def train(args):
# 訓練とテストデータを読み込みます
# Load train and test datas
train_gen, test_gen = load_dataset(train_rate=args.trainrate)
trainx = train_gen.images_original
trainy = train_gen.images_segmented
testx = test_gen.images_original
testy = test_gen.images_segmented
print(trainx.shape)
print(testx.shape)
# Create Reporter Object
reporter = rp.Reporter(parser=parser)
accuracy_fig = reporter.create_figure("Accuracy", ("epoch", "accuracy"),
["train", "test"])
loss_fig = reporter.create_figure("Loss", ("epoch", "loss"),
["train", "test"])
epochs = args.epoch
batch_size = args.batchsize
is_augment = args.augmentation
model = build_model(output_class_num=ld.DataSet.length_category(),
l2_reg=args.l2reg)
model.compile(
loss=original_loss,
#loss='binary_crossentropy',
optimizer=Adam(lr=0.01),
metrics=['accuracy'])
#train_sequence = SequenceGenerator(train_gen, batch_size=batch_size, is_augment=is_augment)
#test_sequence = SequenceGenerator(test_gen, batch_size=batch_size, is_augment=False)
callbacks_list = [
SavePredictionCallback(train_gen, test_gen, reporter),
ReduceLROnPlateau(monitor='loss',
factor=0.5,
patience=5,
min_lr=1e-15,
verbose=1,
mode='auto',
cooldown=0),
ModelCheckpoint(filepath='./model_{epoch:02d}_{val_loss:.2f}.h5',
monitor='loss',
save_best_only=True,
verbose=1,
mode='auto')
]
# model.fit_generator(
# generator=train_sequence,
# validation_data=test_sequence,
# epochs=epochs,
# callbacks=callbacks_list
# )
model.summary()
history = model.fit(x=[trainx],
y=[trainy],
batch_size=batch_size,
epochs=epochs,
validation_data=([testx], [testy]),
callbacks=callbacks_list)
def train(parser):
train, test = load_dataset(train_rate=parser.trainrate)
valid = test.devide(0, int(NUM*0.1))
test = test.devide(int(NUM*0.1), int(NUM*0.3))
#保存ファイル
reporter = rp.Reporter(parser=parser)
accuracy_fig = reporter.create_figure("Accuracy", ("epoch", "accuracy"), ["train", "test"])
loss_fig = reporter.create_figure("Loss", ("epoch", "loss"), ["train", "test"])
#GPU
gpu = parser.gpu
#model
model_unet = model.UNet(size=(128, 128), l2_reg=parser.l2reg).model
#誤差関数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=model_unet.teacher,
logits=model_unet.outputs))
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)
#精度
correct_prediction = tf.equal(tf.argmax(model_unet.outputs, 3), tf.argmax(model_unet.teacher, 3))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#gpu config
gpu_config = tf.ConfigProto(gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.7), device_count={'GPU': 1},
log_device_placement=False, allow_soft_placement=True)
sess = tf.InteractiveSession(config=gpu_config) if gpu else tf.InteractiveSession()
tf.global_variables_initializer().run()
#parameter
epochs = parser.epoch
batch_size = parser.batchsize
is_augment = parser.augmentation
v_images_original = valid.images_original
v_images_original = v_images_original[:,:,:, np.newaxis]
t_images_original = test.images_original
t_images_original = t_images_original[:,:,:, np.newaxis]
train_dict = {model_unet.inputs: v_images_original, model_unet.teacher: valid.images_segmented,
model_unet.is_training: False}
test_dict = {model_unet.inputs: t_images_original, model_unet.teacher: test.images_segmented,
model_unet.is_training: False}
saver = tf.train.Saver()
if not os.path.exists("./checkpoint"):
os.makedirs("./checkpoint")
if CONTINUE:
if os.path.exists("./checkpoint/"+ RESTORE_MODEL):
saver.restore(sess, "./checkpoint/"+ RESTORE_MODEL)
for epoch in range(epochs):
for batch in train(batch_size=batch_size, augment=is_augment):#ここでtrainがシャッフルされる
# バッチデータ
images_original = batch.images_original
if not is_augment:
images_original = images_original[:, :, :, np.newaxis]
inputs = images_original
teacher = batch.images_segmented
sess.run(train_step, feed_dict={model_unet.inputs: inputs, model_unet.teacher: teacher,
model_unet.is_training: True})
#入力データはグレースケールなのでチャネルの分の次元を追加
