def test_read_path_logits(self):
# Create fake logs for the read_path_logits() function to consume.
tempdir = self.get_temp_dir()
writer = tf2.summary.create_file_writer(tempdir, max_queue=0)
with writer.as_default():
# Events matching the path logits pattern
tf2.summary.scalar('rlpathlogits/filters/0', 1.0, step=42)
tf2.summary.scalar('rlpathlogits/filters/1', 2.0, step=42)
tf2.summary.scalar('rlpathlogits/filters/2', 3.0, step=42)
tf2.summary.scalar('rlpathlogits/layers/0/choices/0', 4.0, step=42)
tf2.summary.scalar('rlpathlogits/layers/0/choices/1', 5.0, step=42)
tf2.summary.scalar('rlpathlogits/layers/0/choices/2', 6.0, step=42)
# Events not matching any pattern.
tf2.summary.scalar('global_step/sec', 10.0, step=42)
self.evaluate(writer.init())
self.evaluate(tf.summary.all_v2_summary_ops())
self.evaluate(writer.flush())
# Try to read the events from file.
self.assertAllClose(
analyze_mobile_search_lib.read_path_logits(tempdir),
{
42: {
'filters': [1.0, 2.0, 3.0],
'layers/0/choices': [4.0, 5.0, 6.0],
}
})
def test_read_path_logits_with_invalid_entry(self):
# Create fake logs for the read_path_logits() function to consume.
tempdir = self.get_temp_dir()
writer = tf2.summary.create_file_writer(tempdir, max_queue=0)
with writer.as_default():
# Events matching the path logits pattern
tf2.summary.scalar('rlpathlogits/filters/0', 1.0, step=42)
tf2.summary.scalar('rlpathlogits/filters/2', 3.0, step=42)
tf2.summary.scalar('rlpathlogits/layers/0/choices/0', 4.0, step=42)
tf2.summary.scalar('rlpathlogits/layers/0/choices/1', 5.0, step=42)
tf2.summary.scalar('rlpathlogits/layers/0/choices/2', 6.0, step=42)
# 'rlpathlogits/filters/1' is missing from the logs
self.evaluate(writer.init())
self.evaluate(tf.summary.all_v2_summary_ops())
self.evaluate(writer.flush())
# Try to read the events from file. The events from Step 42 should be
# skipped, since some of the data is incomplete.
self.assertEmpty(analyze_mobile_search_lib.read_path_logits(tempdir))
def _scan_directory(directory, output_format, ssd):
"""Scan a directory for log files and write the final model to stdout."""
if output_format == _OUTPUT_FORMAT_LINES:
print('directory =', directory)
model_spec_filename = os.path.join(directory, 'model_spec.json')
if not tf.io.gfile.exists(model_spec_filename):
print('file {} not found; skipping'.format(model_spec_filename))
if output_format == _OUTPUT_FORMAT_LINES:
print()
return
with tf.io.gfile.GFile(model_spec_filename, 'r') as handle:
model_spec = schema_io.deserialize(handle.read())
paths = []
oneofs = dict()
def populate_oneofs(path, oneof):
paths.append(path)
oneofs[path] = oneof
schema.map_oneofs_with_paths(populate_oneofs, model_spec)
all_path_logits = analyze_mobile_search_lib.read_path_logits(directory)
if not all_path_logits:
print(
'event data missing from directory {}; skipping'.format(directory))
if output_format == _OUTPUT_FORMAT_LINES:
print()
return
global_step = max(all_path_logits)
if output_format == _OUTPUT_FORMAT_LINES:
print('global_step = {:d}'.format(global_step))
all_path_logit_keys = six.viewkeys(all_path_logits[global_step])
oneof_keys = six.viewkeys(oneofs)
if all_path_logit_keys != oneof_keys:
raise ValueError(
'OneOf key mismatch. Present in event files but not in model_spec: {}. '
'Present in model_spec but not in event files: {}'.format(
all_path_logit_keys - oneof_keys,
oneof_keys - all_path_logit_keys))
indices = []
for path in paths:
index = np.argmax(all_path_logits[global_step][path])
indices.append(index)
indices_str = ':'.join(map(str, indices))
if output_format == _OUTPUT_FORMAT_LINES:
print('indices = {:s}'.format(indices_str))
cost_model_time = mobile_cost_model.estimate_cost(indices, ssd)
if output_format == _OUTPUT_FORMAT_LINES:
print('cost_model = {:f}'.format(cost_model_time))
if output_format == _OUTPUT_FORMAT_LINES:
print()
elif output_format == _OUTPUT_FORMAT_CSV:
fields = [indices_str, global_step, directory, cost_model_time]
print(','.join(map(str, fields)))