ['TENSORPACK_FP16'] = 'true'
# create a mask r-cnn model
mask_rcnn_model = ResNetFPNModel(True)
try:
model_dir = os.environ['SM_MODEL_DIR']
except KeyError:
model_dir = '/opt/ml/model'
try:
cls.pretrained_model = os.environ['PRETRAINED_MODEL']
except KeyError:
pass
try:
div = int(eval(os.environ['divisor']))
except KeyError:
div = 1
pass
rpn_anchor_stride = int(16 / div)
rpn_anchor_sizes = (int(32 / div), int(64 / div), int(128 / div),
int(256 / div), int(512 / div))
try:
rpn_anchor_stride = int(eval(os.environ['rpnanchor_stride']))
except KeyError:
pass
try:
nms_topk = int(eval(os.environ['NMS_TOPK']))
except KeyError:
nms_topk = 2
pass
try:
nms_thresh = eval(os.environ['NMS_THRESH'])
except KeyError:
nms_thresh = 0.7
pass
try:
results_per_img = eval(os.environ['res_perimg'])
except KeyError:
results_per_img = 400
pass
# file path to previoulsy trained mask r-cnn model
latest_trained_model = ""
model_search_path = os.path.join(model_dir, "model-*.index")
for model_file in glob.glob(model_search_path):
if model_file > latest_trained_model:
latest_trained_model = model_file
trained_model = latest_trained_model
print(f'Using model: {trained_model}')
# fixed resnet50 backbone weights
cfg.BACKBONE.WEIGHTS = os.path.join(cls.pretrained_model)
cfg.MODE_FPN = True
cfg.MODE_MASK = True
cfg.RPN.ANCHOR_STRIDE = rpn_anchor_stride
cfg.RPN.ANCHOR_SIZES = rpn_anchor_sizes
cfg.RPN.TEST_PRE_NMS_TOPK = int(6000 * nms_topk)
cfg.RPN.TEST_POST_NMS_TOPK = int(1000 * nms_topk)
cfg.RPN.TEST_PER_LEVEL_NMS_TOPK = int(1000 * nms_topk)
# testing -----------------------
cfg.TEST.FRCNN_NMS_THRESH = nms_thresh
cfg.TEST.RESULT_SCORE_THRESH = 0.05
cfg.TEST.RESULT_SCORE_THRESH_VIS = 0.2 # only visualize confident results
cfg.TEST.RESULTS_PER_IM = results_per_img
# calling detection dataset gets the number of coco categories
# and saves in the configuration
DetectionDataset()
finalize_configs(is_training=False)
# Create an inference model
# PredictConfig takes a model, input tensors and output tensors
cls.predictor = OfflinePredictor(
PredictConfig(
model=mask_rcnn_model,
session_init=get_model_loader(trained_model),
input_names=['images', 'orig_image_dims'],
output_names=[
'generate_{}_proposals_topk_per_image/boxes'.format(
'fpn' if cfg.MODE_FPN else 'rpn'),
'generate_{}_proposals_topk_per_image/scores'.format(
'fpn' if cfg.MODE_FPN else 'rpn'),
'fastrcnn_all_scores', 'output/boxes', 'output/scores',
'output/labels', 'output/masks'
]))
return cls.predictor
def get_predictor(cls):
"""load trained model"""
with cls.lock:
# check if model is already loaded
if cls.predictor:
return cls.predictor
os.environ["TENSORPACK_FP16"] = "true"
# create a mask r-cnn model
mask_rcnn_model = ResNetFPNModel(True)
try:
model_dir = os.environ["SM_MODEL_DIR"]
except KeyError:
model_dir = "/opt/ml/model"
try:
resnet_arch = os.environ["RESNET_ARCH"]
except KeyError:
resnet_arch = "resnet50"
# file path to previoulsy trained mask r-cnn model
latest_trained_model = ""
model_search_path = os.path.join(model_dir, "model-*.index")
for model_file in glob.glob(model_search_path):
if model_file > latest_trained_model:
latest_trained_model = model_file
trained_model = latest_trained_model
print(f"Using model: {trained_model}")
# fixed resnet50 backbone weights
cfg.MODE_FPN = True
cfg.MODE_MASK = True
if resnet_arch == "resnet101":
cfg.BACKBONE.RESNET_NUM_BLOCKS = [3, 4, 23, 3]
else:
cfg.BACKBONE.RESNET_NUM_BLOCKS = [3, 4, 6, 3]
cfg_prefix = "CONFIG__"
for key, value in dict(os.environ).items():
if key.startswith(cfg_prefix):
attr_name = key[len(cfg_prefix):]
attr_name = attr_name.replace("__", ".")
