def test_multi_gpu_test_simple_model(self):
gpus = 2
num_samples = 1000
input_dim = 10
output_dim = 1
hidden_dim = 10
epochs = 2
target_gpu_id = [0, 1]
if not check_if_compatible_devices(gpus=gpus):
self.skipTest('multi gpu only')
with self.cached_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(hidden_dim,
input_shape=(input_dim, )))
model.add(keras.layers.Dense(output_dim))
x = np.random.random((num_samples, input_dim))
y = np.random.random((num_samples, output_dim))
parallel_model = multi_gpu_utils.multi_gpu_model(model, gpus=gpus)
parallel_model.compile(loss='mse', optimizer='rmsprop')
parallel_model.fit(x, y, epochs=epochs)
parallel_model = multi_gpu_utils.multi_gpu_model(
model, gpus=target_gpu_id)
parallel_model.compile(loss='mse', optimizer='rmsprop')
parallel_model.fit(x, y, epochs=epochs)
def test_multi_gpu_test_invalid_devices(self):
if not check_if_compatible_devices(gpus=2):
self.skipTest('multi gpu only')
with self.cached_session():
input_shape = (1000, 10)
model = keras.models.Sequential()
model.add(
keras.layers.Dense(10,
activation='relu',
input_shape=input_shape[1:]))
model.add(keras.layers.Dense(1, activation='sigmoid'))
model.compile(loss='mse', optimizer='rmsprop')
x = np.random.random(input_shape)
y = np.random.random((input_shape[0], 1))
with self.assertRaises(ValueError):
parallel_model = multi_gpu_utils.multi_gpu_model(
model, gpus=len(keras.backend._get_available_gpus()) + 1)
parallel_model.fit(x, y, epochs=2)
with self.assertRaises(ValueError):
parallel_model = multi_gpu_utils.multi_gpu_model(
model, gpus=[0, 2, 4, 6, 8])
parallel_model.fit(x, y, epochs=2)
with self.assertRaises(ValueError):
parallel_model = multi_gpu_utils.multi_gpu_model(model, gpus=1)
parallel_model.fit(x, y, epochs=2)
with self.assertRaises(ValueError):
parallel_model = multi_gpu_utils.multi_gpu_model(model,
gpus=[0])
parallel_model.fit(x, y, epochs=2)
def poly_yolo(input_shape=(224, 640, 1),
n_classes=1,
backbone='darknet',
lr=3e-4,
decay=5e-6,
multi_gpu=False,
GPU_COUNT=2,
weight_pt=''):
inpt = Input(input_shape)
# backbone
if backbone == 'darknet':
backmodel = darknet(input_shape=input_shape,
n_classes=n_classes,
include_top=False,
multi_out=True)
if backbone == 'darknet_se':
backmodel = darknet(input_shape=input_shape,
n_classes=n_classes,
n_filters=24,
se=True,
include_top=False,
multi_out=True)
if backbone == 'efficientNet':
backmodel = EfficientNet(input_shape=input_shape,
n_classes=n_classes,
include_top=False,
multi_out=True)
x = backmodel(inpt)
# feature fusion
x = feats_fusion(x, 384)
# head: 1x1
x = Conv2D(2 + 1 + n_classes, 1, strides=1,
padding='same')(x) # x,y,conf,cls
# yt: [b,h,w,2+1+cls]
y_true = Input(
(input_shape[0] // 4, input_shape[1] // 4, 2 + 1 + n_classes))
# loss
loss = Lambda(mix_loss)([y_true, x])
# model
model = Model([inpt, y_true], loss)
if os.path.exists(weight_pt):
print("load weight: ", weight_pt)
model.load_weights(weight_pt, by_name=True, skip_mismatch=True)
single_model = model
if multi_gpu:
model = multi_gpu_model(model, gpus=GPU_COUNT)
model.compile(Adam(lr=lr, decay=decay),
loss=lambda y_true, y_pred: K.mean(y_pred[:, 0]),
metrics=metric_lst)
return model, single_model
def get_Inception_classifier():
inputs = Input((CLASSIFY_INPUT_WIDTH, CLASSIFY_INPUT_HEIGHT, CLASSIFY_INPUT_DEPTH, CLASSIFY_INPUT_CHANNEL))
print('inputs')
print(inputs.get_shape())
# Make inception base
x = inception_base(inputs)
for i in range(INCEPTION_BLOCKS):
x = inception_block(x, filters=INCEPTION_KEEP_FILTERS)
if (i + 1) % INCEPTION_REDUCTION_STEPS == 0 and i != INCEPTION_BLOCKS - 1:
x = reduction_block(x, filters=INCEPTION_KEEP_FILTERS // 2)
print('top')
x = GlobalMaxPooling3D()(x)
print(x.get_shape())
x = Dropout(INCEPTION_DROPOUT)(x)
x = Dense(2, activation='softmax')(x)
print(x.get_shape())
model_s = Model(inputs=inputs, outputs=x)
model = multi_gpu_model(model_s, gpus=4)
model.compile(optimizer=Adam(lr=TRAIN_CLASSIFY_LEARNING_RATE), loss='binary_crossentropy', metrics=['accuracy'])
return model,model_s
def get_DenseNet_classifier():
inputs = Input((32, 32, 32, 1))
x = Conv3D(DENSE_NET_INITIAL_CONV_DIM, (3, 3, 3), padding='same')(inputs)
print('input')
print(x.get_shape())
for i in range(DENSE_NET_BLOCKS):
x = dense_block(x)
if i != DENSE_NET_BLOCKS - 1:
x = transition_block(x)
print('top')
x = GlobalAveragePooling3D()(x)
print(x.get_shape())
if DENSE_NET_ENABLE_DROPOUT:
x = Dropout(DENSE_NET_DROPOUT)(x)
x = Dense(2, activation='softmax')(x)
print(x.get_shape())
model_s = Model(inputs=inputs, outputs=x)
model = multi_gpu_model(model_s,gpus=4)
model.compile(optimizer=Adam(lr=TRAIN_CLASSIFY_LEARNING_RATE), loss='binary_crossentropy', metrics=['accuracy'])
return model,model_s
def __init__(self, data_rows=128, data_cols=128, weight_filepath=None,inference_only=False, net_name='default', gpus=1):
"""Create the PConvUnet. If variable data size, set data_rows and data_cols to None
:param data_rows (int): data height.
:param data_cols (int): data width.
:param inference_only (bool): initialize BN layers for inference.
:param net_name (str): Name of this network (used in logging).
:param gpus (int): How many GPUs to use for training.
