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How to use get_network_by_name method in tempest

Best Python code snippet using tempest_python

FERManager.py

Source:FERManager.py

...40        # Checks if the given net is subclass of CNNetwork41        assert issubclass(type(network), CNNetwork)42        self._nn_dict[network.get_name()] = network43        return44    def get_network_by_name(self, model_name=None):45        """46        This method loads the stored network by its name from the neural network list47        model_name: The name of the neural network to load48        INPUT:49        - model_name: The name of the network to load50        RETURN:51        - The specified network in the network list52        """53        assert model_name is not None54        return self._nn_dict[model_name]55    def code_to_emotion(self, code):56        """57        This method converts the emotion number to the appropriate emotion name58        INPUT:59        - code: The integer code of the emotion60        RETURN:61        - The string name of the respective emotion code, otherwise None62        """63        emotion_names = {0: "Angry/Disgust",64                         1: "Fear",65                         2: "Happy",66                         3: "Sad",67                         4: "Surprise",68                         5: "Neutral"}69        if code >=0 and code <=5:70            return emotion_names.get(code)71        return None72    def start_training(self, network_type):73        """74        This method executes the training and validation process of the models75        INPUT:76        - network_type: The type of the network to train77        """78        self.enable_gpu_support()   # Enables the GPU support when it's available79        if network_type not in self._nn_dict:80            raise ValueError("The given network could not be found in the neural network list!!!.")81        network = self.get_network_by_name(network_type)82        # Checks if the log folder for the VGG16 network exists83        if network.get_name() == 'vgg16':84            if os.path.exists(self._log_dir + "vgg16") == False:85                os.makedirs(self._log_dir + "/vgg16/train/")86        # Checks if the log folder for the Inception-v3 network exists87        if network.get_name() == 'inception-v3':88            if os.path.exists(self._log_dir + "inception_v3") == False:89                os.makedirs(self._log_dir + "/inception_v3/train/")90        print("Training of the "+network_type+" network starts...")91        network.build(self._num_emotions)92        network.training(augmentation=True, early_stopping=True, decay_rate=0.5)  # Decays 50% the learning rate93        return94    def predict(self, dataset_path, network=None):95        """96        This method classifies an unknown image97        INPUT:98        - dataset_path: The path of the dataset99        - network: The network to predict100        """101        assert network is not None102        image_list = os.listdir(dataset_path)103        print("Prediction using the", network,"model...")104        vgg16 = None105        test_model_dir = ""106        inception_resnet_v2 = None107        # Checks for the VGG16 network108        if network == 'vgg16':109            vgg16 = self.get_network_by_name(network)110            test_model_dir = "./logs/vgg16/train/"111        # Checks for the Inception-v3 network112        elif network == 'inception-v3':113            inception_resnet_v2 = self.get_network_by_name(network)114            test_model_dir = "./logs/inception_v3/train/"115        else:116            print("No specified network for testing found!!!")117            return118        # Checks if the test directory contains a test checkpoint119        if os.listdir(test_model_dir):120            file = [file for file in os.listdir(test_model_dir)]121            test_model = load_model(test_model_dir + str(file[0]))122        else:123            print("No saved models available, prediction cannot proceed!!!")124            return125        emotion_predictions = []126        # Predicts the unknown images127        for img_file in image_list:128            # Reads every image from the folder, converts it to grayscale129            img_orig = cv2.imread(dataset_path + img_file)130            img_gray = cv2.imread(dataset_path + img_file, 0)131            predictions = []; calc_predictions = []; y_label = []132            if vgg16:133                predictions = vgg16.predict(img_gray, test_model)134            elif inception_resnet_v2:135                predictions = inception_resnet_v2.predict(img_gray, test_model)136            # print(predictions)137            print("Image file: ", img_file, ":")138            for code, percent in enumerate(predictions):139                emotion = self.code_to_emotion(code)    # Converts the code to the emotion name140                # Checks for which emotion/percentage to print141                if code == 0: print(emotion + ":\t%1.2f%%" % (np.float(percent)))142                elif code == 1: print(emotion + ":\t\t\t%1.2f%%" % (np.float(percent)))143                elif code == 2: print(emotion + ":\t\t\t%1.2f%%" % (np.float(percent)))144                elif code == 3: print(emotion + ":\t\t\t%1.2f%%" % (np.float(percent)))145                elif code == 4: print(emotion + ":\t\t%1.2f%%" % (np.float(percent)))146                elif code == 5: print(emotion + ":\t\t%1.2f%%" % (np.float(percent)))147                else: print("Invalid Code!!!")148                calc_predictions.append(percent)149            highest = self.code_to_emotion(np.argmax(calc_predictions))150            print("Highest:", highest, "\n")151            emotion_predictions.append(highest)152            cv2.imshow(highest, img_orig)153            cv2.waitKey(0)154            cv2.destroyAllWindows()155        self.plot_statistics(emotion_predictions)  # Plots the statistics of the prediction156        return157    # TODO: Implementation of the testing process158    def test(self, network=None):159        """160        This method uses the test set to evaluate the given network161        INPUT:162        - dataset_path: The path of the dataset163        - network: The network to predict164        """165        assert network is not None166        self.enable_gpu_support()  # Enables the GPU support if it's available167        print("Testing using the", network, "model...")168        vgg16 = None169        test_model_dir = ""170        inception_resnet_v2 = None171        # Checks for the VGG16 network172        if network == 'vgg16':173            vgg16 = self.get_network_by_name(network)174            test_model_dir = "./logs/vgg16/train/"175        # Checks for the Inception-v3 network176        elif network == 'inception-v3':177            inception_resnet_v2 = self.get_network_by_name(network)178            test_model_dir = "./logs/inception_v3/train/"179        else:180            print("No specified network for testing found!!!")181            return182        # Checks if the test directory contains a test checkpoint183        if os.listdir(test_model_dir):184            file = [file for file in os.listdir(test_model_dir)]185            test_model = load_model(test_model_dir + str(file[0]))186        else:187            print("No saved models available, prediction cannot proceed!!!")188            return189        result = []190        if vgg16:191            result = vgg16.testing(self._num_emotions, test_model)...

