fix validation precision

onnx2fluid/examples/convert_data_npz_0.py

@@ -11,15 +11,32 @@ import numpy as np
 
 from collections import OrderedDict as Dict
 
+
+def _make_var_name(name):
+    """
+    make a valid variable name in Python code
+    """
+
+    if name == '':
+        return '_'
+    if name[0].isdigit():
+        return 'var_' + name
+    for s in ' *?\\/-:':
+        name = name.replace(s, '_')
+    if name.startswith('_'):
+        name = 'var' + name
+    return name
+
+
 fn = sys.argv[1]
 input_names = sys.argv[2].split(':')
 output_name = sys.argv[3].split(':')
 
-data = np.load(fn)
+data = np.load(fn, encoding='bytes')
 input_data = data['inputs']
 output_data = data['outputs']
 
-inputs = Dict(zip(input_names, [input_data]))
-outputs = Dict(zip(output_name, [output_data]))
+inputs = Dict(zip(map(_make_var_name, input_names), [input_data]))
+outputs = Dict(zip(map(_make_var_name, output_name), [output_data]))
 
 np.savez(fn, inputs=inputs, outputs=outputs)  # overwrite

onnx2fluid/examples/convert_data_pb_0.py

@@ -16,6 +16,23 @@ import onnx.numpy_helper as numpy_helper
 from collections import OrderedDict as Dict
 from glob import glob
 
+
+def _make_var_name(name):
+    """
+    make a valid variable name in Python code
+    """
+
+    if name == '':
+        return '_'
+    if name[0].isdigit():
+        return 'var_' + name
+    for s in ' *?\\/-:':
+        name = name.replace(s, '_')
+    if name.startswith('_'):
+        name = 'var' + name
+    return name
+
+
 data_dir = os.path.dirname(sys.argv[1])
 input_names = sys.argv[2].split(':')
 output_name = sys.argv[3].split(':')
@@ -36,7 +53,7 @@ for fn in glob(os.path.join(data_dir, 'output_*.pb')):
         tensor.ParseFromString(f.read())
     outputs.append(numpy_helper.to_array(tensor))
 
-inputs = Dict(zip(input_names, inputs))
-outputs = Dict(zip(output_name, outputs))
+inputs = Dict(zip(map(_make_var_name, input_names), inputs))
+outputs = Dict(zip(map(_make_var_name, output_name), outputs))
 
 np.savez(data_dir, inputs=inputs, outputs=outputs)

onnx2fluid/examples/onnx_model_zoo.sh

 #! /usr/bin/env sh
 
-get_url="aria2c -c -s8 -x8"
+# setopt SH_WORD_SPLIT # if zsh
+
 base_url="https://s3.amazonaws.com/download.onnx/models/opset_9/"
-flags="-e -o /tmp/export/"
+convert_flags="-e -o /tmp/export/"
+validate_flags1="/tmp/export/model.py"
+validate_flags2="/tmp/export/__model__"
+
+# alias http_get="wget -c" # if no aria2
+alias http_get="aria2c -c -s8 -x8"
+# alias python="python3" # if ...
 
 bvlc_alexnet()
 {
@@ -10,21 +17,24 @@ bvlc_alexnet()
     fn_tar="$bn_tar.tar.gz"
     fn_model="$bn_tar/model.onnx"
 
-    $get_url "$base_url$fn_tar"
+    http_get "$base_url$fn_tar"
     echo "extracting ..."
     tar xf "$fn_tar"
 
-    for npz in $bn_tar/*.npz
+    python -m onnx2fluid $convert_flags "$fn_model"
+    for npz in "$bn_tar"/*.npz
     do
         echo "converting $npz ..."
-        python convert_data_npz_0.py "$npz" "data_0" "prob_1"
-        python -m onnx2fluid $flags "$fn_model" -t $npz
+        python convert_data_npz_0.py "$npz" data_0 prob_1
+        python -m onnx2fluid.validation $validate_flags1 -t "$npz"
+        python -m onnx2fluid.validation $validate_flags2 -t "$npz"
     done
-    for pb_dir in $bn_tar/*/
+    for pb_dir in "$bn_tar"/*/
     do
         echo "converting $pb_dir ..."
-        python convert_data_pb_0.py "$pb_dir" "data_0" "prob_1"
-        python -m onnx2fluid $flags "$fn_model" -t $(dirname "$pb_dir/x").npz
+        python convert_data_pb_0.py "$pb_dir" data_0 prob_1
+        python -m onnx2fluid.validation $validate_flags1 -t $(dirname "$pb_dir/x").npz
+        python -m onnx2fluid.validation $validate_flags2 -t $(dirname "$pb_dir/x").npz
     done
 }
 
