def test_control_flow(self):
@tf.function(input_signature=[tf.TensorSpec([], tf.float32)])
def control_flow(x):
if x <= 0:
return 0.
else:
return x * 3.
to_save = tf.Module()
to_save.control_flow = control_flow
saved_model_dir = tempfile.mkdtemp()
tf.saved_model.save(to_save, saved_model_dir)
tf_model = tf.saved_model.load(saved_model_dir)
concrete_func = tf_model.signatures[
tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
model = coremltools.converters.tensorflow.convert([concrete_func],
inputs={'x': (1, )},
outputs=['Identity'])
self.assertTrue(isinstance(model, coremltools.models.MLModel))
input_data = generate_data(shape=[20])
for data in input_data:
tf_prediction = to_save.control_flow(data).numpy().flatten()
cm_prediction = model.predict({'x': np.array([data])
})['Identity'].flatten()
np.testing.assert_array_almost_equal(tf_prediction,
cm_prediction,
decimal=2)
def run_single_case(args):
""" Run a single CTC loss test case """
np.random.seed(42)
(inputs, target, input_length_data, target_length_data) = test_utils.generate_data(args)
(result, log_probs, gradients, nll) = build_and_run_popart_graph(
inputs, target, input_length_data, target_length_data, args
)
ipu_grad = result[gradients]
ipu_loss = result[nll]
print(ipu_grad)
pytorch_loss, pytorch_res = torch_ctc_loss(
result[log_probs], target, input_length_data, target_length_data, args
)
ipu_grad = np.transpose(ipu_grad, (1, 0, 2)).reshape(pytorch_res.shape)
grad_err = test_utils.getTensorError(ipu_grad, pytorch_res)
loss_err = test_utils.getTensorError(ipu_loss, pytorch_loss)
print("Gradient Error", grad_err)
print("Loss Error", loss_err)
print(f"IPU Loss: {ipu_loss}")
print("Grad result: " + ("Pass" if grad_err < 1e-4 else "FAIL"))
print("Loss result: " + ("Pass" if loss_err < 1e-5 else "FAIL"))
return grad_err, loss_err
def test_subclassed_keras_model(self):
class MyModel(tf.keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.dense1 = tf.keras.layers.Dense(4)
self.dense2 = tf.keras.layers.Dense(5)
@tf.function
def call(self, input_data):
return self.dense2(self.dense1(input_data))
keras_model = MyModel()
inputs = generate_data(shape=(4, 4))
# subclassed model can only be saved as SavedModel format
keras_model._set_inputs(inputs)
keras_model.save(self.saved_model_dir, save_format='tf')
input_name = keras_model.inputs[0].name.split(':')[0]
output_name = keras_model.outputs[0].name.split(':')[0].split('/')[-1]
# convert and validate
model = coremltools.converters.tensorflow.convert(
self.saved_model_dir,
inputs={input_name: (4, 4)},
outputs=[output_name])
self.assertTrue(isinstance(model, coremltools.models.MLModel))
self._test_prediction(keras_model=keras_model,
core_ml_model=model,
inputs=inputs)
def run_single_case(args, splits=[]):
""" Run a single RNN-T loss test case """
np.random.seed(42)
(inputs, target, input_length_data,
target_length_data) = test_utils.generate_data(args)
(
result,
compacted_probs,
gradients,
compacted_gradients,
nll,
) = build_and_run_graph(inputs,
target,
input_length_data,
target_length_data,
args,
splits=splits)
ipu_grad = result[gradients]
# ipu_compacted_grad = result[compacted_gradients]
ipu_loss = result[nll]
pytorch_loss, pytorch_grads_wlogits, pytorch_grads_wlogprobs = reference_rnnt_loss(
inputs, target, input_length_data, target_length_data)
grad_err = test_utils.getTensorError(ipu_grad, pytorch_grads_wlogits)
loss_err = test_utils.getTensorError(ipu_loss, pytorch_loss)
print("Gradient Error", grad_err)
print("Loss Error", loss_err)
print(f"IPU Loss: {ipu_loss}")
print("Grad result: " + ("Pass" if grad_err < 1e-5 else "FAIL"))
print("Loss result: " + ("Pass" if loss_err < 1e-5 else "FAIL"))
return grad_err, loss_err
def test_rnn_no_memory_pass():
T.manual_seed(1111)
input_size = 100
hidden_size = 100
rnn_type = 'gru'
num_layers = 3
num_hidden_layers = 5
dropout = 0.2
nr_cells = 5000
cell_size = 17
sparse_reads = 3
gpu_id = -1
debug = True
lr = 0.001
sequence_max_length = 10
batch_size = 10
cuda = gpu_id
clip = 20
length = 13
rnn = SAM(input_size=input_size,
hidden_size=hidden_size,
rnn_type=rnn_type,
num_layers=num_layers,
num_hidden_layers=num_hidden_layers,
dropout=dropout,
nr_cells=nr_cells,
cell_size=cell_size,
sparse_reads=sparse_reads,
gpu_id=gpu_id,
debug=debug)
optimizer = optim.Adam(rnn.parameters(), lr=lr)
optimizer.zero_grad()
input_data, target_output = generate_data(batch_size, length, input_size,
cuda)
target_output = target_output.transpose(0, 1).contiguous()
(chx, mhx, rv) = (None, None, None)
outputs = []
for x in range(6):
output, (chx, mhx, rv), v = rnn(input_data, (chx, mhx, rv),
pass_through_memory=False)
output = output.transpose(0, 1)
outputs.append(output)
output = functools.reduce(lambda x, y: x + y, outputs)
