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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/08/09 18:52:59 UTC
[GitHub] Roshrini commented on a change in pull request #11952: [MXNET-707]
Add unit test for mxnet to coreml converter
Roshrini commented on a change in pull request #11952: [MXNET-707] Add unit test for mxnet to coreml converter
URL: https://github.com/apache/incubator-mxnet/pull/11952#discussion_r209041165
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File path: tools/coreml/unittest/test_converter_no_pred.py
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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import unittest
+import mxnet as mx
+import numpy as np
+
+from converter._mxnet_converter import convert
+from collections import namedtuple
+from converter import utils
+
+def _mxnet_remove_batch(input_data):
+ for blob in input_data:
+ input_data[blob] = np.reshape(input_data[blob], input_data[blob].shape[1:])
+ return input_data
+
+
+def _get_mxnet_module(net, data_shapes, mode, label_names, input_names=None):
+ """ Given a symbolic graph, input shape and the initialization mode,
+ returns an MXNet module.
+ """
+ mx.random.seed(1993)
+
+ mod = utils.create_module(sym=net, data_shapes=data_shapes, label_shapes=input_names, label_names=label_names)
+
+ if mode == 'random':
+ mod.init_params(
+ initializer=mx.init.Uniform(scale=.1)
+ )
+ elif mode == 'zeros':
+ mod.init_params(
+ initializer=mx.init.Zero()
+ )
+ elif mode == 'ones':
+ mod.init_params(
+ initializer=mx.init.One()
+ )
+ else:
+ Exception(KeyError("%s is not a valid initialization mode" % mode))
+
+ return mod
+
+
+class SingleLayerTest(unittest.TestCase):
+ """
+ Unit test class for testing where converter is able to convert individual layers or not.
+ In order to do so, it converts model and generates preds on both CoreML and MXNet and check they are the same.
+ """
+ def _test_mxnet_model(self, net, input_shape, mode, class_labels=None, coreml_mode=None, label_names=None, delta=1e-3,
+ pre_processing_args=None, input_name='data'):
+ """ Helper method that convert the CoreML model into CoreML and compares the predictions over random data.
+
+ Parameters
+ ----------
+ net: MXNet Symbol Graph
+ The graph that we'll be converting into CoreML.
+
+ input_shape: tuple of ints
+ The shape of input data. Generally of the format (batch-size, channels, height, width)
+
+ mode: (random|zeros|ones)
+ The mode to use in order to set the parameters (weights and biases).
+
+ label_names: list of strings
+ The names of the output labels. Default: None
+
+ delta: float
+ The maximum difference b/w predictions of MXNet and CoreML that is tolerable.
+
+ input_name: str
+ The name of the input variable to the symbolic graph.
+ """
+
+ data_shapes=[(input_name, input_shape)]
+
+ mod = _get_mxnet_module(net, data_shapes, mode, label_names)
+
+ # Generate some dummy data
+ input_data = {input_name: np.random.uniform(-10., 10., input_shape)}
+ Batch = namedtuple('Batch', ['data'])
+ mod.forward(Batch([mx.nd.array(input_data[input_name])]))
+ mxnet_preds = mod.get_outputs()[0].asnumpy().flatten()
+
+ # Get predictions from coreml
+ coreml_model = convert(
+ model=mod,
+ class_labels=class_labels,
+ mode=coreml_mode,
+ input_shape={input_name: input_shape},
+ preprocessor_args=pre_processing_args
+ )
+
+ def test_tiny_inner_product_zero_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 10)
+ net = mx.sym.Variable('data')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
+ self._test_mxnet_model(net, input_shape=input_shape, mode='zeros')
+
+ def test_really_tiny_inner_product_ones_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1)
+ net = mx.sym.Variable('data')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=1)
+ self._test_mxnet_model(net, input_shape=input_shape, mode='ones')
+
+ def test_really_tiny_2_inner_product_ones_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1)
+ net = mx.sym.Variable('data')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
+ self._test_mxnet_model(net, input_shape=input_shape, mode='ones')
+
+ def test_tiny_inner_product_ones_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 10)
+ net = mx.sym.Variable('data')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
+ self._test_mxnet_model(net, input_shape=input_shape, mode='ones')
+
+ def test_tiny_inner_product_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 10)