train_images_original = train.images_original
train_images_original = train_images_original[:,:,:, np.newaxis]
# 評価
if epoch % 1 == 0:
loss_train = sess.run(cross_entropy, feed_dict=train_dict)
loss_test = sess.run(cross_entropy, feed_dict=test_dict)
accuracy_train = sess.run(accuracy, feed_dict=train_dict)
accuracy_test = sess.run(accuracy, feed_dict=test_dict)
print("Epoch:", epoch)
print("[Train] Loss:", loss_train, " Accuracy:", accuracy_train)
print("[Test] Loss:", loss_test, "Accuracy:", accuracy_test)
accuracy_fig.add([accuracy_train, accuracy_test], is_update=True)
loss_fig.add([loss_train, loss_test], is_update=True)
if epoch % 1 == 0:
idx_train = random.randrange(NUM*0.7)#trainサイズ
idx_test = random.randrange(NUM*0.2)#validationとtestが24個しかない
outputs_train = sess.run(model_unet.outputs,
feed_dict={model_unet.inputs: [train_images_original[idx_train]],
model_unet.is_training: False})
outputs_test = sess.run(model_unet.outputs,
feed_dict={model_unet.inputs: [t_images_original[idx_test]],
model_unet.is_training: False})
train_set = [train_images_original[idx_train], outputs_train[0], train.images_segmented[idx_train]] #なぜかtrain.images_segmentedがシャッフルされるTODO
test_set = [t_images_original[idx_test], outputs_test[0], test.images_segmented[idx_test]]
reporter.save_image_from_ndarray(train_set, test_set, train.palette, epoch,
index_void=len(ld.DataSet.CATEGORY)-1)
if epoch % 10 == 0:
if SAVE:
save_path = saver.save(sess, "./checkpoint/save_model_epoch_"+str(epoch)+"_.ckpt")
save_path = saver.save(sess, "./checkpoint/save_model_done_plus.ckpt")
#modelの評価
loss_test = sess.run(cross_entropy, feed_dict=test_dict)
accuracy_test = sess.run(accuracy, feed_dict=test_dict)
print("Result")
print("[Test] Loss:", loss_test, "Accuracy:", accuracy_test)
sess.close()
def train(parser):
# 訓練とテストデータを読み込みます
# Load train and test datas
train, test = load_dataset(train_rate=parser.trainrate)
valid = train.perm(0, 30)
test = test.perm(0, 150)
# 結果保存用のインスタンスを作成します
# Create Reporter Object
reporter = rp.Reporter(parser=parser)
accuracy_fig = reporter.create_figure("Accuracy", ("epoch", "accuracy"),
["train", "test"])
loss_fig = reporter.create_figure("Loss", ("epoch", "loss"),
["train", "test"])
# GPUを使用するか
# Whether or not using a GPU
gpu = parser.gpu
# モデルの生成
# Create a model
model_unet = model.UNet(l2_reg=parser.l2reg).model
# 誤差関数とオプティマイザの設定をします
# Set a loss function and an optimizer
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=model_unet.teacher,
logits=model_unet.outputs))
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)
# 精度の算出をします
# Calculate accuracy
correct_prediction = tf.equal(tf.argmax(model_unet.outputs, 3),
tf.argmax(model_unet.teacher, 3))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# セッションの初期化をします
# Initialize session
gpu_config = tf.ConfigProto(
gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.7),
device_count={'GPU': 1},
log_device_placement=False,
allow_soft_placement=True)
sess = tf.InteractiveSession(
config=gpu_config) if gpu else tf.InteractiveSession()
tf.global_variables_initializer().run()
# モデルの訓練
# Train the model
epochs = parser.epoch
batch_size = parser.batchsize
is_augment = parser.augmentation
train_dict = {
model_unet.inputs: valid.images_original,
model_unet.teacher: valid.images_segmented,
model_unet.is_training: False
}
test_dict = {
model_unet.inputs: test.images_original,
model_unet.teacher: test.images_segmented,
model_unet.is_training: False
}
for epoch in range(epochs):
for batch in train(batch_size=batch_size, augment=is_augment):
# バッチデータの展開
inputs = batch.images_original
teacher = batch.images_segmented
# Training
sess.run(train_step,
feed_dict={
model_unet.inputs: inputs,
model_unet.teacher: teacher,
model_unet.is_training: True
})
# 評価
# Evaluation
if epoch % 1 == 0:
loss_train = sess.run(cross_entropy, feed_dict=train_dict)
loss_test = sess.run(cross_entropy, feed_dict=test_dict)