loss = criterion((output), target_output)
loss.backward()
T.nn.utils.clip_grad_norm(rnn.parameters(), clip)
optimizer.step()
assert target_output.size() == T.Size([27, 10, 100])
assert chx[0].size() == T.Size([num_hidden_layers, 10, 100])
# assert mhx['memory'].size() == T.Size([10,12,17])
assert rv == None
def test_rnn_n():
T.manual_seed(1111)
input_size = 100
hidden_size = 100
rnn_type = 'gru'
num_layers = 3
num_hidden_layers = 5
dropout = 0.2
nr_cells = 200
cell_size = 17
read_heads = 2
sparse_reads = 4
temporal_reads = 3
gpu_id = -1
debug = True
lr = 0.001
sequence_max_length = 10
batch_size = 10
cuda = gpu_id
clip = 20
length = 13
rnn = SDNC(input_size=input_size,
hidden_size=hidden_size,
rnn_type=rnn_type,
num_layers=num_layers,
num_hidden_layers=num_hidden_layers,
dropout=dropout,
nr_cells=nr_cells,
cell_size=cell_size,
read_heads=read_heads,
sparse_reads=sparse_reads,
temporal_reads=temporal_reads,
gpu_id=gpu_id,
debug=debug)
optimizer = optim.Adam(rnn.parameters(), lr=lr)
optimizer.zero_grad()
input_data, target_output = generate_data(batch_size, length, input_size,
cuda)
target_output = target_output.transpose(0, 1).contiguous()
output, (chx, mhx, rv), v = rnn(input_data, None)
output = output.transpose(0, 1)
loss = criterion((output), target_output)
loss.backward()
T.nn.utils.clip_grad_norm_(rnn.parameters(), clip)
optimizer.step()
assert target_output.size() == T.Size([27, 10, 100])
assert chx[0].size() == T.Size([num_hidden_layers, 10, 100])
# assert mhx['memory'].size() == T.Size([10,12,17])
assert rv.size() == T.Size([10, 34])
def test_rnn_1():
T.manual_seed(1111)
input_size = 100
hidden_size = 100
rnn_type = 'lstm'
num_layers = 1
num_hidden_layers = 1
dropout = 0
nr_cells = 100
cell_size = 10
read_heads = 1
sparse_reads = 2
gpu_id = -1
debug = True
lr = 0.001
sequence_max_length = 10
batch_size = 10
cuda = gpu_id
clip = 10
length = 10
rnn = SAM(input_size=input_size,
hidden_size=hidden_size,
rnn_type=rnn_type,
num_layers=num_layers,
num_hidden_layers=num_hidden_layers,
dropout=dropout,
nr_cells=nr_cells,
cell_size=cell_size,
read_heads=read_heads,
sparse_reads=sparse_reads,
gpu_id=gpu_id,
debug=debug)
optimizer = optim.Adam(rnn.parameters(), lr=lr)
rnn = DNI(rnn, optim=optimizer)
optimizer.zero_grad()
input_data, target_output = generate_data(batch_size, length, input_size,
cuda)
target_output = target_output.transpose(0, 1).contiguous()
output, (chx, mhx, rv), v = rnn(input_data, None)
output = output.transpose(0, 1)
loss = criterion((output), target_output)
loss.backward()
T.nn.utils.clip_grad_norm(rnn.parameters(), clip)
assert target_output.size() == T.Size([21, 10, 100])
assert chx[0][0][0].size() == T.Size([10, 100])
# assert mhx['memory'].size() == T.Size([10,1,1])
assert rv.size() == T.Size([10, 10])
def test_basic1():
D = 2
N = 100
pi = np.array([0.99, 0.01])
A = np.array([[0.90, 0.10], [0.10, 0.90]])
mus = np.array([[0., 0.], [20., 20.]])
sigmas = np.array([np.eye(2), 5. * np.eye(2)])
kappa = 0.5
nu = 5
params = [[mus[0], sigmas[0]], [mus[1], sigmas[1]]]
print 'label 1 true'
print mus[0]
print sample_invwishart(sigmas[0], nu)
print kappa
print nu
print 'label 2 true'
print mus[1]
print sample_invwishart(sigmas[1], nu)
print kappa
print nu
obs, sts = generate_data(D, N, pi, A, params)
A_0 = 2 * np.ones((2, 2))
emits = [
NIW(np.ones(2), np.eye(2), kappa, nu),
NIW(20 * np.ones(2), 5. * np.eye(2), kappa, nu)
]
hmm = HMMSVI(obs, A_0, emits, 1., 0.7)
hmm.infer(10, 10, HMMSVI.metaobs_unif, 20)
var_x = hmm.full_local_update()
sts_pred = np.argmax(var_x, axis=1)
print 'hamming distance = ', np.min([
hamming(np.array([0, 1])[sts_pred], sts),
hamming(np.array([1, 0])[sts_pred], sts)
])
print 'label 1 learned'
print hmm.emits[0].mu_N
print sample_invwishart(hmm.emits[0].sigma_N, hmm.emits[0].nu_N)
print hmm.emits[0].kappa_N
print hmm.emits[0].nu_N
print 'label 2 learned'
print hmm.emits[1].mu_N
print sample_invwishart(hmm.emits[1].sigma_N, hmm.emits[1].nu_N)
print hmm.emits[1].kappa_N
print hmm.emits[1].nu_N
def _test_model(self,
keras_model,
model_path,
inputs,
outputs=None,
decimal=4,
use_cpu_only=False,
verbose=False):
keras_model.save(model_path)
# convert and validate
model = coremltools.converters.tensorflow.convert(model_path,
inputs=inputs,
outputs=outputs)
self.assertTrue(isinstance(model, coremltools.models.MLModel))
if verbose:
print('TensorFlow Keras model saved at {}'.format(model_path))
tmp_model_path = self.model_path.rsplit('.')[0] + '.mlmodel'
model.save(tmp_model_path)
print('Core ML model saved at {}'.format(tmp_model_path))
# verify numeric correctness of predictions
# assume one input one output for now
name, shape = list(inputs.items())[0]
data = generate_data(shape=shape)
keras_prediction = keras_model.predict(data)
