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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/03/28 00:07:13 UTC
[GitHub] sandeep-krishnamurthy closed pull request #10118: [MXNET-106][ONNX-MXNET] Adding ONNX Model zoo tests.
sandeep-krishnamurthy closed pull request #10118: [MXNET-106][ONNX-MXNET] Adding ONNX Model zoo tests.
URL: https://github.com/apache/incubator-mxnet/pull/10118
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diff --git a/docs/tutorials/onnx/inference_on_onnx_model.md b/docs/tutorials/onnx/inference_on_onnx_model.md
index 2eb90cd55ab..182a2ae74cd 100644
--- a/docs/tutorials/onnx/inference_on_onnx_model.md
+++ b/docs/tutorials/onnx/inference_on_onnx_model.md
@@ -21,8 +21,8 @@ To run the tutorial you will need to have installed the following python modules
```python
import numpy as np
-import onnx_mxnet
import mxnet as mx
+from mxnet.contrib import onnx as onnx_mxnet
from mxnet import gluon, nd
%matplotlib inline
import matplotlib.pyplot as plt
@@ -75,7 +75,8 @@ Create the model folder and download the zipped model
```python
-os.makedirs(model_folder, exist_ok=True)
+if not os.path.isdir(model_folder):
+ os.makedirs(model_folder)
if not os.path.isfile(archive_file):
wget.download(url, model_folder)
```
@@ -108,7 +109,7 @@ We get the symbol and parameter objects
```python
-sym, params = onnx_mxnet.import_model(onnx_path)
+sym, arg_params, aux_params = onnx_mxnet.import_model(onnx_path)
```
We pick a context, CPU or GPU
@@ -124,9 +125,12 @@ And load them into a MXNet Gluon symbol block. For ONNX models the default input
```python
net = gluon.nn.SymbolBlock(outputs=sym, inputs=mx.sym.var('input_0'))
net_params = net.collect_params()
-for param in params:
+for param in arg_params:
if param in net_params:
- net_params[param]._load_init(params[param], ctx=ctx)
+ net_params[param]._load_init(arg_params[param], ctx=ctx)
+for param in aux_params:
+ if param in net_params:
+ net_params[param]._load_init(aux_params[param], ctx=ctx)
```
We can now cache the computational graph through [hybridization](https://mxnet.incubator.apache.org/tutorials/gluon/hybrid.html) to gain some performance
@@ -165,7 +169,7 @@ We can visualize the network (requires graphviz installed)
```python
-mx.visualization.plot_network(sym, shape={"input_0":inputs[0].shape}, node_attrs={"shape":"oval","fixedsize":"false"})
+mx.visualization.plot_network(sym, node_attrs={"shape":"oval","fixedsize":"false"})
```
diff --git a/example/onnx/super_resolution.py b/example/onnx/super_resolution.py
index 1392b77715c..f7c7886d0df 100644
--- a/example/onnx/super_resolution.py
+++ b/example/onnx/super_resolution.py
@@ -37,9 +37,9 @@ def import_onnx():
download(model_url, 'super_resolution.onnx')
LOGGER.info("Converting onnx format to mxnet's symbol and params...")
- sym, params = onnx_mxnet.import_model('super_resolution.onnx')
+ sym, arg_params, aux_params = onnx_mxnet.import_model('super_resolution.onnx')
LOGGER.info("Successfully Converted onnx format to mxnet's symbol and params...")
