def fc_with_batchnorm(use_feed):
if use_feed:
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
else:
reader = fluid.layers.open_files(filenames=[MNIST_RECORDIO_FILE],
shapes=[[-1, 784], [-1, 1]],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
reader = fluid.layers.io.double_buffer(reader)
img, label = fluid.layers.read_file(reader)
hidden = img
for _ in range(1):
with fluid.name_scope("hidden"):
hidden = fluid.layers.fc(
hidden,
size=200,
act='tanh',
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=1.0)))
hidden = fluid.layers.batch_norm(input=hidden)
with fluid.name_scope("fc_layer"):
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
with fluid.name_scope("loss"):
loss = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.mean(loss)
return loss
def quant_dequant_residual_block(num, quant_skip_pattern=None):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
data1 = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
data2 = fluid.layers.data(
name='matmul_input', shape=[16, 32, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = data1
for _ in six.moves.xrange(num):
conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
hidden = fluid.layers.matmul(hidden, data2, True, True)
if isinstance(quant_skip_pattern, str):
with fluid.name_scope(quant_skip_pattern):
pool1 = fluid.layers.pool2d(
input=hidden, pool_size=2, pool_type='avg', pool_stride=2)
pool2 = fluid.layers.pool2d(
input=hidden, pool_size=2, pool_type='max', pool_stride=2)
pool_add = fluid.layers.elementwise_add(
x=pool1, y=pool2, act='relu')
elif isinstance(quant_skip_pattern, list):
assert len(
quant_skip_pattern
) > 1, 'test config error: the len of quant_skip_pattern list should be greater than 1.'
with fluid.name_scope(quant_skip_pattern[0]):
pool1 = fluid.layers.pool2d(
input=hidden, pool_size=2, pool_type='avg', pool_stride=2)
pool2 = fluid.layers.pool2d(
input=hidden, pool_size=2, pool_type='max', pool_stride=2)
with fluid.name_scope(quant_skip_pattern[1]):
pool_add = fluid.layers.elementwise_add(
x=pool1, y=pool2, act='relu')
else:
pool1 = fluid.layers.pool2d(
input=hidden, pool_size=2, pool_type='avg', pool_stride=2)
pool2 = fluid.layers.pool2d(
input=hidden, pool_size=2, pool_type='max', pool_stride=2)
pool_add = fluid.layers.elementwise_add(x=pool1, y=pool2, act='relu')
fc = fluid.layers.fc(input=pool_add, size=10)
loss = fluid.layers.cross_entropy(input=fc, label=label)
loss = fluid.layers.mean(loss)
return loss
def _residual_unit(self,
input,
num_in_filter,
num_mid_filter,
num_out_filter,
stride,
filter_size,
act=None,
use_se=False,
name=None):
input_data = input
conv0 = self._conv_bn_layer(
input=input,
filter_size=1,
num_filters=num_mid_filter,
stride=1,
padding=0,
if_act=True,
act=act,
name=name + '_expand')
if self.block_stride == 4 and stride == 2:
self.block_stride += 1
if self.block_stride in self.feature_maps:
self.end_points.append(conv0)
with fluid.name_scope('res_conv1'):
conv1 = self._conv_bn_layer(
input=conv0,
filter_size=filter_size,
num_filters=num_mid_filter,
stride=stride,
padding=int((filter_size - 1) // 2),
if_act=True,
act=act,
num_groups=num_mid_filter,
use_cudnn=False,
name=name + '_depthwise')
if use_se:
with fluid.name_scope('se_block'):
conv1 = self._se_block(
input=conv1,
num_out_filter=num_mid_filter,
name=name + '_se')
conv2 = self._conv_bn_layer(
input=conv1,
filter_size=1,
num_filters=num_out_filter,
stride=1,
padding=0,
if_act=False,
name=name + '_linear')
if num_in_filter != num_out_filter or stride != 1:
return conv2
else:
return fluid.layers.elementwise_add(x=input_data, y=conv2, act=None)
def deeplabv3p(img, num_classes):
# Backbone设置：xception 或 mobilenetv2
data, decode_shortcut = resnet_vd(img)
# 编码器解码器设置
#cfg.MODEL.DEFAULT_EPSILON = 1e-5
#if cfg.MODEL.DEEPLAB.ENCODER_WITH_ASPP:
data = encoder(data)