# If outputs are not supplied, get the output name
# from the keras model.
if not outputs:
output_name = keras_model.outputs[0].name
outputs = [output_name.split('/')[1].split(':')[0]]
prediction = model.predict({name: data},
use_cpu_only=use_cpu_only)[outputs[0]]
if verbose:
print('Shape Keras:', keras_prediction.shape, ' vs. Core ML:',
prediction.shape)
print('Input :', data.flatten()[:16])
print('Keras :', keras_prediction.flatten()[:16])
print('Core ML:', prediction.flatten()[:16])
np.testing.assert_array_equal(keras_prediction.shape, prediction.shape)
np.testing.assert_almost_equal(keras_prediction.flatten(),
prediction.flatten(),
decimal=decimal)
return model
def _test_conversion_prediction(self, keras_model, model_path, inputs,
outputs):
# convert and validate
model = coremltools.converters.tensorflow.convert(model_path,
inputs=inputs,
outputs=outputs)
self.assertTrue(isinstance(model, coremltools.models.MLModel))
# verify numeric correctness of predictions
inputs = generate_data(shape=self.input_shape)
keras_prediction = keras_model.predict(inputs)
output_name = keras_model.outputs[0].name.split(':')[0].split('/')[-1]
prediction = model.predict(
{keras_model.inputs[0].name.split(':')[0]: inputs})[output_name]
np.testing.assert_array_equal(keras_prediction.shape, prediction.shape)
np.testing.assert_almost_equal(keras_prediction.flatten(),
prediction.flatten(),
decimal=4)
def _test_keras_model_tf2(self, model, data_mode, decimal, use_cpu_only, has_variables, verbose):
core_ml_model_file = self.model_file.rsplit('.')[0] + '.mlmodel'
input_dict = {inp.op.name: inp.shape.as_list() for inp in model.inputs}
for name, shape in input_dict.items():
input_dict[name] = [dim if dim is not None else 1 for dim in shape]
output_list = ['Identity']
model.save(self.model_file)
# convert Keras model into Core ML model format
core_ml_model = coremltools.converters.tensorflow.convert(
filename=self.model_file,
inputs=input_dict,
outputs=output_list,
use_cpu_only=use_cpu_only)
if verbose:
print('\nKeras model saved at {}'.format(self.model_file))
print('\nCore ML model description:')
from coremltools.models.neural_network.printer import print_network_spec
print_network_spec(core_ml_model.get_spec(), style='coding')
core_ml_model.save(core_ml_model_file)
print('\nCore ML model saved at {}'.format(core_ml_model_file))
core_ml_inputs = {
name: generate_data(shape, data_mode) for name, shape in input_dict.items()
}
# run prediction and compare results
keras_output = model.predict(list(core_ml_inputs.values())[0])
core_ml_output = core_ml_model.predict(
core_ml_inputs, useCPUOnly=use_cpu_only)[output_list[0]]
if verbose:
print('\nPredictions', keras_output.shape, ' vs.', core_ml_output.shape)
print(keras_output.flatten()[:6])
print(core_ml_output.flatten()[:6])
np.testing.assert_array_equal(
keras_output.shape, core_ml_output.shape)
np.testing.assert_almost_equal(
keras_output.flatten(), core_ml_output.flatten(), decimal=decimal)
def _test_model(self,
keras_model,
model_path,
inputs,
outputs,
decimal=4,
verbose=False):
keras_model.save(model_path)
# convert and validate
model = coremltools.converters.tensorflow.convert(model_path,
inputs=inputs,
outputs=outputs,
target_ios='13')
assert isinstance(model, coremltools.models.MLModel)
if verbose:
print('TensorFlow Keras model saved at {}'.format(model_path))
tmp_model_path = self.model_path.rsplit('.')[0] + '.mlmodel'
model.save(tmp_model_path)
print('Core ML model saved at {}'.format(tmp_model_path))
# verify numeric correctness of predictions
# assume one input one output for now
name, shape = list(inputs.items())[0]
data = generate_data(shape=shape)
# self._predict_keras_intermediate_layer(data, 'conv1')
keras_prediction = keras_model.predict(data)
prediction = model.predict({name: data})[outputs[0]]
if verbose:
print('Shape Keras:', keras_prediction.shape, ' vs. Core ML:',
prediction.shape)
print('Input :', data.flatten()[:16])
print('Keras :', keras_prediction.flatten()[:16])
print('Core ML:', prediction.flatten()[:16])
np.testing.assert_array_equal(keras_prediction.shape, prediction.shape)
np.testing.assert_almost_equal(keras_prediction.flatten(),
prediction.flatten(),
decimal=decimal)
def test_basic3():
N = 100
D = 2
pi = np.array([0.999, 0.001])
A = np.array([[0.9, 0.1], [0.1, 0.9]])
mus_0 = np.array([[0., 0.], [20., 20.]])
