def test_read_ascii(self):
"""Test reading ASCII tags and compare to known baseline values."""
self.assertEqual(self.image.datetime, Baseline("""2018:03:12 10:12:07"""))
self.assertEqual(self.image.make, Baseline("""Apple"""))
self.assertEqual(self.image.model, Baseline("""iPhone 7"""))
self.assertEqual(self.image.gps_latitude_ref, Baseline("""N"""))
self.assertEqual(self.image.gps_longitude_ref, Baseline("""W"""))
def test_trainer(self):
batch_size = 3
length = 5
descriptors = torch.FloatTensor(self.config.nclasses,
self.config.descriptor_dim).normal_()
sender = Sender(self.config)
sender.eval()
receiver = Receiver(self.config)
receiver.eval()
exchange_model = ExchangeModel(self.config)
baseline_sender = Baseline(self.config, 'sender')
baseline_receiver = Baseline(self.config, 'receiver')
exchange = Exchange(exchange_model, sender, receiver, baseline_sender,
baseline_receiver, descriptors)
trainer = Trainer(exchange)
image = torch.FloatTensor(batch_size, self.config.image_in).normal_()
target_dist = F.softmax(torch.FloatTensor(
batch_size, self.config.nclasses).normal_(),
dim=1)
target = target_dist.argmax(dim=1)
trainer_loss = trainer.run_step(image, target)
self.assertEqual(trainer_loss.sender_message_loss.numel(), 1)
self.assertEqual(trainer_loss.receiver_message_loss.numel(), 1)
self.assertEqual(trainer_loss.stop_loss.numel(), 1)
self.assertEqual(trainer_loss.baseline_loss_sender.numel(), 1)
self.assertEqual(trainer_loss.baseline_loss_receiver.numel(), 1)
self.assertEqual(trainer_loss.xent_loss.numel(), 1)
def __init__(self, cfg, inference=False, threshold=0.5):
self.device = torch.device(
"cuda") if cfg.MODEL.DEVICE == 'cuda' else torch.device("cpu")
if not inference:
print('load training data')
self.dataloader, class_num = get_train_loader(cfg)
print('load testing data')
if cfg.TEST.MODE == 'face':
self.agedb_30, self.cfp_fp, self.lfw, self.agedb_30_issame, self.cfp_fp_issame, self.lfw_issame = get_val_data(
self.dataloader.dataset.root.parent)
else:
pairs = read_pairs(
os.path.join(cfg.DATASETS.FOLDER, 'pairs.txt'))
self.data, self.data_issame = get_paths(
os.path.join(cfg.DATASETS.FOLDER, 'test'), pairs)
print('load model')
self.model = Baseline(cfg)
self.model = self.model.to(self.device)
self.load_state(cfg)
if cfg.SOLVER.OPT == 'SGD':
self.optimizer = optim.SGD(
[{
'params': self.model.parameters()
}],
lr=cfg.SOLVER.BASE_LR,
momentum=cfg.SOLVER.MOMENTUM,
weight_decay=cfg.SOLVER.WEIGHT_DECAY)
else:
self.optimizer = optim.Adam(
[{
'params': self.model.parameters()
}],
lr=cfg.SOLVER.BASE_LR,
weight_decay=cfg.SOLVER.WEIGHT_DECAY)
self.scheduler = optim.lr_scheduler.CosineAnnealingLR(
self.optimizer,
T_max=cfg.SOLVER.MAX_EPOCH,
eta_min=cfg.SOLVER.ETA_MIN_LR)
checkpoints = cfg.CHECKPOINT.SAVE_DIR
os.makedirs(checkpoints, exist_ok=True)
self.best_score = 0.
self.best_threshold = 0.