#if cfg.MODEL.DEEPLAB.ENABLE_DECODER:
data = decoder(data, decode_shortcut)
# 根据类别数设置最后一个卷积层输出，并resize到图片原始尺寸
param_attr = fluid.ParamAttr(
name=name_scope + 'weights',
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0),
initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.01))
#if not cfg.MODEL.DEEPLAB.DECODER.OUTPUT_IS_LOGITS:
with scope('logit'):
with fluid.name_scope('last_conv'):
logit = conv(
data,
num_classes,
1,
stride=1,
padding=0,
bias_attr=True,
param_attr=param_attr)
#else:
# logit = data
logit = fluid.layers.resize_bilinear(logit, img.shape[2:])
return logit
def deeplabv3p(img, num_classes):
# Backbone设置：xception 或 mobilenetv2
if 'xception' in cfg.MODEL.DEEPLAB.BACKBONE:
data, decode_shortcut = xception(img)
elif 'mobilenet' in cfg.MODEL.DEEPLAB.BACKBONE:
data, decode_shortcut = mobilenetv2(img)
else:
raise Exception("deeplab only support xception and mobilenet backbone")
# 编码器解码器设置
cfg.MODEL.DEFAULT_EPSILON = 1e-5
if cfg.MODEL.DEEPLAB.ENCODER_WITH_ASPP:
data = encoder(data)
if cfg.MODEL.DEEPLAB.ENABLE_DECODER:
data = decoder(data, decode_shortcut)
# 根据类别数设置最后一个卷积层输出，并resize到图片原始尺寸
param_attr = fluid.ParamAttr(
name=name_scope + 'weights',
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0),
initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.01))
with scope('logit'):
with fluid.name_scope('last_conv'):
logit = conv(data,
num_classes,
1,
stride=1,
padding=0,
bias_attr=True,
param_attr=param_attr)
logit = fluid.layers.resize_bilinear(logit, img.shape[2:])
return logit
def _get_outputs(self, input, is_train=True):
"""
Get YOLOv3 head output
Args:
input (list): List of Variables, output of backbone stages
is_train (bool): whether in train or test mode
Returns:
outputs (list): Variables of each output layer
"""
outputs = []
# get last out_layer_num blocks in reverse order
out_layer_num = len(self.anchor_masks)
blocks = input[-1:-out_layer_num - 1:-1]
route = None
for i, block in enumerate(blocks):
if i > 0: # perform concat in first 2 detection_block
block = fluid.layers.concat(input=[route, block], axis=1)
route, tip = self._detection_block(block,
channel=512 // (2**i),
is_test=(not is_train),
name=self.prefix_name +
"yolo_block.{}".format(i))
# out channel number = mask_num * (5 + class_num)
num_filters = len(self.anchor_masks[i]) * (self.num_classes + 5)
with fluid.name_scope('yolo_output'):
block_out = fluid.layers.conv2d(
input=tip,
num_filters=num_filters,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(
name=self.prefix_name +
"yolo_output.{}.conv.weights".format(i)),
bias_attr=ParamAttr(regularizer=L2Decay(0.),
name=self.prefix_name +
"yolo_output.{}.conv.bias".format(i)))
outputs.append(block_out)
if i < len(blocks) - 1:
# do not perform upsample in the last detection_block
route = self._conv_bn(input=route,
ch_out=256 // (2**i),
filter_size=1,
stride=1,
padding=0,
is_test=(not is_train),
name=self.prefix_name +
"yolo_transition.{}".format(i))
# upsample
route = self._upsample(route)
return outputs
def build_net(self, inputs):
# 在两类分割情况下，当loss函数选择dice_loss或bce_loss的时候，最后logit输出通道数设置为1
if self.use_dice_loss or self.use_bce_loss:
self.num_classes = 1
image = inputs['image']
data, decode_shortcuts = self.backbone(image)
decode_shortcut = decode_shortcuts[self.backbone.decode_points]
# 编码器解码器设置
if self.encoder_with_aspp:
data = self._encoder(data)
if self.enable_decoder:
data = self._decoder(data, decode_shortcut)
# 根据类别数设置最后一个卷积层输出，并resize到图片原始尺寸
param_attr = fluid.ParamAttr(
name=name_scope + 'weights',
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0),
initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.01))
with scope('logit'):
with fluid.name_scope('last_conv'):
logit = conv(data,
self.num_classes,
1,
stride=1,
padding=0,
bias_attr=True,
param_attr=param_attr)
