def main(unused_argv):
data_root = os.path.join("outputs", "MNIST")
mnist = None
tf_config = os.environ.get("TF_CONFIG")
if not tf_config or tf_config == "":
raise ValueError("TF_CONFIG not found.")
tf_config_json = json.loads(tf_config)
cluster = tf_config_json.get('cluster')
job_name = tf_config_json.get('task', {}).get('type')
task_index = tf_config_json.get('task', {}).get('index')
job_name = "worker" if job_name == "master" else job_name
sentinel_path = os.path.join(data_root, "complete.txt")
if job_name == "worker" and task_index == 0:
mnist = input_data.read_data_sets(data_root, one_hot=True)
with open(sentinel_path, 'w+') as f:
f.write("download complete")
else:
while not os.path.exists(sentinel_path):
time.sleep(0.01)
mnist = input_data.read_data_sets(data_root, one_hot=True)
if FLAGS.download_only:
sys.exit(0)
print("job name = %s" % job_name)
print("task index = %d" % task_index)
print("number of GPUs = %d" % FLAGS.num_gpus)
# Construct the cluster and start the server
cluster_spec = tf.train.ClusterSpec(cluster)
# Get the number of workers.
num_workers = len(cluster_spec.task_indices("worker"))
if not FLAGS.existing_servers:
# Not using existing servers. Create an in-process server.
server = tf.train.Server(
cluster_spec, job_name=job_name, task_index=task_index)
if job_name == "ps":
server.join()
is_chief = (task_index == 0)
if FLAGS.num_gpus > 0:
# Avoid gpu allocation conflict: now allocate task_num -> #gpu
# for each worker in the corresponding machine
gpu = (task_index % FLAGS.num_gpus)
worker_device = "/job:worker/task:%d/gpu:%d" % (task_index, gpu)
elif FLAGS.num_gpus == 0:
# Just allocate the CPU to worker server
cpu = 0
worker_device = "/job:worker/task:%d/cpu:%d" % (task_index, cpu)
# The device setter will automatically place Variables ops on separate
# parameter servers (ps). The non-Variable ops will be placed on the workers.
# The ps use CPU and workers use corresponding GPU
with tf.device(
tf.train.replica_device_setter(
worker_device=worker_device,
ps_device="/job:ps/cpu:0",
cluster=cluster)):
global_step = tf.Variable(0, name="global_step", trainable=False)
# Variables of the hidden layer
hid_w = tf.Variable(
tf.truncated_normal(
[IMAGE_PIXELS * IMAGE_PIXELS, FLAGS.hidden_units],
stddev=1.0 / IMAGE_PIXELS),
name="hid_w")
hid_b = tf.Variable(tf.zeros([FLAGS.hidden_units]), name="hid_b")
# Variables of the softmax layer
sm_w = tf.Variable(
tf.truncated_normal(
[FLAGS.hidden_units, 10],
stddev=1.0 / math.sqrt(FLAGS.hidden_units)),
name="sm_w")
sm_b = tf.Variable(tf.zeros([10]), name="sm_b")
# Ops: located on the worker specified with task_index
x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS * IMAGE_PIXELS])
y_ = tf.placeholder(tf.float32, [None, 10])
hid_lin = tf.nn.xw_plus_b(x, hid_w, hid_b)
hid = tf.nn.relu(hid_lin)
y = tf.nn.softmax(tf.nn.xw_plus_b(hid, sm_w, sm_b))
cross_entropy = -tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
opt = tf.train.AdamOptimizer(FLAGS.learning_rate)
if FLAGS.sync_replicas:
if FLAGS.replicas_to_aggregate is None:
replicas_to_aggregate = num_workers
else:
replicas_to_aggregate = FLAGS.replicas_to_aggregate
opt = tf.train.SyncReplicasOptimizer(
opt,
replicas_to_aggregate=replicas_to_aggregate,
total_num_replicas=num_workers,
name="mnist_sync_replicas")
train_step = opt.minimize(cross_entropy, global_step=global_step)
if FLAGS.sync_replicas:
local_init_op = opt.local_step_init_op
if is_chief:
local_init_op = opt.chief_init_op
ready_for_local_init_op = opt.ready_for_local_init_op
# Initial token and chief queue runners required by the sync_replicas mode
chief_queue_runner = opt.get_chief_queue_runner()
sync_init_op = opt.get_init_tokens_op()
init_op = tf.global_variables_initializer()
train_dir = tempfile.mkdtemp()
if FLAGS.sync_replicas:
sv = tf.train.Supervisor(
is_chief=is_chief,
logdir=train_dir,
init_op=init_op,
local_init_op=local_init_op,
ready_for_local_init_op=ready_for_local_init_op,
recovery_wait_secs=1,
global_step=global_step)
else:
sv = tf.train.Supervisor(
is_chief=is_chief,
logdir=train_dir,
init_op=init_op,
recovery_wait_secs=1,
global_step=global_step)
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
device_filters=["/job:ps",
"/job:worker/task:%d" % task_index])