value = eval(value)
print(f"update config: {attr_name}={value}")
nested_var = cfg
attr_list = attr_name.split(".")
for attr in attr_list[0:-1]:
nested_var = getattr(nested_var, attr)
setattr(nested_var, attr_list[-1], value)
# calling detection dataset gets the number of coco categories
# and saves in the configuration
DetectionDataset()
finalize_configs(is_training=False)
# Create an inference model
# PredictConfig takes a model, input tensors and output tensors
cls.predictor = OfflinePredictor(
PredictConfig(
model=mask_rcnn_model,
session_init=get_model_loader(trained_model),
input_names=["images", "orig_image_dims"],
output_names=[
"generate_{}_proposals_topk_per_image/boxes".format(
"fpn" if cfg.MODE_FPN else "rpn"),
"generate_{}_proposals_topk_per_image/scores".format(
"fpn" if cfg.MODE_FPN else "rpn"),
"fastrcnn_all_scores",
"output/boxes",
"output/scores",
"output/labels",
"output/masks",
],
))
return cls.predictor
return rs
if __name__ == "__main__":
checkpoint_path = './train_log/train/checkpoint'
hps = get_default_hparams()
mongo_client = MongoClient('localhost', 27017)
db = mongo_client.crypto_currency
collection = db['ohlcv']
market = 'binance'
symbol = 'BNB/BTC'
timewindow = '1h'
query = {'market': market, 'symbol': symbol, 'timewindow': timewindow}
df_data = pd.DataFrame(list(collection.find(query)))[-hps.M - 100:]
df_data = df_data[hps.attributes_normalize_mean]
model = ModelDesc(hps)
pred_config = PredictConfig(session_init=SmartInit(hps.checkpoint_path),
model=model,
input_names=['x'],
output_names=['predict/y_pred']
)
predictor = OfflinePredictor(pred_config)
# input_data = np.random.rand(hps.M, hps.T, hps.D)
input_data = test_segment(df_data, hps)
rs = inference(input_data[-hps.M:], hps=hps, predictor=predictor)
# data loader
audio_meta_class = globals()[hp.embed.audio_meta_class]
params = {'data_path': hp.embed.data_path}
if hp.embed.meta_path:
params['meta_path'] = hp.embed.meta_path
audio_meta = audio_meta_class(**params)
data_loader = DataLoader(audio_meta, hp.embed.batch_size)
# samples
wav, mel_spec, speaker_id = data_loader.dataflow().get_data().next()
ckpt = args.ckpt if args.ckpt else tf.train.latest_checkpoint(hp.logdir)
pred_conf = PredictConfig(
model=model,
input_names=['x'],
output_names=['embedding/embedding', 'prediction'],
session_init=SaverRestore(ckpt) if ckpt else None)
embedding_pred = OfflinePredictor(pred_conf)
embedding, pred_speaker_id = embedding_pred(mel_spec)