image_shape = fluid.layers.shape(image)
logit = fluid.layers.resize_bilinear(logit, image_shape[2:])
if self.num_classes == 1:
out = sigmoid_to_softmax(logit)
out = fluid.layers.transpose(out, [0, 2, 3, 1])
else:
out = fluid.layers.transpose(logit, [0, 2, 3, 1])
pred = fluid.layers.argmax(out, axis=3)
pred = fluid.layers.unsqueeze(pred, axes=[3])
if self.mode == 'train':
label = inputs['label']
mask = label != self.ignore_index
return self._get_loss(logit, label, mask)
elif self.mode == 'eval':
label = inputs['label']
mask = label != self.ignore_index
loss = self._get_loss(logit, label, mask)
return loss, pred, label, mask
else:
if self.num_classes == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = fluid.layers.softmax(logit, axis=1)
return pred, logit
return logit
def _get_outputs(self, input, is_train=True):
outputs = []
# get last out_layer_num blocks in reverse order
out_layer_num = len(self.anchor_masks) # 输出层的数量，有3个输出层。
# 倒序的写法。blocks是input的倒序。用于fpn的3个张量的倒序。
# blocks = [backbone_s32, backbone_s16, backbone_s8]
blocks = input[-1:-out_layer_num - 1:-1]
route = None
for i, block in enumerate(blocks): # blocks = [backbone_s32, backbone_s16, backbone_s8]
if i > 0: # perform concat in first 2 detection_block
block = fluid.layers.concat(input=[route, block], axis=1)
# tip再接卷积进行预测。route用于上采样。
route, tip = self._detection_block(
block,
channel=512 // (2 ** i),
is_test=(not is_train),
name=self.prefix_name + "yolo_block.{}".format(i))
# out channel number = mask_num * (5 + class_num)
num_filters = len(self.anchor_masks[i]) * (self.num_classes + 5)
with fluid.name_scope('yolo_output'):
# 输出卷积层的weights没加正则
# 输出卷积层的bias加了L2正则
block_out = fluid.layers.conv2d(
input=tip,
num_filters=num_filters,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(
name=self.prefix_name +
"yolo_output.{}.conv.weights".format(i)),
bias_attr=ParamAttr(
regularizer=L2Decay(0.),
name=self.prefix_name +
"yolo_output.{}.conv.bias".format(i)))
outputs.append(block_out)
# route用于上采样。
if i < len(blocks) - 1:
# do not perform upsample in the last detection_block
route = self._conv_bn(
input=route,
ch_out=256 // (2 ** i),
filter_size=1,
stride=1,
padding=0,
is_test=(not is_train),
name=self.prefix_name + "yolo_transition.{}".format(i))
# upsample
route = self._upsample(route)
return outputs
def _head(self, input, is_train=True):
outputs = []
# get last out_layer_num blocks in reverse order
out_layer_num = len(self.anchor_masks)
blocks = input[-1:-out_layer_num - 1:-1]
route = None
for i, block in enumerate(blocks):
if i > 0: # perform concat in first 2 detection_block
block = fluid.layers.concat(input=[route, block], axis=1)
route, tip = self._detection_block(
block,
channel=64 * (2**out_layer_num) // (2**i),
is_first=i == 0,
is_test=(not is_train),
conv_block_num=self.conv_block_num,
name=self.prefix_name + "yolo_block.{}".format(i))
# out channel number = mask_num * (5 + class_num)
if self.iou_aware:
num_filters = len(self.anchor_masks[i]) * (
self.num_classes + 6)
else:
num_filters = len(self.anchor_masks[i]) * (
self.num_classes + 5)
with fluid.name_scope('yolo_output'):
block_out = fluid.layers.conv2d(
input=tip,
num_filters=num_filters,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(
name=self.prefix_name +
"yolo_output.{}.conv.weights".format(i)),
bias_attr=ParamAttr(
regularizer=L2Decay(0.),
name=self.prefix_name +
"yolo_output.{}.conv.bias".format(i)))
outputs.append(block_out)
if i < len(blocks) - 1:
# do not perform upsample in the last detection_block
route = self._conv_bn(
input=route,
ch_out=256 // (2**i),
filter_size=1,
stride=1,
padding=0,
is_test=(not is_train),
name=self.prefix_name + "yolo_transition.{}".format(i))
# upsample
route = self._upsample(route)
return outputs
def fc_with_batchnorm(use_feed):
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = img
