train_for_shopee.py

from __future__ import division
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
import random
import pprint
import time
import numpy as np
import pickle
import simplejson
import load_data

from keras import backend as K
from keras.optimizers import Adam
from keras.layers import Input
from keras.models import Model
from keras_frcnn import config, data_generators
from keras_frcnn import losses as losses
import keras_frcnn.roi_helpers as roi_helpers
from keras.utils import generic_utils
from pathlib import Path

# pass the settings from the command line, and persist them in the config object
frcnn_config = config.Config()

if frcnn_config.network == 'vgg':
    frcnn_config.network = 'vgg'
    from keras_frcnn import vgg as nn
elif frcnn_config.network == 'resnet50':
    from keras_frcnn import resnet as nn
    frcnn_config.network = 'resnet50'
else:
    print('Not a valid model')
    raise ValueError

# check if weight path was passed via command line
# read training data config
with open('train_dataset_config.json', 'rb') as dataset_config_file:
    dataset_config = simplejson.load(dataset_config_file)

all_imgs, classes_count, class_mapping = load_data.load(dataset_config)

if 'bg' not in classes_count:
    classes_count['bg'] = 0
    class_mapping['bg'] = len(class_mapping)

frcnn_config.class_mapping = class_mapping

inv_map = {v: k for k, v in class_mapping.items()}

print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))

config_output_filename = 'config.pickle'

with open(config_output_filename, 'wb') as config_f:
    pickle.dump(frcnn_config, config_f)
    print('Config has been written to {}, and can be loaded when testing to ensure correct results'.format(
        config_output_filename))

random.shuffle(all_imgs)

num_imgs = len(all_imgs)

train_imgs = [s for s in all_imgs if s['imageset'] == 'trainval']
val_imgs = [s for s in all_imgs if s['imageset'] == 'test']

print('Num train samples {}'.format(len(train_imgs)))
print('Num val samples {}'.format(len(val_imgs)))

data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, frcnn_config, nn.get_img_output_length,
                                               K.image_dim_ordering(), mode='train')
data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, frcnn_config, nn.get_img_output_length,
                                             K.image_dim_ordering(), mode='val')

if K.image_dim_ordering() == 'th':
    input_shape_img = (3, None, None)
else:
    input_shape_img = (None, None, 3)

img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(None, 4))

# define the base network (resnet here, can be VGG, Inception, etc)
shared_layers = nn.nn_base(img_input, trainable=True)

# define the RPN, built on the base layers
num_anchors = len(frcnn_config.anchor_box_scales) * len(frcnn_config.anchor_box_ratios)
rpn = nn.rpn(shared_layers, num_anchors)

classifier = nn.classifier(shared_layers, roi_input, frcnn_config.num_rois, nb_classes=len(classes_count),
                           trainable=True)

model_rpn = Model(img_input, rpn[:2])
model_classifier = Model([img_input, roi_input], classifier)

# this is a model that holds both the RPN and the classifier, used to load/save weights for the models
model_all = Model([img_input, roi_input], rpn[:2] + classifier)

model_path = 'model_output/'+frcnn_config.model_path
pretrain_model_path = 'pretrain/resnet50_weights_tf_dim_ordering_tf_kernels.h5'

try:
    if Path(model_path).exists() is True:
        model_rpn.load_weights(filepath=model_path, by_name=True)
        model_classifier.load_weights(filepath=model_path, by_name=True)
        print("Succesfully load model parameters")
    elif Path(pretrain_model_path).exists() is True:
        model_rpn.load_weights(filepath=pretrain_model_path, by_name=True)
        model_classifier.load_weights(filepath=pretrain_model_path, by_name=True)
        print("Succesfully load pretrained standard keras model parameters")
except:
    print("No pre trained model")

optimizer = Adam(lr=1e-5)
optimizer_classifier = Adam(lr=1e-5)
model_rpn.compile(optimizer=optimizer, loss=[losses.rpn_loss_cls(num_anchors), losses.rpn_loss_regr(num_anchors)])
model_classifier.compile(optimizer=optimizer_classifier,
                         loss=[losses.class_loss_cls, losses.class_loss_regr(len(classes_count) - 1)],
                         metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
model_all.compile(optimizer='sgd', loss='mae')

epoch_length = 1000
num_epochs = int(100)
iter_num = 0

losses = np.zeros((epoch_length, 5))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
start_time = time.time()

best_loss = np.Inf

class_mapping_inv = {v: k for k, v in class_mapping.items()}
print('Starting training')

vis = True

for epoch_num in range(num_epochs):

    progbar = generic_utils.Progbar(epoch_length)
    print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))

    while True:
        try:

            if len(rpn_accuracy_rpn_monitor) == epoch_length and frcnn_config.verbose:
                mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor)) / len(rpn_accuracy_rpn_monitor)
                rpn_accuracy_rpn_monitor = []
                print('Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'.format(
                    mean_overlapping_bboxes, epoch_length))
                if mean_overlapping_bboxes == 0:
                    print(
                        'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')

