def thigh_incremental_mem(modelObj: DynamicDLModel, trainingData: dict, trainingOutputs,
bs=5, minTrainImages=5):
import dl.common.preprocess_train as pretrain
from dl.common.DataGenerators import DataGeneratorMem
import os
from keras.callbacks import ModelCheckpoint
from keras import optimizers
try:
np
except:
import numpy as np
LABELS_DICT = {
1: 'VL',
2: 'VM',
3: 'VI',
4: 'RF',
5: 'SAR',
6: 'GRA',
7: 'AM',
8: 'SM',
9: 'ST',
10: 'BFL',
11: 'BFS',
12: 'AL'
}
MODEL_RESOLUTION = np.array([1.037037, 1.037037])
MODEL_SIZE = (432, 432)
BAND=49
BATCH_SIZE = bs
CHECKPOINT_PATH = os.path.join("..", "Weights_incremental")
os.makedirs(CHECKPOINT_PATH, exist_ok=True)
image_list, mask_list = pretrain.common_input_process(LABELS_DICT, MODEL_RESOLUTION, MODEL_SIZE, trainingData, trainingOutputs)
output_data_structure = pretrain.input_creation_mem(image_list, mask_list, BAND)
card = len(image_list)
steps = int(float(card) / BATCH_SIZE)
netc = modelObj.model
checkpoint_files = os.path.join(CHECKPOINT_PATH, "weights_thigh - {epoch: 02d} - {loss: .2f}.hdf5")
training_generator = DataGeneratorMem(output_data_structure, list_X=list(range(1, steps * BATCH_SIZE + 1)), batch_size=BATCH_SIZE)
check = ModelCheckpoint(filepath=checkpoint_files, monitor='loss', verbose=0, save_best_only=False,
save_weights_only=True, mode='auto', period=10)
adamlr = optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, amsgrad=True)
netc.compile(loss=pretrain.weighted_loss, optimizer=adamlr)
history = netc.fit_generator(generator=training_generator, steps_per_epoch=steps, epochs=5, callbacks=[check],
verbose=1)
def thigh_incremental_mem(modelObj: DynamicDLModel,
trainingData: dict,
trainingOutputs,
bs=5,
minTrainImages=5):
import dl.common.preprocess_train as pretrain
from dl.common.DataGenerators import DataGeneratorMem
import os
from keras.callbacks import ModelCheckpoint
from keras import optimizers
import time
try:
np
except:
import numpy as np
from dl.labels.thigh import inverse_labels
MODEL_RESOLUTION = np.array([1.037037, 1.037037])
MODEL_SIZE = (432, 432)
BAND = 49
BATCH_SIZE = bs
CHECKPOINT_PATH = os.path.join(".", "Weights_incremental", "thigh")
MIN_TRAINING_IMAGES = minTrainImages
os.makedirs(CHECKPOINT_PATH, exist_ok=True)
t = time.time()
print('Image preprocess')
image_list, mask_list = pretrain.common_input_process(
inverse_labels, MODEL_RESOLUTION, MODEL_SIZE, trainingData,
trainingOutputs)
print('Done. Elapsed', time.time() - t)
nImages = len(image_list)
if nImages < MIN_TRAINING_IMAGES:
print("Not enough images for training")
return
print("image shape", image_list[0].shape)
print("mask shape", mask_list[0].shape)
print('Weight calculation')
t = time.time()
output_data_structure = pretrain.input_creation_mem(
image_list, mask_list, BAND)
print('Done. Elapsed', time.time() - t)
card = len(image_list)
steps = int(float(card) / BATCH_SIZE)
print(f'Incremental learning for thigh with {nImages} images')
t = time.time()
netc = modelObj.model
checkpoint_files = os.path.join(
CHECKPOINT_PATH, "weights - {epoch: 02d} - {loss: .2f}.hdf5")
training_generator = DataGeneratorMem(output_data_structure,
list_X=list(range(steps *
BATCH_SIZE)),
batch_size=BATCH_SIZE,
dim=MODEL_SIZE)
#check = ModelCheckpoint(filepath=checkpoint_files, monitor='loss', verbose=0, save_best_only=False,save_weights_only=True, mode='auto', period=10)
check = ModelCheckpoint(filepath=checkpoint_files,
monitor='loss',