"""
self.weight_filepath = weight_filepath
self.data_rows = data_rows
self.data_cols = data_cols
self.img_overlap = 30
self.inference_only = inference_only
self.net_name = net_name
self.gpus = gpus
assert self.data_rows >= 64, 'Height must be >64 '
assert self.data_cols >= 64, 'Width must be >64 '
self.current_epoch = 0
if self.gpus <= 1:
self.model= self.build_resnet()
self.compile_resnet(self.model)
else:
with tf.device("/cpu:0"):
self.model = self.build_resnet()
self.model = multi_gpu_model(self.model, gpus=self.gpus)
self.compile_resnet(self.model)
def test_multi_gpu_with_siamese_network(self):
gpus = 2
if not check_if_compatible_devices(gpus=gpus):
self.skipTest('multi gpu only')
with self.cached_session():
input_shape = (3, )
nested_model = keras.models.Sequential([
keras.layers.Dense(32, input_shape=input_shape),
keras.layers.Dense(1)
],
name='nested')
input1 = keras.Input(input_shape)
input2 = keras.Input(input_shape)
score1 = nested_model(input1)
score2 = nested_model(input2)
score_sum = keras.layers.Add(name='add')([score1, score2])
siamese = keras.models.Model(inputs=[input1, input2],
outputs=[score_sum, score1, score2],
name='siamese')
parallel_siamese = multi_gpu_utils.multi_gpu_model(siamese, gpus)
self.assertEqual(parallel_siamese.output_names,
['add', 'nested', 'nested_1'])
def build(self) -> None:
"""
Build the Siamese model and compile it to optimize the contrastive loss
using Adam.
Both arms of the Siamese network will use the same embedder model, i.e.
the weights are tied between both arms.
The model will be parallelized over all available GPUs is applicable.
"""
# Both arms of the Siamese network use the same model,
# i.e. the weights are tied.
try:
# The shared weights should be stored on the CPU for easy sharing
# between multiple GPUs.
# FIXME: https://github.com/keras-team/keras/issues/11313
with tf.device('/cpu:0'):
self.siamese_model = self._build_siamese_model()
num_gpus = _get_num_gpus()
self.siamese_model_parallel = multi_gpu_utils.multi_gpu_model(
self.siamese_model, gpus=num_gpus)
logger.info('Parallelizing the embedder model over %d GPUs',
num_gpus)
except ValueError:
self.siamese_model = self._build_siamese_model()
self.siamese_model_parallel = self.siamese_model
logger.info('Running the embedder model on a single GPU')
# Train using Adam to optimize the contrastive loss.
self.siamese_model_parallel.compile(Adam(self.lr), contrastive_loss)
def __load_model_mlp_classifier_action_vlad(n_classes, input_shape, n_gpus,
is_load_weights, weight_path):
"""
Model
"""
# optimizer and loss
loss = keras_utils.LOSSES[0]
metrics = [keras_utils.METRICS[0]]
output_activation = keras_utils.ACTIVATIONS[3]
optimizer = SGD(lr=0.01)
optimizer = Adam(lr=0.01, epsilon=1e-8)
optimizer = Adam(lr=0.01, epsilon=1e-4)
expansion_factor = 5.0 / 4.0
_, n_timesteps, side_dim, _, n_channels_in = input_shape
input_shape = (input_shape[1:])
t_input = Input(shape=input_shape) # (None, 7, 7, 1024)
tensor = t_input
# spatial convolution
n_channels_out = 512
tensor = Conv3D(n_channels_out, kernel_size=(1, 1, 1),
padding='same')(tensor)
tensor = BatchNormalization()(tensor)
tensor = Activation('relu')(tensor)
n_channels_in = n_channels_out
# reshape for vlad
tensor = ReshapeLayer((n_channels_in, ))(tensor)
# vlad layer
max_samples = n_timesteps * side_dim * side_dim
tensor = NetVLAD(n_channels_in, max_samples, 32)(tensor)
# dense layers
tensor = Dropout(0.5)(tensor)
tensor = Dense(256)(tensor)
tensor = BatchNormalization()(tensor)
tensor = LeakyReLU(alpha=0.2)(tensor)
tensor = Dropout(0.25)(tensor)
tensor = Dense(n_classes)(tensor)
t_output = Activation(output_activation)(tensor)
model = Model(input=t_input, output=t_output)
if is_load_weights:
model.load_weights(weight_path)
if n_gpus == 1:
model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
parallel_model = model
else:
parallel_model = multi_gpu_utils.multi_gpu_model(model, n_gpus)
parallel_model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
return model, parallel_model
def __init__(self, img_rows=128, img_cols=128, vgg_weights="imagenet", inference_only=False, net_name='default',
gpus=1, vgg_device=None):
"""Create the PConvUnet. If variable image size, set img_rows and img_cols to None
Args:
img_rows (int): image height.
img_cols (int): image width.
vgg_weights (str): which weights to pass to the vgg network.
inference_only (bool): initialize BN layers for inference.
net_name (str): Name of this network (used in logging).
gpus (int): How many GPUs to use for training.
vgg_device (str): In case of training with multiple GPUs, specify which device to run VGG inference on.
e.g. if training on 8 GPUs, vgg inference could be off-loaded exclusively to one GPU, instead of
running on one of the GPUs which is also training the UNet.