else:
self.device = torch.device(
"cuda") if cfg.TEST.DEVICE == 'cuda' else torch.device("cpu")
print('load model')
self.model = Baseline(cfg)
self.model = self.model.to(self.device)
self.load_state(cfg)
self.threshold = threshold
self.test_transform = trans.Compose([
trans.ToTensor(),
trans.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
def baseline_model(utility_matrix, similarity_matrix):
if os.path.exists("baseline_matrix.pickle"):
baseline = Baseline(utility_matrix)
baseline_matrix = pd.read_pickle("baseline_matrix.pickle")
print("Using already created baseline_matrix.pickle file")
else:
print("Baseline Method Started")
baseline = Baseline(utility_matrix)
baseline_matrix = baseline.get_rating_deviation()
# print("Take Input of user ID and Movie ID to predict the Rating")
# query_user = int(input("Query User ID : "))
# query_movie = int(input("Query Movie ID : "))
# n = 20
# predicted_rating = baseline.get_baseline_rating_prediction(query_user,query_movie,similarity_matrix,baseline_matrix,n)
# print("Predicted Rating using baseline approach : ",predicted_rating)
if os.path.exists("baseline_error_matrix.pickle"):
error_matrix = pd.read_pickle("baseline_error_matrix.pickle")
print("Using already created baseline_error_matrix.pickle file")
size = 20000
utility_matrix = utility_matrix.fillna(0)
for i in range(100):
for j in range(100):
error_matrix.iloc[
i, j] = utility_matrix.iloc[i, j] - error_matrix.iloc[i, j]
ans2 = error_matrix.sum().sum()
matrix = error_matrix.pow(2)
ans = matrix.sum().sum()
ans = ans / size
ans2 = ans / size
print("\nFor Baseline Model")
print("Calculated RMSE is : ", sqrt(ans))
print("Calculated MAE is ", ans2)
else:
baseline.calculate_error(similarity_matrix, baseline_matrix)
def test_read_rational(self):
"""Test reading RATIONAL tags and compare to known baseline values."""
self.assertEqual(str(self.image.gps_altitude)[:13], Baseline("""2189.98969072"""))
self.assertEqual(str(self.image.gps_latitude), Baseline("""(36.0, 3.0, 11.08)"""))
self.assertEqual(str(self.image.gps_longitude), Baseline("""(112.0, 5.0, 4.18)"""))
self.assertEqual(str(self.image.x_resolution), Baseline("""72.0"""))
self.assertEqual(str(self.image.y_resolution), Baseline("""72.0"""))
def test_same_value(self):
"""Test same baselined text for every call on the same line.
Check baseline instantiations on the same line produce the
same instance.
"""
baseline1, baseline2 = Baseline("Hello!"), Baseline("Hello!")
self.assertIs(baseline1, baseline2)
def test_modify_rational(self):
"""Verify that modifying RATIONAL tags updates the tag values as expected."""
self.image.gps_altitude = 123.456789
self.assertEqual(str(self.image.gps_altitude),
Baseline("""123.456789"""))
self.image.gps_latitude = (41.0, 36.0, 33.786)
self.assertEqual(str(self.image.gps_latitude),
Baseline("""(41.0, 36.0, 33.786)"""))
segment_hex = self.image._segments['APP1'].get_segment_hex()
self.assertEqual('\n'.join(textwrap.wrap(segment_hex, 90)),
MODIFY_RATIONAL_HEX_BASELINE)
def test_differing_value(self):
"""Test differing baselined text for every call.
Check that exception is raised, that "atexit" registration
did not occur, and that no files were to be updated.
"""
with self.assertRaises(RuntimeError):
Baseline('junk1'), Baseline('junk2')
with self.assertRaises(RuntimeError):
for text in ['junk1', 'junk2']:
Baseline(text)
self.check_updated_files()
def test_list_attributes_photo(self):
"""Verify that calling dir() on a camera photo lists the expected EXIF attributes."""