# get a random audio of the predicted speaker.
wavfile_pred_speaker = np.array(map(lambda s: audio_meta_train.get_random_audio(s), pred_speaker_id))
length = int(hp.signal.duration * hp.signal.sr)
wav_pred_speaker = np.array(
map(lambda w: fix_length(read_wav(w, hp.signal.sr, duration=hp.signal.duration), length),
wavfile_pred_speaker))
# write audio
tf.summary.audio('wav', wav, hp.signal.sr, max_outputs=10)
game_to_load = "./train_atari.py --task play --env Breakout-v0 --load Breakout-v0.npz"
model = play_one_episode
environment = "//Users/osama/Documents/University/Masters/CE888_Assignment/Assignment_2/Initial_Code/Breakout-v0.npz"
def get_pd(s, t):
return model(s, t)
# IF NUM_ACTIONS Error: Set NUM_ACTIONS to number of actions available in game
# eg num_actions = 4, for breakout
predictor = OfflinePredictor(
PredictConfig(model=model,
session_init=environment,
input_names=['state'],
output_names=['policy']))
print "============= MODEL LODADED ============= \n"
nontrans_imgset = []
imgset = []
actset = []
def image_transform():
for i in range(15):
img = cv2.imread(data_folder + game_name + str(i) + '.png')
nontrans_imgset.append(img)
def get_predictor(cls):
"""load trained model"""
with cls.lock:
# check if model is already loaded
if cls.predictor:
return cls.predictor
# create a mask r-cnn model
mask_rcnn_model = ResNetFPNModel()
try:
model_dir = os.environ["SM_MODEL_DIR"]
except KeyError:
model_dir = "/opt/ml/model"
try:
resnet_arch = os.environ["RESNET_ARCH"]
except KeyError:
resnet_arch = "resnet50"
# file path to previoulsy trained mask r-cnn model
latest_trained_model = ""
model_search_path = os.path.join(model_dir, "model-*.index")
for model_file in glob.glob(model_search_path):
if model_file > latest_trained_model:
latest_trained_model = model_file
trained_model = latest_trained_model[:-6]
print(f"Using model: {trained_model}")
cfg.MODE_FPN = True
cfg.MODE_MASK = True
if resnet_arch == "resnet101":
cfg.BACKBONE.RESNET_NUM_BLOCKS = [3, 4, 23, 3]
else:
cfg.BACKBONE.RESNET_NUM_BLOCKS = [3, 4, 6, 3]
cfg_prefix = "CONFIG__"
for key, value in dict(os.environ).items():
if key.startswith(cfg_prefix):
attr_name = key[len(cfg_prefix) :]
attr_name = attr_name.replace("__", ".")
value = eval(value)
print(f"update config: {attr_name}={value}")
nested_var = cfg
attr_list = attr_name.split(".")
for attr in attr_list[0:-1]:
nested_var = getattr(nested_var, attr)
setattr(nested_var, attr_list[-1], value)
cfg.TEST.RESULT_SCORE_THRESH = cfg.TEST.RESULT_SCORE_THRESH_VIS
cfg.DATA.BASEDIR = "/data"
cfg.DATA.TRAIN = "coco_train2017"
cfg.DATA.VAL = "coco_val2017"
register_coco(cfg.DATA.BASEDIR)
finalize_configs(is_training=False)
# Create an inference model
# PredictConfig takes a model, input tensors and output tensors
input_tensors = mask_rcnn_model.get_inference_tensor_names()[0]
output_tensors = mask_rcnn_model.get_inference_tensor_names()[1]
cls.predictor = OfflinePredictor(
PredictConfig(
model=mask_rcnn_model,
session_init=get_model_loader(trained_model),
input_names=input_tensors,
output_names=output_tensors,
)
)
return cls.predictor
def do_convert(args, logdir1, logdir2):
print("do_convert")
# Load graph
ckpt1 = tf.train.latest_checkpoint(logdir1)
ckpt2 = '{}/{}'.format(
logdir2,
args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir2)
model = Net2ForConvert()
session_inits = []
if ckpt2:
session_inits.append(SaverRestore(ckpt2))
if ckpt1:
session_inits.append(SaverRestore(ckpt1, ignore=['global_step']))
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
else:
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
print("PredictConfig")
pred_conf = PredictConfig(model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=ChainInit(session_inits))
print("OfflinePredictor")
set_env_s = datetime.datetime.now()
predictor = OfflinePredictor(pred_conf)
set_env_e = datetime.datetime.now()
set_env_t = set_env_e - set_env_s
print("Setting Environment time:{}s".format(set_env_t.seconds))
input_name = ''
while True:
input_name = input("Write your audio file\'s path for converting : ")
if input_name == 'quit':
break
elif len(glob.glob(input_name)) == 0:
print("That audio file doesn't exist! Try something else.")