"""
# Settings
self.img_rows = img_rows
self.img_cols = img_cols
self.img_overlap = 30
self.inference_only = inference_only
self.net_name = net_name
self.gpus = gpus
self.vgg_device = vgg_device
# Scaling for VGG input
self.mean = [0.485, 0.456, 0.406]
self.std = [0.229, 0.224, 0.225]
# Assertions
assert self.img_rows >= 64, 'Height must be >64 pixels'
assert self.img_cols >= 64, 'Width must be >64 pixels'
# Set current epoch
self.current_epoch = 0
# VGG layers to extract features from (first maxpooling layers, see pp. 7 of paper)
self.vgg_layers = [3, 6, 10]
# Instantiate the vgg network
if self.vgg_device:
with tf.device(self.vgg_device):
self.vgg = self.build_vgg(vgg_weights)
else:
self.vgg = self.build_vgg(vgg_weights)
# Create UNet-like model
if self.gpus <= 1:
self.model, inputs_mask = self.build_pconv_unet()
self.compile_pconv_unet(self.model, inputs_mask)
else:
with tf.device("/cpu:0"):
self.model, inputs_mask = self.build_pconv_unet()
self.model = multi_gpu_model(self.model, gpus=self.gpus)
self.compile_pconv_unet(self.model, inputs_mask)
def fetch_model_from_arguments(config: Config) -> Tuple[Model, Model]:
with tf.device('/cpu:0'):
loaded_model = False
if config.pretrained_model_file is not None:
print('Loading model from %s' % config.pretrained_model_file,
flush=True)
model = load_model(config.pretrained_model_file)
loaded_model = True
elif config.model_type == 'baseline':
print('Generating baseline model', flush=True)
if config.use_conv_activation:
model = models.baseline2(input_shape=(*config.input_size, 2))
else:
model = models.baseline(input_shape=(*config.input_size, 2))
if config.use_output_postprocessing:
models.add_heatmap_layers(
model, fixation_sigma=config.postprocessing_fixation_sigma)
elif config.model_type == 'transfer':
print('Generating transfer model', flush=True)
if config.use_conv_activation:
model = models.transfer2(input_shape=(*config.input_size, 3))
else:
model = models.transfer(input_shape=(*config.input_size, 3))
if config.use_output_postprocessing:
models.add_heatmap_layers(
model, fixation_sigma=config.postprocessing_fixation_sigma)
else:
print('Unknown model type %s' % config.model_type, flush=True)
exit(-1)
model.summary()
try:
trained_model = multi_gpu_utils.multi_gpu_model(model)
print('Generated multi-gpu model:', flush=True)
trained_model.summary()
except Exception as e:
trained_model = model
print('Failed to generate multi-gpu model:', flush=True)
traceback.print_exc()
print('', flush=True)
print('Compiling model', flush=True)
trained_model.compile(
optimizer=Adam(learning_rate=config.learn_rate),
loss='mean_squared_error',
)
return model, trained_model
def __load_model_mlp_classifier_timeception(n_classes, input_shape, n_gpus,
is_load_weights, weight_path):
"""
Model
"""
# optimizer and loss
loss = keras_utils.LOSSES[0]
metrics = [keras_utils.METRICS[0]]
output_activation = keras_utils.ACTIVATIONS[3]
optimizer = SGD(lr=0.01)
optimizer = Adam(lr=0.01, epsilon=1e-8)
optimizer = Adam(lr=0.01, epsilon=1e-4)
n_tc_layer = 3
expansion_factor = 5.0 / 4.0
_, n_timesteps, side_dim, _, n_channels_in = input_shape
n_groups = int(n_channels_in / 128.0)
print('... n_groups, expansion factor: %d, %.02f' %
(n_groups, expansion_factor))
input_shape = (input_shape[1:])
t_input = Input(shape=input_shape) # (None, 20, 7, 7, 1024)
tensor = t_input
# timeception layers
tensor = timeception.timeception_temporal_convolutions(tensor,
n_tc_layer,
n_groups,
expansion_factor,
is_dilated=True)
# spatio-temporal pooling
tensor = MaxLayer(axis=(1, 2, 3))(tensor)
# dense layers
tensor = Dropout(0.5)(tensor)
tensor = Dense(512)(tensor)
tensor = BatchNormalization()(tensor)
tensor = LeakyReLU(alpha=0.2)(tensor)
tensor = Dropout(0.25)(tensor)
tensor = Dense(n_classes)(tensor)
t_output = Activation(output_activation)(tensor)
model = Model(input=t_input, output=t_output)
if is_load_weights:
model.load_weights(weight_path)
if n_gpus == 1:
model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
parallel_model = model
else:
parallel_model = multi_gpu_utils.multi_gpu_model(model, n_gpus)
parallel_model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
return model, parallel_model
def ConstructModel():
K.clear_session()
if _g.TYPE == '30':
# ModelNet30
model = build_model_30(
input_voxel_size=_g.INPUT_VOXEL_SIZE,
augmenting_dropout_rate=_g.AUGMENTED_DROPOUT_RATE,
conv_num_filters=_g.CONV_NUM_FILTERS,
conv_filter_sizes=_g.CONV_FILTER_SIZES,
conv_strides=_g.CONV_STRIDES,
desc_dims=_g.DESC_DIMS)
elif _g.TYPE == '64':
# KNU_Simplification
model = build_model_64(
input_voxel_size=_g.INPUT_VOXEL_SIZE,
augmenting_dropout_rate=_g.AUGMENTED_DROPOUT_RATE,
conv_num_filters=_g.CONV_NUM_FILTERS,
conv_filter_sizes=_g.CONV_FILTER_SIZES,
conv_strides=_g.CONV_STRIDES,
desc_dims=_g.DESC_DIMS)
elif _g.TYPE == '64deeper':
# KNU_Simplification
model = build_model_64_deeper(
input_voxel_size=_g.INPUT_VOXEL_SIZE,
augmenting_dropout_rate=_g.AUGMENTED_DROPOUT_RATE,
conv_num_filters=_g.CONV_NUM_FILTERS,
conv_filter_sizes=_g.CONV_FILTER_SIZES,
conv_strides=_g.CONV_STRIDES,
desc_dims=_g.DESC_DIMS)
elif _g.TYPE == '128':
# KNU_Simplification
model = build_model_128(
input_voxel_size=_g.INPUT_VOXEL_SIZE,
augmenting_dropout_rate=_g.AUGMENTED_DROPOUT_RATE,
conv_num_filters=_g.CONV_NUM_FILTERS,
conv_filter_sizes=_g.CONV_FILTER_SIZES,
conv_strides=_g.CONV_STRIDES,
desc_dims=_g.DESC_DIMS)
sgd = optimizers.SGD(lr=_g.LEARNING_RATE,
decay=_g.DECAY,
momentum=_g.MOMENTUM)
model = multi_gpu_model(model, gpus=2)
model.compile(loss='binary_crossentropy', optimizer=sgd)
if _g.LOAD_WEIGHT:
model.load_weights(_g.LOAD_WEIGHT_PATH)
return model
def test_multi_gpu_test_multi_io_model(self):
gpus = 2
num_samples = 1000
input_dim_a = 10
input_dim_b = 5
output_dim_a = 1
output_dim_b = 2
hidden_dim = 10
epochs = 2
target_gpu_id = [0, 1]
if not check_if_compatible_devices(gpus=gpus):
self.skipTest('multi gpu only')
with self.cached_session():
input_a = keras.Input((input_dim_a, ))
input_b = keras.Input((input_dim_b, ))
a = keras.layers.Dense(hidden_dim)(input_a)
b = keras.layers.Dense(hidden_dim)(input_b)
c = keras.layers.concatenate([a, b])
output_a = keras.layers.Dense(output_dim_a)(c)
output_b = keras.layers.Dense(output_dim_b)(c)
model = keras.models.Model([input_a, input_b],
[output_a, output_b])
a_x = np.random.random((num_samples, input_dim_a))
b_x = np.random.random((num_samples, input_dim_b))
a_y = np.random.random((num_samples, output_dim_a))
b_y = np.random.random((num_samples, output_dim_b))
parallel_model = multi_gpu_utils.multi_gpu_model(model, gpus=gpus)
parallel_model.compile(loss='mse', optimizer='rmsprop')
parallel_model.fit([a_x, b_x], [a_y, b_y], epochs=epochs)
parallel_model = multi_gpu_utils.multi_gpu_model(
model, gpus=target_gpu_id)
parallel_model.compile(loss='mse', optimizer='rmsprop')
parallel_model.fit([a_x, b_x], [a_y, b_y], epochs=epochs)
def __compile_model_for_finetuning(model, n_gpus):
# optimizer and loss
loss = keras_utils.LOSSES[3]
optimizer = Adam(lr=0.01, epsilon=1e-8)
optimizer = Adam(lr=0.001, epsilon=1e-4)
optimizer = SGD(lr=0.1, momentum=0.9, decay=0.0000001)
optimizer = SGD(lr=0.02, momentum=0.8)
if n_gpus == 1:
model.compile(loss=loss, optimizer=optimizer)
parallel_model = model
else:
parallel_model = multi_gpu_utils.multi_gpu_model(model, n_gpus)
parallel_model.compile(loss=loss, optimizer=optimizer)
