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basic_utils.py
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basic_utils.py
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import json
import os.path
import matplotlib.pyplot as plt
import requests
from hbconfig import Config
def send_message_to_slack(config_name):
project_name = os.path.basename(os.path.abspath("."))
data = {"text": f"The learning is finished with *{project_name}* Project using `{config_name}` config."}
webhook_url = Config.slack.webhook_url
if webhook_url == "":
print(data["text"])
else:
requests.post(Config.slack.webhook_url, data=json.dumps(data))
label_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
def plot_images(images, cls_true, cls_pred=None):
"""
Adapted from https://github.com/Hvass-Labs/TensorFlow-Tutorials/
"""
fig, axes = plt.subplots(3, 3)
for i, ax in enumerate(axes.flat):
# plot img
ax.imshow(images[i, :, :, :], interpolation='spline16')
# show true & predicted classes
cls_true_name = label_names[cls_true[i]]
if cls_pred is None:
xlabel = "{0} ({1})".format(cls_true_name, cls_true[i])
else:
cls_pred_name = label_names[cls_pred[i]]
xlabel = "True: {0}\nPred: {1}".format(cls_true_name, cls_pred_name)
ax.set_xlabel(xlabel)
ax.set_xticks([])
ax.set_yticks([])
plt.show()
def saving_config(path):
with open(path, "w+") as text_file:
text_file.write(f"Config: {Config}")
if Config.get("description", None):
text_file.write("Config: {}".format(Config))
text_file.write("Config Description")
for key, value in Config.description.items():
text_file.write(f" - {key}: {value}")