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DeepLabv3+: add logging

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Starbeamrainbowlabs 2022-12-13 13:20:16 +00:00
parent be4d928319
commit d907dc48e5
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
1 changed files with 10 additions and 3 deletions
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13
aimodel/src/deeplabv3_plus_test.py
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@ -3,6 +3,7 @@
# Required dataset: https://drive.google.com/uc?id=1B9A9UCJYMwTL4oBEo4RZfbMZMaZhKJaz [instance-level-human-parsing.zip] # Required dataset: https://drive.google.com/uc?id=1B9A9UCJYMwTL4oBEo4RZfbMZMaZhKJaz [instance-level-human-parsing.zip]
from datetime import datetime from datetime import datetime
from loguru import logger
import os import os
import cv2 import cv2
@ -24,6 +25,10 @@ DIR_OUTPUT=f"output/{datetime.utcnow().date().isoformat()}_deeplabv3plus_TEST"
os.makedirs(DIR_OUTPUT) os.makedirs(DIR_OUTPUT)
logger.info("DeepLabv3+ TEST")
logger.info(f"> DIR_OUTPUT {DIR_OUTPUT}")
train_images = sorted(glob(os.path.join(DATA_DIR, "Images/*")))[:NUM_TRAIN_IMAGES] train_images = sorted(glob(os.path.join(DATA_DIR, "Images/*")))[:NUM_TRAIN_IMAGES]
train_masks = sorted(glob(os.path.join(DATA_DIR, "Category_ids/*")))[:NUM_TRAIN_IMAGES] train_masks = sorted(glob(os.path.join(DATA_DIR, "Category_ids/*")))[:NUM_TRAIN_IMAGES]
val_images = sorted(glob(os.path.join(DATA_DIR, "Images/*")))[ val_images = sorted(glob(os.path.join(DATA_DIR, "Images/*")))[
@ -64,8 +69,8 @@ def data_generator(image_list, mask_list):
train_dataset = data_generator(train_images, train_masks) train_dataset = data_generator(train_images, train_masks)
val_dataset = data_generator(val_images, val_masks) val_dataset = data_generator(val_images, val_masks)
print("Train Dataset:", train_dataset) logger.info("Train Dataset:", train_dataset)
print("Val Dataset:", val_dataset) logger.info("Val Dataset:", val_dataset)
# ███ ███ ██████ ██████ ███████ ██ # ███ ███ ██████ ██████ ███████ ██
@ -156,7 +161,7 @@ model.compile(
loss=loss, loss=loss,
metrics=["accuracy"], metrics=["accuracy"],
) )
logger.info(">>> Beginning training")
history = model.fit(train_dataset, history = model.fit(train_dataset,
validation_data=val_dataset, validation_data=val_dataset,
epochs=25, epochs=25,
@ -167,6 +172,8 @@ history = model.fit(train_dataset,
) )
], ],
) )
logger.info(">>> Training complete")
logger.info(">>> Plotting graphs")
plt.plot(history.history["loss"]) plt.plot(history.history["loss"])
plt.title("Training Loss") plt.title("Training Loss")