return model, parallel_model
def generate(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
# Load model, or construct model and load weights.
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
is_tiny_version = num_anchors == 6 # default setting
try:
self.yolo_model = load_model(model_path, compile=False)
except:
self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
self.yolo_model.load_weights(
self.model_path) # make sure model, anchors and classes match
else:
assert self.yolo_model.layers[-1].output_shape[-1] == \
num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
'Mismatch between model and given anchor and class sizes'
print('{} model, anchors, and classes loaded.'.format(model_path))
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
np.random.seed(10101) # Fixed seed for consistent colors across runs.
np.random.shuffle(
self.colors) # Shuffle colors to decorrelate adjacent classes.
np.random.seed(None) # Reset seed to default.
# Generate output tensor targets for filtered bounding boxes.
self.input_image_shape = K.placeholder(shape=(2, ))
if self.gpu_num >= 2:
self.yolo_model = multi_gpu_model(self.yolo_model,
gpus=self.gpu_num)
boxes, scores, classes = yolo_eval(self.yolo_model.output,
self.anchors,
len(self.class_names),
self.input_image_shape,
score_threshold=self.score,
iou_threshold=self.iou)
return boxes, scores, classes
def test_multi_gpu_with_multi_input_layers(self):
gpus = 2
if not check_if_compatible_devices(gpus=gpus):
self.skipTest('multi gpu only')
with self.cached_session():
inputs = keras.Input((4, 3))
init_state = keras.Input((3, ))
outputs = keras.layers.SimpleRNN(3, return_sequences=True)(
inputs, initial_state=init_state)
x = [np.random.randn(2, 4, 3), np.random.randn(2, 3)]
y = np.random.randn(2, 4, 3)
model = keras.Model([inputs, init_state], outputs)
parallel_model = multi_gpu_utils.multi_gpu_model(model, gpus=gpus)
parallel_model.compile(loss='mean_squared_error', optimizer='adam')
parallel_model.train_on_batch(x, y)
def test_nested_model_with_tensor_input(self):
gpus = 2
input_dim = 10
shape = (input_dim, )
num_samples = 16
num_classes = 10
if not check_if_compatible_devices(gpus=gpus):
self.skipTest('multi gpu only')
with tf.Graph().as_default(), self.cached_session():
input_shape = (num_samples, ) + shape
x_train = np.random.randint(0, 255, input_shape)
y_train = np.random.randint(0, num_classes, (input_shape[0], ))
y_train = np_utils.to_categorical(y_train, num_classes)
x_train = x_train.astype('float32')
y_train = y_train.astype('float32')
dataset = tf.compat.v1.data.Dataset.from_tensor_slices(
(x_train, y_train))
dataset = dataset.repeat()
dataset = dataset.batch(4)
iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
inputs, targets = iterator.get_next()
input_tensor = keras.layers.Input(tensor=inputs)
model = keras.models.Sequential()
model.add(keras.layers.Dense(3, input_shape=(input_dim, )))
model.add(keras.layers.Dense(num_classes))
output = model(input_tensor)
outer_model = keras.Model(input_tensor, output)
parallel_model = multi_gpu_utils.multi_gpu_model(outer_model,
gpus=gpus)
parallel_model.compile(loss='categorical_crossentropy',
optimizer=optimizer_v1.RMSprop(lr=0.0001,
decay=1e-6),
metrics=['accuracy'],
target_tensors=[targets])
parallel_model.fit(epochs=1, steps_per_epoch=3)
def __build_model(self):
input = Input(shape=self.image_shape, name="the_input")
nb_filter = self.filters
x = Conv2D(nb_filter, (5, 5), strides=(2, 2), kernel_initializer='he_normal', padding='same',
use_bias=False, kernel_regularizer=l2(self.weight_decay))(input)
# 64 + 8 * 8 = 128
x, nb_filter = _dense_block(x, 8, nb_filter, 8, None, self.weight_decay)
# 128
x, nb_filter = _transition_block(x, 128, self.dropout_rate, 2, self.weight_decay)
# 128 + 8 * 8 = 192
x, nb_filter = _dense_block(x, 8, nb_filter, 8, None, self.weight_decay)
# 192->128
x, nb_filter = _transition_block(x, 128, self.dropout_rate, 2, self.weight_decay)
# 128 + 8 * 8 = 192
x, nb_filter = _dense_block(x, 8, nb_filter, 8, None, self.weight_decay)
x = BatchNormalization(axis=-1, epsilon=1.1e-5)(x)
x = Activation('relu')(x)
x = Permute((2, 1, 3), name='permute')(x)
x = TimeDistributed(Flatten(), name='flatten')(x)
y_pred = Dense(self.num_classes, name='out', activation='softmax')(x)
base_model = Model(inputs=input, outputs=y_pred)
labels = Input(shape=(self.maxlen,), dtype='float32', name="the_labels")
input_length = Input(shape=(1,), name="input_length", dtype='int64')
label_length = Input(shape=(1,), name="label_length", dtype='int64')
loss_out = Lambda(_ctc_loss, output_shape=(1,), name='ctc')([labels, y_pred, input_length, label_length])
model = Model(inputs=[input, labels, input_length, label_length], outputs=loss_out)
parallel_model = model
if self.num_gpu > 1:
parallel_model = multi_gpu_model(model, gpus=self.num_gpu)
adam = Adam(self.lr)
parallel_model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=adam, metrics=['accuracy'])
return base_model, model, parallel_model
def get_gpu_model(input_size=None,
activation=None,
initial_weights=None,
is_corruption=False):
ResNet50v2, preprocess_input = Classifiers.get('resnet50v2')
model = ResNet50v2(input_shape=input_size,
weights='imagenet',
classes=1,
include_top=False,
pooling='avg')
model_inputs = model.inputs
model_outsputs = model.output
model_outsputs = Dense(128, activation='relu')(model_outsputs)
model_outsputs = Dense(32, activation='relu')(model_outsputs)
model_outsputs = Dense(1, activation=activation)(model_outsputs)
model = Model(model_inputs, model_outsputs)
model = multi_gpu_model(model, gpus=2)
model.compile(loss=keras.losses.mean_squared_error,
optimizer=keras.optimizers.Adam())
return model
def model_0(self):
self.model_type = 0
bm = self.tc.board_model
x = Input(shape=(self.x.shape[1],), name='board_inputs')
hidden_layers = self.build_hidden_layers(
x=x,
layers=bm['layers'],
params=bm['layer_params'],
dropouts=bm['dropouts']
)
outputs = Dense(1, activation='sigmoid')(hidden_layers)