- return sym, params
+ return sym, arg_params, aux_params
def get_test_image():
"""Download and process the test image"""
@@ -53,12 +53,12 @@ def get_test_image():
input_image = np.array(img_y)[np.newaxis, np.newaxis, :, :]
return input_image, img_cb, img_cr
-def perform_inference(sym, params, input_img, img_cb, img_cr):
+def perform_inference(sym, arg_params, aux_params, input_img, img_cb, img_cr):
"""Perform inference on image using mxnet"""
# create module
mod = mx.mod.Module(symbol=sym, data_names=['input_0'], label_names=None)
mod.bind(for_training=False, data_shapes=[('input_0', input_img.shape)])
- mod.set_params(arg_params=params, aux_params=None)
+ mod.set_params(arg_params=arg_params, aux_params=aux_params)
# run inference
batch = namedtuple('Batch', ['data'])
@@ -79,6 +79,6 @@ def perform_inference(sym, params, input_img, img_cb, img_cr):
return result_img
if __name__ == '__main__':
- MX_SYM, MX_PARAM = import_onnx()
+ MX_SYM, MX_ARG_PARAM, MX_AUX_PARAM = import_onnx()
INPUT_IMG, IMG_CB, IMG_CR = get_test_image()
- perform_inference(MX_SYM, MX_PARAM, INPUT_IMG, IMG_CB, IMG_CR)
+ perform_inference(MX_SYM, MX_ARG_PARAM, MX_AUX_PARAM, INPUT_IMG, IMG_CB, IMG_CR)
diff --git a/python/mxnet/contrib/onnx/_import/import_helper.py b/python/mxnet/contrib/onnx/_import/import_helper.py
index 80541ec3577..175c2fb6a00 100644
--- a/python/mxnet/contrib/onnx/_import/import_helper.py
+++ b/python/mxnet/contrib/onnx/_import/import_helper.py
@@ -27,7 +27,7 @@
from .op_translations import global_avgpooling, global_maxpooling, linalg_gemm
from .op_translations import sigmoid, pad, relu, matrix_multiplication, batch_norm
from .op_translations import dropout, local_response_norm, conv, deconv
-from .op_translations import reshape, cast, split, _slice, transpose, squeeze
+from .op_translations import reshape, cast, split, _slice, transpose, squeeze, flatten
from .op_translations import reciprocal, squareroot, power, exponent, _log
from .op_translations import reduce_max, reduce_mean, reduce_min, reduce_sum
from .op_translations import reduce_prod, avg_pooling, max_pooling
@@ -83,6 +83,7 @@
'Slice' : _slice,
'Transpose' : transpose,
'Squeeze' : squeeze,
+ 'Flatten' : flatten,
#Powers
'Reciprocal' : reciprocal,
'Sqrt' : squareroot,
diff --git a/python/mxnet/contrib/onnx/_import/import_model.py b/python/mxnet/contrib/onnx/_import/import_model.py
index 1df429b4690..d8d32a96a21 100644
--- a/python/mxnet/contrib/onnx/_import/import_model.py
+++ b/python/mxnet/contrib/onnx/_import/import_model.py
@@ -46,5 +46,5 @@ def import_model(model_file):
except ImportError:
raise ImportError("Onnx and protobuf need to be installed")
model_proto = onnx.load(model_file)
- sym, params = graph.from_onnx(model_proto.graph)
- return sym, params
+ sym, arg_params, aux_params = graph.from_onnx(model_proto.graph)
+ return sym, arg_params, aux_params
diff --git a/python/mxnet/contrib/onnx/_import/import_onnx.py b/python/mxnet/contrib/onnx/_import/import_onnx.py
index 56181c777be..037790c8080 100644
--- a/python/mxnet/contrib/onnx/_import/import_onnx.py
+++ b/python/mxnet/contrib/onnx/_import/import_onnx.py
@@ -61,12 +61,12 @@ def _convert_operator(self, node_name, op_name, attrs, inputs):
raise NotImplementedError("Operator {} not implemented.".format(op_name))
if isinstance(op_name, string_types):
new_op = getattr(symbol, op_name, None)
+ if not new_op:
+ raise RuntimeError("Unable to map op_name {} to sym".format(op_name))
if node_name is None:
mxnet_sym = new_op(*inputs, **new_attrs)
else:
mxnet_sym = new_op(name=node_name, *inputs, **new_attrs)
- if not mxnet_sym:
- raise RuntimeError("Unable to map op_name {} to sym".format(op_name))
return mxnet_sym
return op_name
@@ -110,6 +110,10 @@ def from_onnx(self, graph):
self._nodes[name_input] = symbol.Variable(name=name_input)
self._renames[i.name] = name_input
+ # For storing arg and aux params for the graph.