verbose=0,
save_best_only=False,
save_freq='epoch',
save_weights_only=True,
mode='auto')
adamlr = optimizers.Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
amsgrad=True)
netc.compile(loss=pretrain.weighted_loss, optimizer=adamlr)
#history = netc.fit_generator(generator=training_generator, steps_per_epoch=steps, epochs=5, callbacks=[check], verbose=1)
history = netc.fit(x=training_generator,
steps_per_epoch=steps,
epochs=5,
callbacks=[check],
verbose=1)
print('Done. Elapsed', time.time() - t)
def leg_incremental_mem(modelObj: DynamicDLModel, trainingData: dict, trainingOutputs,
bs=5, minTrainImages=5):
import dl.common.preprocess_train as pretrain
from dl.common.DataGenerators import DataGeneratorMem
import os, time
from keras.callbacks import ModelCheckpoint
from keras import optimizers
try:
np
except:
import numpy as np
from dl.labels.leg import inverse_labels
MODEL_RESOLUTION = np.array([1.037037, 1.037037])
MODEL_SIZE = (432, 432)
MODEL_SIZE_SPLIT = (216, 216)
BAND = 64
BATCH_SIZE = bs
CHECKPOINT_PATH = os.path.join(".", "Weights_incremental_split", "leg")
MIN_TRAINING_IMAGES = minTrainImages
os.makedirs(CHECKPOINT_PATH, exist_ok=True)
t = time.time()
print('Image preprocess')
classification = trainingData.get('classification', '')
single_side = False
swap = False
# Note: this is anatomical right, which means it's image left! It's the image right that is swapped
if classification.lower().strip().endswith('right'):
single_side = True
swap = False
elif classification.lower().strip().endswith('left'):
single_side = True
swap = True
if single_side:
image_list, mask_list = pretrain.common_input_process_single(inverse_labels, MODEL_RESOLUTION, MODEL_SIZE,
MODEL_SIZE_SPLIT, trainingData,
trainingOutputs, swap)
else:
image_list, mask_list = pretrain.common_input_process_split(inverse_labels, MODEL_RESOLUTION, MODEL_SIZE,
MODEL_SIZE_SPLIT, trainingData,
trainingOutputs)
print('Done. Elapsed', time.time() - t)
nImages = len(image_list)
if nImages < MIN_TRAINING_IMAGES:
print("Not enough images for training")
return
print("image shape", image_list[0].shape)
print("mask shape", mask_list[0].shape)
print('Weight calculation')
t = time.time()
output_data_structure = pretrain.input_creation_mem(image_list, mask_list, BAND)
print('Done. Elapsed', time.time() - t)
card = len(image_list)
steps = int(float(card) / BATCH_SIZE)
print(f'Incremental learning for leg with {nImages} images')
t = time.time()
netc = modelObj.model
checkpoint_files = os.path.join(CHECKPOINT_PATH, "weights - {epoch: 02d} - {loss: .2f}.hdf5")
training_generator = DataGeneratorMem(output_data_structure, list_X=list(range(steps * BATCH_SIZE)),batch_size=BATCH_SIZE, dim=MODEL_SIZE_SPLIT)
# check = ModelCheckpoint(filepath=checkpoint_files, monitor='loss', verbose=0, save_best_only=False,save_weights_only=True, mode='auto', period=10)
check = ModelCheckpoint(filepath=checkpoint_files, monitor='loss', verbose=0, save_best_only=True, # save_freq='epoch',
save_weights_only=True, mode='auto')
adamlr = optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, amsgrad=True)
netc.compile(loss=pretrain.weighted_loss, optimizer=adamlr)
# history = netc.fit_generator(generator=training_generator, steps_per_epoch=steps, epochs=5, callbacks=[check], verbose=1)
history = netc.fit(x=training_generator, steps_per_epoch=steps, epochs=5, callbacks=[check], verbose=1)
print('Done. Elapsed', time.time() - t)