def build_basic_tensorboard_info(cls):
'''
Create two experiments. Within each, place
values tagged loss/train, loss/val. Dir struct
on return:
tb_root
Exp1
1-file
Exp2
1-file
'''
exp1_dir = os.path.join(cls.tensorboard_dir, 'Exp1')
exp2_dir = os.path.join(cls.tensorboard_dir, 'Exp2')
writer1 = SummaryWriterPlus(exp1_dir)
writer2 = SummaryWriterPlus(exp2_dir)
for epoch in range(5):
val1 = epoch + 1
val2 = epoch + 2
writer1.add_scalar('loss/train', val1, epoch)
writer1.add_scalar('loss/val', val1 - 0.5, epoch)
writer2.add_scalar('loss/train', val2, epoch)
writer2.add_scalar('loss/val', val2 - 0.5, epoch)
return writer1, writer2
def setup_tensorboard(self, logdir, raw_data_dir=True):
'''
Initialize tensorboard. To easily compare experiments,
use runs/exp1, runs/exp2, etc.
Method creates the dir if needed.
Additionally, sets self.csv_pred_writer and self.csv_label_writer
to None, or open CSV writers, depending on the value of raw_data_dir,
see create_csv_writer()
:param logdir: root for tensorboard events
:type logdir: str
'''
if not os.path.isdir(logdir):
os.makedirs(logdir)
# For storing train/val preds/labels
# for every epoch. Used to create charts
# after run is finished:
self.csv_writer = self.create_csv_writer(raw_data_dir)
# Place to store intermediate models:
self.model_archive = \
self.create_model_archive(self.config,
self.num_classes
)
# Use SummaryWriterPlus to avoid confusing
# directory creations when calling add_hparams()
# on the writer:
self.writer = SummaryWriterPlus(log_dir=logdir)
# Intermediate storage for train and val results:
self.results = ResultCollection()
self.log.info(
f"To view tensorboard charts: in shell: tensorboard --logdir {logdir}; then browser: localhost:6006"
)
def prep_model_inference(self, model_path):
'''
1. Parses model_path into its components, and
creates a dict: self.model_props, which
contains the network type, grayscale or not,
whether pretrained, etc.
2. Creates self.csv_writer to write results measures
into csv files. The destination file is determined
as follows:
<script_dir>/runs_raw_inferences/inf_csv_results_<datetime>/<model-props-derived-fname>.csv
3. Creates self.writer(), a tensorboard writer with destination dir:
<script_dir>/runs_inferences/inf_results_<datetime>
4. Creates an ImageFolder classed dataset to self.samples_path
5. Creates a shuffling DataLoader
6. Initializes self.num_classes and self.class_names
7. Creates self.model from the passed-in model_path name
:param model_path: path to model that will be used for
inference by this instance of Inferencer
:type model_path: str
'''
model_fname = os.path.basename(model_path)
# Extract model properties
# from the model filename:
self.model_props = FileUtils.parse_filename(model_fname)
csv_results_root = os.path.join(self.curr_dir, 'runs_raw_inferences')
#self.csv_dir = os.path.join(csv_results_root, f"inf_csv_results_{uuid.uuid4().hex}")
ts = FileUtils.file_timestamp()
self.csv_dir = os.path.join(csv_results_root, f"inf_csv_results_{ts}")
os.makedirs(self.csv_dir, exist_ok=True)
csv_file_nm = FileUtils.construct_filename(self.model_props,
prefix='inf',
suffix='.csv',
incl_date=True)
csv_path = os.path.join(self.csv_dir, csv_file_nm)
self.csv_writer = CSVWriterCloseable(csv_path)
ts = FileUtils.file_timestamp()
tensorboard_root = os.path.join(self.curr_dir, 'runs_inferences')
tensorboard_dest = os.path.join(tensorboard_root, f"inf_results_{ts}")
#f"inf_results_{ts}{uuid.uuid4().hex}")
os.makedirs(tensorboard_dest, exist_ok=True)
self.writer = SummaryWriterPlus(log_dir=tensorboard_dest)
dataset = SingleRootImageDataset(
self.samples_path, to_grayscale=self.model_props['to_grayscale'])
# Make reproducible:
Utils.set_seed(42)
#********Utils.set_seed(56)
self.loader = DataLoader(dataset,
batch_size=self.batch_size,
shuffle=True,
drop_last=True)
self.class_names = dataset.class_names()
self.num_classes = len(self.class_names)
# Get the right type of model,
# Don't bother getting it pretrained,
# of freezing it, b/c we will overwrite
# the weights:
self.model = NetUtils.get_net(
self.model_props['net_name'],
num_classes=self.num_classes,
pretrained=False,
freeze=0,
to_grayscale=self.model_props['to_grayscale'])
self.log.info(f"Tensorboard info written to {tensorboard_dest}")
self.log.info(f"Result measurement CSV file(s) written to {csv_path}")