+ auxDict = {}
+ argDict = {}
+
# constructing nodes, nodes are stored as directed acyclic graph
# converting NodeProto message
for node in graph.node:
@@ -120,19 +124,24 @@ def from_onnx(self, graph):
inputs = [self._nodes[self._renames.get(i, i)] for i in node.input]
mxnet_sym = self._convert_operator(node_name, op_name, onnx_attr, inputs)
- assert len(node.output) == len(mxnet_sym.list_outputs()), (
- "Output dimension mismatch between the onnx operator and the mxnet symbol " +
- "{} vs {} for the operator - {}.".format(
- len(node.output), len(mxnet_sym.list_outputs()), op_name))
- for k, i in zip(list(node.output), range(len(node.output))):
+ for k, i in zip(list(node.output), range(len(mxnet_sym.list_outputs()))):
self._nodes[k] = mxnet_sym[i]
+
+ # splitting params into args and aux params
+ for args in mxnet_sym.list_arguments():
+ if args in self._params:
+ argDict.update({args: nd.array(self._params[args])})
+ for aux in mxnet_sym.list_auxiliary_states():
+ if aux in self._params:
+ auxDict.update({aux: nd.array(self._params[aux])})
+
# now return the outputs
out = [self._nodes[i.name] for i in graph.output]
if len(out) > 1:
out = symbol.Group(out)
else:
out = out[0]
- return out, self._params
+ return out, argDict, auxDict
def _parse_array(self, tensor_proto):
"""Grab data in TensorProto and convert to numpy array."""
diff --git a/python/mxnet/contrib/onnx/_import/op_translations.py b/python/mxnet/contrib/onnx/_import/op_translations.py
index a67c18199eb..de341321785 100644
--- a/python/mxnet/contrib/onnx/_import/op_translations.py
+++ b/python/mxnet/contrib/onnx/_import/op_translations.py
@@ -164,10 +164,14 @@ def matrix_multiplication(attrs, inputs, cls):
def batch_norm(attrs, inputs, cls):
"""Batch normalization."""
- new_attrs = translation_utils._fix_attribute_names(attrs, {'epsilon' : 'eps'})
+ new_attrs = translation_utils._fix_attribute_names(attrs, {'epsilon' : 'eps',
+ 'is_test':'fix_gamma'})
new_attrs = translation_utils._remove_attributes(new_attrs,
- ['spatial', 'is_test', 'consumed_inputs'])
+ ['spatial', 'consumed_inputs'])
new_attrs = translation_utils._add_extra_attributes(new_attrs, {'cudnn_off': 1})
+
+ # in test mode "fix_gamma" should be unset.
+ new_attrs['fix_gamma'] = 0 if new_attrs['fix_gamma'] == 1 else 1
return 'BatchNorm', new_attrs, inputs
@@ -245,7 +249,7 @@ def global_maxpooling(attrs, inputs, cls):
new_attrs = translation_utils._add_extra_attributes(attrs, {'global_pool': True,
'kernel': (1, 1),
'pool_type': 'max'})
- return 'pooling', new_attrs, inputs
+ return 'Pooling', new_attrs, inputs
def global_avgpooling(attrs, inputs, cls):
@@ -253,28 +257,49 @@ def global_avgpooling(attrs, inputs, cls):
new_attrs = translation_utils._add_extra_attributes(attrs, {'global_pool': True,
'kernel': (1, 1),
'pool_type': 'avg'})
- return 'pooling', new_attrs, inputs
+ return 'Pooling', new_attrs, inputs
def linalg_gemm(attrs, inputs, cls):
"""Performs general matrix multiplication and accumulation"""
+ trans_a = 0
+ trans_b = 0
+ alpha = 1
+ beta = 1
+ if 'transA' in attrs:
+ trans_a = attrs['transA']
+ if 'transB' in attrs:
+ trans_b = attrs['transB']
+ if 'alpha' in attrs:
+ alpha = attrs['alpha']
+ if 'beta' in attrs:
+ beta = attrs['beta']
+ flatten_a = symbol.flatten(inputs[0])
+ matmul_op = symbol.linalg_gemm2(A=flatten_a, B=inputs[1],
+ transpose_a=trans_a, transpose_b=trans_b,
+ alpha=alpha)
+ gemm_op = symbol.broadcast_add(matmul_op, beta*inputs[2])
new_attrs = translation_utils._fix_attribute_names(attrs, {'transA': 'transpose_a',
'transB': 'transpose_b'})
new_attrs = translation_utils._remove_attributes(new_attrs, ['broadcast'])
- return translation_utils._fix_gemm('FullyConnected', inputs, new_attrs, cls)
+ return gemm_op, new_attrs, inputs
-def local_response_norm(op_name, attrs, inputs):
+def local_response_norm(attrs, inputs, cls):
"""Local Response Normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'bias': 'knorm',
'size' : 'nsize'})
return 'LRN', new_attrs, inputs
-def dropout(op_name, attrs, inputs):
+def dropout(attrs, inputs, cls):
"""Dropout Regularization."""