for _ in range(1):
with fluid.name_scope("hidden"):
hidden = fluid.layers.fc(
hidden,
size=200,
act='tanh',
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=1.0)))
hidden = fluid.layers.batch_norm(input=hidden)
with fluid.name_scope("fc_layer"):
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
with fluid.name_scope("loss"):
loss = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.mean(loss)
return loss
def residual_unit(self,
input,
num_in_filter,
num_mid_filter,
num_out_filter,
stride,
filter_size,
act=None,
use_se=False,
name=None):
input_data = input
conv0 = self.conv_bn_layer(
input=input,
filter_size=1,
num_filters=num_mid_filter,
stride=1,
padding=0,
if_act=True,
act=act,
name=name + '_expand')
conv1 = self.conv_bn_layer(
input=conv0,
filter_size=filter_size,
num_filters=num_mid_filter,
stride=stride,
padding=int((filter_size - 1) // 2),
if_act=True,
act=act,
num_groups=num_mid_filter,
use_cudnn=False,
name=name + '_depthwise')
if use_se:
with fluid.name_scope('se_block_skip'):
conv1 = self.se_block(
input=conv1,
num_out_filter=num_mid_filter,
name=name + '_se')
conv2 = self.conv_bn_layer(
input=conv1,
filter_size=1,
num_filters=num_out_filter,
stride=1,
padding=0,
if_act=False,
name=name + '_linear')
if num_in_filter != num_out_filter or stride != 1:
return conv2
else:
return fluid.layers.elementwise_add(x=input_data, y=conv2, act=None)
def test_name_scope(self):
with fluid.name_scope("s1"):
a = fluid.layers.data(name='data', shape=[1], dtype='int32')
b = a + 1
with fluid.name_scope("s2"):
c = b * 1
with fluid.name_scope("s3"):
d = c / 1
with fluid.name_scope("s1"):
f = fluid.layers.pow(d, 2.0)
with fluid.name_scope("s4"):
g = f - 1
for op in fluid.default_main_program().block(0).ops:
if op.type == 'elementwise_add':
self.assertEqual(op.desc.attr("op_namescope"), '/s1/')
elif op.type == 'elementwise_mul':
self.assertEqual(op.desc.attr("op_namescope"), '/s1/s2/')
elif op.type == 'elementwise_div':
self.assertEqual(op.desc.attr("op_namescope"), '/s1/s3/')
elif op.type == 'elementwise_sub':
self.assertEqual(op.desc.attr("op_namescope"), '/s4/')
elif op.type == 'pow':
self.assertEqual(op.desc.attr("op_namescope"), '/s1_1/')
def residual_block(num, quant_skip_pattern=None):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
data = fluid.layers.data(name='image',
shape=[1, 1, 32, 32],
dtype='float32',
append_batch_size=False)
label = fluid.layers.data(name='label',
shape=[1, 1],
dtype='int64',
append_batch_size=False)
hidden = data
for _ in six.moves.xrange(num):
conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
matmul_weight = fluid.layers.create_parameter(shape=[1, 16, 32, 32],
dtype='float32')
hidden = fluid.layers.matmul(hidden, matmul_weight, True, True)
if quant_skip_pattern:
with fluid.name_scope(quant_skip_pattern):
pool = fluid.layers.pool2d(input=hidden,
pool_size=2,
pool_type='avg',
pool_stride=2)
else:
pool = fluid.layers.pool2d(input=hidden,
pool_size=2,
pool_type='avg',
pool_stride=2)
fc = fluid.layers.fc(input=pool, size=10)
loss = fluid.layers.cross_entropy(input=fc, label=label)
loss = fluid.layers.mean(loss)
return loss
def conv_net(img, label):
conv_pool_1 = fluid.nets.simple_img_conv_pool(input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
pool_type='max',
act="relu")
conv_pool_2 = fluid.nets.simple_img_conv_pool(input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
pool_type='avg',
act="relu")
with fluid.name_scope("skip_quant"):
hidden = fluid.layers.fc(input=conv_pool_1, size=100, act='relu')
prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
return avg_loss
def net(self, input, class_dim=1000, scale=1.0):
# conv1: 112x112
input = self.conv_bn_layer(input,
filter_size=3,
channels=3,
num_filters=int(32 * scale),
stride=2,
padding=1,
name="conv1")
