def model_fn(inputs, run_mode, **kwargs):
# In train or eval, id_or_labels represents labels. In predict, id_or_labels represents id.
images, id_or_labels, angles = inputs
# Reshape angles from [batch_size] to [batch_size, 1]
angles = tf.expand_dims(angles, 1)
# Apply your version of model
logits = model_v1(images, angles, run_mode)
if run_mode == mox.ModeKeys.PREDICT:
logits = tf.nn.softmax(logits)
# clip logits to get lower loss value.
logits = tf.clip_by_value(logits,
clip_value_min=0.05,
clip_value_max=0.95)
model_spec = mox.ModelSpec(output_info={
'id': id_or_labels,
'logits': logits
})
else:
labels_one_hot = slim.one_hot_encoding(id_or_labels, 2)
loss = tf.losses.softmax_cross_entropy(
logits=logits,
onehot_labels=labels_one_hot,
label_smoothing=0.0,
weights=1.0)
model_spec = mox.ModelSpec(loss=loss, log_info={'loss': loss})
return model_spec
def model_fn(inputs, mode):
images, labels = inputs
# 获取一个resnet50的模型，输入images，输入logits和end_points，这里不关心end_points，仅取logits
logits, _ = mox.get_model_fn(name='resnet_v1_50',
run_mode=mode,
num_classes=data_meta.num_classes,
weight_decay=0.00004)(images)
# 计算交叉熵损失值
labels_one_hot = slim.one_hot_encoding(labels, data_meta.num_classes)
loss = tf.losses.softmax_cross_entropy(logits=logits,
onehot_labels=labels_one_hot)
# 获取正则项损失值，并加到loss上，这里必须要用mox.get_collection代替tf.get_collection
regularization_losses = mox.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
regularization_loss = tf.add_n(regularization_losses)
loss = loss + regularization_loss
# 计算分类正确率
accuracy = tf.reduce_mean(
tf.cast(tf.nn.in_top_k(logits, labels, 1), tf.float32))
# 返回MoXing-TensorFlow用于定义模型的类ModelSpec
return mox.ModelSpec(loss=loss,
log_info={
'loss': loss,
'accuracy': accuracy
})
def model_fn(inputs, run_mode, **kwargs):
x, y_ = inputs
x_image = tf.reshape(x, [-1, 28, 28, 1]) # 转换输入数据shape,以便于用于网络中
W_conv1 = weight_variable('w1', [5, 5, 1, 32])
b_conv1 = bias_variable('b1', [32])
# dcp export only support BiasAdd
# h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_conv1 = tf.nn.relu(tf.nn.bias_add(conv2d(x_image, W_conv1),
b_conv1)) # 第一个卷积层
h_pool1 = max_pool(h_conv1) # 第一个池化层
W_conv2 = weight_variable('w2', [5, 5, 32, 64])
b_conv2 = bias_variable('b2', [64])
h_conv2 = tf.nn.relu(tf.nn.bias_add(conv2d(h_pool1, W_conv2),
b_conv2)) # 第二个卷积层
h_pool2 = max_pool(h_conv2) # 第二个池化层
W_fc1 = weight_variable('w3', [7 * 7 * 64, 1024])
b_fc1 = bias_variable('b3', [1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64]) # reshape成向量
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) # 第一个全连接层
W_fc2 = weight_variable('w4', [1024, 10])
b_fc2 = bias_variable('b4', [10])
y = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2) # softmax层
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
from efficient_ai.config import CompressorSpec
compressor_spec = CompressorSpec(logits=y)
return mox.ModelSpec(loss=cross_entropy,
compressor_spec=compressor_spec,
log_info={'loss': cross_entropy})
def model_fn(inputs, mode, **kwargs):
images, labels = inputs
mox_model_fn = mox.get_model_fn(name=flags.model_name,
run_mode=mode,
num_classes=data_meta.num_classes,
weight_decay=0.00004,
data_format='NCHW',
batch_norm_fused=True)
images = tf.cast(images, tf.float16)
with mox.var_scope(force_dtype=tf.float32):
logits, _ = mox_model_fn(images)
labels_one_hot = slim.one_hot_encoding(labels, data_meta.num_classes)
loss = tf.losses.softmax_cross_entropy(labels_one_hot, logits=logits)
regularization_losses = mox.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
regularization_loss = tf.add_n(regularization_losses)
loss = loss + regularization_loss
logits_fp32 = tf.cast(logits, tf.float32)
accuracy = tf.reduce_mean(
tf.cast(tf.nn.in_top_k(logits_fp32, labels, 1), tf.float32))
export_spec = mox.ExportSpec(inputs_dict={'images': images},
outputs_dict={'logits': logits_fp32})
return mox.ModelSpec(loss=loss,
log_info={
'loss': loss,
