forked from TensorLayer/tensorlayer3
47 lines
2.0 KiB
Python
47 lines
2.0 KiB
Python
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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os.environ['TL_BACKEND'] = 'tensorflow'
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import tensorflow as tf
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import numpy as np
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from generate_anchors import generate_anchors
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def generate_anchors_pre(height, width, feat_stride, anchor_scales=(8,16,32), anchor_ratios=(0.5,1,2)):
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""" A wrapper function to generate anchors given different scales
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Also return the number of anchors in variable 'length'
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"""
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anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
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A = anchors.shape[0]
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shift_x = np.arange(0, width) * feat_stride
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shift_y = np.arange(0, height) * feat_stride
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shift_x, shift_y = np.meshgrid(shift_x, shift_y)
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shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
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K = shifts.shape[0]
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# width changes faster, so here it is H, W, C
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anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
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anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False)
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length = np.int32(anchors.shape[0])
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return anchors, length
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def generate_anchors_pre_tf(height, width, feat_stride=16, anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)):
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shift_x = tf.range(width) * feat_stride # width
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shift_y = tf.range(height) * feat_stride # height
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shift_x, shift_y = tf.meshgrid(shift_x, shift_y)
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sx = tf.reshape(shift_x, shape=(-1,))
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sy = tf.reshape(shift_y, shape=(-1,))
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shifts = tf.transpose(tf.stack([sx, sy, sx, sy]))
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K = tf.multiply(width, height)
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shifts = tf.transpose(tf.reshape(shifts, shape=[1, K, 4]), perm=(1, 0, 2))
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anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
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A = anchors.shape[0]
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anchor_constant = tf.constant(anchors.reshape((1, A, 4)), dtype=tf.int32)
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length = K * A
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anchors_tf = tf.reshape(tf.add(anchor_constant, shifts), shape=(length, 4))
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return tf.cast(anchors_tf, dtype=tf.float32), length
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