def __init__(self, batch=64, use_cpus=False):
image_shape = [batch, 224, 224, 3]
labels_shape = [batch]
# Synthetic image should be within [0, 255].
images = tf.truncated_normal(image_shape,
dtype=tf.float32,
mean=127,
stddev=60,
name='synthetic_images')
# Minor hack to avoid H2D copy when using synthetic data
inputs = tf.contrib.framework.local_variable(images,
name='gpu_cached_images')
labels = tf.random_uniform(labels_shape,
minval=0,
maxval=999,
dtype=tf.int32,
name='synthetic_labels')
model = model_config.get_model_config("resnet101", MockDataset())
logits, aux = model.build_network(inputs,
data_format=use_cpus and "NHWC"
or "NCHW")
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=labels)
# Implement model interface
self.loss = tf.reduce_mean(loss, name='xentropy-loss')
self.optimizer = tf.train.GradientDescentOptimizer(1e-6)
def __init__(self, batch=64, use_cpus=False):
image_shape = [batch, 224, 224, 3]
labels_shape = [batch]
# Synthetic image should be within [0, 255].
images = tf.truncated_normal(
image_shape,
dtype=tf.float32,
mean=127,
stddev=60,
name='synthetic_images')
# Minor hack to avoid H2D copy when using synthetic data
inputs = tf.contrib.framework.local_variable(
images, name='gpu_cached_images')
labels = tf.random_uniform(
labels_shape,
minval=0,
maxval=999,
dtype=tf.int32,
name='synthetic_labels')
model = model_config.get_model_config("resnet101", MockDataset())
logits, aux = model.build_network(
inputs, data_format=use_cpus and "NHWC" or "NCHW")
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels)
# Implement model interface
self.loss = tf.reduce_mean(loss, name='xentropy-loss')
self.optimizer = tf.train.GradientDescentOptimizer(1e-6)
self.variables = ray_tf_utils.TensorFlowVariables(
self.loss, tf.get_default_session())
def _get_logits(self, image):
ctx = get_current_tower_context()
with maybe_freeze_updates(ctx.index > 0):
network = ConvNetBuilder(
image, 3, True,
use_tf_layers=True,
data_format=self.data_format,
dtype=tf.float16 if args.use_fp16 else tf.float32,
variable_dtype=tf.float32)
with custom_getter_scope(network.get_custom_getter()):
dataset = lambda: 1
dataset.name = 'imagenet'
model_conf = model_config.get_model_config('resnet50', dataset)
model_conf.set_batch_size(args.batch)
model_conf.add_inference(network)
return network.affine(1000, activation='linear', stddev=0.001)