+ mode = 'training'
+ if attrs['is_test'] == 0:
+ mode = 'always'
new_attrs = translation_utils._fix_attribute_names(attrs,
{'ratio': 'p'})
new_attrs = translation_utils._remove_attributes(new_attrs, ['is_test'])
+ new_attrs = translation_utils._add_extra_attributes(new_attrs, {'mode': mode})
return 'Dropout', new_attrs, inputs
# Changing shape and type.
@@ -285,6 +310,7 @@ def reshape(attrs, inputs, cls):
def cast(attrs, inputs, cls):
""" Cast input to a given dtype"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'to' : 'dtype'})
+ new_attrs['dtype'] = new_attrs['dtype'].lower()
return 'cast', new_attrs, inputs
def split(attrs, inputs, cls):
@@ -328,6 +354,15 @@ def squeeze(attrs, inputs, cls):
mxnet_op = symbol.split(mxnet_op, axis=i-1, num_outputs=1, squeeze_axis=1)
return mxnet_op, new_attrs, inputs
+
+def flatten(attrs, inputs, cls):
+ """Flattens the input array into a 2-D array by collapsing the higher dimensions."""
+ #Mxnet does not have axis support. By default uses axis=1
+ if 'axis' in attrs and attrs['axis'] != 1:
+ raise RuntimeError("Flatten operator only supports axis=1")
+ new_attrs = translation_utils._remove_attributes(attrs, ['axis'])
+ return 'Flatten', new_attrs, inputs
+
#Powers
def reciprocal(attrs, inputs, cls):
"""Returns the reciprocal of the argument, element-wise."""
@@ -387,8 +422,7 @@ def avg_pooling(attrs, inputs, cls):
'pads': 'pad',
})
new_attrs = translation_utils._add_extra_attributes(new_attrs,
- {'pool_type': 'avg',
- 'pooling_convention': 'valid'
+ {'pooling_convention': 'valid'
})
new_op = translation_utils._fix_pooling('avg', inputs, new_attrs)
@@ -402,9 +436,9 @@ def max_pooling(attrs, inputs, cls):
'strides': 'stride',
'pads': 'pad',
})
+
new_attrs = translation_utils._add_extra_attributes(new_attrs,
- {'pool_type': 'avg',
- 'pooling_convention': 'valid'
+ {'pooling_convention': 'valid'
})
new_op = translation_utils._fix_pooling('max', inputs, new_attrs)
diff --git a/python/mxnet/contrib/onnx/_import/translation_utils.py b/python/mxnet/contrib/onnx/_import/translation_utils.py
index 0fdef647b50..1d84bd70d7e 100644
--- a/python/mxnet/contrib/onnx/_import/translation_utils.py
+++ b/python/mxnet/contrib/onnx/_import/translation_utils.py
@@ -90,10 +90,51 @@ def _fix_pooling(pool_type, inputs, new_attr):
stride = new_attr.get('stride')
kernel = new_attr.get('kernel')
padding = new_attr.get('pad')
- pad_width = (0, 0, 0, 0) + _pad_sequence_fix(padding, len(kernel))
- new_pad_op = symbol.pad(inputs[0], mode='constant', pad_width=pad_width)
- new_pooling_op = symbol.Pooling(new_pad_op, pool_type=pool_type,
- stride=stride, kernel=kernel)
+
+ # Adding default stride.
+ if stride is None:
+ stride = (1,) * len(kernel)
+
+ # Add padding attr if not provided.
+ if padding is None:
+ padding = (0,) * len(kernel) * 2
+
+ # Mxnet Pad operator supports only 4D/5D tensors.
+ # For 1D case, these are the steps:
+ # Step 1. Add extra dummy dimension to make it 4D. Adding to axis = 2
+ # Step 2. Apply padding to this changed tensor
+ # Step 3. Remove the extra dimension added in step 1.
+ if len(kernel) == 1:
+ dummy_axis = 2
+ # setting 0 padding to the new dim to be added.
+ padding = (0, padding[0], 0, padding[1])
+ pad_width = (0, 0, 0, 0) + _pad_sequence_fix(padding, kernel_dim=2)
+
+ # Step 1.
