with open(C.config_filename, 'wb') as config_f:
pickle.dump(C, config_f)
print(
'Config has been written to {}, and can be loaded when testing to ensure correct results'
.format(C.config_filename))
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('Train samples {}, Val samples {}'.format(len(train_imgs),
len(val_imgs)))
data_gen_train = data_generators.get_anchor_gt(train_imgs,
classes_count,
C,
K.image_dim_ordering(),
mode='train')
data_gen_val = data_generators.get_anchor_gt(val_imgs,
classes_count,
C,
K.image_dim_ordering(),
mode='val')
img_input = Input(shape=(None, None, 3))
roi_input = Input(shape=(C.num_rois, 4))
# define the base network (resnet here)
shared_layers = nn.nn_base(img_input, trainable=True)
# define the RPN, built on the base layers
def train():
num_rois = 32
epochs = 250
model = "model_trained/frcnn_resnet.hdf5"
ground_truth = "MIO-TCD-Localization/gt_train.csv"
pretrained_model = "model/resnet50_weights_tf_dim_ordering_tf_kernels.h5"
# load filenames and class mappings
images, label_dict = parse_data(ground_truth)
map1 = parse_mapping(ground_truth)
# shuffle the data
shuffle(images)
# create train and validation data split
train_data = []
val_data = []
# randomly choosing train and test
for image in images:
if np.random.randint(0, 6) == 0:
val_data.append(image)
else:
train_data.append(image)
print("Num train samples", len(train_data))
print("Num val samples", len(val_data))
data_gen_train = get_anchor_gt(train_data,
label_dict,
rn.get_img_output_length,
K.image_dim_ordering(),
mode='train')
data_gen_val = get_anchor_gt(val_data,
label_dict,
rn.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
shared_layers = rn.nn_base(img_input, trainable=True)
# define the RPN using the base layers
num_anchors = 3 * 3 # 3 for number of scales and 3 number of aspect ratios
rpn = rn.rpn(shared_layers, num_anchors)
classifier = rn.classifier(shared_layers,
roi_input,
num_rois,
nb_classes=len(label_dict),
trainable=True)
model_rpn = Model(img_input, rpn[:2])
model_classifier = Model([img_input, roi_input], classifier)
# this model holds both the RPN and the classifier
model_all = Model([img_input, roi_input], rpn[:2] + classifier)
print("Loading pretrained weights from", pretrained_model)
model_rpn.load_weights(pretrained_model, by_name=True)
model_classifier.load_weights(pretrained_model, by_name=True)
optimizer = Adam(lr=1e-5)
optimizer_classifier = Adam(lr=1e-5)
model_rpn.compile(optimizer=optimizer,
loss=[
losses_fn.rpn_loss_cls(num_anchors),
losses_fn.rpn_loss_regr(num_anchors)
])
model_classifier.compile(
optimizer=optimizer_classifier,
loss=[
losses_fn.class_loss_cls,
losses_fn.class_loss_regr(len(label_dict) - 1)
],
metrics={'dense_class_{}'.format(len(label_dict)): 'accuracy'})
model_all.compile(optimizer='sgd', loss='mae')
epoch_length = 250
losses = np.zeros((epoch_length, 5))
best_loss = np.inf
iteration = 0
for epoch in range(epochs):
print("Epoch ", epoch + 1)
# Training on Train Dataset
X, Y, img_data = next(data_gen_train)
P_rpn = model_rpn.predict_on_batch(X)
result = rpn_to_roi(P_rpn[0], P_rpn[1], K.image_dim_ordering(), True,
0.7, 300)
X2, Y1, Y2, IouS = calc_iou(result, img_data, map1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(pos_samples) == 0:
pos_samples = []
else:
pos_samples = pos_samples[0]
neg_samples = np.where(Y1[0, :, -1] == 1)
if len(neg_samples) == 0:
neg_samples = []
else:
neg_samples = neg_samples[0]
if num_rois > 1:
if len(pos_samples) < num_rois // 2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(
pos_samples, num_rois // 2, replace=False).tolist()
try:
selected_neg_samples = np.random.choice(
neg_samples,
num_rois - len(selected_pos_samples),
replace=False).tolist()
except:
selected_neg_samples = np.random.choice(
neg_samples,
num_rois - len(selected_pos_samples),
replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
if np.random.randint(0, 2) == 0:
sel_samples = choice(pos_samples)
else:
sel_samples = choice(neg_samples)
model_classifier.train_on_batch(
[X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
# Testing on Validation Dataset
X, Y, img_data = next(data_gen_val)
P_rpn = model_rpn.predict_on_batch(X)
result = rpn_to_roi(P_rpn[0], P_rpn[1], K.image_dim_ordering(), True,
0.7, 300)
X2, Y1, Y2, IouS = calc_iou(result, img_data, map1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(pos_samples) == 0:
pos_samples = []
else:
pos_samples = pos_samples[0]
neg_samples = np.where(Y1[0, :, -1] == 1)
if len(neg_samples) == 0:
neg_samples = []
else:
neg_samples = neg_samples[0]
if num_rois > 1:
if len(pos_samples) < num_rois // 2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(
pos_samples, num_rois // 2, replace=False).tolist()
try:
selected_neg_samples = np.random.choice(
neg_samples,
num_rois - len(selected_pos_samples),
replace=False).tolist()
except:
selected_neg_samples = np.random.choice(
neg_samples,
num_rois - len(selected_pos_samples),
replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
if np.random.randint(0, 2):
sel_samples = choice(neg_samples)
else:
sel_samples = choice(pos_samples)
loss_class = model_classifier.test_on_batch(
[X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
losses[iteration, 2] = loss_class[1]
losses[iteration, 3] = loss_class[2]
losses[iteration, 4] = loss_class[3]
if iteration == epoch_length * (epoch + 1):
rpn_cls_loss = np.mean(losses[:, 0])
rpn_regr_loss = np.mean(losses[:, 1])
class_cls_loss = np.mean(losses[:, 2])
class_regr_loss = np.mean(losses[:, 3])
class_acc = np.mean(losses[:, 4])
print("Classifier accuracy for bounding boxes from RPN:",
class_acc)
print("Loss RPN classifier:", rpn_cls_loss)
print("Loss RPN regression:", rpn_regr_loss)
print("Loss Detector classifier:", class_cls_loss)
print("Loss Detector regression:", class_regr_loss)
total_loss = rpn_cls_loss + rpn_regr_loss + class_cls_loss + class_regr_loss
if total_loss < best_loss:
best_loss = total_loss
model_all.save_weights(model)
iteration += 1