def call(self, x): ''' To compute rois from infered classification branch and location branch Arguments: x: Return roi_3dtensor: ''' ishape = self.ishape anchor_4dtensor = self.anchor_4dtensor max_num_of_rois = self.num_of_rois nsm_iou_threshold = self.nsm_iou_threshold nsm_score_threshold = self.nsm_score_threshold clzbbe_4dtensor = x # (batch_size, h, w, 6k) clzbbe_3dtensor = clzbbe_4dtensor[0] # (h, w, 6k) roi_2dtensor = nsm( anchor_4dtensor=anchor_4dtensor, clzbbe_3dtensor=clzbbe_3dtensor, max_num_of_rois=max_num_of_rois, nsm_iou_threshold=nsm_iou_threshold, nsm_score_threshold=nsm_score_threshold, ishape=ishape) roi_3dtensor = tf.expand_dims(input=roi_2dtensor, axis=0) # (batch_size, num_of_rois, 4) return roi_3dtensor
def build_infer_model(ishape, resnet_settings, top_down_pyramid_size, k, total_classes, abox_2dtensor, nsm_iou_threshold, nsm_score_threshold, nsm_max_output_size):
'''
'''
use_bias = True
weight_decay = 0.0
trainable = False
bn_trainable = False
input_tensor = Input(shape=ishape, name='input', dtype='float32')
nets = fpn(
input_tensor=input_tensor,
resnet_settings=resnet_settings,
top_down_pyramid_size=top_down_pyramid_size,
use_bias=use_bias,
weight_decay=weight_decay,
trainable=trainable,
bn_trainable=bn_trainable)
tensors = []
for i in range(len(nets)):
tensor = nets[i]
tensor = Conv2D(
filters=k[i]*(total_classes+1+4),
kernel_size=[1, 1],
strides=[1, 1],
padding='same',
use_bias=use_bias,
kernel_regularizer=regularizers.l2(weight_decay),
trainable=trainable,
name='head'+str(i)+'_conv')(tensor)
tensor = tf.reshape(tensor=tensor, shape=[tensor.shape[1]*tensor.shape[2]*k[i], total_classes+1+4]) # (h*w*k, total_classes+1+4)
clz_tensor = tensor[:, :total_classes+1]
clz_tensor = Activation('softmax')(clz_tensor)
loc_tensor = tensor[:, total_classes+1:]
tensor = tf.concat(values=[clz_tensor, loc_tensor], axis=-1) # (h*w*k, total_classes+1+4)
tensors.append(tensor)
tensor = tf.concat(values=tensors, axis=0) # (h1*w1*k1 + h2*w2*k2 + h3*w3*k3 + h4*w4*k4, total_classes+1+4)
tensor, valid_outputs = nsm(
abox_2dtensor=abox_2dtensor,
prediction=tensor,
nsm_iou_threshold=nsm_iou_threshold,
nsm_score_threshold=nsm_score_threshold,
nsm_max_output_size=nsm_max_output_size,
total_classes=total_classes)
valid_outputs = tf.expand_dims(input=valid_outputs, axis=0)
model = Model(inputs=input_tensor, outputs=[tensor, valid_outputs])
model.compile(optimizer=Adam(), loss=None)
return model
start_example_index + num_of_train_examples +
num_of_validation_examples + num_of_test_examples),
ishape=ishape)
for sample_order in range(num_of_test_examples):
x, img_id = next(gen)
# predict proposal
batch_x = tf.expand_dims(input=x, axis=0)
y_pred = rpn_model.predict_on_batch(batch_x)
clzbbe_4dtensor = y_pred # (batch_size, h, w, 6k), batch_size = 1
clzbbe_3dtensor = clzbbe_4dtensor[0] # (h, w, 6k)
roi_2dtensor = nsm(anchor_4dtensor=anchor_4dtensor,
clzbbe_3dtensor=clzbbe_3dtensor,
max_num_of_rois=max_num_of_rois,
nsm_iou_threshold=nsm_iou_threshold,
nsm_score_threshold=nsm_score_threshold,
ishape=ishape)
# predict object proposal
batch_x = [
tf.expand_dims(input=x, axis=0),