+ net = mx.sym.Variable('data')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_inner_product_no_bias(self):
+ np.random.seed(1988)
+ input_shape = (1, 10)
+ net = mx.sym.Variable('data')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5, no_bias=True)
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_softmax_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 10)
+ net = mx.sym.Variable('data')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
+ net = mx.sym.SoftmaxOutput(net, name='softmax')
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random', label_names=['softmax_label'])
+
+ def test_tiny_relu_activation_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 10)
+ net = mx.sym.Variable('data')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
+ net = mx.sym.Activation(net, name='relu1', act_type="relu")
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_sigmoid_activation_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 10)
+ net = mx.sym.Variable('data')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
+ net = mx.sym.Activation(net, name='sigmoid1', act_type="sigmoid")
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_tanh_activation_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 10)
+
+ # Define a model
+ net = mx.sym.Variable('data')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
+ net = mx.sym.Activation(net, name='tanh1', act_type="tanh")
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_really_tiny_conv_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 1
+ kernel = (1 ,1)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # Define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_conv_ones_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 1
+ kernel = (5, 5)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # Define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='ones')
+
+ def test_tiny_conv_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 1
+ kernel = (5, 5)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_asym_conv_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 1
+ kernel = (5 ,3)
+ stride = (1, 1)
+ pad = (0, 0)
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_asym_conv_random_asym_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 28, 18)
+ num_filter = 16
+ kernel = (5, 3)
+ stride = (1, 1)
+ pad = (0, 0)
+ dilate = (1, 1)
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1',
+ dilate=dilate)
+ net = mx.sym.Activation(net, name='tanh', act_type="tanh")
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_conv_valid_pooling_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 1
+ kernel = (2, 2)
+ stride = (2, 2)
+ pad = (0, 0)
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ net = mx.symbol.Pooling(
+ data=net,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='pool_1',
+ pool_type='avg',
+ pooling_convention='valid'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_conv_pooling_full_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 1
+ kernel = (2, 2)
+ stride = (2, 2)
+ pad = (0, 0)
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ net = mx.symbol.Pooling(
+ data=net,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='pool_1',
+ pool_type='avg',
+ pooling_convention='full'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_conv_pooling_full_random_input_with_padding(self):
+ np.random.seed(1988)
+ input_shape = (1, 3, 10, 10)
+ num_filter = 2
+ kernel = (2, 2)
+ stride = (2, 2)
+ pad = (1, 1)
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ net = mx.symbol.Pooling(
+ data=net,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='pool_1',
+ pool_type='avg',
+ pooling_convention='full'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_really_tiny_conv_random_3d_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 3, 10, 10)
+ num_filter = 1
+ kernel = (1, 1)
+ stride = (1, 1)
+ pad = (0, 0)
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_really_tiny_conv_random_input_multi_filter(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 64
+ kernel = (1, 1)
+ stride = (1, 1)
+ pad = (0, 0)
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_conv_random_3d_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 3, 10, 10)
+ num_filter = 1
+ kernel = (5 ,5)
+ stride = (1, 1)
+ pad = (0, 0)
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_conv_random_input_multi_filter(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 64