'accuracy': accuracy
},
export_spec=export_spec)
def model_fn(inputs, run_mode, **kwargs):
x, y_ = inputs
W = tf.get_variable(name='W', initializer=tf.zeros([784, 10]))
b = tf.get_variable(name='b', initializer=tf.zeros([10]))
y = tf.matmul(x, W) + b
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return mox.ModelSpec(log_info={'accuracy': accuracy})
def model_fn(inputs, run_mode, **kwargs):
x, y_ = inputs
W = tf.get_variable(name='W', initializer=tf.zeros([784, 10]))
b = tf.get_variable(name='b', initializer=tf.zeros([10]))
y = tf.matmul(x, W) + b
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
return mox.ModelSpec(loss=cross_entropy, log_info={'loss': cross_entropy})
def model_fn(inputs, run_mode, **kwargs):
x, y_ = inputs
W = tf.get_variable(name='W', initializer=tf.zeros([784, 10]))
b = tf.get_variable(name='b', initializer=tf.zeros([10]))
y = tf.matmul(x, W) + b
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
predictions = tf.argmax(y, 1)
correct_predictions = tf.equal(predictions, tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
export_spec = mox.ExportSpec(inputs_dict={'images': x}, outputs_dict={'predictions': predictions}, version='model')
return mox.ModelSpec(loss=cross_entropy, log_info={'loss': cross_entropy, 'accuracy': accuracy},
export_spec=export_spec)
def model_fn(inputs, run_mode, **kwargs):
x, y_ = inputs
y = tf.keras.layers.Dense(128, activation='relu')(x)
y = tf.keras.layers.Dense(10)(y)
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
export_spec = mox.ExportSpec(inputs_dict={'images': x},
outputs_dict={'logits': y},
version='model')
return mox.ModelSpec(loss=cross_entropy,
log_info={
'loss': cross_entropy,
'accuracy': accuracy
},
export_spec=export_spec)
def model_fn(inputs, mode, **kwargs):
if not flags.gpu_synthetic:
if flags.split_to_device:
images, labels = inputs.get_input(0)
else:
images, labels = inputs
else:
import numpy as np
images = tf.constant(
np.random.randint(low=0,
high=255,
size=[flags.batch_size, 128, 128, 3],
dtype=np.uint8))
labels = tf.constant(
np.random.randint(low=0,
high=999,
size=[flags.batch_size],
dtype=np.int64))
if flags.fp16:
images = tf.cast(images, tf.float16)
def preprocess_fn(images, run_mode, *args):
images = images / 255.0
channels = tf.split(axis=3, num_or_size_splits=3, value=images)
for i in range(3):
channels[i] = (channels[i] - mean[i]) / std[i]
images = tf.concat(axis=3, values=channels)
if flags.data_format == 'NCHW':
images = tf.transpose(images, perm=(0, 3, 1, 2))
return images
model_kwargs = {}
if flags.model_name == 'resnet_v1_50_8k':
if flags.official_stride:
model_kwargs['official'] = True
if flags.fastai_initializer:
model_kwargs['weights_initializer_params'] = {
'factor': 2.0 / 1.3,
'mode': 'FAN_OUT'
}
mox_model_fn = mox.get_model_fn(name=flags.model_name,
run_mode=mode,
num_classes=1000,
preprocess_fn=preprocess_fn,
weight_decay=flags.weight_decay,
data_format=flags.data_format,
batch_norm_fused=True,
batch_renorm=False,
**model_kwargs)
logits, end_points = mox_model_fn(images)
labels_one_hot = slim.one_hot_encoding(labels, 1000)
loss = tf.losses.softmax_cross_entropy(logits=logits,
onehot_labels=labels_one_hot,
label_smoothing=0.0,
weights=1.0)
logits_fp32 = tf.cast(logits, tf.float32)
accuracy_top_1 = tf.reduce_mean(
tf.cast(tf.nn.in_top_k(logits_fp32, labels, 1), tf.float32))
accuracy_top_5 = tf.reduce_mean(
tf.cast(tf.nn.in_top_k(logits_fp32, labels, 5), tf.float32))
log_info = {
'ent_loss': loss,
'top-1': accuracy_top_1,
'top-5': accuracy_top_5
}
regularization_losses = mox.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
if len(regularization_losses
) > 0 and flags.use_optimizer != 'dymomentumw':
regularization_loss = tf.add_n(regularization_losses)
log_info['reg_loss'] = regularization_loss
loss = loss + regularization_loss
log_info['total_loss'] = loss
return mox.ModelSpec(loss=loss, log_info=log_info)