# The chief worker (task_index==0) session will prepare the session,
# while the remaining workers will wait for the preparation to complete.
if is_chief:
print("Worker %d: Initializing session..." % task_index)
else:
print("Worker %d: Waiting for session to be initialized..." %
task_index)
if FLAGS.existing_servers:
server_grpc_url = "grpc://" + task_index
print("Using existing server at: %s" % server_grpc_url)
sess = sv.prepare_or_wait_for_session(server_grpc_url, config=sess_config)
else:
sess = sv.prepare_or_wait_for_session(server.target, config=sess_config)
print("Worker %d: Session initialization complete." % task_index)
if FLAGS.sync_replicas and is_chief:
# Chief worker will start the chief queue runner and call the init op.
sess.run(sync_init_op)
sv.start_queue_runners(sess, [chief_queue_runner])
# Perform training
time_begin = time.time()
print("Training begins @ %f" % time_begin)
local_step = 0
while True:
# Training feed
batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size)
train_feed = {x: batch_xs, y_: batch_ys}
_, step = sess.run([train_step, global_step], feed_dict=train_feed)
local_step += 1
now = time.time()
print("%f: Worker %d: training step %d done (global step: %d)" %
(now, task_index, local_step, step))
if step >= FLAGS.train_steps:
break
time_end = time.time()
print("Training ends @ %f" % time_end)
training_time = time_end - time_begin
print("Training elapsed time: %f s" % training_time)
# Validation feed
val_feed = {x: mnist.validation.images, y_: mnist.validation.labels}
val_xent = sess.run(cross_entropy, feed_dict=val_feed)
print("After %d training step(s), validation cross entropy = %g" %
(FLAGS.train_steps, val_xent))
if job_name == "worker" and task_index == 0:
run = Run.get_context()
run.log("CrossEntropy", val_xent)
from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from azureml.core.run import Run
from sklearn.externals import joblib
import os
import numpy as np
import mylib
os.makedirs('./outputs', exist_ok=True)
X, y = load_diabetes(return_X_y=True)
run = Run.get_context()
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.2,
random_state=0)
data = {"train": {"X": X_train, "y": y_train},
"test": {"X": X_test, "y": y_test}}
# list of numbers from 0.0 to 1.0 with a 0.05 interval
alphas = mylib.get_alphas()
for alpha in alphas:
# Use Ridge algorithm to create a regression model
reg = Ridge(alpha=alpha)
reg.fit(data["train"]["X"], data["train"]["y"])
def run(args):
if args.supress_warnings:
warnings.simplefilter("ignore")
def adjust_path(p):
return os.path.join(args.data_root_dir, p)
args.label_encoder = adjust_path(args.label_encoder)
args.all_imgs_csv = adjust_path(args.all_imgs_csv)
args.val_imgs_csv = adjust_path(args.val_imgs_csv)
args.test_imgs_csv = adjust_path(args.test_imgs_csv)
args.results_dir = adjust_path(args.results_dir)
print(args)
from multihead_trainer import train
from multihead_trainer import torch_transform
# TODO: consolidate logid
def build_logid_string(args, add_timestamp=True):
param_str = "lr{}_dr{}_lrpatience{}_lrfactor{}_{}".format(
args.init_lr, args.dropout, args.lr_patience, args.lr_factor,
args.appearance_network)
if add_timestamp:
param_str += "_" + datetime.datetime.now().strftime("%Y%m%d%H%M")
return param_str
param_str = build_logid_string(args)
# Azure ML
from azureml.core.run import Run
run = Run.get_context()
# log arguments if it's not called by train_cv
if not hasattr(args, 'folds_csv_dir'):
for k, v in vars(args).items():
run.tag(k, str(v))
save_path = os.path.join(args.results_dir, param_str)
os.makedirs(save_path, exist_ok=True)
print("save_path", save_path)
logger.info(
f"cuda.is_available={torch.cuda.is_available()}, n_gpu={torch.cuda.device_count()}"
)
# encode the classes
from sklearn.preprocessing import LabelEncoder
import pickle
if not os.path.exists(args.label_encoder):
logger.warning(f"Fitting a new label encoder at {args.label_encoder}")
all_imgs_df = pd.read_csv(args.all_imgs_csv)
label_encoder = LabelEncoder()
label_encoder.fit(all_imgs_df['label'])
pickle.dump(label_encoder, open(args.label_encoder, "wb"))
else:
logger.info(f"Loading label encoder: {args.label_encoder}")
with open(args.label_encoder, 'rb') as pickle_file:
label_encoder = pickle.load(pickle_file)
logger.info(f"label_encoder.classes_={label_encoder.classes_}")
logger.info("The label encoder has {} classes.".format(
len(label_encoder.classes_)))
# Load image list
all_images_df = pd.read_csv(args.all_imgs_csv)
val_df = pd.read_csv(args.val_imgs_csv)
test_df = pd.read_csv(args.test_imgs_csv)
for df in [all_images_df, val_df, test_df]:
df['image_path'] = df['image_path'].apply(
lambda x: os.path.join(args.data_root_dir, args.img_dir, x))
val_test_image_paths = list(val_df['image_path'].values) + list(
test_df['image_path'].values)
train_df = all_images_df[~all_images_df['image_path'].
