def __init__(
self,
prev_layer,
rois,
pool_height=2,
pool_width=2,
name='roipooling_layer',
):
super(ROIPoolingLayer, self).__init__(prev_layer=prev_layer, name=name)
logging.info("ROIPoolingLayer %s: (%d, %d)" %
(name, pool_height, pool_width))
self.inputs = prev_layer.outputs
try:
from tensorlayer.third_party.roi_pooling.roi_pooling.roi_pooling_ops import roi_pooling
except Exception as e:
logging.info(e)
logging.info(
"HINT: 1. https://github.com/deepsense-ai/roi-pooling 2. tensorlayer/third_party/roi_pooling"
)
self.outputs = roi_pooling(self.inputs, rois, pool_height, pool_width)
# self.all_layers = list(layer.all_layers)
# self.all_params = list(layer.all_params)
# self.all_drop = dict(layer.all_drop)
self.all_layers.append(self.outputs)
def __init__(
self,
#inputs = None,
layer=None,
rois=None,
pool_height=2,
pool_width=2,
name='roipooling_layer',
):
Layer.__init__(self, name=name)
self.inputs = layer.outputs
logging.info("ROIPoolingLayer %s: (%d, %d)" %
(self.name, pool_height, pool_width))
try:
from tensorlayer.third_party.roi_pooling.roi_pooling.roi_pooling_ops import roi_pooling
except Exception as e:
logging.info(e)
logging.info(
"\nHINT: \n1. https://github.com/deepsense-ai/roi-pooling \n2. tensorlayer/third_party/roi_pooling\n"
)
self.outputs = roi_pooling(self.inputs, rois, pool_height, pool_width)
self.all_layers = list(layer.all_layers)
self.all_params = list(layer.all_params)
self.all_drop = dict(layer.all_drop)
self.all_layers.extend([self.outputs])
def __init__(
self,
prev_layer,
rois,
pool_height=2,
pool_width=2,
name='roipooling_layer',
):
super(ROIPoolingLayer, self).__init__(prev_layer=prev_layer, name=name)
logging.info("ROIPoolingLayer %s: (%d, %d)" %
(name, pool_height, pool_width))
self.inputs = prev_layer.outputs
self.outputs = roi_pooling(self.inputs, rois, pool_height, pool_width)
self.all_layers.append(self.outputs)
def __init__(
self,
prev_layer,
rois,
pool_height=2,
pool_width=2,
name='roipooling_layer',
):
Layer.__init__(self, prev_layer=prev_layer, name=name)
self.inputs = prev_layer.outputs
logging.info("ROIPoolingLayer %s: (%d, %d)" % (self.name, pool_height, pool_width))
try:
from tensorlayer.third_party.roi_pooling.roi_pooling.roi_pooling_ops import roi_pooling
except Exception as e:
logging.info(e)
logging.info("HINT: 1. https://github.com/deepsense-ai/roi-pooling 2. tensorlayer/third_party/roi_pooling")
self.outputs = roi_pooling(self.inputs, rois, pool_height, pool_width)
# self.all_layers = list(layer.all_layers)
# self.all_params = list(layer.all_params)
# self.all_drop = dict(layer.all_drop)
self.all_layers.append(self.outputs)
# y = roi_pooling(input, rois, pool_height=2, pool_width=2)
n = InputLayer(input, name='in')
n = ROIPoolingLayer(n, rois=rois, pool_height=2, pool_width=2, name='roi')
y = n.outputs
mean = tf.reduce_mean(y)
grads = tf.gradients(mean, input)
print(type(grads))
print(len(grads))
print(grads)
print(input_value.shape)
with tf.Session('') as sess:
input_const = tf.constant(input_value, tf.float32)
rois_const = tf.constant(rois_value, tf.int32)
y = roi_pooling(input_const, rois_const, pool_height=2, pool_width=2)
mean = tf.reduce_mean(y)
numerical_grad_error_1 = tf.test.compute_gradient_error(
[input_const], [input_value.shape], y, y_shape)
numerical_grad_error_2 = tf.test.compute_gradient_error(
[input_const], [input_value.shape], mean, [])
print(numerical_grad_error_1, numerical_grad_error_2)
with tf.Session('') as sess:
y_output = sess.run(y, feed_dict={input: input_value, rois: rois_value})
print('y: ', y_output)
grads_output = sess.run(grads,
feed_dict={
input: input_value,
rois: rois_value
input = tf.compat.v1.placeholder(tf.float32)
rois = tf.compat.v1.placeholder(tf.int32)
# y = roi_pooling(input, rois, pool_height=2, pool_width=2)
n = InputLayer(input, name='in')
n = ROIPoolingLayer(n, rois=rois, pool_height=2, pool_width=2, name='roi')
y = n.outputs
mean = tf.reduce_mean(input_tensor=y)
grads = tf.gradients(mean, input)
print(type(grads))
print(len(grads))
print(grads)
print(input_value.shape)
with tf.compat.v1.Session('') as sess:
input_const = tf.constant(input_value, tf.float32)
rois_const = tf.constant(rois_value, tf.int32)
y = roi_pooling(input_const, rois_const, pool_height=2, pool_width=2)
mean = tf.reduce_mean(input_tensor=y)
numerical_grad_error_1 = tf.compat.v1.test.compute_gradient_error([input_const], [input_value.shape], y, y_shape)
numerical_grad_error_2 = tf.compat.v1.test.compute_gradient_error([input_const], [input_value.shape], mean, [])
print(numerical_grad_error_1, numerical_grad_error_2)
with tf.compat.v1.Session('') as sess:
y_output = sess.run(y, feed_dict={input: input_value, rois: rois_value})
print('y: ', y_output)
grads_output = sess.run(grads, feed_dict={input: input_value, rois: rois_value})
print('grads: ', grads_output)