sigmas_0 = np.array([np.eye(2), 5 * np.eye(2)])
kappa_0 = 0.5
nu_0 = 5
mu_1, sigma_1 = sample_niw(mus_0[0], sigmas_0[0], kappa_0, nu_0)
mu_2, sigma_2 = sample_niw(mus_0[1], sigmas_0[1], kappa_0, nu_0)
params = [[mu_1, sigma_1], [mu_2, sigma_2]]
obs, _ = generate_data(D, N, pi, A, params)
#plt.scatter(obs[:,0], obs[:,1])
#plt.show()
A_0 = 2 * np.ones((A.shape[0], A.shape[0]))
mus_N = np.array([np.mean(obs, axis=0), np.mean(obs, axis=0)])
sigmas_N = 0.75 * np.array([np.cov(obs.T), np.cov(obs.T)])
A_nat_0 = A_0 - 1
n1s_0 = [
kappa_0 * mus_N[0], kappa_0,
sigmas_N[0] + kappa_0 * np.outer(mus_N[0], mus_N[0]), nu_0 + D + 2
]
n2s_0 = [
kappa_0 * mus_N[1], kappa_0,
sigmas_N[1] + kappa_0 * np.outer(mus_N[1], mus_N[1]), nu_0 + D + 2
]
A_nat_N = A_nat_0[:]
n1s_N = n1s_0[:]
n2s_N = n2s_0[:]
iters = 20
print 'A true'
print A
print 'label 1 true'
#print mus_0[0]
#print sigmas_0[0]
print mu_1
print sigma_1
print 'label 2 true'
#print mus_0[1]
#print sigmas_0[1]
print mu_2
print sigma_2
print 'A_nat_0'
print A_nat_0
for _ in xrange(iters):
mu_1N, sigma_1N, kappa_1N, nu_1N = natural_to_standard(
n1s_N[0], n1s_N[1], n1s_N[2], n1s_N[3])
mu_2N, sigma_2N, kappa_2N, nu_2N = natural_to_standard(
n2s_N[0], n2s_N[1], n2s_N[2], n2s_N[3])
lliks1 = niw.expected_log_likelihood(obs, mu_1N, sigma_1N, kappa_1N,
nu_1N)
lliks2 = niw.expected_log_likelihood(obs, mu_2N, sigma_2N, kappa_2N,
nu_2N)
lliks = np.vstack((lliks1, lliks2)).T.copy(order='C')
lA_mod = dir.expected_sufficient_statistics(A_nat_N + 1)
A_mod = np.exp(lA_mod)
lalpha = fb.forward_msgs(pi, A_mod, lliks)
lbeta = fb.backward_msgs(A_mod, lliks)
lexpected_states, expected_transcounts = fb.expected_statistics(
pi, A_mod, lliks, lalpha, lbeta)
#lexpected_states = lalpha + lbeta
#lexpected_states -= np.max(lexpected_states, axis=1)[:,np.newaxis]
expected_states = np.exp(lexpected_states)
expected_states /= np.sum(expected_states, axis=1)[:, np.newaxis]
#A_ss = np.zeros_like(A_nat_0)
#for i in xrange(1,expected_states.shape[0]):
# A_ss += np.outer(expected_states[i-1], expected_states[i])
A_ss = dir.sufficient_statistics(expected_states)
# convert to natural parameter?
A_ss -= 1
A_nat_N = dir.meanfield_update(A_nat_0, A_ss)
s11, s12, s13 = niw.expected_sufficient_statistics(
obs, expected_states[:, 0].copy(order='C'))
s21, s22, s23 = niw.expected_sufficient_statistics(
obs, expected_states[:, 1].copy(order='C'))
s1s = np.array([s11, s21])
s2s = np.array([s12, s22])
s3s = np.array([s13, s23])
n11, n12, n13, n14 = niw.meanfield_update(n1s_0[0], n1s_0[1], n1s_0[2],
n1s_0[3], s1s[0], s2s[0],
s3s[0])
n21, n22, n23, n24 = niw.meanfield_update(n2s_0[0], n2s_0[1], n2s_0[2],
n2s_0[3], s1s[1], s2s[1],
s3s[1])
n1s_N = [n11, n12, n13, n14]
n2s_N = [n21, n22, n23, n24]
print 'A learned'
A_sample = np.array([
stats.dirichlet.rvs(A_nat_N[i, :] + 1, size=1)[0]
for i in xrange(A.shape[0])
])
print A_sample
mu_0, sigma_0, kappa_0, nu_0 = natural_to_standard(n11, n12, n13, n14)
mu_1, sigma_1 = sample_niw(mu_0, sigma_0, kappa_0, nu_0)
print 'label 1 learned'
print mu_1
print sigma_1
mu_0, sigma_0, kappa_0, nu_0 = natural_to_standard(n21, n22, n23, n24)
mu_2, sigma_2 = sample_niw(mu_0, sigma_0, kappa_0, nu_0)
print 'label 2 learned'
print mu_2
print sigma_2
def _test_tf_model(
self,
graph,
input_shapes,
output_node_names,
data_mode='random',
input_refs=None,
delta=1e-2,
use_cpu_only=False,
graph_optimizations="freeze", # one of ["freeze", "convert_variables_to_constants", None]
quantize_tf_model=False):
"""
Common entry to testing routine.
graph - defined TensorFlow graph.
input_shapes - dict str:shape for each input op (placeholder)
output_node_names - output_node_names, a list of strings
data_mode - auto-generated input vectors, can be 'random', 'zeros', 'ones', 'linear', etc.
input_refs - a dictionary of reference input in tensorFlow axis order, each entry is str:shape.
