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import argparse
import math
import os
import random
import warnings
import time
from keras import backend, Input
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, TensorBoard, Callback
from keras.layers import Masking, LSTM, Bidirectional, Dropout, TimeDistributed, Dense, GRU, SimpleRNN, regularizers
from keras.models import Model
from keras.optimizers import RMSprop
from keras.regularizers import l1_l2, l1, l2
import numpy as np
import tensorflow as tf
from dl.SegmentGenotypingClassesFunctions import ModelEvaluator, get_properties_json, BatchNumpyFileSequence
class MetricLogger(Callback):
def __init__(self, val_data_path, log_path, max_base_count):
super().__init__()
self.model_evaluator = ModelEvaluator(val_data_path, log_path, write_header=True, log_epochs=True,
max_base_count=max_base_count)
def on_epoch_end(self, epoch, logs=None):
self.model_evaluator.eval_model(self.model, epoch)
def on_train_end(self, logs=None):
self.model_evaluator.close_log()
def create_model(num_layers, max_base_count, max_feature_count, max_label_count, use_bidirectional, lstm_units,
implementation, regularizer, learning_rate, layer_type, no_dropout):
model_input = Input(shape=(max_base_count, max_feature_count), name="model_input")
model_masking = Masking(mask_value=0., input_shape=(max_base_count, max_feature_count))(model_input)
if layer_type == "LSTM":
lstm_fn = LSTM
elif layer_type == "GRU":
lstm_fn = GRU
elif layer_type == "RNN":
lstm_fn = SimpleRNN
elif layer_type == "SRU":
raise Exception("SRU not added yet")
else:
raise Exception("Layer type not valid")
first_lstm_layer_fn = lstm_fn(units=lstm_units, unroll=True, implementation=implementation,
activity_regularizer=regularizer, return_sequences=True,
input_shape=(max_base_count, max_feature_count))
first_lstm_layer = (Bidirectional(first_lstm_layer_fn, merge_mode="concat")(model_masking)
if use_bidirectional
else first_lstm_layer_fn(model_masking))
prev_lstm_layer = first_lstm_layer
for _ in range(num_layers):
hidden_lstm_layer_fn = lstm_fn(units=lstm_units, unroll=True, implementation=implementation,
activity_regularizer=regularizer, return_sequences=True,
input_shape=(max_base_count, lstm_units))
hidden_lstm_layer = (Bidirectional(hidden_lstm_layer_fn, merge_mode="concat")(prev_lstm_layer)
if use_bidirectional
else hidden_lstm_layer_fn(prev_lstm_layer))
prev_lstm_layer = hidden_lstm_layer
if not no_dropout:
prev_lstm_layer = Dropout(0.5)(prev_lstm_layer)
model_outputs = [TimeDistributed(Dense(max_label_count, activation="softmax"),
input_shape=(max_base_count, lstm_units), name="main_output")(prev_lstm_layer)]
loss_weights = {"main_output": 1.}
model = Model(inputs=model_input, outputs=model_outputs)
optimizer = RMSprop(lr=learning_rate)
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['acc'], loss_weights=loss_weights)
print("Model summary:", model.summary())
return model
def create_callbacks(model_prefix, min_delta, use_tensorboard, log_metrics, val_data_path, log_path, max_base_count):
callbacks_list = []
filepath = model_prefix + "-weights-improvement-{epoch:02d}-{val_loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks_list.append(checkpoint)
early_stopping = EarlyStopping(monitor="val_loss", patience=3, mode="min", min_delta=min_delta)
callbacks_list.append(early_stopping)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=2, mode="min", factor=0.2, min_lr=0.0001,
cooldown=3, verbose=1)
callbacks_list.append(reduce_lr)
if use_tensorboard:
tensorboard = TensorBoard(log_dir='./logs', histogram_freq=1, write_graph=True,
write_images=True, embeddings_freq=0,
embeddings_layer_names=None,
embeddings_metadata=None)
callbacks_list.append(tensorboard)
if log_metrics:
if log_path is None:
raise ValueError("--log-file undefined when --log-metrics set")
else:
callbacks_list.append(MetricLogger(val_data_path, log_path, max_base_count))
return callbacks_list
def main(args):
log_file_path = None
if args.log_metrics:
if args.log_file is None:
log_file_path = "log_run_{}.csv".format(int(time.time()))
else:
log_file_path = args.log_file
if os.path.exists(log_file_path):
raise Exception("Log path for run should be new")
if args.seed is None:
seed = int(time.time() + 36)
else:
seed = args.seed
random.seed(seed)
np.random.seed(seed)
tf.set_random_seed(seed)
backend.set_learning_phase(1)
init = tf.global_variables_initializer()
# Show device placement:
session = (tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=args.show_mappings))
if args.tensorboard
else tf.Session(config=tf.ConfigProto(log_device_placement=args.show_mappings)))
session.run(init)
if args.platform == "cpu":
implementation = 0
cpu_or_gpu = "/cpu:0"
elif args.platform == "gpu":
implementation = 2
gpu_device = args.gpu_device
cpu_or_gpu = "/gpu:{}".format(gpu_device)
else:
raise ValueError("Platform {} not recognized".format(args.platform))
reg = None
if args.l1 is not None and args.l2 is not None:
reg = regularizers.get(l1_l2(args.l1, args.l2))
elif args.l1 is not None:
reg = regularizers.get(l1(args.l1))
elif args.l2 is not None:
reg = regularizers.get(l2(args.l2))
input_properties_json = get_properties_json(args.input)
val_properties_json = get_properties_json(args.validation)
input_base_count = input_properties_json["max_base_count"]
input_feature_count = input_properties_json["max_feature_count"]
input_label_count = input_properties_json["max_label_count"]
val_feature_count = val_properties_json["max_feature_count"]
val_label_count = val_properties_json["max_label_count"]
input_num_segments = input_properties_json["num_segments_written"]
val_num_segments = val_properties_json["num_segments_written"]
input_mini_batch_size = input_properties_json["mini_batch_size"]
val_mini_batch_size = val_properties_json["mini_batch_size"]
max_base_count = input_base_count
if input_feature_count != val_feature_count:
warnings.warn("Mismatch between input feature count {} and val feature count {}".format(input_feature_count,
val_feature_count))
if input_label_count != val_label_count:
warnings.warn("Mismatch between input label count {} and val label count {}".format(input_label_count,
val_label_count))
max_feature_count = max(input_feature_count, val_feature_count)
max_label_count = max(input_label_count, val_label_count)
print("Creating model and callbacks...")