isin(val_test_image_paths)]
ref_only_df = train_df[train_df['is_ref']]
cons_train_df = train_df[train_df['is_ref'] == False]
cons_val_df = val_df
print("all_images", len(all_images_df), "train", len(train_df), "val",
len(val_df), "test", len(test_df))
run.log("all_images_size", len(all_images_df))
run.log("train_size", len(train_df))
run.log("val_size", len(val_df))
run.log("test_size", len(test_df))
print("ref_only_df", len(ref_only_df), "cons_train_df", len(cons_train_df),
"cons_val_df", len(cons_val_df))
import classif_utils
classif_utils.ClassificationDataset.set_datadir(
os.path.join(args.data_root_dir, args.img_dir))
def plot_pr_curve(plt, dataset_name):
run.log_image(name='{}_{}_{}'.format(
dataset_name,
datetime.datetime.now().strftime("%H:%M:%S"), 'PR-curve'),
plot=plt)
plt.close()
def log_metrics(metrics_results, dataset_name):
from metrics import create_prec_inds_str
import matplotlib
matplotlib.use('Agg') #backend that doesn't display to the user
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
run_metrics = []
for k, v in metrics_results.items():
if ('p_indices' in k) and not ('sanity' in dataset_name):
pind_str = create_prec_inds_str(v, label_encoder)
run.log("{}_{}".format(dataset_name, k), pind_str)
run_metrics.append([
os.path.split(args.val_imgs_csv)[1], dataset_name, k,
pind_str
])
elif isinstance(v, (int, float)):
run.log("{}_{}".format(dataset_name, k), v)
run_metrics.append(
[os.path.split(args.val_imgs_csv)[1], dataset_name, k, v])
return run_metrics
#if da_train, models is actually a dictionary with F1, F2 and G
model, val_metrics = train(ref_only_df,
cons_train_df,
cons_val_df,
label_encoder,
torch_transform,
'label',
args.batch_size,
len(label_encoder.classes_),
args,
args.max_epochs,
results_dir=save_path,
add_perspective=args.add_persp_aug)
print('completed train()')
print('val_metrics', val_metrics)
run_metrics_list = log_metrics(val_metrics, 'val')
predictions_dfs_list = []
from sanitytest_eval import create_eval_dataloaders
evaluator = MetricEmbeddingEvaluator(
model,
args.metric_simul_sidepairs_eval,
sidepairs_agg_method=args.sidepairs_agg,
metric_evaluator_type=args.metric_evaluator_type)
logit_evaluator = LogitEvaluator(model,
args.metric_simul_sidepairs_eval,
sidepairs_agg_method=args.sidepairs_agg)
#figures out label column for sanity test
def get_labelcol_eval(de_imgs_df):
#figuring out if it is a pilltype_id or label_prod_code encoder
#to set the label column of the sanity test set
labels_df = pd.DataFrame({'label': label_encoder.classes_})
img_df = pd.merge(de_imgs_df,
labels_df,
left_on=['label_prod_code'],
right_on=['label'],
how='inner')
if len(img_df) > 1:
labelcol = 'label_prod_code'
else:
labelcol = 'pilltype_id'
print('Selecting {} for sanity test label'.format(labelcol))
return de_imgs_df[labelcol]
def test_model(de_imgs_df,
evaluator,
dataset_name,
run_metrics_list,
predictions_dfs_list,
rotate_aug=None):
if rotate_aug is not None:
dataset_name += "_rotate_aug{}".format(rotate_aug)
print("Evaluating", dataset_name)
eval_dataloader, eval_dataset = create_eval_dataloaders(
de_imgs_df,
label_encoder,
torch_transform,
'label',
24,
rotate_aug=rotate_aug)
ref_dataloader, _ = create_eval_dataloaders(ref_only_df,
label_encoder,
torch_transform,
'label',
24,
rotate_aug=rotate_aug)
dataloader = {'ref': ref_dataloader, 'eval': eval_dataloader}
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("Eval {}: {} images from {} total images".format(
dataset_name, len(eval_dataset), len(de_imgs_df)))
metrics_results, predictions = evaluator.eval_model(
device, dataloader, do_pr_metrics=True, add_single_side_eval=True)
plot_pr_curve(metrics_results['PR-curve'], dataset_name)
run_metrics_list += log_metrics(metrics_results, dataset_name)
predictions['dataset'] = dataset_name
predictions['val_imgs_csv'] = os.path.split(args.val_imgs_csv)[1]
predictions_dfs_list.append(predictions)
return metrics_results, predictions
test_model(test_df, logit_evaluator, 'holdout-logit', run_metrics_list,
predictions_dfs_list)
test_model(test_df, evaluator, 'holdout', run_metrics_list,
predictions_dfs_list)
run_metrics_df = pd.DataFrame(
run_metrics_list, columns=['val_imgs_csv', 'dataset', 'name', 'value'])
all_predictions_df = pd.concat(predictions_dfs_list, ignore_index=True)
# make sure to save both
for target_save_dir in [save_path, 'outputs']:
print(f'saving predictions {target_save_dir}')