@@ -34,15 +44,17 @@ bvlc_googlenet()
     fn_tar="$bn_tar.tar.gz"
     fn_model="$bn_tar/model.onnx"
 
-    $get_url "$base_url$fn_tar"
+    http_get "$base_url$fn_tar"
     echo "extracting ..."
     tar xf "$fn_tar"
 
-    for pb_dir in $bn_tar/*/
+    python -m onnx2fluid $convert_flags "$fn_model"
+    for pb_dir in "$bn_tar"/*/
     do
         echo "converting $pb_dir"
-        python convert_data_pb_0.py "$pb_dir" "data_0" "prob_1"
-        python -m onnx2fluid $flags "$fn_model" -t $(dirname "$pb_dir/x").npz
+        python convert_data_pb_0.py "$pb_dir" data_0 prob_1
+        python -m onnx2fluid.validation $validate_flags1 -t $(dirname "$pb_dir/x").npz
+        python -m onnx2fluid.validation $validate_flags2 -t $(dirname "$pb_dir/x").npz
     done
 }
 
@@ -52,15 +64,17 @@ bvlc_reference_caffenet()
     fn_tar="$bn_tar.tar.gz"
     fn_model="$bn_tar/model.onnx"
 
-    $get_url "$base_url$fn_tar"
+    http_get "$base_url$fn_tar"
     echo "extracting ..."
     tar xf "$fn_tar"
 
-    for pb_dir in $bn_tar/*/
+    python -m onnx2fluid $convert_flags "$fn_model"
+    for pb_dir in "$bn_tar"/*/
     do
         echo "converting $pb_dir"
-        python convert_data_pb_0.py "$pb_dir" "data_0" "prob_1"
-        python -m onnx2fluid $flags "$fn_model" -t $(dirname "$pb_dir/x").npz
+        python convert_data_pb_0.py "$pb_dir" data_0 prob_1
+        python -m onnx2fluid.validation $validate_flags1 -t $(dirname "$pb_dir/x").npz
+        python -m onnx2fluid.validation $validate_flags2 -t $(dirname "$pb_dir/x").npz
     done
 }
 
@@ -70,15 +84,17 @@ bvlc_reference_rcnn_ilsvrc13()
     fn_tar="$bn_tar.tar.gz"
     fn_model="$bn_tar/model.onnx"
 
-    $get_url "$base_url$fn_tar"
+    http_get "$base_url$fn_tar"
     echo "extracting ..."
     tar xf "$fn_tar"
 
-    for pb_dir in $bn_tar/*/
+    python -m onnx2fluid $convert_flags "$fn_model"
+    for pb_dir in "$bn_tar"/*/
     do
         echo "converting $pb_dir"
-        python convert_data_pb_0.py "$pb_dir" "data_0" "fc_rcnn_1"
-        python -m onnx2fluid $flags "$fn_model" -t $(dirname "$pb_dir/x").npz
+        python convert_data_pb_0.py "$pb_dir" data_0 fc-rcnn_1
+        python -m onnx2fluid.validation $validate_flags1 -t $(dirname "$pb_dir/x").npz
+        python -m onnx2fluid.validation $validate_flags2 -t $(dirname "$pb_dir/x").npz
     done
 }
 
@@ -88,22 +104,24 @@ inception_v1()
     fn_tar="$bn_tar.tar.gz"
     fn_model="$bn_tar/model.onnx"
 
-    $get_url "$base_url$fn_tar"
+    http_get "$base_url$fn_tar"
     echo "extracting ..."
     tar xf "$fn_tar"
 
