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PaddleSeg

PaddlePaddle / PaddleSeg

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PaddleSeg
提交 a689b8dd 编写于 作者: C chenguowei01
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add mult grid and rgb change

上级 73fc0c03
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Showing with 130 addition and 127 deletion
pdseg/models/backbone/resnet_vd.py
浏览文件 @ a689b8dd
......@@ -65,6 +65,7 @@ class ResNet():
dilation_dict=None):
layers = self.layers
supported_layers = [18, 34, 50, 101, 152]
mult_grid = [1, 2, 4]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(supported_layers, layers)
......@@ -96,33 +97,38 @@ class ResNet():
num_filters = [64, 128, 256, 512]
if self.stem == 'icnet' or self.stem == 'pspnet' or self.stem == 'deeplab':
conv = self.conv_bn_layer(input=input,
conv = self.conv_bn_layer(
input=input,
num_filters=int(32 * self.scale),
filter_size=3,
stride=2,
act='relu',
name="conv1_1")
conv = self.conv_bn_layer(input=conv,
conv = self.conv_bn_layer(
input=conv,
num_filters=int(32 * self.scale),
filter_size=3,
stride=1,
act='relu',
name="conv1_2")
conv = self.conv_bn_layer(input=conv,
conv = self.conv_bn_layer(
input=conv,
num_filters=int(64 * self.scale),
filter_size=3,
stride=1,
act='relu',
name="conv1_3")
else:
conv = self.conv_bn_layer(input=input,
conv = self.conv_bn_layer(
input=input,
num_filters=int(64 * self.scale),
filter_size=7,
stride=2,
act='relu',
name="conv1")
conv = fluid.layers.pool2d(input=conv,
conv = fluid.layers.pool2d(
input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
......@@ -147,6 +153,8 @@ class ResNet():
else:
conv_name = "res" + str(block + 2) + chr(97 + i)
dilation_rate = get_dilated_rate(dilation_dict, block)
if block == 3:
dilation_rate = dilation_rate * mult_grid[i]
conv = self.bottleneck_block(
input=conv,
......@@ -170,10 +178,8 @@ class ResNet():
np.ceil(
np.array(conv.shape[2:]).astype('int32') / 2))
pool = fluid.layers.pool2d(input=conv,
pool_size=7,
pool_type='avg',
global_pooling=True)
pool = fluid.layers.pool2d(
input=conv, pool_size=7, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
......@@ -198,10 +204,8 @@ class ResNet():
if check_points(layer_count, end_points):
return conv, decode_ends
pool = fluid.layers.pool2d(input=conv,
pool_size=7,
pool_type='avg',
global_pooling=True)
pool = fluid.layers.pool2d(
input=conv, pool_size=7, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
......@@ -234,17 +238,16 @@ class ResNet():
else:
bias_attr = False
conv = fluid.layers.conv2d(input=input,
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) //
2 if dilation == 1 else 0,
padding=(filter_size - 1) // 2 if dilation == 1 else 0,
dilation=dilation,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights",
learning_rate=lr_mult),
param_attr=ParamAttr(name=name + "_weights", learning_rate=lr_mult),
bias_attr=bias_attr,
name=name + '.conv2d.output.1')
......@@ -256,8 +259,8 @@ class ResNet():
input=conv,
act=act,
name=bn_name + '.output.1',
param_attr=ParamAttr(name=bn_name + '_scale',
learning_rate=lr_mult),
param_attr=ParamAttr(
name=bn_name + '_scale', learning_rate=lr_mult),
bias_attr=ParamAttr(bn_name + '_offset', learning_rate=lr_mult),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance',
......@@ -272,22 +275,23 @@ class ResNet():
act=None,
name=None):
lr_mult = self.lr_mult_list[self.curr_stage]
pool = fluid.layers.pool2d(input=input,
pool = fluid.layers.pool2d(
input=input,
pool_size=2,
pool_stride=2,
pool_padding=0,