+ curr_sym = symbol.expand_dims(inputs[0], axis=dummy_axis)
+
+ # Step 2. Common for all tensor sizes
+ new_pad_op = symbol.pad(curr_sym, mode='edge', pad_width=pad_width)
+
+ # Step 3: Removing extra dim added.
+ new_pad_op = symbol.split(new_pad_op, axis=dummy_axis, num_outputs=1, squeeze_axis=1)
+ else:
+ # For 2D/3D cases:
+ # Apply padding
+ pad_width = (0, 0, 0, 0) + _pad_sequence_fix(padding, kernel_dim=len(kernel))
+ curr_sym = inputs[0]
+
+ if pool_type == 'max':
+ # For max pool : mode = 'edge', we should replicate the
+ # edge values to pad, so that we only include input data values
+ # for calculating 'max'
+ new_pad_op = symbol.pad(curr_sym, mode='edge', pad_width=pad_width)
+ else:
+ # For avg pool, we should add 'zeros' for padding so mode='constant'
+ new_pad_op = symbol.pad(curr_sym, mode='constant', pad_width=pad_width)
+
+ # Apply pooling without pads.
+ new_pooling_op = symbol.Pooling(new_pad_op, pool_type=pool_type, stride=stride, kernel=kernel)
return new_pooling_op
def _fix_bias(op_name, attrs, num_inputs):
diff --git a/tests/python-pytest/onnx/backend.py b/tests/python-pytest/onnx/backend.py
index 3b99563bccf..0e0a6a680b7 100644
--- a/tests/python-pytest/onnx/backend.py
+++ b/tests/python-pytest/onnx/backend.py
@@ -94,12 +94,15 @@ def run_node(cls, node, inputs, device='CPU'):
result obtained after running the operator
"""
graph = GraphProto()
- sym, _ = graph.from_onnx(MXNetBackend.make_graph(node, inputs))
- data_names = [i for i in sym.get_internals().list_inputs()]
+ sym, arg_params, aux_params = graph.from_onnx(MXNetBackend.make_graph(node, inputs))
+ data_names = [graph_input for graph_input in sym.list_inputs()
+ if graph_input not in arg_params and graph_input not in aux_params]
data_shapes = []
dim_change_op_types = set(['ReduceMin', 'ReduceMax', 'ReduceMean',
'ReduceProd', 'ReduceSum', 'Slice', 'Pad',
- 'Squeeze', 'Upsample', 'Reshape', 'Conv'])
+ 'Squeeze', 'Upsample', 'Reshape', 'Conv',
+ 'Concat', 'Softmax', 'Flatten', 'Transpose',
+ 'GlobalAveragePool', 'GlobalMaxPool'])
# Adding extra dimension of batch_size 1 if the batch_size is different for multiple inputs.
for idx, input_name in enumerate(data_names):
@@ -123,7 +126,10 @@ def run_node(cls, node, inputs, device='CPU'):
mod.bind(for_training=False, data_shapes=data_shapes, label_shapes=None)
# initializing parameters for calculating result of each individual node
- mod.init_params()
+ if arg_params is None and aux_params is None:
+ mod.init_params()
+ else:
+ mod.set_params(arg_params=arg_params, aux_params=aux_params)
data_forward = []
for idx, input_name in enumerate(data_names):
@@ -162,8 +168,8 @@ def prepare(cls, model, device='CPU', **kwargs):
used to run inference on the input model and return the result for comparison.
"""
graph = GraphProto()
- sym, params = graph.from_onnx(model.graph)
- return MXNetBackendRep(sym, params, device)
+ sym, arg_params, aux_params = graph.from_onnx(model.graph)
+ return MXNetBackendRep(sym, arg_params, aux_params, device)
@classmethod
def supports_device(cls, device):
diff --git a/tests/python-pytest/onnx/backend_rep.py b/tests/python-pytest/onnx/backend_rep.py
index a125086bce2..47ea6c1585a 100644
--- a/tests/python-pytest/onnx/backend_rep.py
+++ b/tests/python-pytest/onnx/backend_rep.py
@@ -37,9 +37,10 @@
class MXNetBackendRep(BackendRep):
"""Running model inference on mxnet engine and return the result
to onnx test infrastructure for comparison."""
