From 610214decad3cb1de057e765f9beb6dcd2fc7c19 Mon Sep 17 00:00:00 2001 From: Rauf Date: Tue, 30 Jul 2019 11:11:45 +0000 Subject: [PATCH] first commit --- .gitignore | 3 + data_loader.py | 75 ++++++++++++++ model.py | 235 +++++++++++++++++++++++++++++++++++++++++++ plots/model.png | Bin 0 -> 10210 bytes plots/train_acc.png | Bin 0 -> 66082 bytes plots/train_loss.png | Bin 0 -> 27002 bytes plots/val_acc.png | Bin 0 -> 67768 bytes plots/val_loss.png | Bin 0 -> 28830 bytes test_net.py | 51 ++++++++++ 9 files changed, 364 insertions(+) create mode 100644 data_loader.py create mode 100644 model.py create mode 100644 plots/model.png create mode 100644 plots/train_acc.png create mode 100644 plots/train_loss.png create mode 100644 plots/val_acc.png create mode 100644 plots/val_loss.png create mode 100644 test_net.py diff --git a/.gitignore b/.gitignore index 894a44c..5bfaca2 100644 --- a/.gitignore +++ b/.gitignore @@ -102,3 +102,6 @@ venv.bak/ # mypy .mypy_cache/ + + +*DS_Store* diff --git a/data_loader.py b/data_loader.py new file mode 100644 index 0000000..c0eed74 --- /dev/null +++ b/data_loader.py @@ -0,0 +1,75 @@ +import os +import cv2 +import numpy as np + + +class SiameseImageLoader: + """ + Image loader for Siamese network + """ + + def __init__(self, dataset_path, number_of_classes=2, input_shape=None, augmentations=None, data_subsets=['train', 'val']): + self.dataset_path = dataset_path + self.data_subsets = data_subsets + self.images_paths = {} + self.images_labels = {} + self.input_shape = input_shape + self.number_of_classes = number_of_classes + self.augmentations = augmentations + self.current_idx = {d: 0 for d in data_subsets} + self._load_images_paths() + self.n_samples = {d: len(self.images_paths[d]) for d in data_subsets} + self.indexes = {d: np.arange(self.n_samples[d]) for d in data_subsets} + for d in self.data_subsets: + np.random.shuffle(self.indexes[d]) + + def _load_images_paths(self): + for d in self.data_subsets: + self.images_paths[d] = [] + self.images_labels[d] = [] + for root, dirs, files in os.walk(self.dataset_path+d): + for f in files: + if f.endswith('.jpg') or f.endswith('.png'): + self.images_paths[d].append(root+'/'+f) + self.images_labels[d].append(root.split('/')[-1]) + + def get_batch(self, batch_size, s="train"): + if self.current_idx[s] + 2*batch_size >= self.n_samples[s]: + np.random.shuffle(self.indexes[s]) + self.current_idx[s] = 0 + + pairs_1 = [] + pairs_2 = [] + + targets = np.zeros((batch_size,)) + for i in range(batch_size): + indx_1 = self.indexes[s][self.current_idx[s] + 2 * i] + indx_2 = self.indexes[s][self.current_idx[s] + 2 * i + 1] + img_1 = cv2.imread( + self.images_paths[s][indx_1]) + img_2 = cv2.imread( + self.images_paths[s][indx_2]) + if self.input_shape: + img_1 = cv2.resize( + img_1, (self.input_shape[0], self.input_shape[1])) + img_2 = cv2.resize( + img_2, (self.input_shape[0], self.input_shape[1])) + if s == 'train' and self.augmentations: + img_1 = self.augmentations(image=img_1)['image'] + img_2 = self.augmentations(image=img_2)['image'] + + pairs_1.append(img_1) + pairs_2.append(img_2) + + if self.images_labels[s][indx_1] == self.images_labels[s][indx_2]: + targets[i] = 1 + else: + targets[i] = 0 + self.current_idx[s] += 2*batch_size + pairs = [np.array(pairs_1), np.array(pairs_2)] + return pairs, targets + + def generate(self, batch_size, s="train"): + while True: + pairs, targets = self.get_batch(batch_size, s) + yield (pairs, targets) diff --git a/model.py b/model.py new file mode 100644 index 0000000..b1612ee --- /dev/null +++ b/model.py @@ -0,0 +1,235 @@ +import os +import numpy as np +import keras.backend as K +from keras.models import Model +from keras import optimizers +from keras.regularizers import l2 +from keras.utils import plot_model +from keras.layers import Dense, Input, Lambda, Dropout, Flatten +from keras.layers import Conv2D, MaxPool2D +from classification_models import Classifiers + + +class SiameseNet: + """ + SiameseNet for image classification + mode = 'l1' -> l1_loss + mode = 'l2' -> l2_loss + + """ + + def __init__(self, input_shape, image_loader, mode='l1', backbone='resnet50', optimizer=optimizers.Adam(lr=1e-4)): + self.input_shape = input_shape + self.backbone = backbone + self.mode = mode + self.optimizer = optimizer + self.model = [] + self._create_model() + self.data_loader = image_loader + self.base_model = [] + + def _create_model(self): + + input_image_1 = Input(self.input_shape) + input_image_2 = Input(self.input_shape) + + if self.backbone == 'simple': + input_image = Input(self.input_shape) + x = Conv2D(64, (10, 10), activation='relu', + kernel_regularizer=l2(2e-4))(input_image) + x = MaxPool2D()(x) + x = Conv2D(128, (7, 7), activation='relu', + kernel_regularizer=l2(2e-4))(x) + x = MaxPool2D()(x) + x = Conv2D(128, (4, 4), activation='relu', + kernel_regularizer=l2(2e-4))(x) + x = MaxPool2D()(x) + x = Conv2D(256, (4, 4), activation='relu', + kernel_regularizer=l2(2e-4))(x) + x = Flatten()(x) + encoded_output = Dense(2048, activation='sigmoid', + kernel_regularizer=l2(1e-3))(x) + self.base_model = Model( + inputs=[input_image], outputs=[encoded_output]) + else: + classifier, preprocess_input = Classifiers.get(self.backbone) + backbone_model = classifier( + input_shape=self.input_shape, weights='imagenet', include_top=False) + + for layer in backbone_model.layers: + layer.trainable = False + + after_backbone = backbone_model.output + x = Flatten()(after_backbone) + x = Dense(512, activation="relu")(x) + x = Dropout(0.1)(x) + x = Dense(256, activation="relu")(x) + x = Dropout(0.1)(x) + encoded_output = Dense(128, activation="relu")(x) + + self.base_model = Model( + inputs=[backbone_model.input], outputs=[encoded_output]) + + image_encoding_1 = self.base_model(input_image_1) + image_encoding_2 = self.base_model(input_image_2) + + if self.mode == 'l1': + L1_layer = Lambda( + lambda tensors: K.abs(tensors[0] - tensors[1])) + distance = L1_layer([image_encoding_1, image_encoding_2]) + + prediction = Dense(units=1, activation='sigmoid')(distance) + metric = 'binary_accuracy' + elif self.mode == 'l2': + + L2_layer = Lambda( + lambda tensors: K.sqrt(K.maximum(K.sum(K.square(tensors[0] - tensors[1]), axis=1, keepdims=True), K.epsilon()))) + distance = L2_layer([image_encoding_1, image_encoding_2]) + + prediction = distance + metric = self.accuracy + self.model = Model( + inputs=[input_image_1, input_image_2], outputs=prediction) + + plot_model(self.model, to_file='plots/model.png') + print('BASE MODEL SUMMARY') + self.base_model.summary() + + print('WHOLE MODEL SUMMARY') + self.model.summary() + self.model.compile(loss=self.contrastive_loss, metrics=[metric], + optimizer=self.optimizer) + + def _write_logs_to_tensorboard(self, current_iteration, train_losses, + train_accuracies, validation_accuracy, + evaluate_each): + """ Writes the logs