When using auto-generated input vectors, set input_refs to None.
delta - maximum difference of normalized TensorFlow and CoreML outputs
use_cpu_only - If True, instantiate and run CoreML model with CPU only
graph_optimizations == "freeze" - Force TensorFlow graph to be frozen before converting.
quantize_tf_model - If True, try to quantize TensorFlow model before converting
"""
# Some file processing
model_dir = tempfile.mkdtemp()
graph_def_file = os.path.join(model_dir, 'tf_graph.pb')
checkpoint_file = os.path.join(model_dir, 'tf_model.ckpt')
static_model_file = os.path.join(model_dir, 'tf_static.pb')
coreml_model_file = os.path.join(model_dir, 'coreml_model.mlmodel')
# add a saver
tf.reset_default_graph()
if graph_optimizations == "freeze":
with graph.as_default() as g:
saver = tf.train.Saver()
if input_refs is None:
feed_dict = {
self._get_tf_tensor_name(graph, name):
generate_data(input_shapes[name], data_mode)
for name in input_shapes
}
else:
feed_dict = {
self._get_tf_tensor_name(graph, name): input_refs[name]
for name in list(input_refs.keys())
}
with tf.Session(graph=graph) as sess:
# initialize
initializer_op = tf.global_variables_initializer()
sess.run(initializer_op)
# run the result
fetches = [
graph.get_operation_by_name(name).outputs[0]
for name in output_node_names
]
result = sess.run(fetches, feed_dict=feed_dict)
# save graph definition somewhere
tf.train.write_graph(sess.graph,
model_dir,
graph_def_file,
as_text=False)
# save the weights if freezing is needed
if not graph_optimizations:
static_model_file = graph_def_file
elif graph_optimizations == "freeze":
saver.save(sess, checkpoint_file)
self._simple_freeze(
input_graph=graph_def_file,
input_checkpoint=checkpoint_file,
output_graph=static_model_file,
output_node_names=",".join(output_node_names))
else:
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, graph.as_graph_def(), output_node_names)
with tf.gfile.GFile(static_model_file, "wb") as f:
f.write(output_graph_def.SerializeToString())
# if TF needs to be quantized, quantize the graph
if quantize_tf_model:
static_model_file = self._quantize_static_tf_model(
model_dir, static_model_file, output_node_names)
# convert to CoreML
mlmodel = coremltools.converters.tensorflow.convert(
static_model_file,
inputs=input_shapes,
outputs=output_node_names,
use_cpu_only=use_cpu_only)
if DEBUG:
print('\n mlmodel description: \n')
from coremltools.models.neural_network.printer import print_network_spec
print_network_spec(mlmodel.get_spec(), style='coding')
mlmodel.save(coreml_model_file)
print('\n mlmodel saved at %s' % coreml_model_file)
# Transpose input data as CoreML requires
coreml_inputs = {
name: tf_transpose(feed_dict[self._get_tf_tensor_name(graph,
name)])
for name in input_shapes
}
# Run predict in CoreML
coreml_output = mlmodel.predict(coreml_inputs, useCPUOnly=use_cpu_only)
for idx, out_name in enumerate(output_node_names):
tf_out = result[idx]
if len(tf_out.shape) == 0:
tf_out = np.array([tf_out])
tp = tf_out.flatten()
coreml_out = coreml_output[out_name]
cp = coreml_out.flatten()
self.assertTrue(tf_out.shape == coreml_out.shape)
for i in range(len(tp)):
max_den = max(1.0, tp[i], cp[i])
self.assertAlmostEqual(tp[i] / max_den,
cp[i] / max_den,
delta=delta)
# Cleanup files - models on disk no longer useful
if os.path.exists(model_dir):
shutil.rmtree(model_dir)
def _test_tf_model_constant(self,
graph,
input_shapes,
output_node_names,
data_mode='random_zero_mean',
delta=1e-2,
use_cpu_only=False,
validate_bool_only=False):
"""
Common entry to testing routine for graphs that have no variables.
Parameters
----------
graph: tf.Graph()
TensorFlow graph.
input_shapes: dict [str : shape]
Shapes for each input (placeholder).
output_node_names: list of str
Output tensor names.
data_mode: str
Data mode for the placeholder data generation.
input_refs: a dictionary of reference input in tensorFlow axis order.
Each entry is str:shape. When using auto-generated input vectors,
set input_refs to None.
delta: float
Delta for error checking, default 1e-2.
use_cpu_only: bool
If true, force use CPU only, default False.
validate_bool_only: bool
If true, only validate it's zero or non-zero, otherwise, validate
float values, default False.