continue
convert_s = datetime.datetime.now()
mfcc, spec, mel_spec = get_mfccs_and_spectrogram(input_name,
trim=False,
isConverting=True)
mfcc = np.expand_dims(mfcc, axis=0)
spec = np.expand_dims(spec, axis=0)
mel_spec = np.expand_dims(mel_spec, axis=0)
output_audio, ppgs = convert(predictor, mfcc, spec, mel_spec)
input_audio, samplerate = load(input_name,
sr=hp.default.sr,
dtype=np.float64)
"""
# F0 adaptation with WORLD Vocoder
f0_conv_s = datetime.datetime.now()
output_audio = f0_adapt(input_audio, output_audio, logdir2, samplerate)
f0_conv_e = datetime.datetime.now()
f0_conv_time = f0_conv_e - f0_conv_s
print("F0 Adapting Time:{}s".format(f0_conv_time.seconds))
"""
# Saving voice-converted audio to 32-bit float wav file
# print(audio.dtype)
output_audio = output_audio.astype(np.float32)
write_wav(path="./converted/" + input_name,
y=output_audio,
sr=hp.default.sr)
# Saving PPGS data to Grayscale Image and raw binary file
ppgs = np.squeeze(ppgs, axis=0)
plt.imsave('./converted/debug/' + input_name + '.png',
ppgs,
cmap='binary')
np.save('./converted/debug/' + input_name + '.npy', ppgs)
convert_e = datetime.datetime.now()
convert_time = convert_e - convert_s
print("Total Converting Time:{}s".format(convert_time.seconds))
ds = DataFlowServer(
AgentPoolFake if args['--fake_agent'] else AgentPool,
local_t_max=LOCAL_TIME_MAX,
gamma=GAMMA)
ds.run()
except KeyboardInterrupt:
pass
import sys
sys.exit(0)
elif args['infer']:
assert args['--load'] is not None
from tensorpack.predict.config import PredictConfig
from tensorpack.tfutils.sessinit import get_model_loader
from tensorpack.predict.base import OfflinePredictor
cfg = PredictConfig(model=Model(),
session_init=get_model_loader(args['--load']),
input_names=['state'],
output_names=['policy', 'value'])
if args['--target'] == 'play':
def play_one_episode(player, func, verbose=False):
player.restart_episode()
def f(s):
# spc = player.get_action_space()
act = func([[s]])
print('act = {}'.format(act))
# if random.random() < 0.001:
# act = spc.sample()
if verbose:
print(act)
return act[0][0], act[1][0]
def ckpt2mel(predictor, ppgs_dir, mel_dir, save_dir):
print("get into ckpt")
for fi in os.listdir(ppgs_dir):
print("fi",fi)
#ppgs_name = os.path.join(ppgs_dir, fi)
mel, ppgs = queue_input(fi, ppgs_dir, mel_dir)
pred_mel = predictor(mel, ppgs)
#print("pred_mel",pred_mel.size())
pred_mel = np.array(pred_mel)
print("pred_mel",pred_mel.shape)
length = pred_mel.shape[2]
width = pred_mel.shape[3]
pred_mel = pred_mel.reshape((length, width))
save_name = fi.split('.npy')[0]
if hp.default.n_mels == 20:
npy_dir = os.path.join(save_dir,'lpc20')
if not os.path.exists(npy_dir):
os.makedirs(npy_dir)
npy_path = os.path.join(npy_dir, '%s_20.npy' %save_name)
np.save(npy_path, pred_mel)
print('saved',npy_dir)if hp.default.n_mels == 32:
npy_dir = os.path.join(save_dir,'lpc32')
if not os.path.exists(npy_dir):
os.makedirs(npy_dir)
npy_path = os.path.join(npy_dir, '%s_32.npy' %save_name)
np.save(npy_path, pred_mel)
print('saved',npy_dir)def do_convert(args, logdir2):
# Load graph
model = Net2()
index = 0
ppgs_dir = hp.convert.ppgs_path
mel_dir = hp.convert.mel_path
#for fi in os.listdir(ppgs_dir):
#print("fi",fi)
#ppgs_path = os.path.join(ppgs_dir, fi)
#df = Net2DataFlow(hp.convert.mel_path, ppgs_path, hp.convert.batch_size)
#print("finish df")
ckpt2 = '{}/{}'.format(logdir2, args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir2)
print("ckpt2",ckpt2)
session_inits = []
if ckpt2:
session_inits.append(SaverRestore(ckpt2))
pred_conf = PredictConfig( model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=ChainInit(session_inits))
predictor = OfflinePredictor(pred_conf)
print("after predictor")
#import pdb;pdb.set_trace()
ckpt2mel(predictor, ppgs_dir, mel_dir, hp.convert.save_path)
print("success")
def get_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('case2', type=str, help='experiment case name of train2')
parser.add_argument('-ckpt', help='checkpoint to load model.')
arguments = parser.parse_args()
return arguments
if __name__ == '__main__':
args = get_arguments()
hp.set_hparam_yaml(args.case2)
logdir_train2 = '{}/{}/train2'.format(hp.logdir_path, args.case2)
print('case2: {},logdir2: {}'.format(args.case2, logdir_train2))
s = datetime.datetime.now()
do_convert(args, logdir2=logdir_train2)
e = datetime.datetime.now()
diff = e - s
print("Done. elapsed time:{}s".format(diff.seconds))