with open(
os.path.join(os.path.dirname(__file__), "grand_canyon.jpg"), "rb"
) as image_file:
image = Image(image_file)
dunder_dir_text = "\n".join(textwrap.wrap(repr(sorted(dir(image))), 90))
self.assertEqual(
dunder_dir_text,
Baseline(
"""
['<unknown EXIF tag 59932>', '<unknown EXIF tag 59933>', '_exif_ifd_pointer',
'_gps_ifd_pointer', '_segments', 'aperture_value', 'brightness_value', 'color_space',
'components_configuration', 'compression', 'datetime', 'datetime_digitized',
'datetime_original', 'delete', 'delete_all', 'exif_version', 'exposure_bias_value',
'exposure_mode', 'exposure_program', 'exposure_time', 'f_number', 'flash',
'flashpix_version', 'focal_length', 'focal_length_in_35mm_film', 'get', 'get_all',
'get_file', 'get_thumbnail', 'gps_altitude', 'gps_altitude_ref', 'gps_datestamp',
'gps_dest_bearing', 'gps_dest_bearing_ref', 'gps_horizontal_positioning_error',
'gps_img_direction', 'gps_img_direction_ref', 'gps_latitude', 'gps_latitude_ref',
'gps_longitude', 'gps_longitude_ref', 'gps_speed', 'gps_speed_ref', 'gps_timestamp',
'has_exif', 'jpeg_interchange_format', 'jpeg_interchange_format_length', 'lens_make',
'lens_model', 'lens_specification', 'list_all', 'make', 'maker_note', 'metering_mode',
'model', 'orientation', 'photographic_sensitivity', 'pixel_x_dimension',
'pixel_y_dimension', 'resolution_unit', 'scene_capture_type', 'scene_type',
'sensing_method', 'shutter_speed_value', 'software', 'subject_area',
'subsec_time_digitized', 'subsec_time_original', 'white_balance', 'x_resolution',
'y_and_c_positioning', 'y_resolution']
"""
),
)
def __init__(self):
config = Baseline()
config_changes = {
'description': 'baseline travel model fast (no build)',
}
config.replace(config_changes)
from psrc.configs.create_travel_model_configuration import create_travel_model_configuration
travel_model_configuration = create_travel_model_configuration(
'baseline_travel_model_psrc_fast_no_build',
emme2_batch_file='MODELUSim.BAT ..\\triptabs',
mode='full')
config['travel_model_configuration'] = travel_model_configuration
config['travel_model_configuration']['locations_to_disaggregate'] = [
'parcel', 'building'
]
del config['travel_model_configuration'][2000]
##fast model doesn't have bank2 and bank3; disable macros using them
del config['travel_model_configuration']['export_macros'][
'tazvmt2.mac']
del config['travel_model_configuration']['export_macros'][
'tazvmt3.mac']
del config['travel_model_configuration']['matrix_variable_map'][
'bank2']
del config['travel_model_configuration']['matrix_variable_map'][
'bank3']
self.merge(config)
#if __name__ == "__main__":
# config = BaselineTravelModelFast()
def test_no_app1_segment(self):
"""Verify behavior of an image without an APP1 segment marker.
Assert the ``has_exif`` attribute is false. Verify non-EXIF ``dir()`` list contents. Then,
check the ``get_file()`` hexadecimal.
"""
image_path = os.path.join(os.path.dirname(__file__), "no_app1.png")
with open(image_path, "rb") as image_file:
my_image = Image(image_file)
self.assertFalse(my_image.has_exif)
self.assertEqual(
str(dir(my_image)),
Baseline("""
['_segments', 'delete', 'delete_all', 'get', 'get_all', 'get_file', 'get_thumbnail', 'has_exif', 'list_all']
"""),
)
with pytest.raises(RuntimeError,
match="image does not contain thumbnail"):
my_image.get_thumbnail()
self.assertEqual(
"\n".join(textwrap.wrap(str(my_image.get_file()), 90)),
NO_APP1_PNG)
def compute_pose(self, view1, view2=None, is_baseline=False):
"""Computes the pose of the new view"""
# procedure for baseline pose estimation
if is_baseline and view2:
match_object = self.matches[(view1.name, view2.name)]
baseline_pose = Baseline(view1, view2, match_object)
view2.R, view2.t = baseline_pose.get_pose(self.K)
rpe1, rpe2 = self.triangulate(view1, view2)
self.errors.append(np.mean(rpe1))
self.errors.append(np.mean(rpe2))
self.done.append(view1)
self.done.append(view2)
# procedure for estimating the pose of all other views
else:
view1.R, view1.t = self.compute_pose_PNP(view1)
errors = []
# reconstruct unreconstructed points from all of the previous views
for i, old_view in enumerate(self.done):
match_object = self.matches[(old_view.name, view1.name)]
_ = remove_outliers_using_F(old_view, view1, match_object)
self.remove_mapped_points(match_object, i)
_, rpe = self.triangulate(old_view, view1)
errors += rpe
self.done.append(view1)
self.errors.append(np.mean(errors))
def test_delete_all_tags(self):
"""Verify deleting all EXIF tags from the Image object."""