- def __init__(self, symbol, params, device):
+ def __init__(self, symbol, arg_params, aux_params, device):
self.symbol = symbol
- self.params = params
+ self.arg_params = arg_params
+ self.aux_params = aux_params
self.device = device
def run(self, inputs, **kwargs):
@@ -67,7 +68,7 @@ def run(self, inputs, **kwargs):
label_names=None)
mod.bind(for_training=False, data_shapes=[('input_0', input_data.shape)],
label_shapes=None)
- mod.set_params(arg_params=self.params, aux_params=None)
+ mod.set_params(arg_params=self.arg_params, aux_params=self.aux_params)
# run inference
batch = namedtuple('Batch', ['data'])
diff --git a/tests/python-pytest/onnx/onnx_backend_test.py b/tests/python-pytest/onnx/onnx_backend_test.py
index 28e2aaefcdd..4ea31e5aac9 100644
--- a/tests/python-pytest/onnx/onnx_backend_test.py
+++ b/tests/python-pytest/onnx/onnx_backend_test.py
@@ -34,7 +34,7 @@
BACKEND_TEST = onnx.backend.test.BackendTest(mxnet_backend, __name__)
-IMPLEMENTED_OPERATORS = [
+IMPLEMENTED_OPERATORS_TEST = [
#Generator Functions
#'test_constant*', # Identity Function
#'test_random_uniform',
@@ -57,37 +57,40 @@
'test_floor',
## Joining and spliting
- #'test_concat.*', #---Failing test
+ 'test_concat',
#Basic neural network functions
'test_sigmoid',
'test_relu',
- #'test_constant_pad',
- #'test_edge_pad',
- #'test_reflect_pad',
+ 'test_constant_pad',
+ 'test_edge_pad',
+ 'test_reflect_pad',
'test_matmul',
'test_leakyrelu',
'test_elu',
- #'test_softmax*',
+ 'test_softmax_example',
+ 'test_softmax_large_number',
+ 'test_softmax_axis_2',
'test_conv',
'test_basic_conv',
- #'test_globalmaxpool',
- #'test_globalaveragepool',
- #'test_batch_norm',
+ 'test_transpose',
+ 'test_globalmaxpool',
+ 'test_globalaveragepool',
+ #'test_batch_norm', - tests to be added
+ #'test_gather',
#Changing shape and type.
'test_reshape_',
- #'test_AvgPool2D*',
- #'test_MaxPool2D*',
- #'test_cast',
+ 'test_cast',
#'test_split',
'test_slice_cpu',
'test_default_axes', #make PR against onnx to fix the test name(grep-able)
'test_slice_neg',
#'test_slice_start_out_of_bounds',
#'test_slice_end_out_of_bounds',
- #'test_transpose*',
+ #'test_transpose',
'test_squeeze_',
+ 'test_flatten_default',
#Powers
'test_reciprocal',
@@ -103,12 +106,62 @@
'test_argmax',
'test_argmin',
'test_max',
- 'test_min'
+ 'test_min',
+
+ #pytorch operator tests
+ #'test_operator_chunk',
+ #'test_operator_clip',
+ 'test_operator_conv',
+ #'test_operator_equal',
+ 'test_operator_exp',
+ #'test_operator_flatten',
+ #'test_operator_max',
+ 'test_operator_maxpool',
+ 'test_operator_non_float_params',
+ 'test_operator_params',
+ 'test_operator_permute2',
+ #'test_operator_transpose',
+ #'test_operator_view'
]
-for op_test in IMPLEMENTED_OPERATORS:
+BASIC_MODEL_TESTS = [
+ 'test_AvgPool2D',
+ 'test_BatchNorm',
+ 'test_ConstantPad2d'
+ 'test_Conv2d',
+ 'test_ELU',
+ 'test_LeakyReLU',
+ 'test_MaxPool',
+ 'test_PReLU',
+ 'test_ReLU',
+ 'test_Sigmoid',
+ 'test_Softmax',
+ 'test_softmax_functional',
+ 'test_softmax_lastdim',
+ 'test_Tanh'
+ ]
+
+STANDARD_MODEL = [
+ 'test_bvlc_alexnet',
+ 'test_densenet121',
+ #'test_inception_v1',
+ #'test_inception_v2',
+ 'test_resnet50',
+ #'test_shufflenet',
+ 'test_squeezenet',
+ 'test_vgg16',
+ 'test_vgg19'
+ ]
+
+for op_test in IMPLEMENTED_OPERATORS_TEST:
BACKEND_TEST.include(op_test)
+for std_model_test in STANDARD_MODEL:
+ BACKEND_TEST.include(std_model_test)
+
+for basic_model_test in BASIC_MODEL_TESTS:
+ BACKEND_TEST.include(basic_model_test)
+
# import all test cases at global scope to make them visible to python.unittest
globals().update(BACKEND_TEST.enable_report().test_cases)
diff --git a/tests/python-pytest/onnx/onnx_test.py b/tests/python-pytest/onnx/onnx_test.py
index 016490a4c4b..ddc633e28f6 100644
--- a/tests/python-pytest/onnx/onnx_test.py
+++ b/tests/python-pytest/onnx/onnx_test.py
@@ -21,19 +21,37 @@
ONNX backend test framework. Once we have PRs on the ONNX repo and get
those PRs merged, this file will get EOL'ed.