+ kernel = (5, 5)
+ stride = (1, 1)
+ pad = (0, 0)
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_conv_random(self):
+ np.random.seed(1988)
+ input_shape = (1, 3, 10, 10)
+ num_filter = 64
+ kernel = (5, 5)
+ stride = (1, 1)
+ pad = (0, 0)
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_flatten(self):
+ np.random.seed(1988)
+ input_shape = (1, 3, 10, 10)
+ num_filter = 64
+ kernel = (5, 5)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ net = mx.sym.Flatten(data=net, name='flatten1')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
+ net = mx.sym.SoftmaxOutput(net, name='softmax')
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random', label_names=['softmax_label'])
+
+ def test_transpose(self):
+ np.random.seed(1988)
+ input_shape = (1, 3, 10, 10)
+ num_filter = 64
+ kernel = (5, 5)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ net = mx.sym.Variable('data')
+ net = mx.sym.transpose(data=net, name='transpose', axes=(0, 1, 2, 3))
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='conv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_reshape(self):
+ np.random.seed(1988)
+ input_shape = (1, 8)
+ net = mx.sym.Variable('data')
+ net = mx.sym.reshape(data=net, shape=(1, 2, 2, 2))
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_synset_random_input(self):
+ np.random.seed(1989)
+ input_shape = (1, 10)
+ net = mx.sym.Variable('data')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
+ net = mx.sym.SoftmaxOutput(net, name='softmax')
+ mod = _get_mxnet_module(net,
+ data_shapes=[('data', input_shape)],
+ mode='random',
+ label_names=['softmax_label'])
+
+ # Generate some dummy data
+ input_data = np.random.uniform(-0.1, 0.1, input_shape)
+
+ Batch = namedtuple('Batch', ['data'])
+ mod.forward(Batch([mx.nd.array(input_data)]))
+
+ kwargs = {'input_shape': {'data': input_shape}}
+ # Get predictions from coreml
+ coreml_model = convert(
+ model=mod,
+ class_labels=['Category1','Category2','Category3','Category4','Category5'],
+ mode='classifier',
+ **kwargs
+ )
+
+ def test_really_tiny_deconv_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 1
+ kernel = (1, 1)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # Define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='deconv_1'
+ )
+ # Test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_deconv_ones_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 1
+ kernel = (5, 5)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # Define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='deconv_1'
+ )
+ # Test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='ones')
+
+ def test_tiny_deconv_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 1
+ kernel = (5, 5)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # Define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='deconv_1'
+ )
+ # Test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_asym_deconv_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 1
+ kernel = (5, 3)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # Define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='deconv_1'
+ )
+ # Test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_asym_deconv_random_asym_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 28, 18)
+ num_filter = 16
+ kernel = (5, 3)
+ stride = (1, 1)
+ pad = (0, 0)
+ dilate = (1, 1)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ dilate=dilate,
+ name='deconv_1'
+ )
+ net = mx.sym.Activation(net, name = 'tanh', act_type = "tanh")
+ # Test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_deconv_pooling_random_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 1
+ kernel = (5, 5)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='deconv_1'
+ )
+ net = mx.symbol.Pooling(
+ data=net,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='pool_1',
+ pool_type='max'
+ )
+ # Test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_really_tiny_deconv_random_3d_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 3, 10, 10)
+ num_filter = 1
+ kernel = (1, 1)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='deconv_1'
+ )
+ # Test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_really_tiny_deconv_random_input_multi_filter(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 64
+ kernel = (1, 1)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='deconv_1'
+ )
+ # Test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_deconv_random_3d_input(self):
