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How to use save_data_as method in SeleniumBase

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train_conv3d-checkpoint.py

Source:train_conv3d-checkpoint.py

1# imports2import warnings3warnings.filterwarnings('ignore')4import os, cv2, shutil, json5import numpy as np, pandas as pd, pickle as pkl6from glob import glob7from time import time8from datetime import datetime9from tqdm import tqdm10from sklearn.metrics import precision_recall_curve, average_precision_score, accuracy_score11from sklearn.metrics.pairwise import cosine_similarity12from sklearn.model_selection import train_test_split13from keras.applications.mobilenet import MobileNet14from keras.models import Model, load_model as K_load_model15from keras.layers import LSTM, Dense, InputLayer16from c3d_model import c3d_model17class DataHandler:18	'''19	Handles all operations with respect to data20	'''21	def __init__(self, videos_path, test_size = 0.05):22		'''23		Initalizes the class variables for data handling24		'''25		self.n_frames = 1626		self.operating_resolution = (224, 224)27		self.test_split = test_size28		self.videos_path = videos_path29		self.image_feature_extractor = self.get_mobilenet_feature_extractor()30	31	def get_mobilenet_feature_extractor(self):32		'''33		Returns the mobilenet feature extractor34		'''35		mobilenet = MobileNet()36		return Model(inputs=mobilenet.inputs, output=mobilenet.get_layer("global_average_pooling2d_1").output)37	def sample_frames(self, video_path):38		'''39		Gets 'n' number of frames, each of resolution 'w' x 'h' and 3 channels (RGB) from a video40		Uses equidistant sampling of frames41		'''42		cap = cv2.VideoCapture(video_path)43		read_count = 144		frames_list = list()45		frame_total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))46		while cap.isOpened():47			isRead, frame = cap.read()48			if not isRead: break49			if read_count % (int(frame_total/self.n_frames) -1) == 0:50				frame = cv2.resize(frame, self.operating_resolution)51				frames_list.append(frame)52			read_count += 153			if len(frames_list) == self.n_frames: break54		return np.array(frames_list[:self.n_frames])55	def get_frame_features(self, frames):56		'''57		Returns features for each frame58		'''59		return np.squeeze(self.image_feature_extractor.predict(frames))60	def extract_video_features(self, video_file):61		'''62		Returns array of fram features for a video63		'''64		frames = self.sample_frames(video_file)65		return self.get_frame_features(frames)66	def prepare_training_data(self, videos_path):67		'''68		Returns data and labels for all videos in a directory69		'''70		folders = sorted(os.listdir(videos_path))71		classes = dict([(folder, idx) for idx, folder in enumerate(folders)])72		n_classes = len(classes)73		frame_features = list()74		labels = list()75		videos_list = list()76		for folder in folders:77			folder_path = os.path.join(self.videos_path, folder)78			video_files = sorted(glob(os.path.join(folder_path, "*")))79			for video_file in video_files:80				frame_features.append(self.extract_video_features(video_file))81				labels.append(classes[folder])82				videos_list.append(video_file)83		return np.array(frame_features), np.array(labels), np.array(videos_list), classes84	def get_training_data(self, save_data_as = None, data_pickle = None):85		'''86		Prepares the preprocessed training data and labels87		'''88		if data_pickle == None:89			if save_data_as == None: save_data_as = "data.pkl"90			if ".pkl" not in save_data_as: save_data_as += ".pkl"91			X, y, video_list, classes = self.prepare_training_data(video_path)92			X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = self.test_split, random_state=42)93			pkl.dump({"X_train": X_train, "y_train": y_train, "X_test": X_test, "y_test": y_test, "classes": classes, "videos": video_list}, open(save_data_as, "wb"))94		else:95			data_dict = pkl.load(open(data_pickle, "rb"))96			X_train, y_train, X_test, y_test, video_list, classes = data_dict["X_train"], data_dict["y_train"], data_dict["X_test"], data_dict["y_test"], data_dict["videos"], data_dict["classes"]97		return X_train, X_test, y_train, y_test, videos, classes98class Trainer(DataHandler):99	'''100	Handles all the training operations101	'''102	def __init__(self, data_to_use = None, operating_resolution = (224, 224)):103		'''104		Initializes the training class variables105		'''106		DataHandler.__init(self, operating_resolution)107		self.operating_resolution = operating_resolution108		self.training_version = str(datetime.datetime.now())[:16].replace("-", "_").replace(" ", "_")109		os.mkdir(self.training_version)110		save_data_as = None111		if data_to_use == None:112			save_data_as = os.path.split(model_path)[0] + "data.pkl"113		self.X_train, self.X_test, self.y_train, self.y_test, self.videos, self.classes = self.get_training_data(save_data_as = save_data_as, data_pickle = data_to_use)114		self.n_classes = len(self.classes)115		# training params116		self.epochs = 50117		self.batch_size = 32118	def train(self, pretrained_model = None, model_path = None):119		'''120		Runs the training121		'''122		if pretrained_model != None: self.c3d_model = load_model(pretrained_model)123		else: self.c3d_model = c3d_model(resolution = self.operating_resolution, n_frames = 16, channels = 3, nb_classes = 3)124		if model_path == None: model_path = "C3D_E{epoch:02d}_VA{val_accuracy:.2f}.hdf5"125		model_path = os.path.join(self.training_version, model_path)126		callbacks = [ModelCheckpoint(model_path, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')]127		128		model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')...