model = create_model(num_layers=args.num_layers,
max_base_count=max_base_count,
max_feature_count=max_feature_count,
max_label_count=max_label_count,
use_bidirectional=args.bidirectional,
lstm_units=args.lstm_units,
implementation=implementation,
layer_type=args.layer_type,
learning_rate=args.learning_rate,
regularizer=reg,
no_dropout=args.no_dropout)
callbacks = create_callbacks(args.model_prefix, args.min_delta, args.tensorboard, args.log_metrics,
args.validation, log_file_path, max_base_count)
input_generator = BatchNumpyFileSequence(args.input, max_base_count, input_properties_json, array_type='train')
val_generator = BatchNumpyFileSequence(args.validation, max_base_count, val_properties_json, array_type='train')
input_updates = math.ceil(input_num_segments / input_mini_batch_size)
val_updates = math.ceil(val_num_segments / val_mini_batch_size)
use_multiprocessing = args.parallel is not None
num_workers = args.parallel if args.parallel is not None else 1
with tf.device(cpu_or_gpu):
print("Training...")
model.fit_generator(generator=input_generator,
steps_per_epoch=input_updates,
validation_data=val_generator,
validation_steps=val_updates,
epochs=args.max_epochs,
callbacks=callbacks,
verbose=args.verbosity,
use_multiprocessing=use_multiprocessing,
workers=num_workers)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", type=str, required=True,
help="Path to directory containing training npz files generated using GenerateDatasetsFromSSI.")
parser.add_argument("-v", "--validation", type=str, required=True,
help="Path to directory containing validation npz files generated using "
"GenerateDatasetsFromSSI.")
parser.add_argument("--bidirectional", dest="bidirectional", action="store_true",
help="When set, train a bidirectional LSTM.")
parser.add_argument("--lstm-units", type=int, default=64, help="Number of LSTM units.")
parser.add_argument("--num-layers", type=int, default=1, help="Number of hidden LSTM layers.")
parser.add_argument("--platform", required=True, type=str, choices=["cpu", "gpu"],
help="Platform to train on: cpu or gpu.")
parser.add_argument("--gpu-device", type=int, default=0, help="Index of the GPU to use, when platform is gpu. "
"Not recommended, set CUDA_VISIBLE_DEVICES instead.")
parser.add_argument("--layer-type", type=str, choices=["LSTM", "RNN", "GRU", "SRU"], default="LSTM",
help="Type of RNN layer to use.")
parser.add_argument("--learning-rate", type=float, default=0.01, help="Learning rate.")
parser.add_argument("--model-prefix", type=str, default="model",
help="Prefix (a short string) to name model checkpoints with")
parser.add_argument("--min-delta", type=float, default=0, help="Minimum delta for loss improvement in each epoch.")
parser.add_argument("--tensorboard", dest="tensorboard", action="store_true",
help="When set, use an interactive session and monitor on tensorboard.")
parser.add_argument("--log-metrics", dest="log_metrics", action="store_true",
help="When set, output a log CSV file containing metric estimates (i.e. precision, recall, F1) "
"for SNPs, refs, and indels. Potentially useful for debugging, but "
"evaluate-genotypes-ssi-keras provides a more standardized means of evaluation.")
parser.add_argument("--show-mappings", dest="show_mappings", action="store_true",
help="When set, show operation placements on cpu/gpu devices.")
parser.add_argument("--verbosity", type=int, default=1, choices=[0, 1, 2],
help="Level of verbosity, 0, not verbose, 1, progress bar, 2, one line per epoch.")
parser.add_argument("--max-epochs", type=int, default=60, help="Maximum number of epochs to train for.")
parser.add_argument("--l1", type=float, help="L1 regularization rate.")
parser.add_argument("--l2", type=float, help="L2 regularization rate.")
parser.add_argument("--no-dropout", dest="no_dropout", action="store_true",
help="When set, doesn't add dropout layer")
parser.add_argument("--parallel", type=int, help="Run training in parallel, with n workers.")
parser.add_argument("-l", "--log-file", type=str,
help="Path to log file to use. Should be new. Only used when --log-metrics is set.")
parser.add_argument("--seed", type=int, help="Random seed to use")
parser_args = parser.parse_args()
main(parser_args)
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