def basic_model_properties(self, cf, variable_input_size): # Define the input size, loss and metrics if cf.dataset.class_mode == 'categorical': if K.image_dim_ordering() == 'th': in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = 'categorical_crossentropy' metrics = ['accuracy'] elif cf.dataset.class_mode == 'detection': # Check model, different detection nets may have different losses and metrics if cf.model_name in ['yolo', 'tiny-yolo']: in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) loss = YOLOLoss(in_shape, cf.dataset.n_classes, cf.dataset.priors) metrics = [YOLOMetrics(in_shape, cf.dataset.n_classes, cf.dataset.priors)] elif cf.model_name == 'ssd300': in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = MultiboxLoss(cf.dataset.n_classes, neg_pos_ratio=2.0).compute_loss metrics = None # TODO: Add metrics for SSD # priors = pickle.load(open('prior_boxes_ssd300.pkl', 'rb')) # metrics = [SSDMetrics(priors, cf.dataset.n_classes)] else: raise NotImplementedError elif cf.dataset.class_mode == 'segmentation': if K.image_dim_ordering() == 'th': if variable_input_size: in_shape = (cf.dataset.n_channels, None, None) else: in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: if variable_input_size: in_shape = (None, None, cf.dataset.n_channels) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights) metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)] else: raise ValueError('Unknown problem type') return in_shape, loss, metrics
def basic_model_properties(self, cf, variable_input_size): # Define the input size, loss and metrics if cf.dataset.class_mode == 'categorical': if K.image_dim_ordering() == 'th': in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = 'categorical_crossentropy' metrics = ['accuracy'] elif cf.dataset.class_mode == 'detection': if cf.model_name in ['yolo', 'tiny-yolo']: in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) loss = YOLOLoss(in_shape, cf.dataset.n_classes, cf.dataset.priors) metrics = [ YOLOMetrics(in_shape, cf.dataset.n_classes, cf.dataset.priors) ] elif cf.model_name in [ 'ssd300', 'ssd300_pretrained', 'ssd_resnet50' ]: # TODO: in_shape ok for ssd? in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) # TODO: extract config parameters from MultiboxLoss mboxloss = MultiboxLoss(cf.dataset.n_classes + 1, alpha=1.0, neg_pos_ratio=2.0, background_label_id=0, negatives_for_hard=100.0) loss = mboxloss.compute_loss metrics = [] # TODO: add mAP metric elif cf.dataset.class_mode == 'segmentation': if K.image_dim_ordering() == 'th': if variable_input_size: in_shape = (cf.dataset.n_channels, None, None) else: in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: if variable_input_size: in_shape = (None, None, cf.dataset.n_channels) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights) metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)] else: raise ValueError('Unknown problem type') return in_shape, loss, metrics
def basic_model_properties(self, cf, variable_input_size): # Define the input size, loss and metrics if cf.dataset.class_mode == 'categorical': if K.image_dim_ordering() == 'th': in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = 'categorical_crossentropy' metrics = ['accuracy'] elif cf.dataset.class_mode == 'detection': if cf.model_name in ['yolo', 'tiny-yolo']: in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) loss = YOLOLoss(in_shape, cf.dataset.n_classes, cf.dataset.priors) metrics = [ YOLOMetrics(in_shape, cf.dataset.n_classes, cf.dataset.priors) ] elif cf.model_name == 'ssd': if K.image_dim_ordering() == 'th': in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = SSDLoss(in_shape, cf.dataset.n_classes + 1, cf.dataset.priors) #+1 to include background #metrics = [SSDMetrics(in_shape, cf.dataset.n_classes, cf.dataset.priors)] metrics = [] else: raise ValueError('Unknown model') elif cf.dataset.class_mode == 'segmentation': if K.image_dim_ordering() == 'th': if variable_input_size: in_shape = (cf.dataset.n_channels, None, None) else: in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: if variable_input_size: in_shape = (None, None, cf.dataset.n_channels) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights) metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)] else: raise ValueError('Unknown problem type') return in_shape, loss, metrics