accuracy_train = sess.run(accuracy, feed_dict=train_dict)
accuracy_test = sess.run(accuracy, feed_dict=test_dict)
print("Epoch:", epoch)
print("[Train] Loss:", loss_train, " Accuracy:", accuracy_train)
print("[Test] Loss:", loss_test, "Accuracy:", accuracy_test)
accuracy_fig.add([accuracy_train, accuracy_test], is_update=True)
loss_fig.add([loss_train, loss_test], is_update=True)
if epoch % 3 == 0:
idx_train = random.randrange(10)
idx_test = random.randrange(100)
outputs_train = sess.run(
model_unet.outputs,
feed_dict={
model_unet.inputs: [train.images_original[idx_train]],
model_unet.is_training: False
})
outputs_test = sess.run(model_unet.outputs,
feed_dict={
model_unet.inputs:
[test.images_original[idx_test]],
model_unet.is_training:
False
})
train_set = [
train.images_original[idx_train], outputs_train[0],
train.images_segmented[idx_train]
]
test_set = [
test.images_original[idx_test], outputs_test[0],
test.images_segmented[idx_test]
]
reporter.save_image_from_ndarray(
train_set,
test_set,
train.palette,
epoch,
index_void=len(ld.DataSet.CATEGORY) - 1)
# 訓練済みモデルの評価
# Test the trained model
loss_test = sess.run(cross_entropy, feed_dict=test_dict)
accuracy_test = sess.run(accuracy, feed_dict=test_dict)
print("Result")
print("[Test] Loss:", loss_test, "Accuracy:", accuracy_test)
def train(parser):
assert parser.num != None, 'please input the number of images. i.e. python3 main.py -n xxx'
#imageの枚数
NUM = parser.num
#画像の学習サイズ
size = tuple(parser.size)
#trainrate
trainrate = parser.trainrate
train, test = load_dataset(train_rate=trainrate, size=size)
valid = train.devide(int(NUM * (trainrate - ((1 - trainrate)))),
int(NUM * trainrate))
test = test.devide(0, int(NUM * (1 - trainrate)))
#保存ファイル
reporter = rp.Reporter(parser=parser)
accuracy_fig = reporter.create_figure("Accuracy", ("epoch", "accuracy"),
["train", "test"])
loss_fig = reporter.create_figure("Loss", ("epoch", "loss"),
["train", "test"])
#restoreするモデル名
CONTINUE = parser.restore
#GPU
gpu = parser.gpu
print(gpu)
#model
model_unet = model.UNet(size=size, l2_reg=parser.l2reg).model
#誤差関数
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=model_unet.teacher,
logits=model_unet.outputs))
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)
#精度
correct_prediction = tf.equal(tf.argmax(model_unet.outputs, 3),
tf.argmax(model_unet.teacher, 3))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#gpu config
gpu_config = tf.ConfigProto(
gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=0.7,
visible_device_list="",
allow_growth=True), #device_count={'GPU': 0},
log_device_placement=False,
allow_soft_placement=True)
if gpu:
sess = tf.InteractiveSession(config=gpu_config)
print("gpu mode")
else:
sess = tf.InteractiveSession()
print("cpu mode")
tf.global_variables_initializer().run()
#parameter
epochs = parser.epoch
batch_size = parser.batchsize
t_images_original = test.images_original
saver = tf.train.Saver(max_to_keep=100)
if not os.path.exists("./checkpoint"):
os.makedirs("./checkpoint")
if CONTINUE is not None:
saver.restore(sess, "./checkpoint/" + CONTINUE)
print("restored")
for epoch in range(epochs):
for batch in train(batch_size=batch_size):
# バッチデータ
images_original = batch.images_original
inputs = images_original
teacher = batch.images_segmented
sess.run(train_step,
feed_dict={
model_unet.inputs: inputs,
model_unet.teacher: teacher,
model_unet.is_training: True
})
train_images_original = train.images_original
# 評価
accuracy_train = 0
loss_train = 0
accuracy_test = 0
loss_test = 0
if epoch % 1 == 0:
num_batch = 0
for batchs in valid(batch_size=batch_size):
num_batch += 1
images = batchs.images_original
segmented = batchs.images_segmented
accuracy_train += sess.run(accuracy,
feed_dict={
model_unet.inputs: images,
model_unet.teacher: segmented,
model_unet.is_training: False
})
loss_train += sess.run(cross_entropy,
feed_dict={
model_unet.inputs: images,
