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train_GhostNet.py
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train_GhostNet.py
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import os
import re
import numpy as np
import glob
import tensorflow as tf
from keras import backend as K
from architectures.GhostNet import GhostNet
from keras.callbacks import ModelCheckpoint, CSVLogger
from keras.layers import Dense, Dropout, GlobalAveragePooling2D
from keras.models import Model
from keras.optimizers import Adam
from keras.utils.np_utils import to_categorical
from keras.losses import CategoricalCrossentropy
from sklearn.utils import class_weight
from sequence_loader import SequenceLoader
from keras import backend as K
import cv2 as cv
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
#os.environ['TF_GPU_ALLOCATOR'] = 'cuda_malloc_async'
#py -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
MODEL_PATH = ".\\nets\\GhostNet\\"
#TRAIN_IMAGES_PATH = "C:\\Users\\Vojta\\DiplomaProjects\\AffectNet\\train_set\\images\\"
TRAIN_IMAGES_PATH = "C:\\Users\\Vojta\\DiplomaProjects\\AffectNet\\train_set\\images2000\\"
TRAIN_LABELS_PATH = "C:\\Users\\Vojta\\DiplomaProjects\\AffectNet\\train_set\\all_labels_exp.npy"
TEST_IMAGES_PATH = "C:\\Users\\Vojta\\DiplomaProjects\\AffectNet\\val_set\\images\\"
TEST_LABELS_PATH = "C:\\Users\\Vojta\\DiplomaProjects\\AffectNet\\val_set\\all_labels_exp.npy"
"""
MODEL_PATH = "./nets/GhostNet/"
TRAIN_IMAGES_PATH = "/sp1/train_set/images/"
TRAIN_LABELS_PATH = "/sp1/train_set/all_labels_exp.npy"
TEST_IMAGES_PATH = "/sp1/val_set/images/"
TEST_LABELS_PATH = "/sp1/val_set/all_labels_exp.npy"
"""
BATCH_SIZE = 8 # BATCH_SIZE * strategy.num_replicas_in_sync
EPOCHS = 1
IMAGE_SHAPE = (224, 224, 3)
AUGMENT = True
SHUFFLE = True
LEARNING_RATE = 0.01
ENDING_STRING = ("AUGFULL" if AUGMENT else "") + ("_SHUFFLE" if SHUFFLE else "")
MODEL_NAME = f"GhostNet_E{EPOCHS}_B{BATCH_SIZE // 3}_Adam{LEARNING_RATE}_{ENDING_STRING}"
def init():
gpus = tf.config.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
#strategy = tf.distribute.MultiWorkerMirroredStrategy()
#strategy = tf.distribute.MirroredStrategy(cross_device_ops = tf.distribute.HierarchicalCopyAllReduce())
#strategy = tf.distribute.MirroredStrategy(cross_device_ops = tf.distribute.NcclAllReduce())
#strategy = tf.distribute.MirroredStrategy(cross_device_ops = tf.distribute.ReductionToOneDevice())
#return strategy
#def load_model(strategy, existingModelPath = None):
def load_model(existingModelPath = None):
if existingModelPath != None:
model = tf.keras.models.load_model(existingModelPath)
else:
#with strategy.scope():
model = GhostNet(classes = 8, input_shape = IMAGE_SHAPE)
model.compile(loss = CategoricalCrossentropy(), optimizer = Adam(learning_rate = LEARNING_RATE), metrics = ['accuracy'])
return model
def atoi(text):
return int(text) if text.isdigit() else text
def natural_keys(text):
return [ atoi(c) for c in re.split(r'(\d+)', text) ]
def load_dataset(labels_path, images_path):
labels = np.load(labels_path)
images_paths_list = glob.glob(images_path + "*.jpg")
images_paths_list.sort(key = natural_keys)
weights = class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(labels), y = labels)
weights = dict(enumerate(weights))
labels = to_categorical(labels, num_classes = 8)
augment = True
shuffle = True
sequence = SequenceLoader(images_paths_list, labels, BATCH_SIZE, IMAGE_SHAPE, shuffle, augment)
return sequence, len(images_paths_list), weights
if __name__ == "__main__":
#strategy = init()
init()
#model = load_model(strategy)
model = load_model()
print(" ***** MODEL LOADED ***** ")
train_sequence, train_labels_count, train_weights = load_dataset(TRAIN_LABELS_PATH, TRAIN_IMAGES_PATH)
test_sequence, test_labels_count, test_weights = load_dataset(TEST_LABELS_PATH, TEST_IMAGES_PATH)
print(" ***** SEQUENCES READY ***** ")
history = model.fit(
train_sequence,
steps_per_epoch = train_labels_count // BATCH_SIZE,
class_weight = train_weights,
epochs = EPOCHS,
validation_data = test_sequence,
validation_steps = test_labels_count // BATCH_SIZE,
callbacks = [
ModelCheckpoint(MODEL_PATH + MODEL_NAME + '_E_{epoch:02d}_{val_loss:.3f}_T.tf', monitor = 'val_acc',
save_best_only = False,
save_weights_only = False,
save_format = 'tf'),
],
workers = 12,
use_multiprocessing = False) # False
"""
use_multiprocessing:
Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes.
"""
print(" ***** MODEL FITTED ***** ")
for layer in model.layers:
if layer is Dropout:
model.layers.remove(layer)
model.save_weights(MODEL_PATH + MODEL_NAME + '_weights', save_format = 'tf', overwrite = True)
model.save(MODEL_PATH + MODEL_NAME, save_format = 'tf', overwrite = True)
print(" ***** ENDING ***** ")
np.save(MODEL_PATH + '_HIST', history.history)
f = open(MODEL_PATH + MODEL_NAME + "/stats.txt", "w")
f.write("accuracy:\n")
f.write(str(history.history['accuracy']))
f.write("\n")
f.write("val_accuracy:\n")
f.write(str(history.history['val_accuracy']))
f.write("\n\n")
model = tf.keras.models.load_model(MODEL_PATH + MODEL_NAME)
labels = np.load(TEST_LABELS_PATH)
predictions = []
images_paths_list = glob.glob(TEST_IMAGES_PATH + "*.jpg")
images_paths_list.sort(key = natural_keys)
errors = 0
for i in range(len(images_paths_list)):
img_path = images_paths_list[i]
img = cv.imread(img_path, 1)
img = img.reshape(1, 224, 224, 3)
prediction = model.predict(img, verbose = 0)[0]
predictions.append(np.argmax(prediction))
if np.argmax(prediction) != labels[i]:
errors += 1
evaluation = (1 - (errors / (i + 1))) * 100
print(f"{i} / {len(images_paths_list)}\t\tSuccess rate: {evaluation:.3f} % ", end = "\r")
print("\n")
evaluation = (1 - (errors / (len(images_paths_list)))) * 100
print(f"{MODEL_NAME}\nImages: {len(images_paths_list)}\nErrors: {errors}\nSuccess rate: {evaluation:.3f} %\nConfusion matrix:\n{tf.math.confusion_matrix(labels, predictions)}")
f.write(f"{MODEL_NAME}\nImages: {len(images_paths_list)}\nErrors: {errors}\nSuccess rate: {evaluation:.3f} %\nConfusion matrix:\n{tf.math.confusion_matrix(labels, predictions)}")
f.close()
print(" ***** STATS SAVED ***** ")