"""
+# pylint: disable=too-many-locals,wrong-import-position,import-error
from __future__ import absolute_import
import sys
import os
import unittest
import logging
import hashlib
+import tarfile
+from collections import namedtuple
import numpy as np
import numpy.testing as npt
from onnx import helper
-import backend as mxnet_backend
+from onnx import numpy_helper
+from onnx import TensorProto
+from mxnet.test_utils import download
+from mxnet.contrib import onnx as onnx_mxnet
+import mxnet as mx
CURR_PATH = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
sys.path.insert(0, os.path.join(CURR_PATH, '../../python/unittest'))
from common import with_seed
+import backend as mxnet_backend
+
+
+URLS = {
+ 'bvlc_googlenet' :
+ 'https://s3.amazonaws.com/onnx-mxnet/model-zoo/bvlc_googlenet.tar.gz',
+ 'bvlc_reference_caffenet' :
+ 'https://s3.amazonaws.com/onnx-mxnet/model-zoo/bvlc_reference_caffenet.tar.gz',
+ 'bvlc_reference_rcnn_ilsvrc13' :
+ 'https://s3.amazonaws.com/onnx-mxnet/model-zoo/bvlc_reference_rcnn_ilsvrc13.tar.gz',
+}
@with_seed()
def test_reduce_max():
@@ -93,9 +111,9 @@ def test_super_resolution_example():
sys.path.insert(0, os.path.join(CURR_PATH, '../../../example/onnx/'))
import super_resolution
- sym, params = super_resolution.import_onnx()
+ sym, arg_params, aux_params = super_resolution.import_onnx()
assert sym is not None
- assert params is not None
+ assert arg_params is not None
inputs = sym.list_inputs()
assert len(inputs) == 9
@@ -116,7 +134,7 @@ def test_super_resolution_example():
'transpose0']):
assert key_item in attrs_keys
- param_keys = params.keys()
+ param_keys = arg_params.keys()
assert len(param_keys) == 8
for i, param_item in enumerate(['param_5', 'param_4', 'param_7', 'param_6',
'param_1', 'param_0', 'param_3', 'param_2']):
@@ -126,11 +144,111 @@ def test_super_resolution_example():
output_img_dim = 672
input_image, img_cb, img_cr = super_resolution.get_test_image()
- result_img = super_resolution.perform_inference(sym, params, input_image,
- img_cb, img_cr)
+ result_img = super_resolution.perform_inference(sym, arg_params, aux_params,
+ input_image, img_cb, img_cr)
assert hashlib.md5(result_img.tobytes()).hexdigest() == '0d98393a49b1d9942106a2ed89d1e854'
assert result_img.size == (output_img_dim, output_img_dim)
+def get_test_files(name):
+ """Extract tar file and returns model path and input, output data"""
+ tar_name = download(URLS.get(name), dirname=CURR_PATH.__str__())
+ # extract tar file
+ tar_path = os.path.join(CURR_PATH, tar_name)
+ tar = tarfile.open(tar_path.__str__(), "r:*")
+ tar.extractall(path=CURR_PATH.__str__())
+ tar.close()
+ data_dir = os.path.join(CURR_PATH, name)
+ model_path = os.path.join(data_dir, 'model.onnx')
+
+ inputs = []
+ outputs = []
+ # get test files
+ for test_file in os.listdir(data_dir):
+ case_dir = os.path.join(data_dir, test_file)
+ # skip the non-dir files
+ if not os.path.isdir(case_dir):
+ continue
+ input_file = os.path.join(case_dir, 'input_0.pb')
+ input_tensor = TensorProto()
+ with open(input_file, 'rb') as proto_file:
+ input_tensor.ParseFromString(proto_file.read())
+ inputs.append(numpy_helper.to_array(input_tensor))
+
+ output_tensor = TensorProto()
+ output_file = os.path.join(case_dir, 'output_0.pb')
+ with open(output_file, 'rb') as proto_file:
