def evaluate(config, train_dir, weights, customize, nevents):
"""Evaluate the trained model in train_dir"""
if config is None:
config = Path(train_dir) / "config.yaml"
assert config.exists(
), "Could not find config file in train_dir, please provide one with -c <path/to/config>"
config, _ = parse_config(config, weights=weights)
if customize:
config = customization_functions[customize](config)
if config["setup"]["dtype"] == "float16":
model_dtype = tf.dtypes.float16
policy = mixed_precision.Policy("mixed_float16")
mixed_precision.set_global_policy(policy)
else:
model_dtype = tf.dtypes.float32
strategy, num_gpus = get_strategy()
# physical_devices = tf.config.list_physical_devices('GPU')
# for dev in physical_devices:
# tf.config.experimental.set_memory_growth(dev, True)
model = make_model(config, model_dtype)
model.build((1, config["dataset"]["padded_num_elem_size"],
config["dataset"]["num_input_features"]))
# need to load the weights in the same trainable configuration as the model was set up
configure_model_weights(model, config["setup"].get("weights_config",
"all"))
if weights:
model.load_weights(weights, by_name=True)
else:
weights = get_best_checkpoint(train_dir)
print(
"Loading best weights that could be found from {}".format(weights))
model.load_weights(weights, by_name=True)
iepoch = int(weights.split("/")[-1].split("-")[1])
for dsname in config["validation_datasets"]:
ds_test, _ = get_heptfds_dataset(dsname,
config,
num_gpus,
"test",
supervised=False)
if nevents:
ds_test = ds_test.take(nevents)
ds_test = ds_test.batch(5)
eval_dir = str(
Path(train_dir) / "evaluation" / "epoch_{}".format(iepoch) /
dsname)
Path(eval_dir).mkdir(parents=True, exist_ok=True)
eval_model(model, ds_test, config, eval_dir)
freeze_model(model, config, train_dir)
def evaluate(config, train_dir, weights, evaluation_dir):
"""Evaluate the trained model in train_dir"""
if config is None:
config = Path(train_dir) / "config.yaml"
assert config.exists(
), "Could not find config file in train_dir, please provide one with -c <path/to/config>"
config, _ = parse_config(config, weights=weights)
if evaluation_dir is None:
eval_dir = str(Path(train_dir) / "evaluation")
else:
eval_dir = evaluation_dir
Path(eval_dir).mkdir(parents=True, exist_ok=True)
if config["setup"]["dtype"] == "float16":
model_dtype = tf.dtypes.float16
policy = mixed_precision.Policy("mixed_float16")
mixed_precision.set_global_policy(policy)
opt = mixed_precision.LossScaleOptimizer(opt)
else:
model_dtype = tf.dtypes.float32
strategy, num_gpus = get_strategy()
ds_test, _ = get_heptfds_dataset(config["validation_dataset"], config,
num_gpus, "test")
ds_test = ds_test.batch(5)
model = make_model(config, model_dtype)
model.build((1, config["dataset"]["padded_num_elem_size"],
config["dataset"]["num_input_features"]))
# need to load the weights in the same trainable configuration as the model was set up
configure_model_weights(model, config["setup"].get("weights_config",
"all"))
if weights:
model.load_weights(weights, by_name=True)
else:
weights = get_best_checkpoint(train_dir)
print(
"Loading best weights that could be found from {}".format(weights))
model.load_weights(weights, by_name=True)
eval_model(model, ds_test, config, eval_dir)
