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RoiPoolingConv.py

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+import tensorflow as tf
+import tensorlayer as tl
+from tensorlayer.layers import Module
+
+class RoiPoolingConv(Module):
+    def __init__(self,pool_size,**kwargs):
+        self.pool_size = pool_size
+        super(RoiPoolingConv,self).__init__(**kwargs)
+    def build(self,input_shape):
+        self.nb_channels = input_shape[0][3]
+
+    def compute_output_shape(self, input_shape):
+        input_shape2 = input_shape[1]
+        return None, input_shape2[1], self.pool_size, self.pool_size, self.nb_channels
+
+    def call(self, x, mask=None):
+        assert (len(x) == 2)
+        img = x[0]
+        rois = x[1]
+        num_rois = tf.shape(rois)[1]
+        batch_size = tf.shape(rois)[0]
+
+        box_index = tf.expand_dims(tf.range(0, batch_size), 1)
+        box_index = tf.tile(box_index, (1, num_rois))
+        box_index = tf.reshape(box_index, [-1])
+
+        rs = tf.image.crop_and_resize(img, tf.reshape(rois, [-1, 4]), box_index, (self.pool_size, self.pool_size))
+
+        final_output = tf.reshape(rs, (batch_size, num_rois, self.pool_size, self.pool_size, self.nb_channels))
+        return final_output