"""
model_dir = tempfile.mkdtemp()
frozen_model_file = os.path.join(model_dir, 'tf_frozen.pb')
coreml_model_file = os.path.join(model_dir, 'coreml_model.mlmodel')
feed_dict = {
self._get_tf_tensor_name(graph, name):
generate_data(input_shapes[name], data_mode)
for name in input_shapes
}
with tf.Session(graph=graph) as sess:
# initialize
sess.run(tf.global_variables_initializer())
# run the result
fetches = [
graph.get_operation_by_name(name).outputs[0]
for name in output_node_names
]
result = sess.run(fetches, feed_dict=feed_dict)
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, # The session is used to retrieve the weights
tf.get_default_graph().as_graph_def(
), # The graph_def is used to retrieve the nodes
output_node_names # The output node names are used to select the useful nodes
)
with tf.gfile.GFile(frozen_model_file, 'wb') as f:
f.write(output_graph_def.SerializeToString())
# convert to CoreML
mlmodel = coremltools.converters.tensorflow.convert(
frozen_model_file,
inputs=input_shapes,
outputs=output_node_names,
use_cpu_only=use_cpu_only)
if DEBUG:
print('\n mlmodel description: \n')
from coremltools.models.neural_network.printer import print_network_spec
print_network_spec(mlmodel.get_spec(), style='coding')
mlmodel.save(coreml_model_file)
print('\n mlmodel saved at %s' % coreml_model_file)
# Transpose input data as CoreML requires
coreml_inputs = {
name: tf_transpose(feed_dict[self._get_tf_tensor_name(graph,
name)])
for name in input_shapes
}
# Run predict in CoreML
coreml_output = mlmodel.predict(coreml_inputs, useCPUOnly=use_cpu_only)
for idx, out_name in enumerate(output_node_names):
tf_out = result[idx]
if len(tf_out.shape) == 0:
tf_out = np.array([tf_out])
tp = tf_out.flatten()
coreml_out = coreml_output[out_name]
cp = coreml_out.flatten()
self.assertTrue(tf_out.shape == coreml_out.shape,
msg=(tf_out.shape, 'vs.', coreml_out.shape))
if validate_bool_only:
cp = np.logical_and(cp, cp)
for i in range(len(tp)):
max_den = max(1.0, tp[i], cp[i])
self.assertAlmostEqual(tp[i] / max_den,
cp[i] / max_den,
delta=delta)
# Cleanup files - models on disk no longer useful
if os.path.exists(model_dir):
shutil.rmtree(model_dir)
def test_basic1():
N = 100
D = 2
pi = np.array([0.999, 0.001])
A = np.array([[0.9, 0.1], [0.1, 0.9]])
mus_0 = np.array([[0., 0.], [20., 20.]])
sigmas_0 = np.array([np.eye(2), 2 * np.eye(2)])
kappa_0 = 0.5
nu_0 = 5
mu_1, sigma_1 = sample_niw(mus_0[0], sigmas_0[0], kappa_0, nu_0)
mu_2, sigma_2 = sample_niw(mus_0[1], sigmas_0[1], kappa_0, nu_0)
print 'label 1 before'
print mu_1
print sigma_1
print 'label 2 before'
print mu_2
print sigma_2
params = [[mu_1, sigma_1], [mu_2, sigma_2]]
obs, _ = generate_data(D, N, pi, A, params)
n1s = [
kappa_0 * mus_0[0], kappa_0,
sigmas_0[0] + kappa_0 * np.outer(mus_0[0], mus_0[0]), nu_0 + D + 2
]
n2s = [
kappa_0 * mus_0[1], kappa_0,
sigmas_0[1] + kappa_0 * np.outer(mus_0[1], mus_0[1]), nu_0 + D + 2
]
lliks1 = niw.expected_log_likelihood(obs, mus_0[0], sigmas_0[0], kappa_0,
nu_0)
lliks2 = niw.expected_log_likelihood(obs, mus_0[1], sigmas_0[1], kappa_0,
nu_0)
lliks = np.vstack((lliks1, lliks2)).T.copy(order='C')
lalpha = fb.forward_msgs(pi, A, lliks)
lbeta = fb.backward_msgs(A, lliks)
lexpected_states = lalpha + lbeta
lexpected_states -= np.max(lexpected_states, axis=1)[:, np.newaxis]
expected_states = np.exp(lexpected_states)
expected_states /= np.sum(expected_states, axis=1)[:, np.newaxis]
s1s = np.sum(expected_states, axis=0)
s2s = np.array([
np.sum(obs * expected_states[:, i, np.newaxis], axis=0)
for i in xrange(2)
])
s3s = np.array([
np.sum([
np.outer(obs[i], obs[i]) * expected_states[i, 0]
for i in xrange(obs.shape[0])
],
axis=0),
np.sum([
np.outer(obs[i], obs[i]) * expected_states[i, 1]
for i in xrange(obs.shape[0])
],
axis=0)
])
n11, n12, n13, n14 = niw.meanfield_update(n1s[0], n1s[1], n1s[2], n1s[3],
s1s[0], s2s[0], s3s[0])
n21, n22, n23, n24 = niw.meanfield_update(n2s[0], n2s[1], n2s[2], n2s[3],
s1s[1], s2s[1], s3s[1])
# Note might need to add small positive to diagonal
mu_0, sigma_0, kappa_0, nu_0 = natural_to_standard(n11, n12, n13, n14)
mu_1, sigma_1 = sample_niw(mu_0, sigma_0, kappa_0, nu_0)
print 'label 1 after'
print mu_1
print sigma_1
mu_0, sigma_0, kappa_0, nu_0 = natural_to_standard(n21, n22, n23, n24)
mu_2, sigma_2 = sample_niw(mu_0, sigma_0, kappa_0, nu_0)
print 'label 2 after'
print mu_2
print sigma_2
def _test_keras_model_tf1(self, model, data_mode, decimal, use_cpu_only, has_variables, verbose):
graph_def_file = os.path.join(self.saved_model_dir, 'graph.pb')
frozen_model_file = os.path.join(self.saved_model_dir, 'frozen.pb')
core_ml_model_file = os.path.join(self.saved_model_dir, 'model.mlmodel')
input_shapes = {inp.op.name: inp.shape.as_list() for inp in model.inputs}
for name, shape in input_shapes.items():
input_shapes[name] = [dim if dim is not None else 1 for dim in shape]
output_node_names = [output.op.name for output in model.outputs]
tf_graph = _keras.get_session().graph
tf.reset_default_graph()
if has_variables:
with tf_graph.as_default():
saver = tf.train.Saver()
# note: if Keras backend has_variable is False, we're not making variables constant
with tf.Session(graph=tf_graph) as sess:
sess.run(tf.global_variables_initializer())
feed_dict = {}
for name, shape in input_shapes.items():
tensor_name = tf_graph.get_operation_by_name(name).outputs[0].name
feed_dict[tensor_name] = generate_data(shape, data_mode)
# run the result
fetches = [
tf_graph.get_operation_by_name(name).outputs[0] for name in output_node_names
]
result = sess.run(fetches, feed_dict=feed_dict)
# save graph definition somewhere
tf.train.write_graph(sess.graph, self.saved_model_dir, graph_def_file, as_text=False)