to a tensorflow log file + This allows us to see the loss curves and the metrics in tensorboard. + If we wrote every iteration, the training process would be slow, so + instead we write the logs every evaluate_each iteration. + Arguments: + current_iteration: iteration to be written in the log file + train_losses: contains the train losses from the last evaluate_each + iterations. + train_accuracies: the same as train_losses but with the accuracies + in the training set. + validation_accuracy: accuracy in the current one-shot task in the + validation set + evaluate each: number of iterations defined to evaluate the one-shot + tasks. + """ + + summary = tf.Summary() + + # Write to log file the values from the last evaluate_every iterations + for index in range(0, evaluate_each): + value = summary.value.add() + value.simple_value = train_losses[index] + value.tag = 'Train Loss' + + value = summary.value.add() + value.simple_value = train_accuracies[index] + value.tag = 'Train Accuracy' + + if index == (evaluate_each - 1): + value = summary.value.add() + value.simple_value = validation_accuracy + value.tag = 'One-Shot Validation Accuracy' + + self.summary_writer.add_summary( + summary, current_iteration - evaluate_each + index + 1) + self.summary_writer.flush() + + def contrastive_loss(self, y_true, y_pred): + '''Contrastive loss from Hadsell-et-al.'06 + http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf + ''' + margin = 1 + sqaure_pred = K.square(y_pred) + margin_square = K.square(K.maximum(margin - y_pred, 0)) + return K.mean(y_true * sqaure_pred + (1 - y_true) * margin_square) + + def accuracy(self, y_true, y_pred): + '''Compute classification accuracy with a fixed threshold on distances. + ''' + return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype))) + + def train_on_batch(self, batch_size=8, s="train"): + generator = self.data_loader.generate(batch_size, s=s) + pairs, targets = next(generator) + train_loss, train_accuracy = self.model.train_on_batch( + pairs, targets) + return train_loss, train_accuracy + + def validate_on_batch(self, batch_size=8, s="val"): + generator = self.data_loader.generate(batch_size, s=s) + pairs, targets = next(generator) + val_loss, val_accuracy = self.model.test_on_batch( + pairs, targets) + return val_loss, val_accuracy + + def train(self, steps_per_epoch, epochs, with_val=True, batch_size=8, verbose=1): + generator_train = self.data_loader.generate(batch_size, 'train') + train_accuracies_epochs = [] + train_losses_epochs = [] + val_accuracies_epochs = [] + val_losses_epochs = [] + for j in range(epochs): + train_accuracies_it = [] + train_losses_it = [] + for i in range(steps_per_epoch): + pairs, targets = next(generator_train) + train_loss_it, train_accuracy_it = self.model.train_on_batch( + pairs, targets) + train_accuracies_it.append(train_accuracy_it) + train_losses_it.append(train_loss_it) + train_loss_epoch = sum(train_losses_it) / len(train_losses_it) + train_accuracy_epoch = sum( + train_accuracies_it) / len(train_accuracies_it) + train_accuracies_epochs.append(train_accuracy_epoch) + train_losses_epochs.append(train_losses_epochs) + + if with_val: + val_loss, val_accuracy = self.validate() + val_accuracies_epochs.append(val_accuracy) + val_losses_epochs.append(val_loss) + if verbose: + print('[Epoch {}] train_loss: {} , train_acc: {}, val_loss: {} , val_acc: {}'.fromat( + j, train_loss_epoch, train_accuracy_epoch, val_loss, val_accuracy)) + else: + if verbose: + print('[Epoch {}] train_loss: {} , train_acc: {}'.fromat( + j, train_loss_epoch, train_accuracy_epoch)) + if with_val: + return train_losses_epochs, train_accuracies_epochs, val_losses_epochs, val_accuracies_epochs + else: + return train_losses_epochs, train_accuracies_epochs + + def validate(self, number_of_comparisons=100, batch_size=8, s="val"): + generator = self.data_loader.generate(batch_size, s=s) + val_accuracies_it = [] + val_losses_it = [] + for _ in range(number_of_comparisons): + pairs, targets = next(generator) + val_loss_it, val_accuracy_it = self.model.test_on_batch( + pairs, targets) + val_accuracies_it.append(val_accuracy_it) + val_losses_it.append(val_loss_it) + val_loss_epoch = sum(val_losses_it) / len(val_losses_it) + val_accuracy_epoch = sum( + val_accuracies_it) / len(val_accuracies_it) + return val_loss_epoch, val_accuracy_epoch + + def generate_encodings(self): + + paths = self.data_loader.images_paths['train'] + labels = self.data_loader.images_labels['train'] + + data = {} + for path in paths: + info = {} + img = cv2.imread(path) + info['encoding'] = self.base_model.predict(img) + + def predict(self, batch_size=8): + pass diff --git a/plots/model.png b/plots/model.png new file mode 100644 index 0000000000000000000000000000000000000000..cba41cb7677e5e31df80f2b04778b3069f0f9376 GIT binary patch literal 10210 zcmb_?cRba9`~RVg5TT^39IGKKJC4e+k{v>1?~#$ciGw7mWJE^UWRGL-k(Ir-2%)mq z?>hJAkMDhd@9+0<-}mG9)lklPz0Yf0*YkQlpVuoySyARJ$z>7*0&!MO7Ojdv;B~^k zPlyTO?8-s!k5n?_uajo5H4)zqU3 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z0nfiTl$xE5|4Anntd&-P2ksjf=mKc)*gILBz~jeE5ewLN3!R1ccM4u6jh;9F2N}`Y zDhXlaFW^$6ssI20 literal 0 HcmV?d00001 diff --git a/test_net.py b/test_net.py new file mode 100644 index 0000000..a86c7de --- /dev/null +++ b/test_net.py @@ -0,0 +1,51 @@ +from model import SiameseNet +from data_loader import SiameseImageLoader +import matplotlib.pyplot as plt +from keras import optimizers +import albumentations as A + + +def plot_grapth(values, y_label, title): + t = list(range(len(values))) + fig, ax = plt.subplots() + ax.plot(t, values) + + ax.set(xlabel='iteration', ylabel='{}'.format(y_label), + title='{}'.format(title)) + ax.grid() + + fig.savefig("plots/{}.png".format(y_label)) + + +n_epochs = 100 +n_iterations = 500 + +augmentations = A.Compose([ + A.RandomBrightnessContrast(p=0.4), + A.RandomGamma(p=0.4), + A.HueSaturationValue(hue_shift_limit=20, + sat_shift_limit=50, val_shift_limit=50, p=0.4), + A.CLAHE(p=0.4), + A.HorizontalFlip(p=0.5), + A.VerticalFlip(p=0.5), + A.Blur(blur_limit=11, p=0.3), + A.GaussNoise(var_limit=(100, 150), p=0.3), + A.CenterCrop(p=1, height=256, width=256) +], p=1) + +loader = SiameseImageLoader( + '/home/rauf/plates_competition/dataset/to_train/', input_shape=(256, 256, 3), augmentations=augmentations) + + +optimizer = optimizers.Adam(lr=1e-5) +# optimizer = optimizers.RMSprop(lr=1e-5) +model = SiameseNet(input_shape=(256, 256, 3), backbone='resnet18', mode='l2', + image_loader=loader, optimizer=optimizer) + +train_losses, train_accuracies, val_losses, val_accuracies = model.train( + steps_per_epoch=20, epochs=20) + +plot_grapth(train_losses, 'train_loss', 'Losses on train') +plot_grapth(train_accuracies, 'train_acc', 'Accuracies on train') +plot_grapth(val_losses, 'val_loss', 'Losses on val') +plot_grapth(val_accuracies, 'val_acc', 'Accuracies on val')