31bfc7c1ce9dac771dbd5e2edf2d5e789d8b1aaa,evaluate.py,,from_pipe,#,18

Before Change

        read_input = True

        while read_input:
            for i in range(0, opts.batch_size):
                raw_image = pipe_in.stdout.read(width * height * 3)

                if len(raw_image) != nbytes:
                    read_input = False
                    break

                image = numpy.fromstring(raw_image, dtype="uint8")
                image = image.reshape((height, width, 3))
                X[i] = image

            if read_input:
                _preds = sess.run(preds, feed_dict={img_placeholder: X})

                for i in range(0, opts.batch_size):

After Change

        while read_input:
            count = 0
            while count < opts.batch_size:
                raw_image = pipe_in.stdout.read(width * height * 3)

                if len(raw_image) != nbytes:
                    last = True
                    X = X[:count]
                    batch_shape = (count, height, width, 3)
                    img_placeholder = tf.placeholder(tf.float32, shape=batch_shape,
                                                     name="img_placeholder")
                    preds = transform.net(img_placeholder)
                    break

                image = numpy.fromstring(raw_image, dtype="uint8")
                image = image.reshape((height, width, 3))
                X[count] = image
                count += 1

            if read_input:
                if last:
                    read_input = False
                _preds = sess.run(preds, feed_dict={img_placeholder: X})

Instances