def test_resnet_architecture_module_is_used_for_resnet(self, m_custom):
self.args.model_type = utils.RESNET_ARCHITECTURE
for supported_mode_type in utils.SUPPORTED_MODEL_MODES:
self.args.model_mode = supported_mode_type
if self.args.model_mode not in self.get_unimplemented_modes_for_model_type(
self.args.model_type):
train_model.get_model(self.args, utils.INPUT_SHAPE)
self.assertTrue(any([m_custom.called]))
def test_raises_valueError_when_unsupported_model_type_is_given(self):
unsupported_model_type = 'xx'
self.args.model_type = unsupported_model_type
if unsupported_model_type in utils.SUPPORTED_MODEL_TYPES:
self.fail(
'Test condition not right: Write the test again with proper unsupported_model_type string'
)
with self.assertRaises(ValueError) as ve:
train_model.get_model(self.args, utils.INPUT_SHAPE)
def test_valueError_not_raised_when_supported_model_type_is_given(self):
try:
for model_type in utils.SUPPORTED_MODEL_TYPES:
self.args.model_type = model_type
if self.args.model_mode not in self.get_unimplemented_modes_for_model_type(
self.args.model_type):
train_model.get_model(self.args, utils.INPUT_SHAPE)
except ValueError:
self.fail(
'{} model architecture should be supported and should not raise error'
.format(self.args.model_type))
def test_inceptionv3_architecture_module_is_used_for_inceptionv3(
self, m_custom, m_finetune, m_baseline):
self.args.model_type = utils.INCEPTIONV3_ARCHITECTURE
for supported_mode_type in utils.SUPPORTED_MODEL_MODES:
self.args.model_mode = supported_mode_type
if self.args.model_mode not in self.get_unimplemented_modes_for_model_type(
self.args.model_type):
train_model.get_model(self.args, utils.INPUT_SHAPE)
self.assertTrue(
any([
m_custom.called, m_baseline.called, m_finetune.called
]))
def test_valueError_not_raised_when_supported_model_mode_is_given(self):
try:
for supported_mode_type in utils.SUPPORTED_MODEL_TYPES:
self.args.model_type = supported_mode_type
for model_mode in utils.SUPPORTED_MODEL_MODES:
self.args.model_mode = model_mode
model_type_unimplemented_exceptions = self.unimplemented_exceptions.get(
self.args.model_type)
if model_type_unimplemented_exceptions is not None \
and self.args.model_mode not in model_type_unimplemented_exceptions:
train_model.get_model(self.args, utils.INPUT_SHAPE)
except ValueError:
self.fail(
'{} model architecture should be supported and should not raise error'
.format(self.args.model_type))
def load_trained_model():
model = get_model()
model.load_weights(MODEL_CHECKPOINT_PATH)
return model
from flask import Blueprint
from flask_restx import Api, fields
from train_model import get_model
api = Api(version='1.0', title='ML API Example', validate=False)
ns = api.namespace('heart', description='Heart disease')
_, model_info = get_model()
feature_row = api.model('featues', {
feat: fields.Float(required=True)
for feat in model_info['required_cols']
})
information = api.model(
'model information', {
'model_name': fields.String(required=True),
'f1-score': fields.Float(required=True),
'required_cols': fields.List(fields.String())
})
prediction = api.model('prediction', {'prediction': fields.Integer()})
api_blueprint = Blueprint('api', __name__)