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completed implementation of cudnn_convt convTransProjection and convTransOperator

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release/0.10.0
wangyang59 8 years ago
parent 07c1ea258f
commit 090c974e4b
4 changed files with 148 additions and 22 deletions
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27
python/paddle/trainer/config_parser.py
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@ -726,7 +726,7 @@ class ConvProjection(ConvBaseProjection):
**xargs):
super(ConvProjection, self).__init__(input_layer_name, **xargs)
parse_conv(conv_conf, input_layer_name, self.proj_conf.conv_conf,
parse_conv(conv_conf, self.input_layer_name, self.proj_conf.conv_conf,
num_filters)
self.proj_conf.output_size = self.proj_conf.conv_conf.output_x * \
self.proj_conf.conv_conf.output_y * \
@ -746,7 +746,7 @@ class ConvTransProjection(ConvBaseProjection):
parse_conv(
conv_conf,
input_layer_name,
self.input_layer_name,
self.proj_conf.conv_conf,
num_filters,
trans=True)
@ -1834,8 +1834,17 @@ class ConvTransLayerBase(LayerBase):
use_gpu = int(g_command_config_args.get("use_gpu", 0))
parallel_nn = int(g_command_config_args.get("parallel_nn", 0))
# cudnn_convt has not been implemented so use exconvt only
self.layer_type = "exconvt"
# Automatically select cudnn_type for GPU and exconvt for CPU
# if set type=exconvt, but still reserve the way user specify
# exconvt or cudnn_convt manually.
if self.layer_type == "cudnn_convt":
config_assert(use_gpu, "cudnn_convt only support GPU")
if (use_gpu == 1 and self.layer_type != "exconvt" and
(parallel_nn == 0 or self.config.device > -1)):
self.layer_type = "cudnn_convt"
else:
self.layer_type = "exconvt"
# need to specify layer in config
self.config.type = self.layer_type
@ -1852,10 +1861,9 @@ class ConvTransLayerBase(LayerBase):
trans=True)
conv_conf = self.config.inputs[input_index].conv_conf
psize = self.calc_parameter_size(conv_conf)
print("output size for %s is %d " % (name, conv_conf.output_x))
self.create_input_parameter(input_index, psize)
self.set_layer_size(
(conv_conf.img_size**2) * self.config.num_filters)
self.set_cnn_layer(name, conv_conf.img_size_y, conv_conf.img_size,
self.config.num_filters)
psize = self.config.size
if shared_biases:
@ -1872,6 +1880,11 @@ class ConvTransLayer(ConvTransLayerBase):
layer_type = 'exconvt'
@config_layer('cudnn_convt')
class ConvTransLayer(ConvTransLayerBase):
layer_type = 'cudnn_convt'
@config_layer('norm')
class NormLayer(LayerBase):
def __init__(self, name, inputs, **xargs):

7
python/paddle/trainer_config_helpers/layers.py
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@ -2046,8 +2046,9 @@ def img_conv_layer(input,
:param trans: true if it is a convTransLayer, false if it is a convLayer
:type trans: bool
:param layer_type: specify the layer_type, default is None. If trans=True,
layer_type has to be "exconvt", otherwise layer_type
has to be either "exconv" or "cudnn_conv"
layer_type has to be "exconvt" or "cudnn_convt",
otherwise layer_type has to be either "exconv" or
"cudnn_conv"
:type layer_type: String
:return: LayerOutput object.
:rtype: LayerOutput
@ -2087,7 +2088,7 @@ def img_conv_layer(input,
if layer_type:
if trans:
assert layer_type in ["exconvt"]
assert layer_type in ["exconvt", "cudnn_convt"]
else:
assert layer_type in ["exconv", "cudnn_conv"]
lt = layer_type

4
python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr
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@ -33,6 +33,8 @@ layers {
bias_parameter_name: "___conv_0__.wbias"
num_filters: 64
shared_biases: true
height: 256
width: 256
}
layers {
name: "__batch_norm_0__"
@ -58,6 +60,8 @@ layers {
}
bias_parameter_name: "___batch_norm_0__.wbias"
moving_average_fraction: 0.9
height: 256
width: 256
}
layers {
name: "__crmnorm_0__"