freeze_model(model, config, ds_test.take(1), train_dir)
def build_model_and_train(config,
checkpoint_dir=None,
full_config=None,
ntrain=None,
ntest=None,
name=None,
seeds=False):
from ray import tune
from ray.tune.integration.keras import TuneReportCheckpointCallback
from raytune.search_space import set_raytune_search_parameters
if seeds:
# Set seeds for reproducibility
random.seed(1234)
np.random.seed(1234)
tf.random.set_seed(1234)
full_config, config_file_stem = parse_config(full_config)
if config is not None:
full_config = set_raytune_search_parameters(search_space=config,
config=full_config)
strategy, num_gpus = get_strategy()
ds_train, num_train_steps = get_datasets(
full_config["train_test_datasets"], full_config, num_gpus, "train")
ds_test, num_test_steps = get_datasets(full_config["train_test_datasets"],
full_config, num_gpus, "test")
ds_val, ds_info = get_heptfds_dataset(
full_config["validation_datasets"][0],
full_config,
num_gpus,
"test",
full_config["setup"]["num_events_validation"],
supervised=False,
)
ds_val = ds_val.batch(5)
if ntrain:
ds_train = ds_train.take(ntrain)
num_train_steps = ntrain
if ntest:
ds_test = ds_test.take(ntest)
num_test_steps = ntest
print("num_train_steps", num_train_steps)
print("num_test_steps", num_test_steps)
total_steps = num_train_steps * full_config["setup"]["num_epochs"]
print("total_steps", total_steps)
callbacks = prepare_callbacks(
full_config,
tune.get_trial_dir(),
ds_val,
)
callbacks = callbacks[:
-1] # remove the CustomCallback at the end of the list
with strategy.scope():
lr_schedule, optim_callbacks = get_lr_schedule(full_config,
steps=total_steps)
callbacks.append(optim_callbacks)
opt = get_optimizer(full_config, lr_schedule)
model = make_model(full_config, dtype=tf.dtypes.float32)
# Run model once to build the layers
model.build((1, full_config["dataset"]["padded_num_elem_size"],
full_config["dataset"]["num_input_features"]))
full_config = set_config_loss(full_config,
full_config["setup"]["trainable"])
configure_model_weights(model, full_config["setup"]["trainable"])
model.build((1, full_config["dataset"]["padded_num_elem_size"],
full_config["dataset"]["num_input_features"]))
loss_dict, loss_weights = get_loss_dict(full_config)
model.compile(
loss=loss_dict,
optimizer=opt,
sample_weight_mode="temporal",
loss_weights=loss_weights,
metrics={
"cls": [
FlattenedCategoricalAccuracy(name="acc_unweighted",
dtype=tf.float64),
FlattenedCategoricalAccuracy(use_weights=True,
name="acc_weighted",
dtype=tf.float64),
]
},
)
model.summary()
callbacks.append(
TuneReportCheckpointCallback(metrics=[
"adam_beta_1",
"charge_loss",
"cls_acc_unweighted",
"cls_loss",
"cos_phi_loss",
"energy_loss",
"eta_loss",
"learning_rate",
"loss",
"pt_loss",
"sin_phi_loss",
"val_charge_loss",
"val_cls_acc_unweighted",
"val_cls_acc_weighted",
"val_cls_loss",
"val_cos_phi_loss",
"val_energy_loss",
"val_eta_loss",
"val_loss",
"val_pt_loss",
"val_sin_phi_loss",
], ), )
try:
model.fit(
ds_train.repeat(),
validation_data=ds_test.repeat(),
epochs=full_config["setup"]["num_epochs"],
callbacks=callbacks,
steps_per_epoch=num_train_steps,
validation_steps=num_test_steps,
)
except tf.errors.ResourceExhaustedError:
logging.warning(
"Resource exhausted, skipping this hyperparameter configuration."