# TODO: this csv can be large. Update the format for the numpy array of prediction scores.
os.makedirs(target_save_dir, exist_ok=True)
all_predictions_df.to_csv(
os.path.join(
target_save_dir, 'eval_predictions_{}'.format(
os.path.basename(args.val_imgs_csv))))
torch.save(
model.state_dict(),
os.path.join(save_path,
'{}.pth'.format(os.path.basename(args.val_imgs_csv))))
return run_metrics_df, all_predictions_df
from __future__ import division
from __future__ import print_function
import sys
import os
import shutil
import argparse
import math
import tensorflow as tf
from azureml.core.run import Run ##### Modified
# Get run when running in remote ##### Modified
if 'run' not in locals(): ##### Modified
run = Run.get_context() ##### Modified
FLAGS = None
batch_size = 100
#
# define functions for Estimator
#
def _my_input_fn(filepath, num_epochs):
# image - 784 (=28 x 28) elements of grey-scaled integer value [0, 1]
# label - digit (0, 1, ..., 9)
data_queue = tf.train.string_input_producer(
[filepath], num_epochs=num_epochs
) # data is repeated and it raises OutOfRange when data is over
def _get_data_from_dataprep(dataprep_json, automl_settings_obj, logger):
current_run = Run.get_submitted_run()
parent_run_id = _get_parent_run_id(current_run._run_id)
print("[ParentRunId:{}]: Start getting data using dataprep.".format(
parent_run_id))
logger.info(
"[ParentRunId:{}]: Start getting data using dataprep.".format(
parent_run_id))
try:
import azureml.train.automl._dataprep_utilities as dataprep_utilities
except Exception as e:
e.error_type = ErrorTypes.Unclassified
log_traceback(e, logger)
logger.error(e)
raise e
fit_iteration_parameters_dict = dict()
class RetrieveNumpyArrayError(Exception):
def __init__(self):
super().__init__()
try:
print("Resolving Dataflows...")
logger.info("Resolving Dataflows...")
dataprep_json_obj = json.loads(dataprep_json)
if 'activities' in dataprep_json_obj: # json is serialized dataflows
dataflow_dict = dataprep_utilities.load_dataflows_from_json(
dataprep_json)
for k in [
'X', 'X_valid', 'sample_weight', 'sample_weight_valid'
]:
fit_iteration_parameters_dict[
k] = dataprep_utilities.try_retrieve_pandas_dataframe(
dataflow_dict.get(k))
for k in ['y', 'y_valid']:
try:
fit_iteration_parameters_dict[
k] = dataprep_utilities.try_retrieve_numpy_array(
dataflow_dict.get(k))
except IndexError:
raise RetrieveNumpyArrayError()
cv_splits_dataflows = []
i = 0
while 'cv_splits_indices_{0}'.format(i) in dataflow_dict:
cv_splits_dataflows.append(
dataflow_dict['cv_splits_indices_{0}'.format(i)])
i = i + 1
fit_iteration_parameters_dict['cv_splits_indices'] = None if len(cv_splits_dataflows) == 0 \
else dataprep_utilities.try_resolve_cv_splits_indices(cv_splits_dataflows)
else: # json is dataprep options
print('Creating Dataflow from options...\r\nOptions:')
logger.info('Creating Dataflow from options...')
print(dataprep_json_obj)
datastore_name = dataprep_json_obj[
'datastoreName'] # mandatory
data_path = dataprep_json_obj['dataPath'] # mandatory
label_column = dataprep_json_obj['label'] # mandatory
separator = dataprep_json_obj.get('columnSeparator', ',')
header = dataprep_json_obj.get('promoteHeader', True)
encoding = dataprep_json_obj.get('encoding', None)
quoting = dataprep_json_obj.get('ignoreNewlineInQuotes', False)
skip_rows = dataprep_json_obj.get('skipRows', 0)
feature_columns = dataprep_json_obj.get('features', [])
from azureml.core import Datastore
import azureml.dataprep as dprep
if header:
header = dprep.PromoteHeadersMode.CONSTANTGROUPED
else:
header = dprep.PromoteHeadersMode.NONE
try:
encoding = dprep.FileEncoding[encoding]
except:
encoding = dprep.FileEncoding.UTF8
ws = Run.get_context().experiment.workspace
datastore = Datastore(ws, datastore_name)
dflow = dprep.read_csv(path=datastore.path(data_path),
separator=separator,
header=header,
encoding=encoding,
quoting=quoting,
skip_rows=skip_rows)
if len(feature_columns) == 0:
X = dflow.drop_columns(label_column)
else:
X = dflow.keep_columns(feature_columns)
print('Inferring types for feature columns...')
logger.info('Inferring types for feature columns...')
sct = X.builders.set_column_types()
sct.learn()
sct.ambiguous_date_conversions_drop()
X = sct.to_dataflow()
y = dflow.keep_columns(label_column)
if automl_settings_obj.task_type.lower() == 'regression':
y = y.to_number(label_column)
print('X:')
print(X)
logger.info('X:')
logger.info(X)
print('y:')
print(y)
logger.info('y:')
logger.info(y)
try:
from azureml.train.automl._dataprep_utilities import try_retrieve_pandas_dataframe_adb
_X = try_retrieve_pandas_dataframe_adb(X)
fit_iteration_parameters_dict['X'] = _X.values
fit_iteration_parameters_dict[
'x_raw_column_names'] = _X.columns.values
except ImportError:
logger.info(
"SDK version does not support column names extraction, fallback to old path"
)
fit_iteration_parameters_dict[
'X'] = dataprep_utilities.try_retrieve_pandas_dataframe(
X)
try:
fit_iteration_parameters_dict[
'y'] = dataprep_utilities.try_retrieve_numpy_array(y)
except IndexError:
raise RetrieveNumpyArrayError()
logger.info("Finish getting data using dataprep.")
