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training_example_torch.py
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training_example_torch.py
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# coding: utf-8
__author__ = 'ZFTurbo: https://kaggle.com/zfturbo'
if __name__ == '__main__':
import os
gpu_use = 0
print('GPU use: {}'.format(gpu_use))
os.environ["KERAS_BACKEND"] = "torch"
os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(gpu_use)
import segmentation_models_3D as sm
import numpy as np
import random
import matplotlib.pyplot as plt
from skimage import measure
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, CSVLogger, EarlyStopping
from keras import backend as K
from keras.src.utils import summary_utils
def get_model_memory_usage(batch_size, model):
shapes_mem_count = 0
internal_model_mem_count = 0
for l in model.layers:
layer_type = l.__class__.__name__
if layer_type == 'Model' or layer_type == 'Functional':
internal_model_mem_count += get_model_memory_usage(batch_size, l)
single_layer_mem = 1
out_shape = l.output.shape
if type(out_shape) is list:
out_shape = out_shape[0]
for s in out_shape:
if s is None:
continue
single_layer_mem *= s
shapes_mem_count += single_layer_mem
trainable_count = summary_utils.count_params(model.trainable_weights)
non_trainable_count = summary_utils.count_params(model.non_trainable_weights)
number_size = 4.0
if K.floatx() == 'float16':
number_size = 2.0
if K.floatx() == 'float64':
number_size = 8.0
total_memory = number_size * (batch_size * shapes_mem_count + trainable_count + non_trainable_count)
gbytes = np.round(total_memory / (1024.0 ** 3), 3) + internal_model_mem_count
return gbytes
def sphere(shape, radius, position):
"""Generate an n-dimensional spherical mask."""
# assume shape and position have the same length and contain ints
# the units are pixels / voxels (px for short)
# radius is a int or float in px
assert len(position) == len(shape)
n = len(shape)
semisizes = (radius,) * len(shape)
# genereate the grid for the support points
# centered at the position indicated by position
grid = [slice(-x0, dim - x0) for x0, dim in zip(position, shape)]
position = np.ogrid[grid]
# calculate the distance of all points from `position` center
# scaled by the radius
arr = np.zeros(shape, dtype=float)
for x_i, semisize in zip(position, semisizes):
# this can be generalized for exponent != 2
# in which case `(x_i / semisize)`
# would become `np.abs(x_i / semisize)`
arr += (x_i / semisize) ** 2
# the inner part of the sphere will have distance below or equal to 1
return arr <= 1.0
def gen_random_volume(debug=False):
img = np.zeros((96, 96, 96, 3), dtype=np.uint8)
mask = np.zeros((96, 96, 96, 2), dtype=np.uint8)
num_sheres = random.randint(2, 4)
min_radius = 3
max_radius = 20
num_cubes = random.randint(2, 4)
min_cube_side = 3
max_cube_side = 15
# Background
dark_color0 = random.randint(0, 100)
dark_color1 = random.randint(0, 100)
dark_color2 = random.randint(0, 100)
img[..., 0] = dark_color0
img[..., 1] = dark_color1
img[..., 2] = dark_color2
# Spheres
for i in range(num_sheres):
light_color0 = random.randint(dark_color0+1, 255)
light_color1 = random.randint(dark_color1+1, 255)
light_color2 = random.randint(dark_color2+1, 255)
center_0 = random.randint(0, img.shape[0] - 1)
center_1 = random.randint(0, img.shape[1] - 1)
center_2 = random.randint(0, img.shape[2] - 1)
r1 = random.randint(min_radius, max_radius)
# print(r1, (center_0, center_1, center_2), (light_color0, light_color1, light_color2))
s = sphere(img.shape[:-1], r1, (center_0, center_1, center_2))
tmp = img.copy()
tmp[s] = (light_color0, light_color1, light_color2)