self.image.delete_all()
segment_hex = (binascii.hexlify(
self.image._segments["APP1"].get_segment_bytes()).decode(
"utf-8").upper())
self.assertEqual("\n".join(textwrap.wrap(segment_hex, 90)),
DELETE_ALL_HEX_BASELINE)
with TemporaryFile("w+b") as temporary_file_stream:
temporary_file_stream.write(self.image.get_file())
temporary_file_stream.seek(0)
reloaded_image = Image(temporary_file_stream)
dunder_dir_text = "\n".join(
textwrap.wrap(repr(sorted(dir(reloaded_image))), 90))
self.assertEqual(
dunder_dir_text,
Baseline("""
['<unknown EXIF tag 59932>', '<unknown EXIF tag 59933>', '_exif_ifd_pointer',
'_gps_ifd_pointer', '_segments', 'delete', 'delete_all', 'exif_version', 'get', 'get_all',
'get_file', 'get_thumbnail', 'has_exif', 'list_all']
"""),
)
def test_index_accessor():
"""Test accessing attributes using index syntax."""
with open(os.path.join(os.path.dirname(__file__), "grand_canyon.jpg"),
"rb") as image_file:
image = Image(image_file)
assert image["datetime"] == Baseline("""2018:03:12 10:12:07""")
def execute_demo(language):
if language == 'english':
word_emb = load_word_embeddings('english')
elif language == 'spanish':
word_emb = load_word_embeddings('spanish')
data = Dataset(language)
print("{}: {} training - {} dev".format(language, len(data.trainset),
len(data.devset)))
#for sent in data.trainset:
# Gold label -> 0 if the word is not complex, 1 if the word is complex.
#print(sent['sentence'], sent['target_word'], sent['gold_label'])
baseline = Baseline(language)
model = Model(language)
model.train(data.trainset, word_emb)
predictions = model.test(data.devset, word_emb)
gold_labels = [sent['gold_label'] for sent in data.devset]
report_score(gold_labels, predictions)
def __init__(self):
config = Baseline()
config_changes = {
'description':
'run travel model for baseyear and then extract travel data',
'models': [],
'models_in_year': {
2000: [],
},
'years': (2000, 2001),
}
from psrc.configs.create_travel_model_configuration import create_travel_model_configuration
travel_model_configuration = create_travel_model_configuration(
'baseline_travel_model_psrc_2008_lmwang',
emme2_batch_file='MODEL1-0.BAT',
mode='full',
years_to_run={
2000: '2000_v1.0aTG',
2005: '2006_v1.0aTG',
2010: '2010_v1.0aTG',
2015: '2010_v1.0aTG_2015',
2020: '2020_v1.0aTG'
})
config['travel_model_configuration'] = travel_model_configuration
config.replace(config_changes)
self.merge(config)
def run(dataset):
n_features = len(meta[dataset]['val_name'])
result_online = Result('%s-%s' %(dataset, 'aws-online'), aws=True)
result_baseline = Result('%s-%s' %(dataset, 'aws-baseline'), aws=True)
result_active = Result('%s-%s' %(dataset, 'aws-active'), aws=True)
for repeat in range(0, n_repeat):
print 'Round %d of %d'% (repeat, n_repeat - 1)
ex = AWSOnline(meta[dataset]['model_id'], 1, 0, n_features, meta[dataset]['val_name'], ftype='uniform', error=.1)
test_x, test_y = load_svmlight_file('/Users/Fan/dev/ML/code/binary-classifiers/targets/%s/test.scale' % dataset, n_features)
test_x = test_x.todense()
test_y = [a if a == 1 else 0 for a in test_y]
train_x, train_y = [], []
for i in result_active.index:
q_by_u = result_active.Q_by_U[i]
print 'Active learning with budget %d / %d' % (q_by_u, q_by_u * (n_features + 1))
main = ActiveLearning(ex, (None, None), (test_x, test_y), n_features,
q_by_u * (n_features + 1), 5)
L_unif, L_test = main.do()
result_active.L_unif[i].append(L_unif)
result_active.L_test[i].append(L_test)
result_active.nquery[i].append(ex.get_n_query())
ex = AWSOnline(meta[dataset]['model_id'], 1, 0, n_features, meta[dataset]['val_name'], ftype='uniform', error=.1)
for i in result_online.index:
q_by_u = result_online.Q_by_U[i]
print 'collecting up to budget %d / %d' % (q_by_u, q_by_u * (n_features + 1))
ex.collect_up_to_budget(q_by_u * (n_features + 1))
train_x.extend(ex.pts_near_b)
train_y.extend(ex.pts_near_b_labels)
print 'retraining with %d points' % len(train_y)