            X, Y, img_data = next(data_gen_train)

            loss_rpn = model_rpn.train_on_batch(X, Y)

            P_rpn = model_rpn.predict_on_batch(X)

            R = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], frcnn_config, K.image_dim_ordering(), use_regr=True,
                                       overlap_thresh=0.7,
                                       max_boxes=300)
            # note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
            X2, Y1, Y2, IouS = roi_helpers.calc_iou(R, img_data, frcnn_config, class_mapping)

            if X2 is None:
                rpn_accuracy_rpn_monitor.append(0)
                rpn_accuracy_for_epoch.append(0)
                continue

            neg_samples = np.where(Y1[0, :, -1] == 1)
            pos_samples = np.where(Y1[0, :, -1] == 0)

            if len(neg_samples) > 0:
                neg_samples = neg_samples[0]
            else:
                neg_samples = []

            if len(pos_samples) > 0:
                pos_samples = pos_samples[0]
            else:
                pos_samples = []

            rpn_accuracy_rpn_monitor.append(len(pos_samples))
            rpn_accuracy_for_epoch.append((len(pos_samples)))

            if frcnn_config.num_rois > 1:
                if len(pos_samples) < frcnn_config.num_rois // 2:
                    selected_pos_samples = pos_samples.tolist()
                else:
                    selected_pos_samples = np.random.choice(pos_samples, frcnn_config.num_rois // 2,
                                                            replace=False).tolist()
                try:
                    selected_neg_samples = np.random.choice(neg_samples,
                                                            frcnn_config.num_rois - len(selected_pos_samples),
                                                            replace=False).tolist()
                except:
                    selected_neg_samples = np.random.choice(neg_samples,
                                                            frcnn_config.num_rois - len(selected_pos_samples),
                                                            replace=True).tolist()

                sel_samples = selected_pos_samples + selected_neg_samples
            else:
                # in the extreme case where num_rois = 1, we pick a random pos or neg sample
                selected_pos_samples = pos_samples.tolist()
                selected_neg_samples = neg_samples.tolist()
                if np.random.randint(0, 2):
                    sel_samples = random.choice(neg_samples)
                else:
                    sel_samples = random.choice(pos_samples)

            loss_class = model_classifier.train_on_batch([X, X2[:, sel_samples, :]],
                                                         [Y1[:, sel_samples, :], Y2[:, sel_samples, :]])

            losses[iter_num, 0] = loss_rpn[1]
            losses[iter_num, 1] = loss_rpn[2]

            losses[iter_num, 2] = loss_class[1]
            losses[iter_num, 3] = loss_class[2]
            losses[iter_num, 4] = loss_class[3]

            iter_num += 1

            progbar.update(iter_num,
                           [('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])),
                            ('detector_cls', np.mean(losses[:iter_num, 2])),
                            ('detector_regr', np.mean(losses[:iter_num, 3]))])

            if iter_num == epoch_length:
                loss_rpn_cls = np.mean(losses[:, 0])
                loss_rpn_regr = np.mean(losses[:, 1])
                loss_class_cls = np.mean(losses[:, 2])
                loss_class_regr = np.mean(losses[:, 3])
                class_acc = np.mean(losses[:, 4])

                mean_overlapping_bboxes = float(sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
                rpn_accuracy_for_epoch = []

                if frcnn_config.verbose:
                    print('Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(
                        mean_overlapping_bboxes))
                    print('Classifier accuracy for bounding boxes from RPN: {}'.format(class_acc))
                    print('Loss RPN classifier: {}'.format(loss_rpn_cls))
                    print('Loss RPN regression: {}'.format(loss_rpn_regr))
                    print('Loss Detector classifier: {}'.format(loss_class_cls))
                    print('Loss Detector regression: {}'.format(loss_class_regr))
                    print('Elapsed time: {}'.format(time.time() - start_time))

                curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
                iter_num = 0
                start_time = time.time()

                if curr_loss < best_loss:
                    if frcnn_config.verbose:
                        print('Total loss decreased from {} to {}, saving weights'.format(best_loss, curr_loss))
                    best_loss = curr_loss

                    model_all.save_weights(model_path)

                break

        except Exception as e:
            print('Exception: {}'.format(e))
            continue

print('Training complete, exiting.')