model = Model(inputs=x, outputs=outputs)
if bm['use_multi_gpu']:
model = multi_gpu_model(model, gpus=bm['gpu_counts'])
optimizer = OPTIMIZER_MAP[bm['optimizer']](**bm['optimizer_params'])
model.compile(optimizer=optimizer, loss='binary_crossentropy',
metrics=['accuracy', auc_roc])
return model
def build_gan(self):
# set if generator is going to use spectral norm
image, pc, elev, azim = self.train_source.next_batch()
elev_code = Input(shape=(1, ), name='elev_code')
azim_code = Input(shape=(1, ), name='azim_code')
pc_code = Input(shape=(self.pc_code_dim, ), name='pc_code')
noise_code = Input(shape=(self.noise_dim, ), name='noise_code')
model_name = "pc2pix"
image_size = image.shape[1]
if self.color:
input_shape = (image_size, image_size, 3)
else:
input_shape = (image_size, image_size, 1)
inputs = Input(shape=input_shape, name='image_input')
if self.gen_spectral_normalization:
optimizer = Adam(lr=4e-4, beta_1=0.0, beta_2=0.9)
else:
optimizer = Adam(lr=2e-4, beta_1=0.5, beta_2=0.999)
# build discriminator
# by default, discriminator uses SN
if self.gpus <= 1:
self.discriminator = model.discriminator(
input_shape, pc_code_dim=self.pc_code_dim)
if self.dw is not None:
print("loading discriminator weights: ", self.dw)
self.discriminator.load_weights(self.dw)
self.discriminator_single = self.discriminator
else:
with tf.device("/cpu:0"):
self.discriminator_single = model.discriminator(
input_shape, pc_code_dim=self.pc_code_dim)
if self.dw is not None:
print("loading discriminator weights: ", self.dw)
self.discriminator_single.load_weights(self.dw)
self.discriminator = multi_gpu_model(self.discriminator_single,
gpus=self.gpus)
loss = ['binary_crossentropy', 'mae', self.elev_loss, self.azim_loss]
loss_weights = [1., 10., 10., 10.]
self.discriminator.compile(loss=loss,
loss_weights=loss_weights,
optimizer=optimizer)
self.discriminator_single.summary()
path = os.path.join(self.model_dir, "discriminator.png")
plot_model(self.discriminator_single, to_file=path, show_shapes=True)
# build generator
# try SN to see if mode collapse is avoided
if self.gpus <= 1:
self.generator = model.generator(
input_shape,
noise_code=noise_code,
pc_code=pc_code,
elev_code=elev_code,
azim_code=azim_code,
spectral_normalization=self.gen_spectral_normalization,
color=self.color)
if self.gw is not None:
print("loading generator weights: ", self.gw)
self.generator.load_weights(self.gw)
self.generator_single = self.generator
else:
with tf.device("/cpu:0"):
self.generator_single = model.generator(
input_shape,
noise_code=noise_code,
pc_code=pc_code,
elev_code=elev_code,
azim_code=azim_code,
spectral_normalization=self.gen_spectral_normalization,
color=self.color)
if self.gw is not None:
print("loading generator weights: ", self.gw)
self.generator_single.load_weights(self.gw)
self.generator = multi_gpu_model(self.generator_single,
gpus=self.gpus)
self.generator_single.summary()
path = os.path.join(self.model_dir, "generator.png")
plot_model(self.generator_single, to_file=path, show_shapes=True)
self.discriminator.trainable = False
if self.gen_spectral_normalization:
optimizer = Adam(lr=1e-4, beta_1=0.0, beta_2=0.9)
else:
optimizer = Adam(lr=1e-4, beta_1=0.5, beta_2=0.999)
if self.gpus <= 1:
self.adversarial = Model(
[noise_code, pc_code, elev_code, azim_code],
self.discriminator(
self.generator([noise_code, pc_code, elev_code,
azim_code])),
name=model_name)
self.adversarial_single = self.adversarial
else:
with tf.device("/cpu:0"):
self.adversarial_single = Model(
[noise_code, pc_code, elev_code, azim_code],
self.discriminator(
self.generator(
[noise_code, pc_code, elev_code, azim_code])),
name=model_name)
self.adversarial = multi_gpu_model(self.adversarial_single,
gpus=self.gpus)
self.adversarial.compile(loss=loss,
loss_weights=loss_weights,
optimizer=optimizer)
self.adversarial_single.summary()
path = os.path.join(self.model_dir, "adversarial.png")
plot_model(self.adversarial_single, to_file=path, show_shapes=True)
print("Using split file: ", self.split_file)
print("1 epoch datalen: ", self.epoch_datalen)
print("1 epoch train steps: ", self.train_steps)
print("Using pc codes: ", self.pc_codes_filename)
def main():
load_size = 512
batch_size = 32
num_classes = 228
num_channels = 3
epochs = 10
data_augmentation = True
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_fashion_trained_model.h5'
image_dim = 64
X_load_batch = np.zeros((load_size, image_dim, image_dim, num_channels),
dtype='uint8')
Y_load_batch = np.zeros((load_size, num_classes), dtype='uint8')
# The data, split between train and test sets:
#x_train = np.load('x_train_size_256.npy')
#y_train = np.load('y_train_size_256.npy')
#print('reading train data done')
x_eval = np.load(
'D:/PrivacyPreservingDistributedDL/Filters/x_eval_all_64.npy')
#y_eval = np.load('y_eval_size_256.npy')
#print('reading eval data done')
#x_test = np.load('D:/PrivacyPreservingDistributedDL/Filters/x_test.npy')
#y_test = np.load('D:/PrivacyPreservingDistributedDL/Filters/y_test.npy')
x_Test = np.load(
'D:/PrivacyPreservingDistributedDL/Filters/x_TestSub_64.npy')
print('reading test done')
### creating the filename and label vectors
train_labels = sps.load_npz(
"D:/PrivacyPreservingDistributedDL/Filters/train_image_mat.npz"
).todense()
############### loading validation image mat
eval_labels = sps.load_npz(
"D:/PrivacyPreservingDistributedDL/Filters/eval_image_mat.npz"
).todense()
train_labels = train_labels[0:150000, ]
train_set_size = train_labels.shape[0] - 1
eval_set_size = eval_labels.shape[0] - 1
label_size = train_labels.shape[1] - 1
print('train_set_size ', train_set_size)
print('eval_set_size ', eval_set_size)
print('label_size ', label_size)
#x_train = np.zeros(shape=(train_set_size,image_dim,image_dim,3), dtype='uint8')
y_train = train_labels[0:train_set_size + 1, 1:229]
y_eval = eval_labels[0:eval_set_size + 1, 1:229]
#x_eval = np.zeros(shape=(eval_set_size,image_dim,image_dim,3), dtype='uint8')
#y_eval = np.zeros(shape=(eval_set_size,label_size),dtype='uint8')