model_unet.teacher: segmented,
model_unet.is_training: False
})
accuracy_train /= num_batch
loss_train /= num_batch
num_batch = 0
for batchs in test(batch_size=batch_size):
num_batch += 1
images = batchs.images_original
segmented = batchs.images_segmented
accuracy_test += sess.run(accuracy,
feed_dict={
model_unet.inputs: images,
model_unet.teacher: segmented,
model_unet.is_training: False
})
loss_test += sess.run(cross_entropy,
feed_dict={
model_unet.inputs: images,
model_unet.teacher: segmented,
model_unet.is_training: False
})
accuracy_test /= num_batch
loss_test /= num_batch
print("Epoch:", epoch)
print("[Train] Loss:", loss_train, " Accuracy:", accuracy_train)
print("[Test] Loss:", loss_test, "Accuracy:", accuracy_test)
accuracy_fig.add([accuracy_train, accuracy_test], is_update=True)
loss_fig.add([loss_train, loss_test], is_update=True)
if epoch % 1 == 0:
idx_train = random.randrange(int(NUM * trainrate))
idx_test = random.randrange(int(NUM * (1 - trainrate)))
outputs_train = sess.run(
model_unet.outputs,
feed_dict={
model_unet.inputs: [train_images_original[idx_train]],
model_unet.is_training: False
})
outputs_test = sess.run(model_unet.outputs,
feed_dict={
model_unet.inputs:
[t_images_original[idx_test]],
model_unet.is_training:
False
})
train_set = [
train_images_original[idx_train], outputs_train[0],
train.images_segmented[idx_train]
]
test_set = [
t_images_original[idx_test], outputs_test[0],
test.images_segmented[idx_test]
]
reporter.save_image_from_ndarray(
train_set, test_set, train.palette, epoch,
index_void=0) #index_void = background
if epoch % 10 == 0:
if SAVE:
save_path = saver.save(
sess,
"./checkpoint/save_model_epoch_" + str(epoch) + "_.ckpt")
save_path = saver.save(sess, "./checkpoint/save_model_done.ckpt")
#modelの評価
for batchs in test(batch_size=batch_size):
images = batchs.images_original
segmented = batchs.image_segmented
accuracy_test += sess.run(accuracy,
feed_dict={
model_unet.inputs: images,
model_unet.teacher: segmented,
model_unet.is_training: False
})
loss_test += sess.run(cross_entropy,
feed_dict={
model_unet.inputs: images,
model_unet.teacher: segmented,
model_unet.is_training: False
})
accuracy_test /= num_batch
loss_test /= num_batch
print("Result")
print("[Test] Loss:", loss_test, "Accuracy:", accuracy_test)
sess.close()
def implement(parser):
#image data
train = load_dataset()
#reporter
reporter = rp.Reporter(parser=parser)
# GPU
gpu = parser.gpu
#model
model_unet = model.UNet(size=(1024, 1024), l2_reg=parser.l2reg).model
# Initialize session
gpu_config = tf.ConfigProto(gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.7), device_count={'GPU': 1},
log_device_placement=False, allow_soft_placement=True)
sess = tf.InteractiveSession(config=gpu_config) if gpu else tf.InteractiveSession()
tf.global_variables_initializer().run()
is_augment = parser.augmentation
#Sarver
saver = tf.train.Saver()
if not os.path.exists("./checkpoint"):
os.makedirs("./checkpoint")
if not os.path.exists("./output"):
os.makedirs("./output")
if not os.path.exists("./input"):
os.makedirs("./input")
saver.restore(sess, "./checkpoint/save_model_512new_epoch_70.ckpt")
train_images_original = train.images_original
train_images_original = train_images_original[:,:,:, np.newaxis]
for k in range(NUM):
print("number: ", k)
idx_train = k
outputs_train = sess.run(model_unet.outputs,
feed_dict={model_unet.inputs: [train_images_original[idx_train]],
model_unet.is_training: False})
images_original_size = train.images_original_size
print(images_original_size[idx_train])
pred_image = reporter.cast_to_out_image(outputs_train[0], images_original_size[idx_train])
#インプット画像の確認
image_in_np = np.squeeze(train_images_original[idx_train])
image_in_pil = reporter.cast_to_out_image_in(image_in_np, images_original_size[idx_train])
if k >= 40:
#reporter.save_simple_image(pred_image, k-40, "./output", "hv_")