-    for npz in $bn_tar/*.npz
+    python -m onnx2fluid $convert_flags "$fn_model"
+    for npz in "$bn_tar"/*.npz
     do
         echo "converting $npz ..."
-        python convert_data_npz_0.py "$npz" "data_0" "prob_1"
-        python -m onnx2fluid $flags "$fn_model" -t $npz
+        python convert_data_npz_0.py "$npz" data_0 prob_1
+        python -m onnx2fluid.validation $validate_flags1 -t "$npz"
+        python -m onnx2fluid.validation $validate_flags2 -t "$npz"
     done
-
-    for pb_dir in $bn_tar/*/
+    for pb_dir in "$bn_tar"/*/
     do
-        echo "converting $pb_dir"
-        python convert_data_pb_0.py "$pb_dir" "data_0" "prob_1"
-        python -m onnx2fluid $flags "$fn_model" -t $(dirname "$pb_dir/x").npz
+        echo "converting $pb_dir ..."
+        python convert_data_pb_0.py "$pb_dir" data_0 prob_1
+        python -m onnx2fluid.validation $validate_flags1 -t $(dirname "$pb_dir/x").npz
+        python -m onnx2fluid.validation $validate_flags2 -t $(dirname "$pb_dir/x").npz
     done
 }
 
@@ -113,22 +131,24 @@ inception_v2()
     fn_tar="$bn_tar.tar.gz"
     fn_model="$bn_tar/model.onnx"
 
-    $get_url "$base_url$fn_tar"
+    http_get "$base_url$fn_tar"
     echo "extracting ..."
     tar xf "$fn_tar"
 
-    for npz in $bn_tar/*.npz
+    python -m onnx2fluid $convert_flags "$fn_model"
+    for npz in "$bn_tar"/*.npz
     do
         echo "converting $npz ..."
-        python convert_data_npz_0.py "$npz" "data_0" "prob_1"
-        python -m onnx2fluid $flags "$fn_model" -t $npz
+        python convert_data_npz_0.py "$npz" data_0 prob_1
+        python -m onnx2fluid.validation $validate_flags1 -t "$npz"
+        python -m onnx2fluid.validation $validate_flags2 -t "$npz"
     done
-
-    for pb_dir in $bn_tar/*/
+    for pb_dir in "$bn_tar"/*/
     do
-        echo "converting $pb_dir"
-        python convert_data_pb_0.py "$pb_dir" "data_0" "prob_1"
-        python -m onnx2fluid $flags "$fn_model" -t $(dirname "$pb_dir/x").npz
+        echo "converting $pb_dir ..."
+        python convert_data_pb_0.py "$pb_dir" data_0 prob_1
+        python -m onnx2fluid.validation $validate_flags1 -t $(dirname "$pb_dir/x").npz
+        python -m onnx2fluid.validation $validate_flags2 -t $(dirname "$pb_dir/x").npz
     done
 }
 
@@ -138,22 +158,24 @@ resnet50()
     fn_tar="$bn_tar.tar.gz"
     fn_model="$bn_tar/model.onnx"
 
-    $get_url "$base_url$fn_tar"
+    http_get "$base_url$fn_tar"
     echo "extracting ..."
     tar xf "$fn_tar"
 
-    for npz in $bn_tar/*.npz
+    python -m onnx2fluid $convert_flags "$fn_model"
+    for npz in "$bn_tar"/*.npz
     do
         echo "converting $npz ..."
-        python convert_data_npz_0.py "$npz" "gpu_0/data_0" "gpu_0/softmaxout_1"
-        python -m onnx2fluid $flags "$fn_model" -t $npz
+        python convert_data_npz_0.py "$npz" gpu_0/data_0 gpu_0/softmaxout_1
+        python -m onnx2fluid.validation $validate_flags1 -t "$npz"
+        python -m onnx2fluid.validation $validate_flags2 -t "$npz"
     done
-
-    for pb_dir in $bn_tar/*/
+    for pb_dir in "$bn_tar"/*/
     do
-        echo "converting $pb_dir"
-        python convert_data_pb_0.py "$pb_dir" "gpu_0/data_0" "gpu_0/softmaxout_1"
-        python -m onnx2fluid $flags "$fn_model" -t $(dirname "$pb_dir/x").npz
+        echo "converting $pb_dir ..."
+        python convert_data_pb_0.py "$pb_dir" gpu_0/data_0 gpu_0/softmaxout_1
+        python -m onnx2fluid.validation $validate_flags1 -t $(dirname "$pb_dir/x").npz
+        python -m onnx2fluid.validation $validate_flags2 -t $(dirname "$pb_dir/x").npz
     done
 }
 