return fit_iteration_parameters_dict
except Exception as e:
print("[ParentRunId:{0}]: Error from resolving Dataflows: {1} {2}".
format(parent_run_id, e.__class__, e))
logger.error(
"[ParentRunId:{0}]: Error from resolving Dataflows: {1} {2}".
format(parent_run_id, e.__class__, e))
if isinstance(e, RetrieveNumpyArrayError):
logger.debug("Label column (y) does not exist in user's data.")
e.error_type = ErrorTypes.User
elif "The provided path is not valid." in str(e):
logger.debug("User's data is not accessible from remote run.")
e.error_type = ErrorTypes.User
elif "Required secrets are missing. Please call use_secrets to register the missing secrets." in str(
e):
logger.debug(
"User should use Datastore to data that requires secrets.")
e.error_type = ErrorTypes.User
else:
e.error_type = ErrorTypes.Client
log_traceback(e, logger)
raise RuntimeError("Error during extracting Dataflows")
def _set_problem_info_for_setup(fit_iteration_parameters_dict,
automl_settings_obj, task_type, preprocess,
enable_subsampling, num_iterations,
logger):
current_run = Run.get_submitted_run()
logger.info(
"Start to set problem info for the setup for run id {}.".format(
current_run._run_id))
logger.info("Setup experiment.")
try:
experiment = current_run.experiment
parent_run_id = _get_parent_run_id(current_run._run_id)
data_store = experiment.workspace.get_default_datastore()
found_data_store = True
logger.info("Using data store.")
except Exception as e:
logger.warning(
"Getting data store, fallback to default {}".format(e))
found_data_store = False
logger.info("Caching supported {}.".format(sdk_has_cache_capability
and found_data_store))
print("caching supported {}".format(sdk_has_cache_capability
and found_data_store))
if sdk_has_validate_data_dict:
# The newest version of validate_training_data_dict should contains check_x_y
logger.info("Using validate_training_data_dict now.")
validate_training_data_dict(
data_dict=fit_iteration_parameters_dict,
automl_settings=automl_settings_obj)
else:
logger.info("Using validate_training_data now.")
validate_training_data(
X=fit_iteration_parameters_dict.get('X'),
y=fit_iteration_parameters_dict.get('y'),
X_valid=fit_iteration_parameters_dict.get('X_valid'),
y_valid=fit_iteration_parameters_dict.get('y_valid'),
sample_weight=fit_iteration_parameters_dict.get(
'sample_weight'),
sample_weight_valid=fit_iteration_parameters_dict.get(
'sample_weight_valid'),
cv_splits_indices=fit_iteration_parameters_dict.get(
'cv_splits_indices'),
automl_settings=automl_settings_obj)
check_x_y(fit_iteration_parameters_dict.get('X'),
fit_iteration_parameters_dict.get('y'),
automl_settings_obj)
if sdk_has_cache_capability and found_data_store:
data_splits_validated = True
try:
start = time.time()
transformed_data_context = _get_transformed_data_context(
X=fit_iteration_parameters_dict.get('X'),
y=fit_iteration_parameters_dict.get('y'),
X_valid=fit_iteration_parameters_dict.get('X_valid'),
y_valid=fit_iteration_parameters_dict.get('y_valid'),
sample_weight=fit_iteration_parameters_dict.get(
'sample_weight'),
sample_weight_valid=fit_iteration_parameters_dict.get(
'sample_weight_valid'),
x_raw_column_names=fit_iteration_parameters_dict.get(
'x_raw_column_names'),
cv_splits_indices=fit_iteration_parameters_dict.get(
'cv_splits_indices'),
automl_settings_obj=automl_settings_obj,
data_store=data_store,
run_target='remote',
parent_run_id=parent_run_id,
logger=logger)
end = time.time()
print("time taken for transform {}".format(end - start))
logger.info("time taken for transform {}".format(end - start))
if sdk_has_validate_data_splits:
try:
logger.info("Validating data splits now.")
_validate_data_splits(
X=transformed_data_context.X,
y=transformed_data_context.y,
X_valid=transformed_data_context.X_valid,
y_valid=transformed_data_context.y_valid,
cv_splits=transformed_data_context.cv_splits,
automl_settings=automl_settings_obj)
data_splits_validated = True
except Exception as data_split_exception:
data_splits_validated = False
logger.error("Meeting validation errors {}.".format(
data_split_exception))
log_traceback(data_split_exception, logger)
raise data_split_exception
logger.info("Start setting problem info.")
automl.set_problem_info(
transformed_data_context.X,
transformed_data_context.y,
automl_settings_obj.task_type,
current_run=current_run,
preprocess=automl_settings_obj.preprocess,
lag_length=automl_settings_obj.lag_length,
transformed_data_context=transformed_data_context,
enable_cache=automl_settings_obj.enable_cache,
subsampling=enable_subsampling)
except Exception as e:
if sdk_has_validate_data_splits and not data_splits_validated:
logger.error(
"sdk_has_validate_data_splits is True and data_splits_validated is False {}."
