def test_openvino_predict_xshards(self):
self.load_resnet()
input_data_list = [np.array([self.input] * 4),
np.concatenate([np.array([self.input] * 2),
np.zeros([1, 3, 224, 224])])]
sc = init_nncontext()
rdd = sc.parallelize(input_data_list, numSlices=2)
shards = SparkXShards(rdd)
def pre_processing(images):
return {"x": images}
shards = shards.transform_shard(pre_processing)
result = self.est.predict(shards)
result_c = result.collect()
assert isinstance(result, SparkXShards)
assert result_c[0]["prediction"].shape == (4, 1000)
assert result_c[1]["prediction"].shape == (3, 1000)
assert self.check_result(result_c[0]["prediction"], 4)
assert self.check_result(result_c[1]["prediction"], 2)
assert not self.check_result(result_c[1]["prediction"][2:], 1)
def test_nnEstimator(self):
from bigdl.dllib.nnframes import NNModel
linear_model = Sequential().add(Linear(2, 2))
mse_criterion = MSECriterion()
df, _ = self.get_estimator_df()
est = Estimator.from_bigdl(model=linear_model,
loss=mse_criterion,
optimizer=Adam(),
feature_preprocessing=SeqToTensor([2]),
label_preprocessing=SeqToTensor([2]))
res0 = est.predict(df)
res0_c = res0.collect()
est.fit(df, 2, batch_size=4)
nn_model = NNModel(est.get_model(),
feature_preprocessing=SeqToTensor([2]))
res1 = nn_model.transform(df)
res2 = est.predict(df)
res1_c = res1.collect()
res2_c = res2.collect()
assert type(res1).__name__ == 'DataFrame'
assert type(res2).__name__ == 'DataFrame'
assert len(res1_c) == len(res2_c)
for idx in range(len(res1_c)):
assert res1_c[idx]["prediction"] == res2_c[idx]["prediction"]
with tempfile.TemporaryDirectory() as tempdirname:
temp_path = os.path.join(tempdirname, "model")
est.save(temp_path)
est2 = Estimator.from_bigdl(model=linear_model, loss=mse_criterion)
est2.load(temp_path,
optimizer=Adam(),
loss=mse_criterion,
feature_preprocessing=SeqToTensor([2]),
label_preprocessing=SeqToTensor([2]))
est2.set_constant_gradient_clipping(0.1, 1.2)
est2.clear_gradient_clipping()
res3 = est2.predict(df)
res3_c = res3.collect()
assert type(res3).__name__ == 'DataFrame'
assert len(res1_c) == len(res3_c)
for idx in range(len(res1_c)):
assert res1_c[idx]["prediction"] == res3_c[idx]["prediction"]
est2.fit(df, 4, batch_size=4)
data = self.sc.parallelize([((2.0, 1.0), (1.0, 2.0)),
((1.0, 2.0), (2.0, 1.0)),
((2.0, 1.0), (1.0, 2.0)),
((1.0, 2.0), (2.0, 1.0))])
data_shard = SparkXShards(data)
data_shard = data_shard.transform_shard(
lambda feature_label_tuple: {
"x":
np.stack([
np.expand_dims(np.array(feature_label_tuple[0][0]), axis=0
),
np.expand_dims(np.array(feature_label_tuple[0][1]), axis=0)
],
axis=1),
"y":
np.stack([
np.expand_dims(np.array(feature_label_tuple[1][0]), axis=0
),
np.expand_dims(np.array(feature_label_tuple[1][1]), axis=0)
],
axis=1)
})
res4 = est.predict(data_shard)
res4_c = res4.collect()
assert type(res4).__name__ == 'SparkXShards'
for idx in range(len(res4_c)):
assert abs(res4_c[idx]["prediction"][0][0] -
res3_c[idx]["prediction"][0]) == 0
assert abs(res4_c[idx]["prediction"][0][1] -
res3_c[idx]["prediction"][1]) == 0
est.fit(data_shard, 1, batch_size=4)
res5 = est.predict(data_shard)
res5_c = res5.collect()
res6 = est.predict(df)
res6_c = res6.collect()
for idx in range(len(res5_c)):
assert abs(res5_c[idx]["prediction"][0][0] -
res6_c[idx]["prediction"][0]) == 0
assert abs(res5_c[idx]["prediction"][0][1] -
res6_c[idx]["prediction"][1]) == 0
