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new a33ded7 fix [#7368] Caffe converter test fails, causing CI to halt
for all PRs (#7381)
a33ded7 is described below
commit a33ded796325c8da4eebab07a18ddc616ad28098
Author: joey2014 <joe...@foxmail.com>
AuthorDate: Wed Aug 9 15:19:21 2017 -0500
fix [#7368] Caffe converter test fails, causing CI to halt for all PRs
(#7381)
* support convert mtcnn and MobileNet model
* pass python lint
* put "import re" before "import caffe_parser" as lint required
* correct missed checkin and pass pylint
* When converting vgg16 model, the layer fc6 input name pool5 is changed to
flatten_0 by _parse_proto(). It misleads adding the layer to group.
* revert disable vgg-16 resnet converter check (#7369)
---
tools/caffe_converter/convert_model.py | 13 ++++++++++++-
tools/caffe_converter/convert_symbol.py | 21 ++++++++++-----------
tools/caffe_converter/test_converter.py | 2 +-
3 files changed, 23 insertions(+), 13 deletions(-)
diff --git a/tools/caffe_converter/convert_model.py
b/tools/caffe_converter/convert_model.py
index c04a2aa..1624a01 100644
--- a/tools/caffe_converter/convert_model.py
+++ b/tools/caffe_converter/convert_model.py
@@ -26,6 +26,17 @@ import mxnet as mx
import numpy as np
from convert_symbol import convert_symbol
+def prob_label(arg_names):
+ candidates = [arg for arg in arg_names if
+ not arg.endswith('data') and
+ not arg.endswith('_weight') and
+ not arg.endswith('_bias') and
+ not arg.endswith('_gamma') and
+ not arg.endswith('_beta')]
+ if len(candidates) == 0:
+ return 'prob_label'
+ return candidates[-1]
+
def convert_model(prototxt_fname, caffemodel_fname, output_prefix=None):
"""Convert caffe model
@@ -198,7 +209,7 @@ def convert_model(prototxt_fname, caffemodel_fname,
output_prefix=None):
assert len(layer_blobs) == 0
if output_prefix is not None:
- model = mx.mod.Module(symbol=sym, label_names=[arg_names[-1], ])
+ model = mx.mod.Module(symbol=sym, label_names=[prob_label(arg_names),
])
model.bind(data_shapes=[('data', tuple(input_dim))])
model.init_params(arg_params=arg_params, aux_params=aux_params)
model.save_checkpoint(output_prefix, 0)
diff --git a/tools/caffe_converter/convert_symbol.py
b/tools/caffe_converter/convert_symbol.py
index dde3c26..13b55fe 100644
--- a/tools/caffe_converter/convert_symbol.py
+++ b/tools/caffe_converter/convert_symbol.py
@@ -145,6 +145,16 @@ def _parse_proto(prototxt_fname):
param_string = ''
skip_layer = False
name = re.sub('[-/]', '_', layer.name)
+ for k in range(len(layer.bottom)):
+ if layer.bottom[k] in _output_name:
+ _output_name[layer.bottom[k]]['count'] =
_output_name[layer.bottom[k]]['count']+1
+ else:
+ _output_name[layer.bottom[k]] = {'count':0}
+ for k in range(len(layer.top)):
+ if layer.top[k] in _output_name:
+ _output_name[layer.top[k]]['count'] =
_output_name[layer.top[k]]['count']+1
+ else:
+ _output_name[layer.top[k]] = {'count':0, 'name':name}
if layer.type == 'Convolution' or layer.type == 4:
type_string = 'mx.symbol.Convolution'
param_string = _convert_conv_param(layer.convolution_param)
@@ -270,17 +280,6 @@ def _parse_proto(prototxt_fname):
for j in range(len(layer.top)):
mapping[layer.top[j]] = name
output_name = name
- for k in range(len(layer.bottom)):
- if layer.bottom[k] in _output_name:
- _output_name[layer.bottom[k]]['count'] =
_output_name[layer.bottom[k]]['count']+1
- else:
- _output_name[layer.bottom[k]] = {'count':0}
- for k in range(len(layer.top)):
- if layer.top[k] in _output_name:
- _output_name[layer.top[k]]['count'] =
_output_name[layer.top[k]]['count']+1
- else:
- _output_name[layer.top[k]] = {'count':0, 'name':name}
-
output_name = []
for i in _output_name:
if 'name' in _output_name[i] and _output_name[i]['count'] == 0:
diff --git a/tools/caffe_converter/test_converter.py
b/tools/caffe_converter/test_converter.py
index db17c64..cdf8331 100644
--- a/tools/caffe_converter/test_converter.py
+++ b/tools/caffe_converter/test_converter.py
@@ -95,7 +95,7 @@ def main():
assert gpus, 'At least one GPU is needed to run test_converter in GPU
mode'
batch_size = 32 * len(gpus)
- models = ['bvlc_googlenet']
+ models = ['bvlc_googlenet', 'vgg-16', 'resnet-50']
val = download_data()
for m in models:
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