# online
e = RBFKernelRetraining(ex.batch_predict, (train_x, train_y), (test_x, test_y), n_features)
L_unif, L_test = e.grid_retrain_in_x()
result_online.L_unif[i].append(L_unif)
result_online.L_test[i].append(L_test)
result_online.nquery[i].append(ex.get_n_query())
# baseline
e = Baseline(ex.batch_predict, (train_x, train_y), (test_x, test_y), n_features)
L_unif, L_test = e.do()
result_baseline.L_unif[i].append(L_unif)
result_baseline.L_test[i].append(L_test)
result_baseline.nquery[i].append(ex.get_n_query())
print result_online
print result_baseline
print result_active
def execute(language):
language = language
data = Dataset(language)
print("{}: {} training - {} dev".format(language, len(data.trainset),
len(data.devset)))
baseline = Baseline(language)
estimator = SVC(gamma=300)
title = 'Spanish Learning Curves (SVM, γ=300)'
X, y = baseline.train(data.trainset)
plot_learning_curve(estimator,
title,
X,
y,
ylim=None,
n_jobs=1,
train_sizes=np.linspace(.1, 1.0, 5))
predictions = baseline.test(data.devset)
gold_labels = [sent['gold_label'] for sent in data.devset]
target_words = [sent['target_word'] for sent in data.devset]
prediction = []
for i in predictions:
prediction.append(i)
df = pd.DataFrame(columns=['target_word', 'prediction'])
df["target_word"] = target_words
df['gold_label'] = gold_labels
df['prediction'] = prediction
df.to_csv('out_s2.csv')
report_score(gold_labels, predictions)
def test_nic_name_map(self, mocked_check_output):
"""Test nic_name_map attribute of NetworkAdapters."""
self.assertEqual(
str(
sorted(self.test_adapters.nic_name_map,
key=self.test_adapters.nic_name_map.get)),
Baseline("""['Ethernet Adapter', 'Wi-Fi Adapter']"""))
def test_gitlab_issue_23():
"""Regression test for GitLab issue 23.
Verify reading ASCII tags containing a smaller length value than specified by the size field.
"""
image_under_test = Image(
os.path.join(os.path.dirname(__file__), "excess_ascii_null_bytes.jpg"))
with pytest.warns(RuntimeWarning,
match="ASCII tag contains 2 fewer bytes than specified"):
assert image_under_test.model == Baseline("""iPhone""")
with pytest.warns(
RuntimeWarning,
match="ASCII tag contains 13 fewer bytes than specified"):
assert image_under_test.software == Baseline("""Photoshop Express""")
def test_product_name(self, mocked_check_output):
"""Test product_name property of the Nic class."""
self.assertEqual(self.test_nic.product_name,
Baseline("""Dummy Adapter"""))
with self.assertRaisesRegex(
AttributeError,
"'Nic' attribute 'product_name' is not settable"):
self.test_nic.product_name = "New Name"
def test_set_static_address(self, mocked_check_output, mocked_call_output):
"""Test set_static_address method of the Nic class."""
self.assertEqual(
str(
self.test_nic.set_static_address('192.168.0.2',
'255.255.255.0',
'192.168.0.1')),
Baseline("""0"""))
def test_get_method():
"""Test behavior when accessing tags using the ``get()`` method."""
with open(os.path.join(os.path.dirname(__file__), 'grand_canyon.jpg'), 'rb') as image_file:
image = Image(image_file)
assert image.get('fake_attribute') is None
assert image.get('light_source', default=-1) == -1 # tag not in image
assert image.get('make') == Baseline("""Apple""")
def test_modify_rational(self):
"""Verify that modifying RATIONAL tags updates the tag values as expected."""
self.image.gps_altitude = 123.456789
self.assertEqual(str(self.image.gps_altitude), Baseline("""123.456789"""))
self.image.gps_latitude = (41.0, 36.0, 33.786)
self.assertEqual(
str(self.image.gps_latitude), Baseline("""(41.0, 36.0, 33.786)""")
)
segment_hex = (
binascii.hexlify(self.image._segments["APP1"].get_segment_bytes())
.decode("utf-8")
.upper()
)
self.assertEqual(
"\n".join(textwrap.wrap(segment_hex, 90)), MODIFY_RATIONAL_HEX_BASELINE
)
def test_nic_connection_id_map(self, mocked_check_output):
"""Test nic_connection_id_map attribute of NetworkAdapters."""