@@ -163,15 +185,17 @@ shufflenet()
     fn_tar="$bn_tar.tar.gz"
     fn_model="$bn_tar/model.onnx"
 
-    $get_url "$base_url$fn_tar"
+    http_get "$base_url$fn_tar"
     echo "extracting ..."
     tar xf "$fn_tar"
 
-    for pb_dir in $bn_tar/*/
+    python -m onnx2fluid $convert_flags "$fn_model"
+    for pb_dir in "$bn_tar"/*/
     do
-        echo "converting $pb_dir"
-        python convert_data_pb_0.py "$pb_dir" "gpu_0/data_0" "gpu_0/softmaxout_1"
-        python -m onnx2fluid $flags "$fn_model" -t $(dirname "$pb_dir/x").npz
+        echo "converting $pb_dir ..."
+        python convert_data_pb_0.py "$pb_dir" gpu_0/data_0 gpu_0/softmaxout_1
+        python -m onnx2fluid.validation $validate_flags1 -t $(dirname "$pb_dir/x").npz
+        python -m onnx2fluid.validation $validate_flags2 -t $(dirname "$pb_dir/x").npz
     done
 }
 
@@ -181,15 +205,17 @@ squeezenet()
     fn_tar="$bn_tar.tar.gz"
     fn_model="$bn_tar/model.onnx"
 
-    $get_url "$base_url$fn_tar"
+    http_get "$base_url$fn_tar"
     echo "extracting ..."
     tar xf "$fn_tar"
 
-    for pb_dir in $bn_tar/*/
+    python -m onnx2fluid $convert_flags "$fn_model"
+    for pb_dir in "$bn_tar"/*/
     do
         echo "converting $pb_dir"
-        python convert_data_pb_0.py "$pb_dir" "data_0" "softmaxout_1"
-        python -m onnx2fluid $flags "$fn_model" -t $(dirname "$pb_dir/x").npz
+        python convert_data_pb_0.py "$pb_dir" data_0 softmaxout_1
+        python -m onnx2fluid.validation $validate_flags1 -t $(dirname "$pb_dir/x").npz
+        python -m onnx2fluid.validation $validate_flags2 -t $(dirname "$pb_dir/x").npz
     done
 }
 
@@ -199,15 +225,17 @@ tiny_yolov2()
     fn_tar="$bn_tar.tar.gz"
     fn_model="$bn_tar/model.onnx"
 
-    $get_url "https://onnxzoo.blob.core.windows.net/models/opset_8/tiny_yolov2/$fn_tar"
+    http_get "https://onnxzoo.blob.core.windows.net/models/opset_8/tiny_yolov2/$fn_tar"
     echo "extracting ..."
     tar xf "$fn_tar"
 
-    for pb_dir in $bn_tar/*/
+    python -m onnx2fluid $convert_flags "$fn_model" -xy
+    for pb_dir in "$bn_tar"/*/
     do
         echo "converting $pb_dir"
-        python convert_data_pb_0.py "$pb_dir" "image" "grid"
-        python -m onnx2fluid $flags "$fn_model" -t $(dirname "$pb_dir/x").npz -x
+        python convert_data_pb_0.py "$pb_dir" image grid
+        python -m onnx2fluid.validation $validate_flags1 -t $(dirname "$pb_dir/x").npz
+        python -m onnx2fluid.validation $validate_flags2 -t $(dirname "$pb_dir/x").npz
     done
 }
 
@@ -217,15 +245,17 @@ vgg19()
     fn_tar="$bn_tar.tar.gz"
     fn_model="$bn_tar/model.onnx"
 
-    $get_url "$base_url$fn_tar"
+    http_get "$base_url$fn_tar"
     echo "extracting ..."
     tar xf "$fn_tar"
 
-    for pb_dir in $bn_tar/*/
+    python -m onnx2fluid $convert_flags "$fn_model"
+    for pb_dir in "$bn_tar"/*/
     do
         echo "converting $pb_dir"
-        python convert_data_pb_0.py "$pb_dir" "data_0" "prob_1"
-        python -m onnx2fluid $flags "$fn_model" -t $(dirname "$pb_dir/x").npz
+        python convert_data_pb_0.py "$pb_dir" data_0 prob_1
+        python -m onnx2fluid.validation $validate_flags1 -t $(dirname "$pb_dir/x").npz
+        python -m onnx2fluid.validation $validate_flags2 -t $(dirname "$pb_dir/x").npz
     done
 }
 