img[s] = tmp[s]
mask[s] = (255, 0)
# print(img.min(), img.max(), img.mean(), img.dtype)
# Cubes
for i in range(num_cubes):
light_color0 = random.randint(dark_color0 + 1, 255)
light_color1 = random.randint(dark_color1 + 1, 255)
light_color2 = random.randint(dark_color2 + 1, 255)
range0_start = random.randint(0, img.shape[0] - max_cube_side)
range0_end = range0_start + random.randint(min_cube_side, max_cube_side)
range1_start = random.randint(0, img.shape[1] - max_cube_side)
range1_end = range1_start + random.randint(min_cube_side, max_cube_side)
range2_start = random.randint(0, img.shape[2] - max_cube_side)
range2_end = range2_start + random.randint(min_cube_side, max_cube_side)
img[range0_start:range0_end, range1_start:range1_end, range2_start:range2_end] = (light_color0, light_color1, light_color2)
mask[range0_start:range0_end, range1_start:range1_end, range2_start:range2_end] = (0, 255)
# Debug
if debug:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='3d')
verts, faces, normals, values = measure.marching_cubes(img[..., 1], 127)
ax.plot_trisurf(
verts[:, 0],
verts[:, 1],
faces,
verts[:, 2],
cmap='Spectral',
antialiased=False,
linewidth=0.0
)
plt.show()
# White noise
density = random.uniform(0, 0.1)
for i in range(img.shape[0]):
for j in range(img.shape[1]):
for k in range(img.shape[2]):
if random.random() < density:
img[i, j, k] = (
random.randint(0, 255),
random.randint(0, 255),
random.randint(0, 255),
)
return img, mask
def batch_generator(batch_size, preprocess_input):
while True:
image_list = []
mask_list = []
for i in range(batch_size):
img, mask = gen_random_volume()
image_list.append(img)
mask_list.append(mask)
image_list = np.array(image_list, dtype=np.float32)
image_list = preprocess_input(image_list)
mask_list = np.array(mask_list, dtype=np.float32)
mask_list /= 255.0
# print(image_list.shape, mask_list.shape)
yield image_list, mask_list
def train_model_example():
encoder_weights = 'imagenet'
shape_size = (96, 96, 96, 3)
backbone = 'resnet18'
num_classes = 2
batch_size_train = 12
batch_size_valid = 12
learning_rate = 0.0001
patience = 10
epochs = 50
steps_per_epoch = 100
validation_steps = 20
model = sm.Unet(
backbone,
input_shape=shape_size,
encoder_weights=encoder_weights,
classes=num_classes,
)
print(model.summary())
print(get_model_memory_usage(batch_size_train, model))
preprocess_input = sm.get_preprocessing(backbone)
optim = Adam(learning_rate=learning_rate)
loss_to_use = sm.losses.bce_jaccard_loss
model.compile(optimizer=optim, loss=loss_to_use, metrics=[sm.metrics.iou_score, sm.metrics.f1_score])
cache_model_path = '{}_temp.keras'.format(backbone)
best_model_path = '{}'.format(backbone) + '-{val_iou_score:.4f}-{epoch:02d}.keras'
callbacks = [
ModelCheckpoint(cache_model_path, monitor='val_loss', verbose=0),
ModelCheckpoint(best_model_path, monitor='val_loss', verbose=0),
ReduceLROnPlateau(monitor='val_iou_score', factor=0.95, patience=3, min_lr=1e-9, min_delta=1e-8, verbose=1, mode='max'),
CSVLogger('history_{}_lr_{}.csv'.format(backbone, learning_rate), append=True),
EarlyStopping(monitor='val_iou_score', patience=patience, verbose=0, mode='max'),
]
gen_train = batch_generator(
batch_size_train,
preprocess_input
)
gen_valid = batch_generator(
batch_size_valid,
preprocess_input,
)
history = model.fit(
gen_train,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=gen_valid,
validation_steps=validation_steps,
verbose=1,
initial_epoch=0,
callbacks=callbacks
)
max_iou = max(history.history['val_iou_score'])
print('Training finished. Max IoU: {}'.format(max_iou))
if __name__ == '__main__':
# gen_random_volume(debug=True)
train_model_example()