def basic_model_properties(self, cf, variable_input_size): # Define the input size, loss and metrics if cf.dataset.class_mode == 'categorical': if K.image_dim_ordering() == 'th': in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = 'categorical_crossentropy' metrics = ['accuracy'] elif cf.dataset.class_mode == 'detection': if "yolo" in cf.model_name: in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) # TODO detection : check model, different detection nets may have different losses and metrics loss = YOLOLoss(in_shape, cf.dataset.n_classes, cf.dataset.priors) metrics = [YOLOMetrics(in_shape, cf.dataset.n_classes, cf.dataset.priors)] elif "ssd" in cf.model_name: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels,) loss = SSDLoss(cf.dataset.n_classes) metrics = [SSDMetrics()] elif cf.dataset.class_mode == 'segmentation': if K.image_dim_ordering() == 'th': if variable_input_size: in_shape = (cf.dataset.n_channels, None, None) else: in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: if variable_input_size: in_shape = (None, None, cf.dataset.n_channels) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights) metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)] else: raise ValueError('Unknown problem type') return in_shape, loss, metrics
def basic_model_properties(self, cf, variable_input_size): # Define the input size, loss and metrics if cf.dataset.class_mode == 'categorical': if K.image_dim_ordering() == 'th': in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = 'categorical_crossentropy' metrics = ['accuracy'] elif cf.dataset.class_mode == 'detection': if cf.model_name == 'ssd': in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels,) loss = MultiboxLoss(cf.dataset.n_classes, neg_pos_ratio=2.0).compute_loss metrics = None else: # YOLO in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) loss = YOLOLoss(in_shape, cf.dataset.n_classes, cf.dataset.priors) metrics = [YOLOMetrics(in_shape, cf.dataset.n_classes, cf.dataset.priors)] elif cf.dataset.class_mode == 'segmentation': if K.image_dim_ordering() == 'th': if variable_input_size: in_shape = (cf.dataset.n_channels, None, None) else: in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: if variable_input_size: in_shape = (None, None, cf.dataset.n_channels) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights) metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)] else: raise ValueError('Unknown problem type') return in_shape, loss, metrics
def make_segmentor(self): segmentor = build_segnet(self.img_shape, self.n_classes, l2_reg=0., init='glorot_uniform', path_weights=None, freeze_layers_from=None, use_unpool=False, basic=False) lr = 1e-04 optimizer = RMSprop(lr=lr, rho=0.9, epsilon=1e-8, clipnorm=10) print( ' Optimizer segmentor: rmsprop. Lr: {}. Rho: 0.9, epsilon=1e-8, ' 'clipnorm=10'.format(lr)) the_loss = cce_flatt(self.cf.dataset.void_class, self.cf.dataset.cb_weights) metrics = [IoU(self.cf.dataset.n_classes, self.cf.dataset.void_class)] segmentor.compile(loss=the_loss, metrics=metrics, optimizer=optimizer) return segmentor
def train(dataset, model_name, learning_rate, weight_decay, num_epochs, max_patience, batch_size, optimizer, savepath, train_path, valid_path, test_path, crop_size=(224, 224), in_shape=(3, None, None), n_classes=5, gtSet=None, void_class=[4], w_balance=None, weights_file=False, show_model=False, plot_hist=True, train_model=True): # Remove void classes from number of classes n_classes = n_classes - len(void_class) # Mask folder (For different polyp groundtruths) if gtSet is not None: mask_floder = 'masks' + str(gtSet) else: mask_floder = 'masks' # TODO: Get the number of images directly from data loader n_images_train = 30 # 547 n_images_val = 20 # 183 n_images_test = 20 # 182 # Normalization mean and std computed on training set for RGB pixel values print ('\n > Computing mean and std for normalization...') if False: rgb_mean, rgb_std = compute_mean_std(os.path.join(train_path, 'images'), os.path.join(train_path, mask_floder), n_classes) rescale = None else: rgb_mean = None rgb_std = None rescale = 1/255. print ('Mean: ' + str(rgb_mean)) print ('Std: ' + str(rgb_std)) # Compute class balance weights if w_balance is not None: class_balance_weights = compute_class_balance(masks_path=train_path + mask_floder, n_classes=n_classes, method=w_balance, void_labels=void_class ) print ('Class balance weights: ' + str(class_balance_weights)) else: class_balance_weights = None # Build model print ('\n > Building model (' + model_name + ')...') if model_name == 'fcn8': model = build_fcn8(in_shape, l2_reg=weight_decay, nclasses=n_classes, weights_file=weights_file, deconv='deconv') model.output else: raise ValueError('Unknown model') # Create the optimizer print ('\n > Creating optimizer ({}) with lr ({})...'.format(optimizer, learning_rate)) if optimizer == 'rmsprop': opt = RMSprop(lr=learning_rate, rho=0.9, epsilon=1e-8, clipnorm=10) else: raise ValueError('Unknown optimizer') # Compile model print ('\n > Compiling model...') model.compile(loss=cce_flatt(void_class, class_balance_weights), optimizer=opt) # Show model structure if show_model: model.summary() plot(model, to_file=savepath+'model.png') # Create the data generators print ('\n > Reading training set...') dg_tr = ImageDataGenerator(crop_size=crop_size, # Crop the image to a fixed size featurewise_center=False, # Substract mean - dataset samplewise_center=False, # Substract mean - sample featurewise_std_normalization=False, # Divide std - dataset samplewise_std_normalization=False, # Divide std - sample rgb_mean=rgb_mean, rgb_std=rgb_std, gcn=False, # Global contrast normalization zca_whitening=False, # Apply ZCA whitening rotation_range=180, # Rnd rotation degrees 0-180 width_shift_range=0.0, # Rnd horizontal shift height_shift_range=0.0, # Rnd vertical shift shear_range=0.5, # 0.5, # Shear in radians zoom_range=0.1, # Zoom channel_shift_range=0., # Channel shifts fill_mode='constant', # Fill mode cval=0., # Void image value void_label=void_class[0], # Void class value horizontal_flip=True, # Rnd horizontal flip vertical_flip=True, # Rnd vertical flip rescale=rescale, # Rescaling factor spline_warp=False, # Enable elastic deformation warp_sigma=10, # Elastic deformation sigma warp_grid_size=3 # Elastic deformation gridSize ) train_gen = dg_tr.flow_from_directory(train_path + 'images', batch_size=batch_size, gt_directory=train_path + mask_floder, target_size=crop_size, class_mode='seg_map', classes=n_classes, # save_to_dir=savepath, # Save DA save_prefix='data_augmentation', save_format='png') print ('\n > Reading validation set...') dg_va = ImageDataGenerator(rgb_mean=rgb_mean, rgb_std=rgb_std, rescale=rescale) valid_gen = dg_va.flow_from_directory(valid_path + 'images', batch_size=1, gt_directory=valid_path + mask_floder, target_size=None, class_mode='seg_map', classes=n_classes) print ('\n > Reading testing set...') dg_ts = ImageDataGenerator(rgb_mean=rgb_mean, rgb_std=rgb_std, rescale=rescale) test_gen = dg_ts.flow_from_directory(test_path + 'images', batch_size=1, gt_directory=test_path + mask_floder, target_size=None, class_mode='seg_map', classes=n_classes, shuffle=False) # Define the jaccard validation callback eval_model = Evaluate_model(n_classes=n_classes, void_label=void_class[0], save_path=savepath, valid_gen=valid_gen, valid_epoch_length=n_images_val, valid_metrics=['val_loss', 'val_jaccard', 'val_acc', 'val_jaccard_perclass']) # Define early stopping callbacks early_stop_jac = EarlyStopping(monitor='val_jaccard', mode='max', patience=max_patience, verbose=0) early_stop_jac_class = [] for i in range(n_classes): early_stop_jac_class += [EarlyStopping(monitor=str(i)+'_val_jacc_percl', mode='max', patience=max_patience, verbose=0)] # Define model saving callbacks checkp_jac = ModelCheckpoint(filepath=savepath+"weights.hdf5", verbose=0, monitor='val_jaccard', mode='max', save_best_only=True, save_weights_only=True) checkp_jac_class = [] for i in range(n_classes): checkp_jac_class += [ModelCheckpoint(filepath=savepath+"weights"+str(i)+".hdf5", verbose=0, monitor=str(i)+'_val_jacc_percl', mode='max', save_best_only=True, save_weights_only=True)] # Train the model