tf.expand_dims(input=roi_2dtensor, axis=0)
]
y_pred = detection_model.predict_on_batch(batch_x)
clzbbe_tensor = y_pred[0]
clz_2dtensor = clzbbe_tensor[:, :len(classes)]
bbe_2dtensor = clzbbe_tensor[:, len(classes):]
pred_bbox2d = bbe2box2d(box_2dtensor=roi_2dtensor,
bbe_2dtensor=bbe_2dtensor)
def build_infer_model(ishape, resnet_settings, k, total_classes, abox_2dtensor,
nsm_iou_threshold, nsm_score_threshold,
nsm_max_output_size):
'''
'''
use_bias = True
weight_decay = 0.0
trainable = False
bn_trainable = False
input_tensor = Input(shape=ishape, name='input', dtype='uint8')
tensor = tf.cast(x=input_tensor, dtype='float32')
tensors = resnet(input_tensor=tensor,
block_settings=resnet_settings,
use_bias=use_bias,
weight_decay=weight_decay,
trainable=trainable,
bn_trainable=bn_trainable)
tensor = tensors[-1]
head_dims = tensor.shape[3]
tensor = Conv2D(filters=head_dims,
kernel_size=[3, 3],
strides=[1, 1],
padding='same',
use_bias=use_bias,
kernel_regularizer=regularizers.l2(weight_decay),
trainable=trainable,
name='conv6')(tensor)
tensor = BatchNormalization(trainable=bn_trainable,
name='conv6_bn')(tensor)
tensor = Activation('relu')(tensor)
tensor = Conv2D(filters=k * (total_classes + 1 + 4),
kernel_size=[1, 1],
strides=[1, 1],
padding='same',
use_bias=use_bias,
kernel_regularizer=regularizers.l2(weight_decay),
trainable=trainable,
name='conv7')(tensor)
tensor = tf.reshape(tensor=tensor, shape=[-1, total_classes + 5
]) # (h*w*k, total_classes+1+4)
clz_tensor = tensor[:, :1 + total_classes]
clz_tensor = Activation('softmax')(clz_tensor)
loc_tensor = tensor[:, 1 + total_classes:]
tensor = tf.concat(values=[clz_tensor, loc_tensor],
axis=-1) # (h*w*k, total_classes+1+4)
tensor, valid_outputs = nsm(abox_2dtensor=abox_2dtensor,
prediction=tensor,
nsm_iou_threshold=nsm_iou_threshold,
nsm_score_threshold=nsm_score_threshold,
nsm_max_output_size=nsm_max_output_size,
total_classes=total_classes)
valid_outputs = tf.cast(x=valid_outputs, dtype='float32')
valid_outputs = tf.expand_dims(input=valid_outputs, axis=0)
model = Model(inputs=input_tensor, outputs=[tensor, valid_outputs])
model.compile(
optimizer=Adam(),
loss=[lambda y_true, y_pred: 0.0, lambda y_true, y_pred: 0.0])
return model
total_faces = 0 total_pred_faces = 0 TP = 0 FP = 0 FN = 0 for batch in range(total_test_examples): batchx_4dtensor, batchy_2dtensor, bboxes, _ = next(gen) batch_loss = model.test_on_batch(batchx_4dtensor, batchy_2dtensor) loss[batch] = batch_loss prediction = model.predict_on_batch(batchx_4dtensor) # (h1*w1*k1 + h2*w2*k2 + h3*w3*k3, total_classes+1+4) boxclz_2dtensor, valid_outputs = nsm( abox_2dtensor=abox_2dtensor, prediction=prediction, nsm_iou_threshold=nsm_iou_threshold, nsm_score_threshold=nsm_score_threshold, nsm_max_output_size=nsm_max_output_size, total_classes=total_classes) boxclz_2dtensor = boxclz_2dtensor[:valid_outputs] pred_bboxes = list(boxclz_2dtensor.numpy()) total_bboxes = len(bboxes) total_pred_bboxes = len(pred_bboxes) true_positives = 0 for i in range(total_bboxes): for j in range(total_pred_bboxes): iou = comiou(bbox=bboxes[i], pred_bbox=pred_bboxes[j]) if iou >= iou_thresholds[1]: true_positives += 1
