mirror of
https://github.com/clearml/clearml
synced 2025-01-31 17:17:00 +00:00
66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
# TRAINS - Example of manual graphs and statistics reporting
|
|
#
|
|
import os
|
|
from PIL import Image
|
|
import numpy as np
|
|
import logging
|
|
from trains import Task
|
|
|
|
|
|
task = Task.init(project_name="examples", task_name="Manual reporting")
|
|
|
|
# standard python logging
|
|
logging.info("This is an info message")
|
|
|
|
# this is loguru test example
|
|
try:
|
|
from loguru import logger
|
|
logger.info("That's it, beautiful and simple logging! (using ANSI colors)")
|
|
except ImportError:
|
|
pass
|
|
|
|
# get TRAINS logger object for any metrics / reports
|
|
logger = Task.current_task().get_logger()
|
|
|
|
# log text
|
|
logger.report_text("hello")
|
|
|
|
# report scalar values
|
|
logger.report_scalar("example_scalar", "series A", iteration=0, value=100)
|
|
logger.report_scalar("example_scalar", "series A", iteration=1, value=200)
|
|
|
|
# report histogram
|
|
histogram = np.random.randint(10, size=10)
|
|
logger.report_histogram("example_histogram", "random histogram", iteration=1, values=histogram,
|
|
xaxis="title x", yaxis="title y")
|
|
|
|
# report confusion matrix
|
|
confusion = np.random.randint(10, size=(10, 10))
|
|
logger.report_matrix("example_confusion", "ignored", iteration=1, matrix=confusion, xaxis="title X", yaxis="title Y")
|
|
|
|
# report 3d surface
|
|
logger.report_surface("example_surface", "series1", iteration=1, matrix=confusion,
|
|
xaxis="title X", yaxis="title Y", zaxis="title Z")
|
|
|
|
# report 2d scatter plot
|
|
scatter2d = np.hstack((np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1))))
|
|
logger.report_scatter2d("example_scatter", "series_xy", iteration=1, scatter=scatter2d,
|
|
xaxis="title x", yaxis="title y")
|
|
|
|
# report 3d scatter plot
|
|
scatter3d = np.random.randint(10, size=(10, 3))
|
|
logger.report_scatter3d("example_scatter_3d", "series_xyz", iteration=1, scatter=scatter3d,
|
|
xaxis="title x", yaxis="title y", zaxis="title z")
|
|
|
|
# reporting images
|
|
m = np.eye(256, 256, dtype=np.float)
|
|
logger.report_image("test case", "image float", iteration=1, image=m)
|
|
m = np.eye(256, 256, dtype=np.uint8)*255
|
|
logger.report_image("test case", "image uint8", iteration=1, image=m)
|
|
m = np.concatenate((np.atleast_3d(m), np.zeros((256, 256, 2), dtype=np.uint8)), axis=2)
|
|
logger.report_image("test case", "image color red", iteration=1, image=m)
|
|
image_open = Image.open(os.path.join("samples", "picasso.jpg"))
|
|
logger.report_image("test case", "image PIL", iteration=1, image=image_open)
|
|
# flush reports (otherwise it will be flushed in the background, every couple of seconds)
|
|
logger.flush()
|