train_conv3d.py

Source:train_conv3d.py

1# imports2import warnings3warnings.filterwarnings('ignore')4import os, cv2, shutil, json5import numpy as np, pandas as pd, pickle as pkl6from glob import glob7from time import time8from datetime import datetime9from tqdm import tqdm10from sklearn.metrics import precision_recall_curve, average_precision_score, accuracy_score11from sklearn.metrics.pairwise import cosine_similarity12from sklearn.model_selection import train_test_split13from keras.applications.mobilenet import MobileNet14from keras.models import Model, load_model as K_load_model15from keras.layers import LSTM, Dense, InputLayer16from keras.callbacks.callbacks import ModelCheckpoint17from c3d_model import c3d_model18class DataHandler:19	'''20	Handles all operations with respect to data21	'''22	def __init__(self, videos_path = "/mnt/E2F262F2F262C9FD/PROJECTS/media_retrieval/Datasets/KTH/train/", test_size = 0.05):23		'''24		Initalizes the class variables for data handling25		'''26		self.n_frames = 1627		self.operating_resolution = (224, 224)28		self.test_split = test_size29		self.videos_path = videos_path30	def sample_frames(self, video_path):31		'''32		Gets 'n' number of frames, each of resolution 'w' x 'h' and 3 channels (RGB) from a video33		Uses equidistant sampling of frames34		'''35		cap = cv2.VideoCapture(video_path)36		read_count = 137		frames_list = list()38		frame_total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))39		while cap.isOpened():40			isRead, frame = cap.read()41			if not isRead: break42			if read_count % (int(frame_total/self.n_frames) -1) == 0:43				frame = cv2.resize(frame, self.operating_resolution)44				frames_list.append(frame)45			read_count += 146			if len(frames_list) == self.n_frames: break47		return np.array(frames_list[:self.n_frames])48	def extract_video_features(self, video_file):49		'''50		Returns array of fram features for a video51		'''52		return self.sample_frames(video_file)53	def prepare_training_data(self, videos_path):54		'''55		Returns data and labels for all videos in a directory56		'''57		folders = sorted(os.listdir(videos_path))58		classes = dict([(folder, idx) for idx, folder in enumerate(folders)])59		n_classes = len(classes)60		frame_features = list()61		labels = list()62		videos_list = list()63		for folder in folders:64			folder_path = os.path.join(self.videos_path, folder)65			video_files = sorted(glob(os.path.join(folder_path, "*")))66			for video_file in video_files:67				frame_features.append(self.extract_video_features(video_file))68				labels.append(classes[folder])69				videos_list.append(video_file)70		return np.array(frame_features), np.array(labels), np.array(videos_list), classes71	def get_training_data(self, save_data_as = None, data_pickle = None):72		'''73		Prepares the preprocessed training data and labels74		'''75		if data_pickle == None:76			if save_data_as == None: save_data_as = "data.pkl"77			if ".pkl" not in save_data_as: save_data_as += ".pkl"78			X, y, video_list, classes = self.prepare_training_data(self.videos_path)79			X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = self.test_split, random_state=42)80			pkl.dump({"X_train": X_train, "y_train": y_train, "X_test": X_test, "y_test": y_test, "classes": classes, "videos": video_list}, open(save_data_as, "wb"))81		else:82			data_dict = pkl.load(open(data_pickle, "rb"))83			X_train, y_train, X_test, y_test, video_list, classes = data_dict["X_train"], data_dict["y_train"], data_dict["X_test"], data_dict["y_test"], data_dict["videos"], data_dict["classes"]84		return X_train, X_test, y_train, y_test, video_list, classes85class Trainer(DataHandler):86	'''87	Handles all the training operations88	'''89	def __init__(self, data_to_use = "/mnt/E2F262F2F262C9FD/PROJECTS/media_retrieval/training/Conv3D/2020_04_24_19:06/data.pkl", operating_resolution = (224, 224)):90		'''91		Initializes the training class variables92		'''93		DataHandler.__init__(self)94		self.operating_resolution = operating_resolution95		self.training_version = str(datetime.now())[:16].replace("-", "_").replace(" ", "_")96		os.mkdir(self.training_version)97		save_data_as = None98		if data_to_use == None:99			save_data_as = os.path.join(self.training_version,  "data.pkl")100		self.X_train, self.X_test, self.y_train, self.y_test, self.videos, self.classes = self.get_training_data(save_data_as = save_data_as, data_pickle = data_to_use)101		self.n_classes = len(self.classes)102		# training params103		self.epochs = 50104		self.batch_size = 32105	def train(self, pretrained_model = None, model_path = None):106		'''107		Runs the training108		'''109		if pretrained_model != None: self.c3d_model = load_model(pretrained_model)110		else: self.c3d_model = c3d_model(resolution = self.operating_resolution, n_frames = 16, channels = 3, nb_classes = 3)111		if model_path == None: model_path = "C3D_E{epoch:02d}_VA{val_accuracy:.2f}.hdf5"112		model_path = os.path.join(self.training_version, model_path)113		callbacks = [ModelCheckpoint(model_path, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')]114		115		self.c3d_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')116		self.c3d_model.fit(self.X_train, self.y_train, epochs=self.epochs, batch_size=self.batch_size, validation_split=validation_split, shuffle=True, verbose=2, callbacks = callbacks)117if __name__ == '__main__':118	tr = Trainer()...

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