.format(e))
log_traceback(e, logger)
raise e
else:
logger.warning(
"Setup failed, fall back to old model {}".format(e))
print("Setup failed, fall back to old model {}".format(e))
automl.set_problem_info(
X=fit_iteration_parameters_dict.get('X'),
y=fit_iteration_parameters_dict.get('y'),
task_type=task_type,
current_run=current_run,
preprocess=preprocess,
subsampling=enable_subsampling)
else:
logger.info("Start setting problem info using old model.")
if sdk_has_validate_data_splits:
_validate_data_splits(
X=fit_iteration_parameters_dict.get('X'),
y=fit_iteration_parameters_dict.get('y'),
X_valid=fit_iteration_parameters_dict.get('X_valid'),
y_valid=fit_iteration_parameters_dict.get('y_valid'),
cv_splits=fit_iteration_parameters_dict.get(
'cv_splits_indices'),
automl_settings=automl_settings_obj)
automl.set_problem_info(X=fit_iteration_parameters_dict.get('X'),
y=fit_iteration_parameters_dict.get('y'),
task_type=task_type,
current_run=current_run,
preprocess=preprocess,
subsampling=enable_subsampling)
test_size=0.2,
random_state=42)
vectorizer = CountVectorizer()
vectorizer.fit(x_train)
X_train = vectorizer.transform(x_train)
X_test = vectorizer.transform(x_test)
test_data = X_test[1, :]
test_data_array = test_data.toarray()
test_data_list = test_data_array.tolist()
print("len test_data_list", len(test_data_list))
print("len test_data_list 0", len(test_data_list[0]))
with open("test_data.txt", "w") as fp:
json.dump(test_data_list, fp)
run = Run.get_context(allow_offline=True)
def main():
# Add arguments to script
parser = argparse.ArgumentParser()
parser.add_argument(
"--C",
type=float,
default=1.0,
help=
"Inverse of regularization strength. Smaller values cause stronger regularization"
)
parser.add_argument("--max_iter",
type=int,
def load_and_clean(dataset_name):
dataframe = load_data(dataset_name)
dataframe = extract_features(dataframe)
features, labels = clean_data(dataframe)
return features, labels
try:
# Get workspace if run locally
ws = Workspace.from_config()
except:
# Get workspace if run remotely
ws = Run.get_context().experiment.workspace
# Run
run = Run.get_context()
# Load and clean data
features_train, labels_train = load_and_clean('energy-forecast-data-training')
features_val, labels_val = load_and_clean('energy-forecast-data-validation')
def main():
# Add arguments to script
parser = argparse.ArgumentParser()
parser.add_argument('--n_estimators',
type=int,
def train(config,
evaluate_only=False,
outdir=".",
detail=False,
azureml=False):
filename = config.model.filename
categories_file = config.dataset.categories
wav_directory = config.dataset.path
batch_size = config.training.batch_size
hidden_units = config.model.hidden_units
architecture = config.model.architecture
num_layers = config.model.num_layers
use_gpu = config.training.use_gpu
run = None
if azureml:
from azureml.core.run import Run
run = Run.get_context()
if run is None:
print("### Run.get_context() returned None")
else:
print("### Running in Azure Context")
valid_layers = [1, 2, 3]
if num_layers not in valid_layers:
raise Exception(
"--num_layers can only be one of these values {}".format(
valid_layers))
if not os.path.isdir(outdir):
os.makedirs(outdir)
if not filename:
filename = "{}{}KeywordSpotter.pt".format(architecture, hidden_units)
config.model.filename = filename
# load the featurized data
if not os.path.isdir(wav_directory):
print("### Error: please specify valid --dataset folder location: {}".
format(wav_directory))
sys.exit(1)
if not categories_file:
categories_file = os.path.join(wav_directory, "categories.txt")
with open(categories_file, "r") as f:
keywords = [x.strip() for x in f.readlines()]
training_file = os.path.join(wav_directory, "training_list.npz")
testing_file = os.path.join(wav_directory, "testing_list.npz")
validation_file = os.path.join(wav_directory, "validation_list.npz")
if not os.path.isfile(training_file):
print("Missing file {}".format(training_file))
print("Please run make_datasets.py")
sys.exit(1)
if not os.path.isfile(validation_file):
print("Missing file {}".format(validation_file))
print("Please run make_datasets.py")
sys.exit(1)
if not os.path.isfile(testing_file):
print("Missing file {}".format(testing_file))
print("Please run make_datasets.py")
sys.exit(1)
model = None
device = torch.device("cpu")
if use_gpu:
if torch.cuda.is_available():
device = torch.device("cuda")
else:
print("### CUDA not available!!")