#reporter.save_simple_image(image_in_pil, k-40, "./input", "hv_")
pass
else:
reporter.save_simple_image(pred_image, k, "./output", "hh_")
reporter.save_simple_image(image_in_pil, k, "./input", "hh_")
print("Result")
sess.close()
def debug(parser):
# load test dataset
_, test = load_dataset(train_rate=parser.trainrate)
test = test.perm(0, 150)
saver = tf.train.import_meta_graph(
os.path.join(parser.saverpath, 'model.meta'))
# Create Reporter Object
reporter = rp.Reporter(result_dir=parser.saverpath, parser=parser)
# Whether or not using a GPU
gpu = parser.gpu
# Create a model
model_unet = model.UNet(l2_reg=parser.l2reg).model
# Set a loss function
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=model_unet.teacher,
logits=model_unet.outputs))
# Calculate accuracy
correct_prediction = tf.equal(tf.argmax(model_unet.outputs, 3),
tf.argmax(model_unet.teacher, 3))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Calculate mean iou
labels = tf.reshape(model_unet.teacher,
[tf.shape(model_unet.teacher)[0], -1])
preds = tf.reshape(tf.clip_by_value(model_unet.outputs, 0, 10000),
[tf.shape(model_unet.outputs)[0], -1])
weights = tf.cast(
tf.less_equal(preds,
len(ld.DataSet.CATEGORY) - 1),
tf.int32) # Ignoring all labels greater than or equal to n_classes.
miou, update_op_miou = tf.metrics.mean_iou(labels=labels,
predictions=preds,
num_classes=len(
ld.DataSet.CATEGORY),
weights=weights)
# shape of each
shape_teacher = tf.shape(model_unet.teacher)
shape_output = tf.shape(model_unet.outputs)
# Initialize session
gpu_config = tf.ConfigProto(
gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.7),
device_count={'GPU': 1},
log_device_placement=False,
allow_soft_placement=True)
sess = tf.InteractiveSession(
config=gpu_config) if gpu else tf.InteractiveSession()
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
saver.restore(sess, tf.train.latest_checkpoint(parser.saverpath))
print("Saver restore model variables from ",
tf.train.latest_checkpoint(parser.saverpath))
# Set up the test dataset
test_dict = {
model_unet.inputs: test.images_original[0:2],
model_unet.teacher: test.images_segmented[0:2],
model_unet.is_training: False
}
# Test the trained model
loss_test = sess.run(cross_entropy, feed_dict=test_dict)
accuracy_test = sess.run(accuracy, feed_dict=test_dict)
sess.run(update_op_miou, feed_dict=test_dict)
miou_test = sess.run(miou, feed_dict=test_dict)
shapes_test1 = sess.run(shape_teacher, feed_dict=test_dict)
shapes_test2 = sess.run(shape_output, feed_dict=test_dict)
print("TEST Result")
print("[Test] Loss:", loss_test, "Accuracy:", accuracy_test, " Mean IOU:",
miou_test, " Shape", shapes_test1, shapes_test2,
len(ld.DataSet.CATEGORY))
def train(parser):
# Load train and test datas
print("Start training")
train, test = load_dataset(train_rate=parser.trainrate)
valid = train.perm(0, 30)
test = test.perm(0, 150)
# Create Reporter Object
reporter = rp.Reporter(parser=parser)
#accuracy_fig = reporter.create_figure("Accuracy", ("epoch", "accuracy"), ["train", "test"])
#loss_fig = reporter.create_figure("Loss", ("epoch", "loss"), ["train", "test"])
# Whether or not using a GPU
gpu = parser.gpu
# Create a model
model_unet = model.UNet(l2_reg=parser.l2reg).model
# Set a loss function and an optimizer
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=model_unet.teacher,
logits=model_unet.outputs))
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)
# Calculate accuracy
correct_prediction = tf.equal(tf.argmax(model_unet.outputs, 3),
tf.argmax(model_unet.teacher, 3))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Calculate mean iou
labels = tf.reshape(model_unet.teacher,
[tf.shape(model_unet.teacher)[0], -1])
preds = tf.reshape(tf.clip_by_value(model_unet.outputs, 0, 10000),
[tf.shape(model_unet.outputs)[0], -1])
weights = tf.cast(
tf.less_equal(preds,
len(ld.DataSet.CATEGORY) - 1),
tf.int32) # Ignoring all labels greater than or equal to n_classes.
