def _predict(cls, model, xs_2d):
# override TrainTestModel._predict
f = list(xs_2d)
for i, item in enumerate(f):
f[i] = list(item)
score, _, _ = svmutil.svm_predict([0] * len(f), f, model)
ys_label_pred = np.array(score)
return ys_label_pred
def _predict(cls, model, xs_2d):
# override TrainTestModel._predict
f = list(xs_2d)
for i, item in enumerate(f):
f[i] = list(item)
score, _, _ = svmutil.svm_predict([0] * len(f), f, model)
ys_label_pred = np.array(score)
return ys_label_pred
def _predict(cls, model, xs_2d):
# override TrainTestModel._predict
try:
svmutil
except NameError:
from vmaf import svmutil
f = list(xs_2d)
for i, item in enumerate(f):
f[i] = list(item)
score, _, _ = svmutil.svm_predict([0] * len(f), f, model)
ys_label_pred = np.array(score)
return ys_label_pred
def _run_on_asset(self, asset):
# Override Executor._run_on_asset(self, asset), which runs a
# FeatureAssembler, collect a feature vector, run
# TrainTestModel.predict() on it, and return a Result object
# (in this case, both Executor._run_on_asset(self, asset) and
# QualityRunner._read_result(self, asset) get bypassed.
vmaf_fassembler = self._get_vmaf_feature_assembler_instance(asset)
vmaf_fassembler.run()
feature_result = vmaf_fassembler.results[0]
# =====================================================================
try:
svmutil
except NameError:
from vmaf import svmutil
# SVR predict
model = svmutil.svm_load_model(self.SVM_MODEL_FILE)
ordered_scaled_scores_list = []
for scores_key in self.SVM_MODEL_ORDERED_SCORES_KEYS:
scaled_scores = self._rescale(
feature_result[scores_key],
self.FEATURE_RESCALE_DICT[scores_key])
ordered_scaled_scores_list.append(scaled_scores)
scores = []
for score_vector in zip(*ordered_scaled_scores_list):
vif, adm, ansnr, motion = score_vector
xs = [[vif, adm, ansnr, motion]]
score = svmutil.svm_predict([0], xs, model)[0][0]
score = self._post_correction(motion, score)
scores.append(score)
result_dict = {}
# add all feature result
result_dict.update(feature_result.result_dict)
# add quality score
result_dict[self.get_scores_key()] = scores
return Result(asset, self.executor_id, result_dict)
def _run_on_asset(self, asset):
# Override Executor._run_on_asset(self, asset), which runs a
# FeatureAssembler, collect a feature vector, run
# TrainTestModel.predict() on it, and return a Result object
# (in this case, both Executor._run_on_asset(self, asset) and
# QualityRunner._read_result(self, asset) get bypassed.
vmaf_fassembler = self._get_vmaf_feature_assembler_instance(asset)
vmaf_fassembler.run()
feature_result = vmaf_fassembler.results[0]
# =====================================================================
# SVR predict
model = svmutil.svm_load_model(self.SVM_MODEL_FILE)
ordered_scaled_scores_list = []
for scores_key in self.SVM_MODEL_ORDERED_SCORES_KEYS:
scaled_scores = self._rescale(feature_result[scores_key],
self.FEATURE_RESCALE_DICT[scores_key])
ordered_scaled_scores_list.append(scaled_scores)
scores = []
for score_vector in zip(*ordered_scaled_scores_list):
vif, adm, ansnr, motion = score_vector
xs = [[vif, adm, ansnr, motion]]
score = svmutil.svm_predict([0], xs, model)[0][0]
score = self._post_correction(motion, score)
scores.append(score)
result_dict = {}
# add all feature result
result_dict.update(feature_result.result_dict)
# add quality score
result_dict[self.get_scores_key()] = scores
return Result(asset, self.executor_id, result_dict)