)
skiplog_file_path = Path(full_config["raytune"]["local_dir"]
) / name / "skipped_configurations.txt"
lines = [
"{}: {}\n".format(item[0], item[1]) for item in config.items()
]
with open(skiplog_file_path, "a") as f:
f.write("#" * 80 + "\n")
for line in lines:
f.write(line)
logging.warning(line[:-1])
f.write("#" * 80 + "\n\n")
def hypertune(config, outdir, ntrain, ntest, recreate):
config_file_path = config
config, _ = parse_config(config, ntrain=ntrain, ntest=ntest)
# Override number of epochs with max_epochs from Hyperband config if specified
if config["hypertune"]["algorithm"] == "hyperband":
config["setup"]["num_epochs"] = config["hypertune"]["hyperband"][
"max_epochs"]
strategy, num_gpus = get_strategy()
ds_train, ds_info = get_heptfds_dataset(
config["training_dataset"], config, num_gpus, "train",
config["setup"]["num_events_train"])
ds_test, _ = get_heptfds_dataset(config["testing_dataset"], config,
num_gpus, "test",
config["setup"]["num_events_test"])
ds_val, _ = get_heptfds_dataset(
config["validation_datasets"][0],
config,
num_gpus,
"test",
config["setup"]["num_events_validation"],
supervised=False,
)
ds_val = ds_val.batch(5)
num_train_steps = 0
for _ in ds_train:
num_train_steps += 1
num_test_steps = 0
for _ in ds_test:
num_test_steps += 1
model_builder, optim_callbacks = hypertuning.get_model_builder(
config, num_train_steps)
callbacks = prepare_callbacks(
config,
outdir,
ds_val,
)
callbacks.append(optim_callbacks)
callbacks.append(
tf.keras.callbacks.EarlyStopping(patience=20, monitor="val_loss"))
tuner = get_tuner(config["hypertune"], model_builder, outdir, recreate,
strategy)
tuner.search_space_summary()
tuner.search(
ds_train.repeat(),
epochs=config["setup"]["num_epochs"],
validation_data=ds_test.repeat(),
steps_per_epoch=num_train_steps,
validation_steps=num_test_steps,
callbacks=callbacks,
)
print("Hyperparameter search complete.")
shutil.copy(config_file_path, outdir + "/config.yaml"
) # Copy the config file to the train dir for later reference
tuner.results_summary()
for trial in tuner.oracle.get_best_trials(num_trials=10):
print(trial.hyperparameters.values, trial.score)
def find_lr(config, outdir, figname, logscale):
"""Run the Learning Rate Finder to produce a batch loss vs. LR plot from
which an appropriate LR-range can be determined"""
config, _ = parse_config(config)
# Decide tf.distribute.strategy depending on number of available GPUs
strategy, num_gpus = get_strategy()
ds_train, num_train_steps = get_datasets(config["train_test_datasets"],
config, num_gpus, "train")
with strategy.scope():
opt = tf.keras.optimizers.Adam(
learning_rate=1e-7
) # This learning rate will be changed by the lr_finder
if config["setup"]["dtype"] == "float16":
model_dtype = tf.dtypes.float16
policy = mixed_precision.Policy("mixed_float16")
mixed_precision.set_global_policy(policy)
opt = mixed_precision.LossScaleOptimizer(opt)
else:
model_dtype = tf.dtypes.float32
model = make_model(config, model_dtype)
config = set_config_loss(config, config["setup"]["trainable"])
# Run model once to build the layers
model.build((1, config["dataset"]["padded_num_elem_size"],
config["dataset"]["num_input_features"]))
configure_model_weights(model, config["setup"]["trainable"])
loss_dict, loss_weights = get_loss_dict(config)
model.compile(
loss=loss_dict,
optimizer=opt,
sample_weight_mode="temporal",
loss_weights=loss_weights,
metrics={
"cls": [
FlattenedCategoricalAccuracy(name="acc_unweighted",
dtype=tf.float64),
FlattenedCategoricalAccuracy(use_weights=True,