test_getNetworkByName.py

Source:test_getNetworkByName.py

...20            raise21        else:22            try:23                for item in range(0, no_of_api_calls):24                    response = self.get_network_by_name(25                        ncm_url, params_from_inputfile.get_anchor(0)26                    )27                    logfile.log_debug(28                        "Received Response Code: " + str(response.status_code)29                    )30                    logfile.log_debug("Received Response Text: " + str(response.text))31                    # logfile.log_debug("Received Response Header SessionId: " + response.headers.get('SessionId'))32                    self.get_network_by_name_assertion_check(33                        logfile,34                        response,35                        params_from_inputfile.get_response_code(0, item),36                        params_from_inputfile.get_response_text(0, item),37                    )38            except HTTPError as http_err:39                logfile.log_debug(utils.printException())40                logfile.log_debug(41                    "Error - HTTP error occurred: '{0}' ".format(http_err)42                )43                raise44            except Exception as err:45                logfile.log_debug(utils.printException())46                logfile.log_debug("Error - other error occurred: '{0}' ".format(err))47                raise48    @classmethod49    def get_network_by_name(self, ncm_url, anchor):50        headers = {"accept": "application/json"}51        response = requests.get(ncm_url + anchor, headers=headers)52        return response53    @classmethod54    def get_network_by_name_assertion_check(55        self, logfile, response, responseCode=None, responseText=None56    ):57        msgResCode = "Response Code does not match, expected: {0}, actual: {1}".format(58            responseCode, response.status_code59        )60        utils.logAssert(response.status_code == responseCode, msgResCode, logfile)61        if responseText:62            if type(responseText) == str:63                msgText = "Response Text does not contain expected string, expected: {0}, actual: {1}".format(...

loader.py

Source:loader.py

1import importlib2__all__ = [3    'get_network_by_name',4]5def get_network_by_name(name, input_shape):6    module = importlib.import_module('badukai.networks.' + name)7    constructor = getattr(module, 'layers')...

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