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fixed a bug in parse_conv in config_parser.py

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avx_docs
wangyang59 9 years ago
parent af7a50c0d4
commit 1c58e27fdc
2 changed files with 188 additions and 5 deletions
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17
python/paddle/trainer/config_parser.py
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@ -649,7 +649,8 @@ class ConvProjection(Projection):
parse_conv(conv_conf,
input_layer_name,
self.proj_conf.conv_conf)
self.proj_conf.conv_conf,
num_filters)
# TODO: support rectangle input
self.proj_conf.output_size = (self.proj_conf.conv_conf.output_x ** 2) * num_filters
@ -730,7 +731,8 @@ class ConvOperator(Operator):
parse_conv(conv_conf,
MakeLayerNameInSubmodel(input_layer_names[0]),
self.operator_conf.conv_conf)
self.operator_conf.conv_conf,
num_filters)
self.operator_conf.output_size = (self.operator_conf.conv_conf.output_x ** 2) * num_filters
config_assert(len(input_layer_names) == 2, "Conv is binary operator")
@ -1097,7 +1099,7 @@ def parse_norm(norm, input_layer_name, norm_conf):
caffe_mode: compute the output size using floor instead of ceil,
which is consistent of caffe and CuDNN's convention.
'''
def parse_conv(conv, input_layer_name, conv_conf, trans=False):
def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False):
conv_conf.filter_size = conv.filter_size
conv_conf.filter_size_y = conv.filter_size_y
conv_conf.channels = conv.channels
@ -1106,10 +1108,11 @@ def parse_conv(conv, input_layer_name, conv_conf, trans=False):
conv_conf.stride = conv.stride
conv_conf.stride_y = conv.stride_y
conv_conf.groups = conv.groups
conv_conf.filter_channels = conv.channels / conv.groups
conv_conf.caffe_mode = conv.caffe_mode
if not trans:
conv_conf.filter_channels = conv.channels / conv.groups
img_pixels = g_layer_map[input_layer_name].size / conv.channels
print('channels=%d size=%d'%(conv.channels,
g_layer_map[input_layer_name].size))
@ -1123,6 +1126,8 @@ def parse_conv(conv, input_layer_name, conv_conf, trans=False):
conv_conf.img_size, conv_conf.filter_size,
conv_conf.padding, conv_conf.stride, conv_conf.caffe_mode)
else:
conv_conf.filter_channels = num_filters / conv.groups
outputSize = g_layer_map[input_layer_name].size / conv.channels
print('channels=%d size=%d'%(conv.channels,
g_layer_map[input_layer_name].size))
@ -1616,7 +1621,8 @@ class ConvLayerBase(LayerBase):
parse_conv(
self.inputs[input_index].conv,
input_layer.name,
self.config.inputs[input_index].conv_conf)
self.config.inputs[input_index].conv_conf,
num_filters)
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))
@ -1676,6 +1682,7 @@ class ConvTransLayerBase(LayerBase):
self.inputs[input_index].conv,
input_layer.name,
self.config.inputs[input_index].conv_conf,
num_filters,
trans=True)
conv_conf = self.config.inputs[input_index].conv_conf
psize = self.calc_parameter_size(conv_conf)

176
python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr
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@ -0,0 +1,176 @@
type: "nn"
layers {
name: "image"
type: "data"
size: 51529
active_type: ""
}
layers {
name: "__conv_0__"
type: "exconvt"
size: 4194304
active_type: ""
inputs {
input_layer_name: "image"
input_parameter_name: "___conv_0__.w0"
conv_conf {
filter_size: 32
channels: 1
stride: 1
padding: 1
groups: 1
filter_channels: 64
output_x: 227
img_size: 256
caffe_mode: true
filter_size_y: 32
padding_y: 1
stride_y: 1
}
}
bias_parameter_name: "___conv_0__.wbias"
num_filters: 64
shared_biases: true
}
layers {
name: "__batch_norm_0__"
type: "batch_norm"
size: 4194304
active_type: "relu"
inputs {
input_layer_name: "__conv_0__"
input_parameter_name: "___batch_norm_0__.w0"
image_conf {
channels: 64
img_size: 256
}
}
inputs {
input_layer_name: "__conv_0__"
input_parameter_name: "___batch_norm_0__.w1"
}
inputs {
input_layer_name: "__conv_0__"
input_parameter_name: "___batch_norm_0__.w2"
}
bias_parameter_name: "___batch_norm_0__.wbias"
moving_average_fraction: 0.9
}
layers {
name: "__crmnorm_0__"
type: "norm"
size: 4194304
active_type: ""
inputs {
input_layer_name: "__batch_norm_0__"
norm_conf {
norm_type: "cmrnorm-projection"
channels: 64
size: 32
scale: 0.0004
pow: 0.75
output_x: 256
img_size: 256
blocked: false
}
}
}
layers {
name: "__pool_0__"
type: "pool"
size: 3240000
active_type: ""
inputs {
input_layer_name: "__conv_0__"
pool_conf {
pool_type: "max-projection"
channels: 64
size_x: 32
stride: 1
output_x: 225
img_size: 256
padding: 0
size_y: 32
stride_y: 1
output_y: 225
img_size_y: 256
padding_y: 0
}
}
}
parameters {
name: "___conv_0__.w0"
size: 65536
initial_mean: 0.0
initial_std: 0.0441941738242
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___conv_0__.wbias"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 64
dims: 1
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___batch_norm_0__.w0"
size: 64
initial_mean: 1.0
initial_std: 0.0
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___batch_norm_0__.w1"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 64
initial_strategy: 0
initial_smart: false
is_static: true
is_shared: true
}
parameters {
name: "___batch_norm_0__.w2"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 64
initial_strategy: 0
initial_smart: false
is_static: true
is_shared: true
}
parameters {
name: "___batch_norm_0__.wbias"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 64
initial_strategy: 0
initial_smart: false
}
input_layer_names: "image"
output_layer_names: "__pool_0__"
output_layer_names: "__crmnorm_0__"
sub_models {
name: "root"
layer_names: "image"
layer_names: "__conv_0__"
layer_names: "__batch_norm_0__"
layer_names: "__crmnorm_0__"
layer_names: "__pool_0__"
input_layer_names: "image"
output_layer_names: "__pool_0__"
output_layer_names: "__crmnorm_0__"
is_recurrent_layer_group: false
}
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