name="acc_weighted",
dtype=tf.float64),
]
},
)
model.summary()
max_steps = 200
lr_finder = LRFinder(max_steps=max_steps)
callbacks = [lr_finder]
model.fit(
ds_train.repeat(),
epochs=max_steps,
callbacks=callbacks,
steps_per_epoch=1,
)
lr_finder.plot(save_dir=outdir, figname=figname, log_scale=logscale)
def compute_validation_loss(config, train_dir, weights):
"""Evaluate the trained model in train_dir"""
if config is None:
config = Path(train_dir) / "config.yaml"
assert config.exists(
), "Could not find config file in train_dir, please provide one with -c <path/to/config>"
config, _ = parse_config(config, weights=weights)
if config["setup"]["dtype"] == "float16":
model_dtype = tf.dtypes.float16
policy = mixed_precision.Policy("mixed_float16")
mixed_precision.set_global_policy(policy)
else:
model_dtype = tf.dtypes.float32
strategy, num_gpus = get_strategy()
ds_test, num_test_steps = get_datasets(config["train_test_datasets"],
config, num_gpus, "test")
with strategy.scope():
model = make_model(config, model_dtype)
model.build((1, config["dataset"]["padded_num_elem_size"],
config["dataset"]["num_input_features"]))
# need to load the weights in the same trainable configuration as the model was set up
configure_model_weights(model,
config["setup"].get("weights_config", "all"))
if weights:
model.load_weights(weights, by_name=True)
else:
weights = get_best_checkpoint(train_dir)
print("Loading best weights that could be found from {}".format(
weights))
model.load_weights(weights, by_name=True)
loss_dict, loss_weights = get_loss_dict(config)
model.compile(
loss=loss_dict,
# sample_weight_mode="temporal",
loss_weights=loss_weights,
metrics={
"cls": [
FlattenedCategoricalAccuracy(name="acc_unweighted",
dtype=tf.float64),
FlattenedCategoricalAccuracy(use_weights=True,
name="acc_weighted",
dtype=tf.float64),
] + [
SingleClassRecall(
icls, name="rec_cls{}".format(icls), dtype=tf.float64)
for icls in range(config["dataset"]["num_output_classes"])
]
},
)
losses = model.evaluate(
x=ds_test,
steps=num_test_steps,
return_dict=True,
)
with open("{}/losses.txt".format(train_dir), "w") as loss_file:
loss_file.write(json.dumps(losses) + "\n")
def train(config, weights, ntrain, ntest, nepochs, recreate, prefix, plot_freq,
customize, comet_offline):
# tf.debugging.enable_check_numerics()
"""Train a model defined by config"""
config_file_path = config
config, config_file_stem = parse_config(config,
nepochs=nepochs,
weights=weights)
if plot_freq:
config["callbacks"]["plot_freq"] = plot_freq
if customize:
config = customization_functions[customize](config)
# Decide tf.distribute.strategy depending on number of available GPUs
horovod_enabled = config["setup"]["horovod_enabled"]
if horovod_enabled:
num_gpus = initialize_horovod()
else:
strategy, num_gpus = get_strategy()
outdir = ""
if not horovod_enabled or hvd.rank() == 0:
outdir = create_experiment_dir(prefix=prefix + config_file_stem + "_",
suffix=platform.node())
shutil.copy(
config_file_path, outdir + "/config.yaml"
) # Copy the config file to the train dir for later reference
try:
if comet_offline:
print("Using comet-ml OfflineExperiment, saving logs locally.")
from comet_ml import OfflineExperiment
experiment = OfflineExperiment(
project_name="particleflow-tf",
auto_metric_logging=True,
auto_param_logging=True,
auto_histogram_weight_logging=True,
auto_histogram_gradient_logging=False,
auto_histogram_activation_logging=False,
offline_directory=outdir + "/cometml",
)
else:
print("Using comet-ml Experiment, streaming logs to www.comet.ml.")