if (train_model): print('\n > Training the model...') cb = [eval_model, early_stop_jac, checkp_jac] + checkp_jac_class hist = model.fit_generator(train_gen, samples_per_epoch=n_images_train, nb_epoch=num_epochs, callbacks=cb) # Compute test metrics print('\n > Testing the model...') model.load_weights(savepath + "weights.hdf5") color_map = [ (255/255., 0, 0), # Background (192/255., 192/255., 128/255.), # Polyp (128/255., 64/255., 128/255.), # Lumen (0, 0, 255/255.), # Specularity (0, 255/255., 0), # (192/255., 128/255., 128/255.), # (64/255., 64/255., 128/255.), # ] test_metrics = compute_metrics(model, test_gen, n_images_test, n_classes, metrics=['test_loss', 'test_jaccard', 'test_acc', 'test_jaccard_perclass'], color_map=color_map, tag="test", void_label=void_class[0], out_images_folder=savepath, epoch=0, save_all_images=True, useCRF=False) for k in sorted(test_metrics.keys()): print('{}: {}'.format(k, test_metrics[k])) if (train_model): # Save the results print ("\n > Saving history...") with open(savepath + "history.pickle", 'w') as f: pickle.dump([hist.history, test_metrics], f) # Load the results print ("\n > Loading history...") with open(savepath + "history.pickle") as f: history, test_metrics = pickle.load(f) # print (str(test_metrics)) # Show the trained model history if plot_hist: print('\n > Show the trained model history...') plot_history(history, savepath, n_classes)
x_train -= x_train_mean x_test -= x_train_mean x_train /= (x_train_std + 1e-7) x_test /= (x_train_std + 1e-7) # plot data if(not args.nodisplay): for idx in range(25): plt.subplot(5,10,2*idx+1) plt.imshow(x_train[idx,:,:,0]) plt.subplot(5,10,2*idx+2) plt.imshow(y_train[idx,:,:,0]) plt.show() if(not args.nomodel): loss = cce_flatt(void_class, None) metrics = [IoU(n_classes, void_class)] #opt = RMSprop(lr=0.001, clipnorm=10) opt = Nadam(lr=0.002) model = build_fcn8(in_shape, n_classes, 0.) model.compile(loss=loss, metrics=metrics, optimizer=opt) cb = [EarlyStopping(monitor='val_loss', min_delta = 0.0001, patience=2)] model.fit(x_train, y_train, epochs=1000, batch_size=16, callbacks=cb, validation_data=(x_valid,y_valid)) score = model.evaluate(x_test, y_test) #, batch_size=128) y_pred = model.predict(x_test) print(score)
def basic_model_properties(self, cf, variable_input_size): # Define the input size, loss and metrics if cf.dataset.class_mode == 'categorical': if K.image_dim_ordering() == 'th': in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = 'categorical_crossentropy' metrics = ['accuracy'] elif cf.dataset.class_mode == 'detection': if 'yolo' in cf.model_name: in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) loss = YOLOLoss(in_shape, cf.dataset.n_classes, cf.dataset.priors) metrics = [YOLOMetrics(in_shape, cf.dataset.n_classes, cf.dataset.priors)] elif cf.model_name == 'ssd': in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = MultiboxLoss(cf.dataset.n_classes, neg_pos_ratio=2.0).compute_loss metrics = [] # TODO: There is no metrics for the ssd model else: raise ValueError('Uknown "' + cf.model_name + '" name for the ' + cf.dataset.class_mode + ' problem type.' 'Only is implemented for: {yolo, tiny-yolo, ssd}') elif cf.dataset.class_mode == 'segmentation': if K.image_dim_ordering() == 'th': if variable_input_size: in_shape = (cf.dataset.n_channels, None, None) else: in_shape = (cf.dataset.n_channels, cf.target_size_train[0], cf.target_size_train[1]) else: if variable_input_size: in_shape = (None, None, cf.dataset.n_channels) else: in_shape = (cf.target_size_train[0], cf.target_size_train[1], cf.dataset.n_channels) loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights) metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)] # if cf.model_name == 'fcn8': # loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights) # metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)] # # elif 'segnet' in cf.model_name: # loss = 'categorical_crossentropy' # metrics = [] # # else: # raise ValueError('Uknown "'+cf.model_name+'" name for the '+cf.dataset.class_mode+' problem type.' # 'Only is implemented for: {fc8, segnet}') else: raise ValueError('Unknown problem type') return in_shape, loss, metrics