# freeze_graph() has been raising error with tf.keras models since no
# later than TensorFlow 1.6, so we're not using freeze_graph() here.
# See: https://github.com/tensorflow/models/issues/5387
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, # The session is used to retrieve the weights
tf_graph.as_graph_def(), # The graph_def is used to retrieve the nodes
output_node_names # The output node names are used to select the useful nodes
)
with tf.gfile.GFile(frozen_model_file, 'wb') as f:
f.write(output_graph_def.SerializeToString())
_keras.clear_session()
# convert to Core ML model format
core_ml_model = coremltools.converters.tensorflow.convert(
frozen_model_file,
inputs=input_shapes,
outputs=output_node_names,
use_cpu_only=use_cpu_only)
if verbose:
print('\nFrozen model saved at {}'.format(frozen_model_file))
print('\nCore ML model description:')
from coremltools.models.neural_network.printer import print_network_spec
print_network_spec(core_ml_model.get_spec(), style='coding')
core_ml_model.save(core_ml_model_file)
print('\nCore ML model saved at {}'.format(core_ml_model_file))
# transpose input data as Core ML requires
core_ml_inputs = {
name: tf_transpose(feed_dict[self._get_tf_tensor_name(tf_graph, name)])
for name in input_shapes
}
# run prediction in Core ML
core_ml_output = core_ml_model.predict(core_ml_inputs, useCPUOnly=use_cpu_only)
for idx, out_name in enumerate(output_node_names):
tf_out = result[idx]
if len(tf_out.shape) == 0:
tf_out = np.array([tf_out])
tp = tf_out.flatten()
coreml_out = core_ml_output[out_name]
cp = coreml_out.flatten()
self.assertTrue(tf_out.shape == coreml_out.shape)
for i in range(len(tp)):
max_den = max(1.0, tp[i], cp[i])
self.assertAlmostEqual(tp[i] / max_den, cp[i] / max_den, delta=10 ** -decimal)
def _test_keras_model(self,
model,
data_mode='random',
delta=1e-2,
use_cpu_only=True,
has_variables=True):
"""Saves out the backend TF graph from the Keras model and tests it
"""
model_dir = tempfile.mkdtemp()
graph_def_file = os.path.join(model_dir, 'tf_graph.pb')
checkpoint_file = os.path.join(model_dir, 'tf_model.ckpt')
frozen_model_file = os.path.join(model_dir, 'tf_frozen.pb')
coreml_model_file = os.path.join(model_dir, 'coreml_model.mlmodel')
input_shapes = {
inp.op.name: inp.shape.as_list()
for inp in model.inputs
}
for name, shape in input_shapes.items():
input_shapes[name] = [
dim if dim is not None else 1 for dim in shape
]
output_node_names = [output.op.name for output in model.outputs]
tf_graph = K.get_session().graph
tf.reset_default_graph()
if has_variables:
with tf_graph.as_default() as g:
saver = tf.train.Saver()
# TODO - if Keras backend has_variable is False, we're not making variables constant
with tf.Session(graph=tf_graph) as sess:
sess.run(tf.global_variables_initializer())
feed_dict = {}
for name, shape in input_shapes.items():
tensor_name = tf_graph.get_operation_by_name(
name).outputs[0].name
feed_dict[tensor_name] = generate_data(shape, data_mode)
# run the result
fetches = [
tf_graph.get_operation_by_name(name).outputs[0]
for name in output_node_names
]
result = sess.run(fetches, feed_dict=feed_dict)
# save graph definition somewhere
tf.train.write_graph(sess.graph,
model_dir,
graph_def_file,
as_text=False)