pool_type='avg',
ceil_mode=True)
conv = fluid.layers.conv2d(input=pool,
conv = fluid.layers.conv2d(
input=pool,
num_filters=num_filters,
filter_size=filter_size,
stride=1,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights",
learning_rate=lr_mult),
param_attr=ParamAttr(name=name + "_weights", learning_rate=lr_mult),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
......@@ -296,24 +300,20 @@ class ResNet():
return fluid.layers.batch_norm(
input=conv,
act=act,
param_attr=ParamAttr(name=bn_name + '_scale',
learning_rate=lr_mult),
param_attr=ParamAttr(
name=bn_name + '_scale', learning_rate=lr_mult),
bias_attr=ParamAttr(bn_name + '_offset', learning_rate=lr_mult),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def shortcut(self, input, ch_out, stride, is_first, name):
ch_in = input.shape[1]
print('shortcut:', stride, is_first, ch_in, ch_out)
if ch_in != ch_out or stride != 1:
if is_first or stride == 1:
return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
else:
return self.conv_bn_layer_new(input,
ch_out,
1,
stride,
name=name)
return self.conv_bn_layer_new(
input, ch_out, 1, stride, name=name)
elif is_first:
return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
else:
......@@ -326,7 +326,8 @@ class ResNet():
name,
is_first=False,
dilation=1):
conv0 = self.conv_bn_layer(input=input,
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
filter_size=1,
dilation=1,
......@@ -335,50 +336,48 @@ class ResNet():
name=name + "_branch2a")
if dilation > 1:
conv0 = self.zero_padding(conv0, dilation)
conv1 = self.conv_bn_layer(input=conv0,
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
dilation=dilation,
stride=stride,
act='relu',
name=name + "_branch2b")
conv2 = self.conv_bn_layer(input=conv1,
conv2 = self.conv_bn_layer(
input=conv1,
num_filters=num_filters * 4,
dilation=1,
filter_size=1,
act=None,
name=name + "_branch2c")
short = self.shortcut(input,
short = self.shortcut(
input,
num_filters * 4,
stride,
is_first=is_first,
name=name + "_branch1")
print(input.shape, short.shape, conv2.shape)
print(stride)
return fluid.layers.elementwise_add(x=short,
y=conv2,
act='relu',
name=name + ".add.output.5")
return fluid.layers.elementwise_add(
x=short, y=conv2, act='relu', name=name + ".add.output.5")
def basic_block(self, input, num_filters, stride, is_first, name):
conv0 = self.conv_bn_layer(input=input,
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
filter_size=3,
act='relu',
stride=stride,
name=name + "_branch2a")
conv1 = self.conv_bn_layer(input=conv0,
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
act=None,
name=name + "_branch2b")
short = self.shortcut(input,
num_filters,
stride,
is_first,
name=name + "_branch1")
short = self.shortcut(
input, num_filters, stride, is_first, name=name + "_branch1")
return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
......
pdseg/reader.py
浏览文件 @ a689b8dd
......@@ -318,6 +318,8 @@ class SegDataset(object):
raise ValueError("Dataset mode={} Error!".format(mode))
# Normalize image
if cfg.AUG.TO_RGB:
img = img[..., ::-1]
img = self.normalize_image(img)
if ModelPhase.is_train(mode) or ModelPhase.is_eval(mode):
......
pdseg/utils/config.py
浏览文件 @ a689b8dd
......@@ -117,6 +117,8 @@ cfg.AUG.RICH_CROP.CONTRAST_JITTER_RATIO = 0.5
cfg.AUG.RICH_CROP.BLUR = False
# 图像启动模糊百分比，0-1
cfg.AUG.RICH_CROP.BLUR_RATIO = 0.1
# 图像是否切换到rgb模式
cfg.AUG.TO_RGB = True
########################### 训练配置 ##########################################
# 模型保存路径
......
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