Negative dimension size for 'pool2/MaxPool' (op: 'MaxPool') with input shapes:[?,1,112,128].

[SANTO-P]

Converting model...
[name: "data"
type: "Input"
top: "data"
input_param {
shape {
dim: 1
dim: 3
dim: 224
dim: 224
}
}
, name: "conv1_1"
type: "Convolution"
bottom: "data"
top: "conv1_1"
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
}
, name: "relu1_1"
type: "ReLU"
bottom: "conv1_1"
top: "conv1_1"
, name: "conv1_2"
type: "Convolution"
bottom: "conv1_1"
top: "conv1_2"
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
}
, name: "relu1_2"
type: "ReLU"
bottom: "conv1_2"
top: "conv1_2"
, name: "pool1"
type: "Pooling"
bottom: "conv1_2"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
, name: "conv2_1"
type: "Convolution"
bottom: "pool1"
top: "conv2_1"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
, name: "relu2_1"
type: "ReLU"
bottom: "conv2_1"
top: "conv2_1"
, name: "conv2_2"
type: "Convolution"
bottom: "conv2_1"
top: "conv2_2"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
, name: "relu2_2"
type: "ReLU"
bottom: "conv2_2"
top: "conv2_2"
, name: "pool2"
type: "Pooling"
bottom: "conv2_2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
, name: "conv3_1"
type: "Convolution"
bottom: "pool2"
top: "conv3_1"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
, name: "relu3_1"
type: "ReLU"
bottom: "conv3_1"
top: "conv3_1"
, name: "conv3_2"
type: "Convolution"
bottom: "conv3_1"
top: "conv3_2"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
, name: "relu3_2"
type: "ReLU"
bottom: "conv3_2"
top: "conv3_2"
, name: "conv3_3"
type: "Convolution"
bottom: "conv3_2"
top: "conv3_3"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
, name: "relu3_3"
type: "ReLU"
bottom: "conv3_3"
top: "conv3_3"
, name: "pool3"
type: "Pooling"
bottom: "conv3_3"
top: "pool3"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
, name: "conv4_1"
type: "Convolution"
bottom: "pool3"
top: "conv4_1"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
, name: "relu4_1"
type: "ReLU"
bottom: "conv4_1"
top: "conv4_1"
, name: "conv4_2"
type: "Convolution"
bottom: "conv4_1"
top: "conv4_2"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
, name: "relu4_2"
type: "ReLU"
bottom: "conv4_2"
top: "conv4_2"
, name: "conv4_3"
type: "Convolution"
bottom: "conv4_2"
top: "conv4_3"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
, name: "relu4_3"
type: "ReLU"
bottom: "conv4_3"
top: "conv4_3"
, name: "pool4"
type: "Pooling"
bottom: "conv4_3"
top: "pool4"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
, name: "conv5_1"
type: "Convolution"
bottom: "pool4"
top: "conv5_1"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
, name: "relu5_1"
type: "ReLU"
bottom: "conv5_1"
top: "conv5_1"
, name: "conv5_2"
type: "Convolution"
bottom: "conv5_1"
top: "conv5_2"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
, name: "relu5_2"
type: "ReLU"
bottom: "conv5_2"
top: "conv5_2"
, name: "conv5_3"
type: "Convolution"
bottom: "conv5_2"
top: "conv5_3"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
, name: "relu5_3"
type: "ReLU"
bottom: "conv5_3"
top: "conv5_3"
, name: "pool5"
type: "Pooling"
bottom: "conv5_3"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
, name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
inner_product_param {
num_output: 4096
}
, name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
, name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
, name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
inner_product_param {
num_output: 4096
}
, name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
, name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
, name: "fc8_softlabel"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_softlabel"
inner_product_param {
num_output: 20
}
, name: "softmax"
type: "Softmax"
bottom: "fc8_softlabel"
top: "fc8_softlabel"
, name: "fc8_landmarks"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_landmarks"
inner_product_param {
num_output: 12
}
, name: "fc8_visibility_1"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_visibility_1"
inner_product_param {
num_output: 3
}
, name: "fc8_visibility_2"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_visibility_2"
inner_product_param {
num_output: 3
}
, name: "fc8_visibility_3"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_visibility_3"
inner_product_param {
num_output: 3
}
, name: "fc8_visibility_4"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_visibility_4"
inner_product_param {
num_output: 3
}
, name: "fc8_visibility_5"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_visibility_5"
inner_product_param {
num_output: 3
}
, name: "fc8_visibility_6"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_visibility_6"
inner_product_param {
num_output: 3
}
, name: "fc8"
type: "Concat"
bottom: "fc8_landmarks"
bottom: "fc8_visibility_1"
bottom: "fc8_visibility_2"
bottom: "fc8_visibility_3"
bottom: "fc8_visibility_4"
bottom: "fc8_visibility_5"
bottom: "fc8_visibility_6"
top: "fc8"
]
CREATING MODEL
Traceback (most recent call last):
File "caffe2keras.py", line 49, in
main(arguments)
File "caffe2keras.py", line 35, in main
debug=args.debug)
File "/root/santosh/try/keras/keras/caffe/convert.py", line 44, in caffe_to_keras
tuple(input_dim[1:]), debug)
File "/root/santosh/try/keras/keras/caffe/convert.py", line 320, in create_model
padding=border_mode, name=name)(input_layers)
File "/root/santosh/try/keras/keras/engine/base_layer.py", line 460, in call
output = self.call(inputs, **kwargs)
File "/root/santosh/try/keras/keras/layers/pooling.py", line 158, in call
data_format=self.data_format)
File "/root/santosh/try/keras/keras/layers/pooling.py", line 221, in _pooling_function
pool_mode='max')
File "/root/santosh/try/keras/keras/backend/tensorflow_backend.py", line 4210, in pool2d
data_format=tf_data_format)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_ops.py", line 2142, in max_pool
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 4604, in max_pool
data_format=data_format, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 3392, in create_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1734, in init
control_input_ops)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1570, in _create_c_op
raise ValueError(str(e))
ValueError: Negative dimension size caused by subtracting 2 from 1 for 'pool2/MaxPool' (op: 'MaxPool') with input shapes: [?,1,112,128].