# This will do preprocessing and realtime data augmentation:
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=
False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=
20, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=
0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=
0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
# Compute quantities required for feature-wise normalization
# (std, mean, and principal components if ZCA whitening is applied).
# read images in batchs (start with 100000)
#print('x_train shape:', x_train.shape)
#print(x_Test.shape[0], 'Test samples')
"""
for i in range(0,x_train.shape[0]-1):
image = x_train[i,:,:,:]
plt.imshow(image)
plt.show()
time.sleep(1)
"""
# Convert class vectors to binary class matrices.
#y_train = keras.utils.to_categorical(y_train, num_classes)
#y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(
Conv2D(32, (3, 3),
padding='same',
input_shape=(image_dim, image_dim, 3))) #conv1
model.add(Activation('relu'))
#print('x_train shape for Conv1:', x_train.shape[1:])
model.add(Conv2D(32, (3, 3))) #conv2
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same')) #conv3
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3))) #conv4
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('sigmoid'))
model = multi_gpu_model(model, gpus=2)
# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
def _sd(y_true, y_pred):
return 1 - K.mean(K.square(K.abs(y_true - K.round(K.abs(y_pred))))) * 5
def _ap(y_true, y_pred):
y_pred_pos = K.round(K.clip(y_pred, 0, 1))
y_pred_neg = 1 - y_pred_pos
y_pos = K.round(K.clip(y_true, 0, 1))
y_neg = 1 - y_pos
tp = K.sum(y_pos * y_pred_pos)
#tn = K.sum(y_neg * y_pred_neg,axis=1)
fp = K.sum(y_neg * y_pred_pos)
fn = K.sum(y_pos * y_pred_neg)
precision = tp / (tp + fp)
recall = tp / (tp + fn)
return (precision + recall) / 2
def _f1(y_true, y_pred):
y_pred_pos = K.round(K.clip(y_pred, 0, 1))
y_pred_neg = 1 - y_pred_pos
y_pos = K.round(K.clip(y_true, 0, 1))
y_neg = 1 - y_pos
tp = K.sum(y_pos * y_pred_pos)
#tn = K.sum(y_neg * y_pred_neg,axis=1)
fp = K.sum(y_neg * y_pred_pos)
fn = K.sum(y_pos * y_pred_neg)
precision = tp / (tp + fp)
recall = tp / (tp + fn)
return 2 * (precision * recall) / (precision + recall)
def _sumLabels(y_true, y_pred):
return K.sum(y_true, axis=1)
def abs_KL_div(y_true, y_pred):
y_true = K.clip(y_true, K.epsilon(), None)
y_pred = K.clip(y_pred, K.epsilon(), None)
return K.sum(K.abs((y_true - y_pred) * (K.log(y_true / y_pred))),
axis=-1)
# Let's train the model using adam
model.compile(loss='binary_crossentropy',
optimizer=opt,
metrics=[_ap, _f1])
#x_eval = x_eval.astype('float32')
#x_eval /= 255
#x_Test /= 255
if not data_augmentation:
print('Not using data augmentation.')
"""
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_eval, y_eval),
shuffle=True)
"""
else:
print('Using real-time data augmentation.')
for e in tqdm(range(epochs)):
print('Epoch', e)
n_load_batch = int(np.ceil(train_set_size / load_size))
for load_batch in tqdm(range(1, n_load_batch + 1)):
if load_batch == n_load_batch:
load_sizel = train_set_size - load_size * (load_batch - 1)
else:
load_sizel = load_size
#######load the images, resize them
r_start = (load_batch - 1) * load_size
#r_end = r_start+load_sizel
#with tf.device("/cpu:0"):
for i in range(0, load_sizel):
img = mpimg.imread(
'D:/dataset/inputs/{}.jpg'.format(r_start + i + 1))
X_load_batch[i, ] = img_as_ubyte(
cv2.resize(img, (image_dim, image_dim),
interpolation=cv2.INTER_AREA))
Y_load_batch[i, ] = y_train[r_start + i + 1, ]
print('\n Epoch: ', e)
model.fit_generator(datagen.flow(X_load_batch[0:load_sizel, ],
Y_load_batch[0:load_sizel],
batch_size=batch_size),
verbose=1)
model.evaluate(x_eval, y_eval, verbose=1)
"""
for x_batch, y_batch in datagen.flow(X_load_batch[0:load_sizel,], y_train[0:load_sizel], batch_size=batch_size):
model.fit(x_batch, y_batch,shuffle=True)
batches += 1
if batches >= load_sizel / batch_size:
# we need to break the loop by hand because
# the generator loops indefinitely
break
"""
preds = model.predict(x_Test)
preds[preds >= 0.5] = 1
preds[preds < 0.5] = 0
#print('Shape of y_test: ',y_eval.shape)
#print('Shape of preds: ',preds.shape)
#print ('msd accuracy =',1-np.mean(np.square(y_eval-preds)))
############## save Test matrix
np.save(
'D:/PrivacyPreservingDistributedDL/Filters/y_TestSub_size_64_all.npy',
preds)
print("Test matrix saved.")
# Save model and weights
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)