print("Loading {}...".format(testing_file))
test_data = AudioDataset(testing_file, config.dataset, keywords)
log = None
if not evaluate_only:
print("Loading {}...".format(training_file))
training_data = AudioDataset(training_file,
config.dataset,
keywords,
training=True)
print("Loading {}...".format(validation_file))
validation_data = AudioDataset(validation_file, config.dataset,
keywords)
if training_data.mean is not None:
fname = os.path.join(outdir, "mean.npy")
print("Saving {}".format(fname))
np.save(fname, training_data.mean)
fname = os.path.join(outdir, "std.npy")
print("Saving {}".format(fname))
np.save(fname, training_data.std)
# use the training_data mean and std variation
test_data.mean = training_data.mean
test_data.std = training_data.std
validation_data.mean = training_data.mean
validation_data.std = training_data.std
print("Training model {}".format(filename))
model = create_model(config.model, training_data.input_size,
training_data.num_keywords)
if device.type == 'cuda':
model.cuda() # move the processing to GPU
start = time.time()
log = model.fit(training_data, validation_data, config.training,
config.model.sparsify, device, detail, run)
end = time.time()
passed, total, rate = model.evaluate(training_data, batch_size, device)
print("Training accuracy = {:.3f} %".format(rate * 100))
torch.save(model.state_dict(), os.path.join(outdir, filename))
print(
"Evaluating {} keyword spotter using {} rows of featurized test audio..."
.format(architecture, test_data.num_rows))
if model is None:
msg = "Loading trained model with input size {}, hidden units {} and num keywords {}"
print(
msg.format(test_data.input_size, hidden_units,
test_data.num_keywords))
model = create_model(config.model, test_data.input_size,
test_data.num_keywords)
model.load_dict(torch.load(filename))
if model and device.type == 'cuda':
model.cuda() # move the processing to GPU
results_file = os.path.join(outdir, "results.txt")
passed, total, rate = model.evaluate(test_data, batch_size, device,
results_file)
print("Testing accuracy = {:.3f} %".format(rate * 100))
if not evaluate_only:
name = os.path.splitext(filename)[0] + ".onnx"
print("saving onnx file: {}".format(name))
model.export(os.path.join(outdir, name), device)
config.dataset.sample_rate = test_data.sample_rate
config.dataset.input_size = test_data.audio_size
config.dataset.num_filters = test_data.input_size
config.dataset.window_size = test_data.window_size
config.dataset.shift = test_data.shift
logdata = {
"accuracy_val": rate,
"training_time": end - start,
"log": log
}
d = TrainingConfig.to_dict(config)
logdata.update(d)
logname = os.path.join(outdir, "train_results.json")
save_json(logdata, logname)
return rate, log
def main(
root_dir: str,
input_dir: str,
output_dir: str,
stop_words: List,
timestamp_interval: int,
force: bool,
) -> None:
"""
Main function for receiving args, and passing them through to form recognizer postprocessing function
Parameters
----------
root_dir: str
Root datastore being used
input_dir: str
Directory containing input data
output_dir: str
Path to save outputs to
stop_words: Array
stop words that will not be counted when computing
character level frequency
timestamp_interval: int
Interval used to split clapperboards into seperate events
force: bool
Flag that specifies whether current run
should overwrite outputs from previous run
"""
log.info("Clapperboard Selection Step")
# Resolve paths
input_dir = join(root_dir, input_dir)
output_dir = join(root_dir, output_dir)
run = Run.get_context()
# convert tuple to list type
stop_words = list(stop_words)
log.info("Checking if output from previous run exists...")
if os.path.exists(output_dir) and not force:
log.info("Output path already exists, please use --force to overwrite the results. Skipping...")
return
# set ocr recognizer credentials
ocr_credentials = {"key": run.get_secret("ocrkey"),
"endpoint": run.get_secret("ocrendpoint")}
log.info("Beginning process to select best clapperboards..")
# Create directory to store results in
os.makedirs(output_dir, exist_ok=True)
log.info("Running Clapperboard Selection Step")
# Build dataframe
image_obj = []
for file in os.listdir(input_dir):
if file.endswith(".jpeg"):
image = file
timestamp = int(Path(file).stem.split("=")[-1])
image_obj.append(dict(image=image, timestamp=timestamp))
output_file = os.path.join(output_dir, "selected_clapperboards.csv")
image_df = pd.DataFrame(image_obj)
image_df = image_df.sort_values(by=["timestamp"])
image_df["image"] = image_df["image"].apply(
lambda x: join(input_dir, x)
)
# feed video dataframe into the function to select clapperboards
_ = get_best_clapperboard(
image_df=image_df,
ocr_credentials=ocr_credentials,
output_file=output_file,
tolerance=timestamp_interval,
stop_words=stop_words,
)
log.info("Finished Running Clapperboard Selection Step")
def init():
global current_run
current_run = Run.get_context()
def main():
num_classes = 3
# create checkpoint dir
out_dir = './outputs' if args.out_dir is None else args.out_dir
checkpoint_dir = os.path.join(out_dir, experiment_name, 'checkpoints')
os.makedirs(checkpoint_dir, exist_ok=True)