@@ -235,15 +265,17 @@ zfnet512()
     fn_tar="$bn_tar.tar.gz"
     fn_model="$bn_tar/model.onnx"
 
-    $get_url "$base_url$fn_tar"
+    http_get "$base_url$fn_tar"
     echo "extracting ..."
     tar xf "$fn_tar"
 
-    for pb_dir in $bn_tar/*/
+    python -m onnx2fluid $convert_flags "$fn_model"
+    for pb_dir in "$bn_tar"/*/
     do
         echo "converting $pb_dir"
-        python convert_data_pb_0.py "$pb_dir" "gpu_0/data_0" "gpu_0/softmax_1"
-        python -m onnx2fluid $flags "$fn_model" -t $(dirname "$pb_dir/x").npz
+        python convert_data_pb_0.py "$pb_dir" gpu_0/data_0 gpu_0/softmax_1
+        python -m onnx2fluid.validation $validate_flags1 -t $(dirname "$pb_dir/x").npz
+        python -m onnx2fluid.validation $validate_flags2 -t $(dirname "$pb_dir/x").npz
     done
 }
 

onnx2fluid/onnx2fluid/__main__.py

@@ -89,7 +89,7 @@ parser.add_argument(
     '--precision',
     '-p',
     type=int,
-    default=4,
+    default=3,
     help='assertion decimal for validation',
 )
 args = parser.parse_args()

onnx2fluid/onnx2fluid/cmdline.py

@@ -84,13 +84,13 @@ def main(**kwargs):
 
         # in fact fluid can not fully clear the context
         # continuous validation may be inaccurate
-        precision = 10**-kwargs.get('precision', 4)
+        decimal = kwargs.get('precision', 3)
 
         logger.info('starting validation on desc ...')
         passed &= validate(
             shutil.os.path.join(save_dir, '__model__'),
             golden_data_filename,
-            precision=precision,
+            decimal=decimal,
         )
 
         logger.info('starting validation on code ...')
@@ -98,7 +98,7 @@ def main(**kwargs):
             shutil.os.path.join(save_dir, model_basename),
             golden_data_filename,
             model_func_name=model_func_name,
-            precision=precision,
+            decimal=decimal,
             save_inference_model=debug,  # this overwrite desc file for test
         )
 
@@ -131,12 +131,13 @@ if __name__ == '__main__':
         level=logging.DEBUG,
     )
 
-    #    main(model=['../examples/t5.onnx'],
-    #         output_dir='/tmp/export/',
-    #         embed_params=False,
-    #         pedantic=False,
-    #         test_data='../examples/t5.npz',
-    #         debug=True)
+    main(
+        model=['../examples/t1.onnx'],
+        output_dir='/tmp/export/',
+        embed_params=False,
+        pedantic=False,
+        test_data='../examples/t1.npz',
+        debug=True)
 
     main(
         model=['../examples/inception_v2/model.onnx'],

onnx2fluid/onnx2fluid/symbolic.py

@@ -250,20 +250,17 @@ def _default(prog, op_type, inputs, outputs, attrs, *args, name='', **kwargs):
         arg_name,
     ))
 
-    for var_out in var_outs:
-        prog.VarDesc(var_out)
-
     # dummy var_out
     num_vars = len(var_outs)
     num_args = len(fluid_output_args)
     if num_vars < num_args:
         assert fill_name_field, 'name required to name dummy output variables'
         for idx_out in range(num_vars, num_args):
-            var_out = _make_var_name(name + '.' +
-                                     fluid_output_args[idx_out].lower())
+            var_out = name + '.' + fluid_output_args[idx_out]  # dummy output
             var_outs.append(var_out)
-            prog.VarDesc(var_out)
 
+    for var_out in var_outs:
+        prog.VarDesc(var_out)
     prog.OpDesc(fluid_op, (var_inps, *fluid_input_args),
                 (var_outs, *fluid_output_args), fluid_attrs)
 
@@ -301,7 +298,7 @@ def _pad_if_asymmetric(prog, pads, val_name, value_infos):  # pads: SSEE
     if symmetric:
         return pads[:ndims], val_name
 
