def __init__(self, fit_kwargs, compile_kwargs, name, TPA_view_IDs, TPA_dense_units=TPA_DENSE_DEFAULT, **kwargs):
vb.print_general("Initializing {} - Baseline1...".format(name))
vb.print_general("Model will be trained on {} views".format(TPA_view_IDs))
def downsampling(tpa_view_id):
return TimeDistributed(AvgPool2D(pool_size=(4, 4), strides=None), name='TPA{}_'.format(tpa_view_id))
io_TPAs = [_build_TPA_embedding(id, TPA_dense_units, block1=downsampling(id))
for id in TPA_view_IDs]
i_TPAs = [x[0] for x in io_TPAs]
o_TPAs = [x[1] for x in io_TPAs]
if len(TPA_view_IDs) > 1:
TPA_merged = Concatenate(name='view_concat', axis=-1)([*o_TPAs])
if len(TPA_view_IDs) == 1:
TPA_merged = [*o_TPAs]
rnn = RNN(int(TPA_dense_units*len(io_TPAs)*RNN_UNITS_SCALING), activation='tanh',
recurrent_activation='sigmoid', return_sequences=True, name="TPA_GRU")(TPA_merged)
TPA_dense = TimeDistributed(
Dense(project.N_CLASSES, activation=None), name="TPA_dense")(rnn)
TPA_classification = Activation(
activation='softmax', name='TPA_classification')(TPA_dense)
model = Model(i_TPAs, TPA_classification,
name="Model_{}xTPA".format(len(TPA_view_IDs)))
Models_Training.__init__(self, name=name, model=model, fit_kwargs=fit_kwargs,
compile_kwargs=compile_kwargs, TPA_view_IDs=TPA_view_IDs)
def train(self):
if self.pretraining:
vb.print_general("Pretraining...")
self.model.compile(**self.precompile_kwargs)
self.model.fit(**self.prefit_kwargs)
self.pretrained = True
if self.pretraining and not self.pretrained:
raise Exception("Pretrain the model first!")
if self.pretrained:
print("\n\n\n\n\n\n")
vb.print_general("Training...")
self.model.compile(**self.compile_kwargs)
self.model.fit(**self.fit_kwargs)
self.trained = True
"rb") as f:
optimal_threshold = pickle.load(f)
#2A. Read in list of clips to test on.
subjects = ds.read_test_subjects()
test_set_path = ds.read_test_set_path()
clips_fps = glob.glob(
os.path.join(test_set_path, "subject*", "*", "*ID*.TXT"))
prefixes = list(set([fp.split("ID")[0] for fp in clips_fps]))
if len(clips_fps) / len(prefixes) != 3:
raise Exception(
"It seems that there are some missing/additional views in your test set"
)
#2B. loop
vb.print_general("Testing {} on {}".format(config_json_name,
test_set_path))
vb.print_general(
"Testing: executing main loop (loading in data + prediction)...")
view_IDs = model_config["view_IDs"]
predictions_dict, sample_classes_dict, labels_dict, timestamps_dict = [
dict() for i in range(4)
]
prefixes = prefixes
for prefix in prefixes:
arrays = []
timestamps = []
for id in view_IDs:
# + Z-score!
a, ts = tpa_tools.txt2np(prefix + "ID" + id + ".TXT")
arrays.append((a - mu) / sigma)
timestamps.append(ts)