+ np.random.seed(1988)
+ input_shape = (1, 3, 10, 10)
+ num_filter = 1
+ kernel = (5, 5)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='deconv_1'
+ )
+ # Test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_tiny_deconv_random_input_multi_filter(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 10, 10)
+ num_filter = 64
+ kernel = (5 ,5)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ name='deconv_1'
+ )
+ # Test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_deconv_random(self):
+ np.random.seed(1988)
+ input_shape = (1, 10, 4, 4)
+ num_filter = 3
+ kernel = (2, 2)
+ stride = (1, 1)
+ pad = (0, 0)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ no_bias=False,
+ name='deconv_1'
+ )
+ # test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_deconv_random_output_shape(self):
+ np.random.seed(1988)
+ input_shape = (1, 10, 4, 4)
+ num_filter = 3
+ kernel = (2, 2)
+ stride = (1, 1)
+ pad = (0, 0)
+ target_shape = (5, 5)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ no_bias=False,
+ target_shape=target_shape,
+ name='deconv_1'
+ )
+ # test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_deconv_random_padding(self):
+ np.random.seed(1988)
+ input_shape = (1, 10, 9, 9)
+ num_filter = 3
+ kernel = (3, 3)
+ stride = (3, 3)
+ pad = (2, 2)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ no_bias=False,
+ name='deconv_1')
+ # test the mxnet model
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_conv_random_padding_odd(self):
+ np.random.seed(1988)
+ input_shape = (1, 10, 6, 6)
+ num_filter = 3
+ kernel = (5, 5)
+ stride = (1, 1)
+ pad = (3, 3)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ no_bias=False,
+ name='conv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_conv_random_padding_even(self):
+ np.random.seed(1988)
+ input_shape = (1, 10, 6, 6)
+ num_filter = 3
+ kernel = (5, 5)
+ stride = (1, 1)
+ pad = (2, 2)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Convolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ no_bias=False,
+ name='conv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_deconv_random_all_inputs(self):
+ np.random.seed(1988)
+ input_shape = (1, 10, 5, 5)
+ num_filter = 3
+ kernel = (3, 3)
+ stride = (2, 2)
+ pad = (1, 1)
+ dilate = (1, 1)
+ target_shape = (11, 11)
+
+ # define a model
+ net = mx.sym.Variable('data')
+ net = mx.symbol.Deconvolution(
+ data=net,
+ num_filter=num_filter,
+ kernel=kernel,
+ stride=stride,
+ pad=pad,
+ no_bias=False,
+ target_shape=target_shape,
+ dilate=dilate,
+ name='deconv_1'
+ )
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random')
+
+ def test_batch_norm(self):
+ np.random.seed(1988)
+ input_shape = (1, 1, 2, 3)
+
+ net = mx.sym.Variable('data')
+ gamma = mx.sym.Variable('gamma')
+ beta = mx.sym.Variable('beta')
+ moving_mean = mx.sym.Variable('moving_mean')
+ moving_var = mx.sym.Variable('moving_var')
+ net = mx.symbol.BatchNorm(
+ data=net,
+ gamma=gamma,
+ beta=beta,
+ moving_mean=moving_mean,
+ moving_var=moving_var,
+ use_global_stats=True,
+ name='batch_norm_1')
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random', delta=1e-2)
+
+ def test_batch_norm_no_global_stats(self):
+ """ This test should throw an exception since converter doesn't support
+ conversion of MXNet models that use local batch stats (i.e.
+ use_global_stats=False). The reason for this is CoreML doesn't support
+ local batch stats.
+ """
+ np.random.seed(1988)
+ input_shape = (1, 1, 2, 3)
+
+ net = mx.sym.Variable('data')
+ gamma = mx.sym.Variable('gamma')
+ beta = mx.sym.Variable('beta')
+ moving_mean = mx.sym.Variable('moving_mean')
+ moving_var = mx.sym.Variable('moving_var')
+ net = mx.symbol.BatchNorm(
+ data=net,
+ gamma=gamma,
+ beta=beta,
+ moving_mean=moving_mean,
+ moving_var=moving_var,
+ use_global_stats=False,
+ name='batch_norm_1')
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random', delta=1e-2)
+
+ def test_pre_processing_args(self):
+ np.random.seed(1988)
+ input_shape = (1, 10)
+ net = mx.sym.Variable('data')
+ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=5)
+ net = mx.sym.SoftmaxOutput(net, name='softmax')
+ self._test_mxnet_model(net, input_shape=input_shape, mode='random', label_names=['softmax_label'],
+ pre_processing_args={'red_bias':0, 'blue_bias':0, 'green_bias':0, 'image_scale':1})
+
+ def test_different_input_variables(self):
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