132
python/paddle/trainer_config_helpers/tests/configs/protostr/projections.protostr
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@ -154,13 +154,38 @@ layers {
inputs {
input_layer_name: "img"
}
inputs {
input_layer_name: "img"
proj_conf {
type: "conv"
name: "___mixed_6__.w1"
input_size: 1024
output_size: 57600
conv_conf {
filter_size: 3
channels: 1
stride: 1
padding: 0
groups: 1
filter_channels: 1
output_x: 30
img_size: 32
caffe_mode: true
filter_size_y: 3
padding_y: 0
stride_y: 1
output_y: 30
img_size_y: 32
}
}
}
inputs {
input_layer_name: "filter"
}
operator_confs {
type: "conv"
input_indices: 0
input_indices: 1
input_indices: 2
input_sizes: 1024
input_sizes: 576
output_size: 57600
@ -186,38 +211,110 @@ layers {
layers {
name: "__mixed_7__"
type: "mixed"
size: 254016
active_type: ""
inputs {
input_layer_name: "img"
}
inputs {
input_layer_name: "img"
proj_conf {
type: "convt"
name: "___mixed_7__.w1"
input_size: 1024
output_size: 254016
conv_conf {
filter_size: 3
channels: 1
stride: 2
padding: 1
groups: 1
filter_channels: 64
output_x: 32
img_size: 63
caffe_mode: true
filter_size_y: 3
padding_y: 1
stride_y: 2
output_y: 32
img_size_y: 63
}
}
}
inputs {
input_layer_name: "filter"
}
operator_confs {
type: "convt"
input_indices: 0
input_indices: 2
input_sizes: 1024
input_sizes: 576
output_size: 254016
conv_conf {
filter_size: 3
channels: 1
stride: 2
padding: 1
groups: 1
filter_channels: 64
output_x: 32
img_size: 63
caffe_mode: true
filter_size_y: 3
padding_y: 1
stride_y: 2
output_y: 32
img_size_y: 63
}
num_filters: 64
}
}
layers {
name: "__mixed_8__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_4__"
input_parameter_name: "___mixed_7__.w0"
input_parameter_name: "___mixed_8__.w0"
proj_conf {
type: "fc"
name: "___mixed_7__.w0"
name: "___mixed_8__.w0"
input_size: 300
output_size: 100
}
}
inputs {
input_layer_name: "__mixed_5__"
input_parameter_name: "___mixed_7__.w1"
input_parameter_name: "___mixed_8__.w1"
proj_conf {
type: "trans_fc"
name: "___mixed_7__.w1"
name: "___mixed_8__.w1"
input_size: 100
output_size: 100
}
}
inputs {
input_layer_name: "__mixed_6__"
input_parameter_name: "___mixed_7__.w2"
input_parameter_name: "___mixed_8__.w2"
proj_conf {
type: "fc"
name: "___mixed_7__.w2"
name: "___mixed_8__.w2"
input_size: 57600
output_size: 100
}
}
inputs {
input_layer_name: "__mixed_7__"
input_parameter_name: "___mixed_8__.w3"
proj_conf {
type: "fc"
name: "___mixed_8__.w3"
input_size: 254016
output_size: 100
}
}
drop_rate: 0.5
}
parameters {
@ -281,7 +378,7 @@ parameters {
initial_smart: true
}
parameters {
name: "___mixed_7__.w0"
name: "___mixed_8__.w0"
size: 30000
initial_mean: 0.0
initial_std: 0.057735026919
@ -291,7 +388,7 @@ parameters {
initial_smart: true
}
parameters {
name: "___mixed_7__.w1"
name: "___mixed_8__.w1"
size: 10000
initial_mean: 0.0
initial_std: 0.1
@ -301,7 +398,7 @@ parameters {
initial_smart: true
}
parameters {
name: "___mixed_7__.w2"
name: "___mixed_8__.w2"
size: 5760000
initial_mean: 0.0
initial_std: 0.00416666666667
@ -310,10 +407,20 @@ parameters {
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___mixed_8__.w3"
size: 25401600
initial_mean: 0.0
initial_std: 0.00198412698413
dims: 254016
dims: 100
initial_strategy: 0
initial_smart: true
}
input_layer_names: "test"
input_layer_names: "img"
input_layer_names: "filter"
output_layer_names: "__mixed_7__"
output_layer_names: "__mixed_8__"
sub_models {
name: "root"
layer_names: "test"
@ -328,10 +435,11 @@ sub_models {
layer_names: "filter"
layer_names: "__mixed_6__"
layer_names: "__mixed_7__"
layer_names: "__mixed_8__"
input_layer_names: "test"
input_layer_names: "img"
input_layer_names: "filter"
output_layer_names: "__mixed_7__"
output_layer_names: "__mixed_8__"
is_recurrent_layer_group: false
}

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