-    val_padded = val_name + '_padded'
+    val_padded = val_name + '_padded'  # explicit variable
     prog.Op(
         '',
         'Pad',
@@ -637,6 +634,8 @@ def BatchNormalization(prog,
     val_y, = outputs
     var_x = _make_var_name(val_x)
     var_y = _make_var_name(val_y)
+    var_saved_mean = name + '.saved_mean'  # dummy output
+    var_saved_variance = name + '.saved_variance'  # dummy output
 
     # interpretation
     fluid_op = 'batch_norm'
@@ -645,10 +644,10 @@ def BatchNormalization(prog,
     name_attr = ', name={}'.format(repr(name)) if name else ''
     if embed_params:
         assert name != ''
-        var_scale = '{}.w_0'.format(name)
-        var_b = '{}.b_0'.format(name)
-        var_mean = '{}.w_1'.format(name)
-        var_var = '{}.w_2'.format(name)
+        var_scale = name + '.w_0'
+        var_b = name + '.b_0'
+        var_mean = name + '.w_1'
+        var_var = name + '.w_2'
         value_infos[val_scale].setdefault('embeded_as', []).append(var_scale)
         value_infos[val_b].setdefault('embeded_as', []).append(var_b)
         value_infos[val_mean].setdefault('embeded_as', []).append(var_mean)
@@ -663,8 +662,6 @@ def BatchNormalization(prog,
                       ', moving_mean_name={}, moving_variance_name={}').format(
                           repr(var_scale), repr(var_b), repr(var_mean),
                           repr(var_var))
-    var_saved_mean = '{}.saved_mean'.format(name)  # dropped var
-    var_saved_variance = '{}.saved_variance'.format(name)  # dropped var
 
     # generationvalue_infos
     prog.Code('{} = layers.{}({}, is_test=True, data_layout="NCHW"'
@@ -795,7 +792,7 @@ def Constant(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
 
 
 #    dtype = np.dtype('float32') # HINT: force to float32
-    shape = attrs.get('shape', None)  # additional, maybe var_name
+    shape = attrs.get('shape', None)  #
     if shape is None:
         shape = _shape_or_none(value_infos, val_output)
     if shape is None:
@@ -807,6 +804,7 @@ def Constant(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
 
     # generation
     if value.size == 1:  # scalar
+        value = value[0]
         fluid_op = 'fill_constant'
         prog.Code('{} = layers.{}(shape={}, dtype={}, value={})'.format(
             var_output,
@@ -814,12 +812,22 @@ def Constant(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
             # attrs
             shape,
             repr(dtype.name),
-            value[0],  # shape can be list or var_name
+            value,
         ))
-        value_infos[val_output]['const_value'] = value[0]
+        value_infos[val_output]['const_value'] = value
         prog.VarDesc(var_output)
+        prog.OpDesc(
+            fluid_op,
+            ([], ),
+            ([var_output], 'Out'),
+            dict(
+                shape=shape,
+                dtype=dtype.name,
+                value=value,
+            ),
+        )
     else:  # list parameter -> const_value
-        prog.Code('{} = {}'.format(
+        prog.Code('# {} = {} # passed directly as literal'.format(
             var_output,
             value.tolist(),
         ))
@@ -834,6 +842,7 @@ def ConstantOfShape(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
     # I/O
     val_shape, = inputs
     val_output, = outputs
+    var_shape = _make_var_name(val_shape)
 
     shape = _const_weight_or_none(value_infos, val_shape)
     if shape is None:
@@ -843,8 +852,9 @@ def ConstantOfShape(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
         'this is not supported')
     dtype = attrs['value'].dtype
     attrs = attrs.copy()
-    attrs.update(dict(shape=shape, dtype=dtype))  # pass var_name
+    attrs.update(dict(shape=shape, dtype=dtype))  # pass const
 