+ output_tensor.ParseFromString(proto_file.read())
+ outputs.append(numpy_helper.to_array(output_tensor))
+
+ return model_path, inputs, outputs
+
+def test_bvlc_googlenet():
+ """ Tests Googlenet model"""
+ model_path, inputs, outputs = get_test_files('bvlc_googlenet')
+ logging.info("Translating Googlenet model from ONNX to Mxnet")
+ sym, arg_params, aux_params = onnx_mxnet.import_model(model_path)
+
+ # run test for each test file
+ for input_data, output_data in zip(inputs, outputs):
+ # create module
+ mod = mx.mod.Module(symbol=sym, data_names=['input_0'], context=mx.cpu(), label_names=None)
+ mod.bind(for_training=False, data_shapes=[('input_0', input_data.shape)], label_shapes=None)
+ mod.set_params(arg_params=arg_params, aux_params=aux_params,
+ allow_missing=True, allow_extra=True)
+ # run inference
+ batch = namedtuple('Batch', ['data'])
+ mod.forward(batch([mx.nd.array(input_data)]), is_train=False)
+
+ # verify the results
+ npt.assert_equal(mod.get_outputs()[0].shape, output_data.shape)
+ npt.assert_almost_equal(output_data, mod.get_outputs()[0].asnumpy(), decimal=3)
+ logging.info("Googlenet model conversion Successful")
+
+def test_bvlc_reference_caffenet():
+ """Tests the bvlc cafenet model"""
+ model_path, inputs, outputs = get_test_files('bvlc_reference_caffenet')
+ logging.info("Translating Caffenet model from ONNX to Mxnet")
+ sym, arg_params, aux_params = onnx_mxnet.import_model(model_path)
+
+ # run test for each test file
+ for input_data, output_data in zip(inputs, outputs):
+ # create module
+ mod = mx.mod.Module(symbol=sym, data_names=['input_0'], context=mx.cpu(), label_names=None)
+ mod.bind(for_training=False, data_shapes=[('input_0', input_data.shape)], label_shapes=None)
+ mod.set_params(arg_params=arg_params, aux_params=aux_params,
+ allow_missing=True, allow_extra=True)
+ # run inference
+ batch = namedtuple('Batch', ['data'])
+ mod.forward(batch([mx.nd.array(input_data)]), is_train=False)
+
+ # verify the results
+ npt.assert_equal(mod.get_outputs()[0].shape, output_data.shape)
+ npt.assert_almost_equal(output_data, mod.get_outputs()[0].asnumpy(), decimal=3)
+ logging.info("Caffenet model conversion Successful")
+
+def test_bvlc_rcnn_ilsvrc13():
+ """Tests the bvlc rcnn model"""
+ model_path, inputs, outputs = get_test_files('bvlc_reference_rcnn_ilsvrc13')
+ logging.info("Translating rcnn_ilsvrc13 model from ONNX to Mxnet")
+ sym, arg_params, aux_params = onnx_mxnet.import_model(model_path)
+
+ # run test for each test file
+ for input_data, output_data in zip(inputs, outputs):
+ # create module
+ mod = mx.mod.Module(symbol=sym, data_names=['input_0'], context=mx.cpu(), label_names=None)
+ mod.bind(for_training=False, data_shapes=[('input_0', input_data.shape)], label_shapes=None)
+ mod.set_params(arg_params=arg_params, aux_params=aux_params,
+ allow_missing=True, allow_extra=True)
+ # run inference
+ batch = namedtuple('Batch', ['data'])
+ mod.forward(batch([mx.nd.array(input_data)]), is_train=False)
+
+ # verify the results
+ npt.assert_equal(mod.get_outputs()[0].shape, output_data.shape)
+ npt.assert_almost_equal(output_data, mod.get_outputs()[0].asnumpy(), decimal=3)
+ logging.info("rcnn_ilsvrc13 model conversion Successful")
+
+
if __name__ == '__main__':
unittest.main()
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