# 56x56
input = self.depthwise_separable(input,
num_filters1=32,
num_filters2=64,
num_groups=32,
stride=1,
scale=scale,
name="conv2_1")
input = self.depthwise_separable(input,
num_filters1=64,
num_filters2=128,
num_groups=64,
stride=2,
scale=scale,
name="conv2_2")
# 28x28
input = self.depthwise_separable(input,
num_filters1=128,
num_filters2=128,
num_groups=128,
stride=1,
scale=scale,
name="conv3_1")
input = self.depthwise_separable(input,
num_filters1=128,
num_filters2=256,
num_groups=128,
stride=2,
scale=scale,
name="conv3_2")
# 14x14
input = self.depthwise_separable(input,
num_filters1=256,
num_filters2=256,
num_groups=256,
stride=1,
scale=scale,
name="conv4_1")
input = self.depthwise_separable(input,
num_filters1=256,
num_filters2=512,
num_groups=256,
stride=2,
scale=scale,
name="conv4_2")
# 14x14
for i in range(5):
input = self.depthwise_separable(input,
num_filters1=512,
num_filters2=512,
num_groups=512,
stride=1,
scale=scale,
name="conv5" + "_" + str(i + 1))
# 7x7
input = self.depthwise_separable(input,
num_filters1=512,
num_filters2=1024,
num_groups=512,
stride=2,
scale=scale,
name="conv5_6")
input = self.depthwise_separable(input,
num_filters1=1024,
num_filters2=1024,
num_groups=1024,
stride=1,
scale=scale,
name="conv6")
input = fluid.layers.pool2d(input=input,
pool_size=0,
pool_stride=1,
pool_type='avg',
global_pooling=True)
with fluid.name_scope('last_fc'):
output = fluid.layers.fc(input=input,
size=class_dim,
act='softmax',
param_attr=ParamAttr(initializer=MSRA(),
name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
return output
def _get_outputs(self, input, is_train=True):
"""
Get YOLOv3 head output
Args:
input (list): List of Variables, output of backbone stages
is_train (bool): whether in train or test mode
Returns:
outputs (list): Variables of each output layer
"""
# get last out_layer_num blocks in reverse order
out_layer_num = len(self.anchor_masks)
blocks = input[-1:-out_layer_num - 1:-1]
# fpn
yolo_feats = []
route = None
for i, block in enumerate(blocks):
if i > 0: # perform concat in first 2 detection_block
block = fluid.layers.concat(input=[route, block], axis=1)
route, tip = self._detection_block(
block,
channel=512 // (2**i),
is_first=i == 0,
is_test=(not is_train),
conv_block_num=self.conv_block_num,
name=self.prefix_name + "fpn.{}".format(i))
yolo_feats.append(tip)
if i < len(blocks) - 1:
# do not perform upsample in the last detection_block
route = self._conv_bn(input=route,
ch_out=512 // (2**i),
filter_size=1,
stride=1,
padding=0,
act=self.act,
name=self.prefix_name +
"fpn_transition.{}".format(i))
# upsample
route = self._upsample(route)
# pan
pan_feats = [yolo_feats[-1]]
route = yolo_feats[out_layer_num - 1]
for i in reversed(range(out_layer_num - 1)):
channel = 512 // (2**i)
route = self._conv_bn(input=route,
ch_out=channel,
filter_size=3,
stride=2,
padding=1,
act=self.act,
name=self.prefix_name +
"pan_transition.{}".format(i))
block = yolo_feats[i]
block = fluid.layers.concat(input=[route, block], axis=1)
route, tip = self._detection_block(
block,
channel=channel,
is_first=False,
is_test=(not is_train),
conv_block_num=self.conv_block_num,
name=self.prefix_name + "pan.{}".format(i))
pan_feats.append(tip)
pan_feats = pan_feats[::-1]
outputs = []
for i, block in enumerate(pan_feats):
if self.iou_aware:
num_filters = len(
self.anchor_masks[i]) * (self.num_classes + 6)
else:
num_filters = len(
self.anchor_masks[i]) * (self.num_classes + 5)
with fluid.name_scope('yolo_output'):
block_out = fluid.layers.conv2d(
input=block,
num_filters=num_filters,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(
name=self.prefix_name +
"yolo_output.{}.conv.weights".format(i)),
bias_attr=ParamAttr(regularizer=L2Decay(0.),
name=self.prefix_name +
"yolo_output.{}.conv.bias".format(i)))
outputs.append(block_out)
return outputs