+    prog.Code('# shape:{}={} # const as literal'.format(var_shape, shape))
     prog.Op(
         '',
         'Constant',
@@ -898,10 +908,10 @@ def Conv(prog,
     name_attr = ', name={}'.format(repr(name)) if name else ''
     if embed_params:
         assert name != ''
-        var_w = '{}.w_0'.format(name)
+        var_w = name + '.w_0'
         value_infos[val_w].setdefault('embeded_as', []).append(var_w)
         if has_bias:
-            var_b = '{}.b_0'.format(name)
+            var_b = name + '.b_0'
             value_infos[val_b].setdefault('embeded_as', []).append(var_b)
             param_attr = ''
         else:
@@ -935,7 +945,7 @@ def Conv(prog,
                   param_attr,
                   name_attr,
               ))
-    var_conv = _make_var_name(name + '.conv')  # hidden variable
+    var_conv = name + '.conv'  # hidden variable
     prog.OpDesc(
         fluid_op,
         ([var_x, var_w], 'Input', 'Filter'),  # , 'Bias', 'ResidualData'
@@ -951,8 +961,8 @@ def Conv(prog,
         prog.IntermediateOp(
             '',
             'Add',
-            [var_conv, var_b],
-            [var_y],  # var
+            [var_conv, var_b],  #
+            [val_y],
             dict(axis=1),
             value_infos=value_infos,
             name=(name + '.bias'),
@@ -1007,10 +1017,10 @@ def ConvTranspose(prog,
     name_attr = ', name={}'.format(repr(name)) if name else ''
     if embed_params:
         assert name != ''
-        var_w = '{}.w_0'.format(name)
+        var_w = name + '.w_0'
         value_infos[val_w].setdefault('embeded_as', []).append(var_w)
         if has_bias:
-            var_b = '{}.b_0'.format(name)
+            var_b = name + '.b_0'
             value_infos[val_b].setdefault('embeded_as', []).append(var_b)
             param_attr = ''
         else:
@@ -1045,7 +1055,7 @@ def ConvTranspose(prog,
                   param_attr,
                   name_attr,
               ))
-    var_conv = _make_var_name(name + '.conv')  # hidden variable
+    var_conv = name + '.conv'  # hidden variable
     prog.OpDesc(
         fluid_op,
         ([var_x, var_w], 'Input', 'Filter'),  # , 'Bias', 'ResidualData'
@@ -1062,8 +1072,8 @@ def ConvTranspose(prog,
         prog.IntermediateOp(
             '',
             'Add',
-            [var_conv, var_b],
-            [var_y],  # var
+            [var_conv, var_b],  #
+            [val_y],
             dict(axis=1),
             value_infos=value_infos,
             name=(name + '.bias'),
@@ -1414,14 +1424,14 @@ def Reshape(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
     val_data, val_shape = inputs
     val_reshaped, = outputs
     var_data = _make_var_name(val_data)
+    var_shape = _make_var_name(val_shape)
     var_reshaped = _make_var_name(val_reshaped)
 
     # interpretation
-    var_shape = _make_var_name(val_shape)  # for code
     shape = _const_weight_or_none(value_infos, val_shape)
     is_const_shape = shape and 'const_value' in value_infos[val_shape]
     if shape is None:
-        shape = _shape_or_none(value_infos, var_reshaped)
+        shape = _shape_or_none(value_infos, val_reshaped)
     assert shape is not None, (
         'given shape is neither const value nor deductible from output, '
         'this is not supported')
@@ -1429,6 +1439,7 @@ def Reshape(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
     name_attr = ', name={}'.format(repr(name)) if name else ''
 
     # generation
+    prog.Code('# shape:{}={} # const as literal'.format(var_shape, shape))
     if is_const_shape:
         prog.Code('{} = layers.{}({}'
                   ', shape={}'
@@ -1437,16 +1448,17 @@ def Reshape(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
                       fluid_op,
                       var_data,
                       # attrs
-                      var_shape,
+                      shape,
                       name_attr,
                   ))
     else:
-        var_shape_int32 = var_shape + '_int32'
+        val_shape_int32 = val_shape + '_int32'  # explicit variable
+        var_shape_int32 = _make_var_name(val_shape_int32)
         prog.Op(
             '',
             'Cast',
-            [var_shape],
-            [var_shape_int32],  # var
+            [val_shape],
+            [val_shape_int32],  # var
             dict(to=np.dtype('int32')),  # use np.dtype
             value_infos=value_infos,
             name=(name + '_cast'),
@@ -1464,7 +1476,7 @@ def Reshape(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
                       name_attr,
                   ))
     fluid_op = 'reshape2'
-    var_xshape = _make_var_name(name + '.xshape')
+    var_xshape = name + '.xshape'  # dummy output
     prog.VarDesc(var_reshaped)
     prog.VarDesc(var_xshape)
     if is_const_shape:
@@ -1580,6 +1592,7 @@ def Tile(prog, inputs, outputs, attrs, value_infos, name='', *args, **kwargs):
     val_input, val_repeats = inputs
     val_output, = outputs
     var_input = _make_var_name(val_input)
+    var_repeats = _make_var_name(val_repeats)
     var_output = _make_var_name(val_output)
 