# write logs to ./logs, which AML uploads to Artifact Service and makes available to a TensorBoard instance.
# also log some metrics through AML's Run object
run = Run.get_context()
logger_train = Logger('train', './logs', run)
logger_val = Logger('val', './logs', run)
log_sample_img_gt(sample_images_train, sample_images_val, logger_train,
logger_val)
logging.info('Logged ground truth image samples')
# larger model
if model_choice == 'unet':
model = Unet(feature_scale=feature_scale,
n_classes=num_classes,
is_deconv=True,
in_channels=3,
is_batchnorm=True)
# year 2 best solution XD_XD's model, as the baseline model
elif model_choice == 'unet_baseline':
model = UnetBaseline(feature_scale=feature_scale,
n_classes=num_classes,
is_deconv=True,
in_channels=3,
is_batchnorm=True)
else:
sys.exit(
'Invalid model_choice {}, choose unet_baseline or unet'.format(
model_choice))
model = model.to(device=device,
dtype=dtype) # move the model parameters to CPU/GPU
criterion = nn.CrossEntropyLoss(weight=loss_weights).to(device=device,
dtype=dtype)
# can also use Nesterov momentum in optim.SGD
# optimizer = optim.SGD(model.parameters(), lr=learning_rate,
# momentum=0.9, nesterov=True)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# resume from a checkpoint if provided
starting_epoch = 0
best_acc = 0.0
if os.path.isfile(starting_checkpoint_path):
logging.info(
'Loading checkpoint from {0}'.format(starting_checkpoint_path))
checkpoint = torch.load(starting_checkpoint_path)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
starting_epoch = checkpoint['epoch']
best_acc = checkpoint.get('best_acc', 0.0)
else:
logging.info(
'No valid checkpoint is provided. Start to train from scratch...')
model.apply(weights_init)
if evaluate_only:
val_loss, val_acc = evaluate(loader_val, model, criterion)
print('Evaluated on val set, loss is {}, accuracy is {}'.format(
val_loss, val_acc))
return
step = starting_epoch * len(dset_train)
for epoch in range(starting_epoch, num_epochs):
logging.info('Epoch {} of {}'.format(epoch, num_epochs))
# train for one epoch
step = train(loader_train, model, criterion, optimizer, epoch, step,
logger_train)
# evaluate on val set
logging.info(
'Evaluating model on the val set at the end of epoch {}...'.format(
epoch))
val_loss, val_acc = evaluate(loader_val, model, criterion)
logging.info('\nEpoch {}, val loss is {}, val accuracy is {}\n'.format(
epoch, step, val_loss, val_acc))
logger_val.scalar_summary('val_loss', val_loss, step + 1)
logger_val.scalar_summary('val_acc', val_acc, step + 1)
# TODO log the val images too
# record the best accuracy; save checkpoint for every epoch
is_best = val_acc > best_acc
best_acc = max(val_acc, best_acc)
checkpoint_path = os.path.join(
checkpoint_dir, 'checkpoint_epoch{}_{}.pth.tar'.format(
epoch, strftime("%Y-%m-%d-%H-%M-%S", localtime())))
logging.info(
'Saving to checkoutpoint file at {}. Is it the highest accuracy checkpoint so far: {}'
.format(checkpoint_path, str(is_best)))
save_checkpoint(
{
'epoch':
epoch + 1, # saved checkpoints are numbered starting from 1
'arch': model_choice,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_acc': best_acc
},
is_best,
checkpoint_path,
checkpoint_dir)
def main():
# Prepare parser for parameters to tune
parser = argparse.ArgumentParser()
parser.add_argument(
'--learning_rate',
type=float,
default=0.1,
help=
"Inverse of regularization strength. Smaller values cause stronger regularization"
)
parser.add_argument(
'--n_estimators',
type=int,
default=20,
help=
"Maximum number of iterations to converge, similar to max_iter in Logistic Regression"
)
parser.add_argument(
'--max_features',
type=int,
default=5,
help=
"Number of features to consider in one pass, i.e. how large could the tree grow"
)
parser.add_argument(
'--max_depth',
type=int,
default=2,
help="Maximum number of splits, i.e. how bushy could the tree grow")
args = parser.parse_args()
# Prepare the dataset to match the expected format
path = 'https://raw.githubusercontent.com/allaccountstaken/automl_v_hyperdrive/main/data/camel_data_after2010Q3.csv'
ds = load_data(path)
X, y = clean_data(ds)
#Consider for internal datasets:
#from azureml.data.dataset_factory import TabularDatasetFactory
#ds = TabularDatasetFactory.from_delimited_files(path)
# Perorm train-test-split
X_train, X_test, y_train, y_test = train_test_split(X,
y.ravel(),
train_size=0.7,
random_state=123)
# Additionally consider scalling (this may not be important for tree-based models)
scaler = StandardScaler()
X_scaler = scaler.fit(X_train)
X_train_scaled = X_scaler.transform(X_train)
X_test_scaled = X_scaler.transform(X_test)
# Prepare Azure run context to log tunning progress
run = Run.get_context()
run.log("Learning rate:", np.float(args.learning_rate))
run.log("Number of estimators:", np.int(args.n_estimators))
run.log("Number of features:", np.int(args.max_features))
run.log("Max tree depth:", np.int(args.max_depth))
# Instanciated and fit GBM classifier using sklearn library
model = GradientBoostingClassifier(learning_rate=args.learning_rate,
n_estimators=args.n_estimators,
max_features=args.max_features,
max_depth=args.max_depth,
random_state=123)
model.fit(X_train_scaled, y_train)
y_pred = model.predict(X_test_scaled)
# Report performance metrics of the trained model using testing subset
recall = recall_score(y_test, y_pred, average='binary')
run.log("Recall", np.round(np.float(recall), 5))