from comet_ml import Experiment
experiment = Experiment(
project_name="particleflow-tf",
auto_metric_logging=True,
auto_param_logging=True,
auto_histogram_weight_logging=True,
auto_histogram_gradient_logging=False,
auto_histogram_activation_logging=False,
)
except Exception as e:
print("Failed to initialize comet-ml dashboard: {}".format(e))
experiment = None
if experiment:
experiment.set_name(outdir)
experiment.log_code("mlpf/tfmodel/model.py")
experiment.log_code("mlpf/tfmodel/utils.py")
experiment.log_code(config_file_path)
ds_train, num_train_steps = get_datasets(config["train_test_datasets"],
config, num_gpus, "train")
ds_test, num_test_steps = get_datasets(config["train_test_datasets"],
config, num_gpus, "test")
ds_val, ds_info = get_heptfds_dataset(
config["validation_datasets"][0],
config,
num_gpus,
"test",
config["setup"]["num_events_validation"],
supervised=False,
)
ds_val = ds_val.batch(5)
if ntrain:
ds_train = ds_train.take(ntrain)
num_train_steps = ntrain
if ntest:
ds_test = ds_test.take(ntest)
num_test_steps = ntest
print("num_train_steps", num_train_steps)
print("num_test_steps", num_test_steps)
total_steps = num_train_steps * config["setup"]["num_epochs"]
print("total_steps", total_steps)
if horovod_enabled:
model, optim_callbacks, initial_epoch = model_scope(
config, total_steps, weights, horovod_enabled)
else:
with strategy.scope():
model, optim_callbacks, initial_epoch = model_scope(
config, total_steps, weights)
callbacks = prepare_callbacks(
config,
outdir,
ds_val,
comet_experiment=experiment,
horovod_enabled=config["setup"]["horovod_enabled"])
verbose = 1
if horovod_enabled:
callbacks.append(hvd.callbacks.BroadcastGlobalVariablesCallback(0))
callbacks.append(hvd.callbacks.MetricAverageCallback())
verbose = 1 if hvd.rank() == 0 else 0
num_train_steps /= hvd.size()
num_test_steps /= hvd.size()
callbacks.append(optim_callbacks)
model.fit(
ds_train.repeat(),
validation_data=ds_test.repeat(),
epochs=initial_epoch + config["setup"]["num_epochs"],
callbacks=callbacks,
steps_per_epoch=num_train_steps,
validation_steps=num_test_steps,
initial_epoch=initial_epoch,
verbose=verbose,
)
def debug_data(config, ntrain, ntest):
"""Train a model defined by config"""
config, config_file_stem, global_batch_size, n_train, n_test, n_epochs, weights = parse_config(
config,
ntrain,
ntest,
weights=None,
)
dataset_def = get_dataset_def(config)
ds_train, ds_test, dataset_transform = get_train_val_datasets(
config, global_batch_size=1, n_train=n_train, n_test=n_test)
# cand_counts = np.zeros(8)
# for data_item in tqdm(ds_train, desc="Counting"):
# import pdb; pdb.set_trace()
# cand_vals, cand_count = np.unique(np.argmax(data_item[1]['cls'], axis=2), return_counts=True)
# cand_counts[cand_vals.astype("int32")] += cand_count
# print("cand_counts: ", cand_counts)
dsf = CMSDatasetFactory(config)
ds_train, _ = dsf.get_dataset(split="train")
ds_test, _ = dsf.get_dataset(split="test")
for data_item in tqdm(ds_train, desc="Counting"):
import pdb
pdb.set_trace()
def train(config, weights, ntrain, ntest, nepochs, recreate, prefix, plot_freq,
customize):
try:
from comet_ml import Experiment
experiment = Experiment(
project_name="particleflow-tf",
auto_metric_logging=True,
auto_param_logging=True,
auto_histogram_weight_logging=True,
auto_histogram_gradient_logging=False,
auto_histogram_activation_logging=False,
)
except Exception as e:
print("Failed to initialize comet-ml dashboard")
experiment = None
"""Train a model defined by config"""
config_file_path = config
config, config_file_stem = parse_config(config,
nepochs=nepochs,
weights=weights)
if plot_freq:
config["callbacks"]["plot_freq"] = plot_freq
if customize:
config = customization_functions[customize](config)
if recreate or (weights is None):
outdir = create_experiment_dir(prefix=prefix + config_file_stem + "_",
suffix=platform.node())
else:
outdir = str(Path(weights).parent)
# Decide tf.distribute.strategy depending on number of available GPUs
strategy, num_gpus = get_strategy()