#miou, update_op_miou = tf.metrics.mean_iou(labels = labels,
# predictions = preds,
# num_classes = len(ld.DataSet.CATEGORY),
# weights=weights)
# Initialize session
saver = tf.train.Saver()
gpu_config = tf.ConfigProto(
gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.7),
device_count={'GPU': 1},
log_device_placement=False,
allow_soft_placement=True)
sess = tf.InteractiveSession(
config=gpu_config) if gpu else tf.InteractiveSession()
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
# Train the model
epochs = parser.epoch
batch_size = parser.batchsize
is_augment = parser.augmentation
train_dict = {
model_unet.inputs: valid.images_original,
model_unet.teacher: valid.images_segmented,
model_unet.is_training: False
}
test_dict = {
model_unet.inputs: test.images_original,
model_unet.teacher: test.images_segmented,
model_unet.is_training: False
}
for epoch in range(epochs):
for batch in train(batch_size=batch_size, augment=is_augment):
# input images
inputs = batch.images_original
teacher = batch.images_segmented
# Training
sess.run(train_step,
feed_dict={
model_unet.inputs: inputs,
model_unet.teacher: teacher,
model_unet.is_training: True
})
# Evaluation
if epoch % 1 == 0:
loss_train = sess.run(cross_entropy, feed_dict=train_dict)
loss_test = sess.run(cross_entropy, feed_dict=test_dict)
accuracy_train = sess.run(accuracy, feed_dict=train_dict)
accuracy_test = sess.run(accuracy, feed_dict=test_dict)
#sess.run(update_op_miou, feed_dict=train_dict)
#sess.run(update_op_miou, feed_dict=test_dict)
#miou_train = sess.run(miou, feed_dict=train_dict)
#miou_test = sess.run(miou, feed_dict=test_dict)
print("Epoch:", epoch)
print("[Train] Loss:", loss_train, " Accuracy:", accuracy_train)
print("[Test] Loss:", loss_test, "Accuracy:", accuracy_test)
#accuracy_fig.add([accuracy_train, accuracy_test], is_update=True)
#loss_fig.add([loss_train, loss_test], is_update=True)
if epoch % 3 == 0:
saver.save(sess, os.path.join(reporter._result_dir, 'model'))
idx_train = random.randrange(10)
idx_test = random.randrange(100)
outputs_train = sess.run(
model_unet.outputs,
feed_dict={
model_unet.inputs: [train.images_original[idx_train]],
model_unet.is_training: False
})
outputs_test = sess.run(model_unet.outputs,
feed_dict={
model_unet.inputs:
[test.images_original[idx_test]],
model_unet.is_training:
False
})
train_set = [
train.images_original[idx_train], outputs_train[0],
train.images_segmented[idx_train]
]
test_set = [
test.images_original[idx_test], outputs_test[0],
test.images_segmented[idx_test]
]
reporter.save_image_from_ndarray(
train_set,
test_set,
train.palette,
epoch,
index_void=len(ld.DataSet.CATEGORY) - 1)
# Test the trained model
loss_test = sess.run(cross_entropy, feed_dict=test_dict)
accuracy_test = sess.run(accuracy, feed_dict=test_dict)
# sess.run(update_op_miou, feed_dict=test_dict)
# miou_test = sess.run(miou, feed_dict=test_dict)
print("Result")
print("[Test] Loss:", loss_test, "Accuracy:", accuracy_test)
save_path = saver.save(sess, os.path.join(reporter._result_dir, 'model'))
print("Model saved in file: ", save_path)
for ii in range(100):
outputs_test = sess.run(model_unet.outputs,
feed_dict={
model_unet.inputs:
[test.images_original[ii]],
model_unet.is_training: False
})
test_set = [
test.images_original[ii], outputs_test[0],
test.images_segmented[ii], test.filenames[ii]
]
reporter.save_image_from_ndarray([],
test_set,
test.palette,
1000000,
index_void=len(ld.DataSet.CATEGORY) -
1,
fnames=test.filenames[ii])