self.assertEqual(
str(
sorted(self.test_adapters.nic_connection_id_map,
key=self.test_adapters.nic_connection_id_map.get)),
Baseline(
"""['Local Area Connection', 'Wireless Area Connection']"""))
def test_modify_ascii_same_len(self):
"""Verify that writing a same length string to an ASCII tag updates the tag."""
self.image.model = "MyCamera"
self.assertEqual(self.image.model, Baseline("""MyCamera"""))
segment_hex = self.image._segments['APP1'].get_segment_hex()
self.assertEqual('\n'.join(textwrap.wrap(segment_hex, 90)),
MODIFY_ASCII_SAME_LEN_HEX_BASELINE)
def test_modify_ascii_shorter(self):
"""Verify that writing a shorter string to an ASCII tag updates the tag."""
self.image.model = "MyCam"
self.assertEqual(self.image.model, Baseline("""MyCam"""))
segment_hex = self.image._segments['APP1'].get_segment_hex()
self.assertEqual('\n'.join(textwrap.wrap(segment_hex, 90)),
MODIFY_ASCII_SHORTER_HEX_BASELINE)
def __init__(self):
config = Baseline()
if self.multiple_runs:
config.sample_inputs()
config['number_of_runs'] = 99
config['seed'] = 1
config_changes = {
'description': 'baseline travel model fast',
}
config.replace(config_changes)
from psrc.configs.create_travel_model_configuration import create_travel_model_configuration
travel_model_configuration = create_travel_model_configuration(
'baseline_travel_model_psrc_fast_hana',
emme2_batch_file='MODELUSim.BAT ..\\triptabs',
mode='skims',
years_to_run={
2005: '2005_06',
2010: '2010_06',
2015: '2010_06'
})
config['travel_model_configuration'] = travel_model_configuration
config['travel_model_configuration']['locations_to_disaggregate'] = [
'parcel', 'building'
]
##fast model doesn't have bank2 and bank3; disable macros using them
del config['travel_model_configuration']['export_macros'][
'tazvmt2.mac']
del config['travel_model_configuration']['export_macros'][
'tazvmt3.mac']
del config['travel_model_configuration']['matrix_variable_map'][
'bank2']
del config['travel_model_configuration']['matrix_variable_map'][
'bank3']
config['travel_model_configuration']['export_macros'][
'get_link_attributes.mac'] = {
'bank': 'bank1',
'scenario': -1,
'path': 'export_macros'
}
config['travel_model_configuration']['node_matrix_variable_map'] = {
"bank1": {
"attr_on_links.rpt": {
"timau": "am_pk_travel_time",
"len": "distance"
},
"tveham.rpt": {
"@tveh": "vehicle_volume"
}
}
}
#config['travel_model_configuration'][2015]['models'] = list(config['travel_model_configuration'][2015].get('models'))
#config['travel_model_configuration'][2015]['models'].append('opus_emme2.models.restore_trip_tables')
self.merge(config)
def test_read(self):
"""Test reading tags and compare to known baseline values."""
self.assertEqual(repr(self.image.color_space), "<ColorSpace.SRGB: 1>")
self.assertEqual(str(self.image.datetime_original),
Baseline("""2019:02:08 21:44:35"""))
self.assertEqual(str(self.image.gps_latitude),
Baseline("""(79.0, 36.0, 54.804590935844615)"""))
self.assertEqual(str(self.image.gps_longitude),
Baseline("""(47.0, 25.0, 34.489798675854615)"""))
self.assertEqual(self.image.make, Baseline("""EXIF Package"""))
self.assertEqual(self.image.model, Baseline("""Little Endian"""))
self.assertEqual(str(self.image.resolution_unit), Baseline("""2"""))
self.assertEqual(repr(self.image.saturation),
Baseline("""<Saturation.LOW: 1>"""))
self.assertEqual(repr(self.image.sharpness),
Baseline("""<Sharpness.SOFT: 1>"""))
self.assertEqual(str(self.image.x_resolution), Baseline("""200.0"""))
self.assertEqual(str(self.image.y_resolution), Baseline("""200.0"""))
def __init__(self):
config = Baseline()
if self.multiple_runs:
config.sample_inputs()
config['years'] = (2001, 2005)
config['number_of_runs'] = 50
config['seed'] = 1
config['description'] = 'baseline multiple runs'
self.merge(config)