#if "CPU" not in strategy.extended.worker_devices[0]:
# nvidia_smi_call = "nvidia-smi --query-gpu=timestamp,name,pci.bus_id,pstate,power.draw,temperature.gpu,utilization.gpu,utilization.memory,memory.total,memory.free,memory.used --format=csv -l 1 -f {}/nvidia_smi_log.csv".format(outdir)
# p = subprocess.Popen(shlex.split(nvidia_smi_call))
ds_train, num_train_steps = get_datasets(config["train_test_datasets"],
config, num_gpus, "train")
ds_test, num_test_steps = get_datasets(config["train_test_datasets"],
config, num_gpus, "test")
ds_val, ds_info = get_heptfds_dataset(
config["validation_dataset"], config, num_gpus, "test",
config["setup"]["num_events_validation"])
ds_val = ds_val.batch(5)
if ntrain:
ds_train = ds_train.take(ntrain)
num_train_steps = ntrain
if ntest:
ds_test = ds_test.take(ntest)
num_test_steps = ntest
print("num_train_steps", num_train_steps)
print("num_test_steps", num_test_steps)
total_steps = num_train_steps * config["setup"]["num_epochs"]
print("total_steps", total_steps)
if experiment:
experiment.set_name(outdir)
experiment.log_code("mlpf/tfmodel/model.py")
experiment.log_code("mlpf/tfmodel/utils.py")
experiment.log_code(config_file_path)
shutil.copy(config_file_path, outdir + "/config.yaml"
) # Copy the config file to the train dir for later reference
with strategy.scope():
lr_schedule, optim_callbacks = get_lr_schedule(config,
steps=total_steps)
opt = get_optimizer(config, lr_schedule)
if config["setup"]["dtype"] == "float16":
model_dtype = tf.dtypes.float16
policy = mixed_precision.Policy("mixed_float16")
mixed_precision.set_global_policy(policy)
opt = mixed_precision.LossScaleOptimizer(opt)
else:
model_dtype = tf.dtypes.float32
model = make_model(config, model_dtype)
# Build the layers after the element and feature dimensions are specified
model.build((1, config["dataset"]["padded_num_elem_size"],
config["dataset"]["num_input_features"]))
initial_epoch = 0
if weights:
# We need to load the weights in the same trainable configuration as the model was set up
configure_model_weights(
model, config["setup"].get("weights_config", "all"))
model.load_weights(weights, by_name=True)
initial_epoch = int(weights.split("/")[-1].split("-")[1])
model.build((1, config["dataset"]["padded_num_elem_size"],
config["dataset"]["num_input_features"]))
config = set_config_loss(config, config["setup"]["trainable"])
configure_model_weights(model, config["setup"]["trainable"])
model.build((1, config["dataset"]["padded_num_elem_size"],
config["dataset"]["num_input_features"]))
print("model weights")
tw_names = [m.name for m in model.trainable_weights]
for w in model.weights:
print("layer={} trainable={} shape={} num_weights={}".format(
w.name, w.name in tw_names, w.shape, np.prod(w.shape)))
loss_dict, loss_weights = get_loss_dict(config)
model.compile(
loss=loss_dict,
optimizer=opt,
sample_weight_mode="temporal",
loss_weights=loss_weights,
metrics={
"cls": [
FlattenedCategoricalAccuracy(name="acc_unweighted",
dtype=tf.float64),
FlattenedCategoricalAccuracy(use_weights=True,
name="acc_weighted",
dtype=tf.float64),
] + [
SingleClassRecall(
icls, name="rec_cls{}".format(icls), dtype=tf.float64)
for icls in range(config["dataset"]["num_output_classes"])
]
},
)
model.summary()
callbacks = prepare_callbacks(config["callbacks"],
outdir,
ds_val,
ds_info,
comet_experiment=experiment)
callbacks.append(optim_callbacks)
fit_result = model.fit(
ds_train.repeat(),
validation_data=ds_test.repeat(),
epochs=initial_epoch + config["setup"]["num_epochs"],
callbacks=callbacks,
steps_per_epoch=num_train_steps,
validation_steps=num_test_steps,
initial_epoch=initial_epoch,
)
history_path = Path(outdir) / "history"
history_path = str(history_path)
with open("{}/history.json".format(history_path), "w") as fi:
json.dump(fit_result.history, fi)
weights = get_best_checkpoint(outdir)
print("Loading best weights that could be found from {}".format(weights))
model.load_weights(weights, by_name=True)
model.save(outdir + "/model_full", save_format="tf")
print("Training done.")