# Score trained model.
#scores = model.evaluate(x_eval, y_eval, verbose=1)
#print('Test loss:', scores[0])
#print('Test accuracy:', scores[1])
print(model.summary())
#------------------------------------------------------#
# 创建yolo模型
#------------------------------------------------------#
model_body = yolo_body((input_shape[0], input_shape[1], 3),
anchors_mask,
num_classes,
phi=phi)
if model_path != '':
#------------------------------------------------------#
# 载入预训练权重
#------------------------------------------------------#
print('Load weights {}.'.format(model_path))
model_body.load_weights(model_path, by_name=True, skip_mismatch=True)
if ngpus_per_node > 1:
model = multi_gpu_model(model_body, gpus=ngpus_per_node)
model = get_train_model(model, input_shape, num_classes, anchors,
anchors_mask, label_smoothing)
else:
model = get_train_model(model_body, input_shape, num_classes, anchors,
anchors_mask, label_smoothing)
#---------------------------#
# 读取数据集对应的txt
#---------------------------#
with open(train_annotation_path, encoding='utf-8') as f:
train_lines = f.readlines()
with open(val_annotation_path, encoding='utf-8') as f:
val_lines = f.readlines()
num_train = len(train_lines)
num_val = len(val_lines)
def __init__(self, model, num_gpus):
parallel_model = multi_gpu_model(model, num_gpus)
self.__dict__.update(parallel_model.__dict__)
self._model = model
if K._BACKEND == 'tensorflow':
from tensorflow.python.client import device_lib
def get_available_gpus():
local_device_protos = device_lib.list_local_devices()
return [
x.name for x in local_device_protos if x.device_type == 'GPU'
]
ngpus = len(get_available_gpus())
print("[INFO] training with {} GPUs...".format(ngpus))
import tensorflow as tf
with tf.device("/cpu:0"):
original_built_model = model.build(input_shape=dataset.input_shape,
num_classes=dataset.num_classes)
built_model = multi_gpu_model(original_built_model, gpus=ngpus)
elif K._BACKEND == 'cntk':
built_model = model.build(input_shape=dataset.input_shape,
num_classes=dataset.num_classes)
else:
print("Multi GPU not available on this backend.")
# import numpy as np
# class_weights = np.ones(dataset.num_classes)
# model compilation with loss and accuracy
def custom_loss(y_true, y_pred):
final_loss = 0.
if dataset.enable_boundingbox:
obj_true = y_true[..., dataset.num_classes]
if G <= 1:
print("[INFO] training with 1 GPU...")
model = MiniGoogLeNet.build(width=32, height=32, depth=3, classes=10)
# otherwise, we are compiling using multiple GPUs
else:
print("[INFO] training with {} GPUs...".format(G))
# we'll store a copy of the model on *every* GPU and then combine
# the results from the gradient updates on the CPU
with tf.device("/cpu:0"):
# initialize the model
model = MiniGoogLeNet.build(width=32, height=32, depth=3, classes=10)
# make the model parallel
model = multi_gpu_model(model, gpus=G)
# initialize the optimizer and model
print("[INFO] compiling model...")
opt = SGD(lr=INIT_LR, momentum=0.9)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# train the network
print("[INFO] training network...")
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=64 * G),
validation_data=(testX, testY),
steps_per_epoch=len(trainX) // (64 * G),
epochs=NUM_EPOCHS,
callbacks=callbacks,
return -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1)) \
-K.sum((1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0))
return binary_focal_loss_fixed
# def false_rates(y_true, y_pred):
# false_neg = ...
# false_pos = ...
# return {
# 'false_neg': false_neg,
# 'false_pos': false_pos,
# }
parallel_model = multi_gpu_model(model, gpus=G)
if FL:
parallel_model.compile(optimizer='adam',
loss=binary_focal_loss(gamma=0., alpha=.5),
metrics=['accuracy'])
else:
parallel_model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
###########################################################
#Data generator#
###########################################################
# Data Generator for training (always use json files)
train_datagen = MB.ImageDataGenerator(
rescale=1. / 255,
# shear_range=0.2,
def __load_model_mlp_classifier_video_graph(centroids, n_classes,
input_shape_x, n_gpus,
is_load_weights, weight_path):
"""
Model
"""
# optimizer and loss
loss = keras_utils.LOSSES[0]
metrics = [keras_utils.METRICS[0]]
output_activation = keras_utils.ACTIVATIONS[3]
optimizer = SGD(lr=0.01)
optimizer = Adam(lr=0.01, epsilon=1e-8)
optimizer = Adam(lr=0.01, epsilon=1e-4)
expansion_factor = 5.0 / 4.0
n_groups = int(input_shape_x[-1] / 128.0)
# per-layer kernel size and max pooling for centroids and timesteps
n_graph_layers = 2
# time kernel
t_kernel_size = 7
t_max_size = 3
# node kernel
c_kernel_size = 7
c_max_size = 3
c_avg_size = 4
# space kernel
s_kernel_size = 2
s_kernel_size = 1
n_centroids, _ = centroids.shape
_, n_timesteps, side_dim, side_dim, n_channels_in = input_shape_x
t_input_x = Input(shape=(n_timesteps, side_dim, side_dim, n_channels_in),
name='input_x') # (None, 64, 1024)
t_input_c = Input(tensor=tf.constant(centroids, dtype=tf.float32),
name='input_n') # (1, 100, 1024)
tensor = t_input_x
# spatial convolution
n_channels_in = 1024
tensor = Conv3D(n_channels_in, (1, s_kernel_size, s_kernel_size),
padding='VALID',
name='conv_s')(tensor)
tensor = BatchNormalization()(tensor)
tensor = LeakyReLU(alpha=0.2)(tensor)
# pool over space
tensor = MaxLayer(axis=(2, 3), is_keep_dim=True,
name='global_pool_s')(tensor) # (None, 64, 7, 7, 1024)
# centroid-attention
tensor = videograph.node_attention(
tensor, t_input_c, n_channels_in,
activation_type='relu') # (N, 100, 64, 7, 7, 1024)
# graph embeddings
tensor = videograph.graph_embedding(tensor, n_graph_layers, c_avg_size,
c_kernel_size, t_kernel_size,
c_max_size,
t_max_size) # (N, 100, 64, 7, 7, 1024)
# centroid pooling
tensor = MeanLayer(axis=(1, ), name='global_pool_n')(tensor)
# temporal pooling
tensor = MaxLayer(axis=(1, 2, 3), name='global_pool_t')(tensor)
# activity classification
tensor = Dropout(0.25)(tensor)
tensor = Dense(512)(tensor)
tensor = BatchNormalization()(tensor)
tensor = LeakyReLU(alpha=0.2)(tensor)
tensor = Dropout(0.25)(tensor)
tensor = Dense(n_classes)(tensor)
t_output = Activation(output_activation)(tensor)
model = Model(input=[t_input_x, t_input_c], output=t_output)
if is_load_weights:
model.load_weights(weight_path)
if n_gpus == 1:
model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
parallel_model = model
else:
parallel_model = multi_gpu_utils.multi_gpu_model(model, n_gpus)
parallel_model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
return model, parallel_model
def main(
net_name,
A_name_list=['A_downstream', 'A_upstream', 'A_neighbors'],
run_name=None,
flatten_A=False,
val_split_proportion=.1,
test_split_proportion=.1,
loss_function='mse',
batch_size=4,
time_window=150,
average_interval=None,
max_time=3599,
epochs=50,
no_liu=False,
attn_dim=64,
attn_heads=4,
attn_depth=2,
attn_residual_connection=False,
gat_highway_connection=False,
layer_norm=False,
rnn_dim=64,
stateful_rnn=False,
dense_dim=64,
dropout_rate=.3,
attn_dropout=0.,
seed=123,
per_step_metrics=False,
old_model=False,
num_gpus=1,
no_plots=False,
use_gcn=False,
gcn_filter_type='localpool',
gcn_chebyshev_degree=2,
):
if use_gcn:
assert 'A_eye' in A_name_list
tf.set_random_seed(seed)
np.random.seed(seed)
net_dir = os.path.join('data', 'networks', net_name)
sn = SumoNetwork.from_preexisting_directory(net_dir)
lanes = sn.lanes_with_detectors()
num_lanes = len(lanes)
data_dir = os.path.join(net_dir, 'preprocessed_data')
x_feature_subset = [
'e1_0/occupancy', 'e1_0/speed', 'e1_1/occupancy', 'e1_1/speed',
'liu_estimated_veh', 'green'
]
y_feature_subset = ['e2_0/nVehSeen', 'e2_0/maxJamLengthInVehicles']
if no_liu:
x_feature_subset.remove('liu_estimated_veh')
write_dir = os.path.join(net_dir, 'models')
if not os.path.isdir(write_dir):
os.makedirs(write_dir)
with tf.device('/cpu:0'):
batch_gen = TFBatcher(data_dir,
batch_size,
time_window,
average_interval=average_interval,
val_proportion=val_split_proportion,
shuffle=True,
A_name_list=A_name_list,
x_feature_subset=x_feature_subset,
y_feature_subset=y_feature_subset,
flatten_A=flatten_A,
max_time=max_time,
gpu_prefetch=True)
Xtens = batch_gen.X
Atens = tf.cast(batch_gen.A, tf.float32)
# X dimensions: timesteps x lanes x feature dim
X_in = Input(batch_shape=(None, None, num_lanes, len(x_feature_subset)),
name='X',
tensor=Xtens)
# A dimensions: timesteps x num edge types x lanes x lanes
if not flatten_A:
num_edge_types = len(A_name_list)
else:
num_edge_types = 1
A_in = Input(batch_shape=(None, None, num_edge_types, num_lanes,
num_lanes),
name='A',
tensor=Atens)
attn_dim = iterfy(attn_dim) * attn_depth
attn_heads = iterfy(attn_heads) * attn_depth
def make_model(X_in, A_in):
if use_gcn:
X = gcn_encoder(X_in,
A_in,
gcn_filter_type,
attn_dim,
dropout_rate,
dense_dim,
cheb_polynomial_degree=gcn_chebyshev_degree,
layer_norm=layer_norm)
else:
X = gat_encoder(X_in,
A_in,
attn_dim,
attn_heads,
dropout_rate,
attn_dropout,
gat_activation='relu',
dense_dim=dense_dim,
gat_highway_connection=gat_highway_connection,
layer_norm=layer_norm,
residual_connection=attn_residual_connection)
if stateful_rnn:
reshape_batch_size = batch_size
else:
reshape_batch_size = None
reshaped_1 = ReshapeFoldInLanes(batch_size=reshape_batch_size)(X)
encoded = rnn_encode(reshaped_1, [rnn_dim],
'GRU',
stateful=stateful_rnn)
decoded = rnn_attn_decode('GRU',
rnn_dim,
encoded,
stateful=stateful_rnn)
reshaped_decoded = ReshapeUnfoldLanes(num_lanes)(decoded)
output = TimeDistributed(
Dense(len(y_feature_subset), activation='relu'))(reshaped_decoded)
outputs = output_tensor_slices(output, y_feature_subset)
model = Model([X_in, A_in], outputs)
return model
if num_gpus > 1:
with tf.device('/cpu:0'):
base_model = make_model(X_in, A_in)
model = multi_gpu_model(base_model, num_gpus)
else:
base_model = make_model(X_in, A_in)
model = base_model
Ytens = batch_gen.Y_slices
if loss_function.lower() == 'mse':
losses = ['mse', negative_masked_mse]
metrics = [
negative_masked_mae, negative_masked_huber, negative_masked_mape
]
elif loss_function.lower() == 'mae':
losses = ['mae', negative_masked_mae]
metrics = [
negative_masked_mse, negative_masked_huber, negative_masked_mape
]
elif loss_function.lower() == 'huber':
losses = [huber, negative_masked_huber]
metrics = [
negative_masked_mse, negative_masked_mae, negative_masked_mape
]
model.compile(
optimizer='Adam',
loss=losses,
metrics=metrics,
target_tensors=Ytens,
)
model.summary(print_fn=_logger.info)
verbose = 1
if val_split_proportion > 0:
do_validation = True
else:
do_validation = False
callback_list = make_callbacks(model, write_dir, do_validation, run_name,
base_model)
# record hyperparameters
hyperparams = dict(
net_name=net_name,
A_name_list=A_name_list,
no_liu=no_liu,
x_feature_subset=x_feature_subset,
y_feature_subset=y_feature_subset,
flatten_A=flatten_A,
param_count=model.count_params(),
val_split_proportion=val_split_proportion,
test_split_proportion=test_split_proportion,
loss_function=loss_function,
batch_size=batch_size,
time_window=time_window,
average_interval=average_interval,
max_time=max_time,
epochs=epochs,
attn_dim=attn_dim,
attn_depth=attn_depth,
attn_residual_connection=attn_residual_connection,
layer_norm=layer_norm,
gat_highway_connection=gat_highway_connection,
attn_heads=attn_heads,
rnn_dim=rnn_dim,
stateful_rnn=stateful_rnn,
dense_dim=dense_dim,
dropout_rate=dropout_rate,
attn_dropout=attn_dropout,
seed=seed,
num_gpus=num_gpus,
use_gcn=use_gcn,
gcn_filter_type=gcn_filter_type,
gcn_chebyshev_degree=gcn_chebyshev_degree,
)
logdir = get_logging_dir(callback_list)
if not os.path.exists(logdir):
os.makedirs(logdir)
with open(os.path.join(logdir, 'params.json'), 'w') as f:
json.dump(hyperparams, f)
_logger.info('Run dir: %s', logdir)
# Guess at the number of steps per simulation. This only affects Keras's
# progress bar per training epoch so it can be wrong.
if per_step_metrics:
timesteps_per_simulation = 3600
steps = batch_gen.num_train_batches * math.ceil(
timesteps_per_simulation / time_window)
else:
steps = batch_gen.num_train_batches
set_callback_params(callback_list, epochs, batch_size, verbose,
do_validation, model, steps)
fit_loop_init(model, callback_list, batch_gen)
with K.get_session().as_default():
fit_loop_tf(model,
callback_list,
batch_gen,
epochs,
per_step_metrics=per_step_metrics)
predict_eval_tf(model,
get_logging_dir(callback_list),
batch_gen,
plot_results=not (no_plots))
if hasattr(model, 'history'):
return model.history #pylint: disable=no-member