     # interpretation
@@ -1589,6 +1602,7 @@ def Tile(prog, inputs, outputs, attrs, value_infos, name='', *args, **kwargs):
     name_attr = ', name={}'.format(repr(name)) if name else ''
 
     # generation
+    prog.Code('# repeats:{}={} # const as literal'.format(var_repeats, repeats))
     prog.Code('{} = layers.{}({}'
               ', expand_times={}'
               '{})'

onnx2fluid/onnx2fluid/validation.py

@@ -37,7 +37,7 @@ def _ensure_list(obj):
 def validate(fluid_model_filename,
              golden_data_filename,
              model_func_name='inference',
-             precision=1e-4,
+             decimal=3,
              save_inference_model=False):
     """
     inferece the converted Paddle fluid model, validate with given golden data
@@ -91,16 +91,17 @@ def validate(fluid_model_filename,
     # load data
     logger.info('using golden data %s', golden_data_filename)
     if golden_data_filename.endswith('.npz'):
-        test_data = np.load(golden_data_filename)
+        test_data = np.load(golden_data_filename, encoding='bytes')
         input_data = test_data['inputs'].tolist()
         output_data = test_data['outputs'].tolist()
     else:
-        test_data = np.load(golden_data_filename).tolist()
+        test_data = np.load(golden_data_filename, encoding='bytes').tolist()
         input_data = test_data['inputs']
         output_data = test_data['outputs']
     input_data = _flatten_dict(input_data)
     output_data = _flatten_dict(output_data)
-    logger.info('found %d I/O golden data, starting test ...', len(test_data))
+    logger.info('found %d I/O golden data, starting test ...',
+                len(input_data) + len(output_data))
 
     # DEBUG: reload test for python code
     if basename.endswith('.py') and save_inference_model:
@@ -124,7 +125,7 @@ def validate(fluid_model_filename,
     for (name, truth), output in zip(output_data.items(), outputs):
         logger.info('testing output {} ...'.format(name))
         try:
-            np.testing.assert_almost_equal(output, truth, decimal=precision)
+            np.testing.assert_almost_equal(output, truth, decimal=decimal)
         except AssertionError as e:
             passed = False
             logger.error('failed: %s\n', e)
@@ -165,7 +166,7 @@ if __name__ == '__main__':
         '--precision',
         '-p',
         type=int,
-        default=4,
+        default=3,
         help='assertion decimal for validation',
     )
     args = parser.parse_args()
@@ -177,10 +178,10 @@ if __name__ == '__main__':
     debug = args.debug
     fluid_model_filename = args.model[0]
     golden_data_filename = args.test_data
-    precision = args.precision
+    decimal = args.precision
 
     validate(
         fluid_model_filename,
         golden_data_filename,
-        precision=precision,
+        decimal=decimal,
         save_inference_model=debug)

onnx2fluid/onnx2fluid/writer.py

@@ -328,7 +328,7 @@ class Writer(object):
         else:
             var_name = make_var_name(name)
             attr_name = make_attr_name(name)
-            prog.Code('# parameter: {}'.format(name))
+            prog.Code('# parameter {}: {}'.format(name, var_name))
             prog.Code('{} = ParamAttr(name={})'  # , trainable=True
                       .format(attr_name, repr(var_name)))
             prog.Code(
@@ -355,13 +355,13 @@ class Writer(object):
             if remove_batch:
                 shape = shape[1:]
 
-            prog.Code('# input: {}'.format(name))
+            prog.Code('# input {}: {}'.format(name, var_name))
             prog.Code((
                 '{} = layers.data(name={}, shape={}, dtype={}, '
                 'append_batch_size={})'  # , stop_gradient=True
             ).format(
                 var_name,
-                repr(name),
+                repr(var_name),
                 shape,
                 repr(value_info['dtype'].name),
                 remove_batch,