问题描述
我正在尝试对BRATS挑战中的图像进行细分。我在这两个存储库的组合中使用U-net：
I am trying to segment images from the BRATS challenge. I am using U-net in a combination of these two repositories:
何时我尝试输出预测统计信息，出现不匹配形状错误：
When I try to output the prediction statistics a mismatch shape error come up:
我正在使用240x240的图像切片，batch_verification_size = 500
I am using image slices 240x240, with a batch_verification_size = 500
然后，
- 这是形状test_x：（500，240，240， 1）
- 这是形状test_y：（500， 240，240，1）
- 这是形状测试x：（500，240，240，1）
- 这是形状测试y：（ 500，240，240，1）
- 这是形状批次x：（500，240，240，1）
- 这是形状批次y ：（500，240，240，1）
- 这是形状预测：（500，240，240，1）
- 这是成本： Tensor（ add_88：0，shape =（），dtype = float32）
- 这是成本：Tensor（ Mean_2：0，shape =（），dtype = float32）
- 这是形状预测：（？，？，？，1）
- 这是形状批处理x：（500，240，240，1）
- 这是形状批次y：（500、240、240、1）
- this is shape test_x: (500, 240, 240, 1)
- this is shape test_y: (500, 240, 240, 1)
- this is shape test x: (500, 240, 240, 1)
- this is shape test y: (500, 240, 240, 1)
- this is shape batch x: (500, 240, 240, 1)
- this is shape batch y: (500, 240, 240, 1)
- this is shape prediction: (500, 240, 240, 1)
- this is cost : Tensor("add_88:0", shape=(), dtype=float32)
- this is cost : Tensor("Mean_2:0",shape=(), dtype=float32)
- this is shape prediction: (?, ?, ?, 1)
- this is shape batch x: (500, 240, 240, 1)
- this is shape batch y: (500, 240, 240, 1)
240 x 240 x 500 = 28800000
我不知道为什么要请求57600
240 x 240 x 500 = 28800000I don't know why is requesting 57600
似乎错误是由 output_minibatch_stats 函数：
summary_str, loss, acc, predictions = sess.run([self.summary_op,
self.net.cost, self.net.accuracy,
self.net.predicter],
feed_dict={self.net.x: batch_x,
self.net.y: batch_y,
self.net.keep_prob: 1.})
因此sess.run tf函数出了点问题。以下是一些出现错误的代码。有人知道会发生什么吗？
Therefore something is wrong in sess.run tf function. Below is some code where the error come up. Anybody got any idea what would happen?
def store_prediction(self, sess, batch_x, batch_y, name):
print('track 1')
prediction = sess.run(self.net.predicter, feed_dict={self.net.x: batch_x,
self.net.y: batch_y,
self.net.keep_prob: 1.})
print('track 2')
pred_shape = prediction.shape
loss = sess.run(self.net.cost, feed_dict={self.net.x: batch_x,
self.net.y: batch_y, `
self.net.keep_prob: 1.})
print('track 3')
logging.info("Verification error= {:.1f}%, loss= {:.4f}".format(error_rate(prediction,
util.crop_to_shape(batch_y,
prediction.shape)),
loss))
print('track 4')
print('this is shape batch x: ' + str(batch_x.shape))
print('this is shape batch y: ' + str(batch_y.shape))
print('this is shape prediction: ' + str(prediction.shape))
#img = util.combine_img_prediction(batch_x, batch_y, prediction)
print('track 5')
#util.save_image(img, "%s/%s.jpg"%(self.prediction_path, name))
return pred_shape
def output_epoch_stats(self, epoch, total_loss, training_iters, lr):
logging.info("Epoch {:}, Average loss: {:.4f}, learning rate: {:.4f}".format(epoch, (total_loss / training_iters), lr))
def output_minibatch_stats(self, sess, summary_writer, step, batch_x, batch_y):
print('this is shape cost : ' + str(self.net.cost.shape))
print('this is cost : ' + str(self.net.cost))
print('this is acc : ' + str(self.net.accuracy.shape))
print('this is cost : ' + str(self.net.accuracy))
print('this is shape prediction: ' + str(self.net.predicter.shape))
print('this is shape batch x: ' + str(batch_x.shape))
print('this is shape batch y: ' + str(batch_y.shape))
# Calculate batch loss and accuracy
summary_str, loss, acc, predictions = sess.run([self.summary_op,
self.net.cost,
self.net.accuracy,
self.net.predicter],
feed_dict={self.net.x: batch_x,
self.net.y: batch_y,
self.net.keep_prob: 1.})
print('track 6')
summary_writer.add_summary(summary_str, step)
print('track 7')
summary_writer.flush()
logging.info("Iter {:}, Minibatch Loss= {:.4f}, Training Accuracy= {:.4f}, Minibatch error= {:.1f}%".format(step,
loss,
acc,
error_rate(predictions, batch_y)))
print('track 8')
推荐答案
您在训练期间在张量流管道中将批量大小设置为1，但是批量喂入500只在您的测试数据中。这就是为什么网络仅请求形状为57600的张量的原因。
您可以将训练批大小设置为500或将测试批大小设置为1。
You set your batch size as 1 in your tensorflow pipeline during training but feeding in 500 batch size in your testing data. Thats why the network requests only a tensor of shape 57600.You can either set your training batch size 500 or testing batch size as 1.
这篇关于张量流，图像分割卷积InvalidArgumentError：重塑的输入是具有28800000值的张量，但请求的形状为57600的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！