# freeze_graph() has been raising error with tf.keras models since no
# later than TensorFlow 1.6, so we're not using freeze_graph() here.
# See: https://github.com/tensorflow/models/issues/5387
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, # The session is used to retrieve the weights
tf_graph.as_graph_def(
), # The graph_def is used to retrieve the nodes
output_node_names # The output node names are used to select the useful nodes
)
with tf.gfile.GFile(frozen_model_file, "wb") as f:
f.write(output_graph_def.SerializeToString())
K.clear_session()
# convert to CoreML
mlmodel = coremltools.converters.tensorflow.convert(
frozen_model_file,
inputs=input_shapes,
outputs=output_node_names,
use_cpu_only=use_cpu_only)
if DEBUG:
print('\n mlmodel description: \n')
from coremltools.models.neural_network.printer import print_network_spec
print_network_spec(mlmodel.get_spec(), style='coding')
mlmodel.save(coreml_model_file)
print('\n mlmodel saved at %s' % (coreml_model_file))
# Transpose input data as CoreML requires
coreml_inputs = {
name:
tf_transpose(feed_dict[self._get_tf_tensor_name(tf_graph, name)])
for name in input_shapes
}
# Run predict in CoreML
coreml_output = mlmodel.predict(coreml_inputs, useCPUOnly=use_cpu_only)
for idx, out_name in enumerate(output_node_names):
tf_out = result[idx]
if len(tf_out.shape) == 0:
tf_out = np.array([tf_out])
tp = tf_out.flatten()
coreml_out = coreml_output[out_name]
cp = coreml_out.flatten()
self.assertTrue(tf_out.shape == coreml_out.shape)
for i in range(len(tp)):
max_den = max(1.0, tp[i], cp[i])
self.assertAlmostEqual(tp[i] / max_den,
cp[i] / max_den,
delta=delta)
# Cleanup files - models on disk no longer useful
if os.path.exists(model_dir):
shutil.rmtree(model_dir)
def test_basic2():
N = 100
D = 2
pi = np.array([0.999, 0.001])
A = np.array([[0.9, 0.1], [0.1, 0.9]])
mus_0 = np.array([[0., 0.], [20., 20.]])
sigmas_0 = np.array([np.eye(2), 5 * np.eye(2)])
kappa_0 = 0.5
nu_0 = 5
mu_1, sigma_1 = sample_niw(mus_0[0], sigmas_0[0], kappa_0, nu_0)
mu_2, sigma_2 = sample_niw(mus_0[1], sigmas_0[1], kappa_0, nu_0)
params = [[mu_1, sigma_1], [mu_2, sigma_2]]
obs, _ = generate_data(D, N, pi, A, params)
plt.scatter(obs[:, 0], obs[:, 1])
plt.show()
mus_N = np.array([np.mean(obs, axis=0), np.mean(obs, axis=0)])
sigmas_N = 0.75 * np.array([np.cov(obs.T), np.cov(obs.T)])
n1s_0 = [
kappa_0 * mus_N[0], kappa_0,
sigmas_N[0] + kappa_0 * np.outer(mus_N[0], mus_N[0]), nu_0 + D + 2
]
n2s_0 = [
kappa_0 * mus_N[1], kappa_0,
sigmas_N[1] + kappa_0 * np.outer(mus_N[1], mus_N[1]), nu_0 + D + 2
]
n1s_N = n1s_0[:]
n2s_N = n2s_0[:]
iters = 20
print 'label 1 true'
print mus_0[0]
print sigmas_0[0]
print 'label 2 true'
print mus_0[1]
print sigmas_0[1]
for _ in xrange(iters):
mu_1N, sigma_1N, kappa_1N, nu_1N = natural_to_standard(
n1s_N[0], n1s_N[1], n1s_N[2], n1s_N[3])
mu_2N, sigma_2N, kappa_2N, nu_2N = natural_to_standard(
n2s_N[0], n2s_N[1], n2s_N[2], n2s_N[3])
lliks1 = niw.expected_log_likelihood(obs, mu_1N, sigma_1N, kappa_1N,
nu_1N)
lliks2 = niw.expected_log_likelihood(obs, mu_2N, sigma_2N, kappa_2N,
nu_2N)
lliks = np.vstack((lliks1, lliks2)).T.copy(order='C')
lalpha = fb.forward_msgs(pi, A, lliks)
lbeta = fb.backward_msgs(A, lliks)
lexpected_states = lalpha + lbeta
lexpected_states -= np.max(lexpected_states, axis=1)[:, np.newaxis]
expected_states = np.exp(lexpected_states)
expected_states /= np.sum(expected_states, axis=1)[:, np.newaxis]
s11, s12, s13 = niw.expected_sufficient_statistics(
obs, expected_states[:, 0].copy(order='C'))
s21, s22, s23 = niw.expected_sufficient_statistics(
obs, expected_states[:, 1].copy(order='C'))
s1s = np.array([s11, s21])
s2s = np.array([s12, s22])
s3s = np.array([s13, s23])
n11, n12, n13, n14 = niw.meanfield_update(n1s_0[0], n1s_0[1], n1s_0[2],
n1s_0[3], s1s[0], s2s[0],
s3s[0])
n21, n22, n23, n24 = niw.meanfield_update(n2s_0[0], n2s_0[1], n2s_0[2],
n2s_0[3], s1s[1], s2s[1],
s3s[1])
n1s_N = [n11, n12, n13, n14]
n2s_N = [n21, n22, n23, n24]
mu_0, sigma_0, kappa_0, nu_0 = natural_to_standard(n11, n12, n13, n14)
mu_1, sigma_1 = sample_niw(mu_0, sigma_0, kappa_0, nu_0)
print 'label 1 learned'
print mu_1
print sigma_1
mu_0, sigma_0, kappa_0, nu_0 = natural_to_standard(n21, n22, n23, n24)
mu_2, sigma_2 = sample_niw(mu_0, sigma_0, kappa_0, nu_0)
print 'label 2 learned'
print mu_2
print sigma_2