Comments (4)
It is correct for isotropic Gaussian blur kernels. The range of lambda is from 1.0 to 5.0 for scaling factor (X2). We randomly sampled the lambda when generating the blur kernel for training.
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Please @liujianzhao6328057 I'm facing a problem when i load the pretrained model , specially when it reads the checkpoint
this is the error .. how did you kindly solve it please ??
NotFoundError (see above for traceback): Key MODEL/conv7/kernel/Adam_3 not found in checkpoint
[[Node: save/RestoreV2_69 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2_69/tensor_names, save/RestoreV2_69/shape_and_slices)]]
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Please @JWSoh I'm facing a problem when i load the pretrained model , specially when it reads the checkpoint
this is the error .. how did you kindly solve it please ??
NotFoundError (see above for traceback): Key MODEL/conv7/kernel/Adam_3 not found in checkpoint
[[Node: save/RestoreV2_69 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2_69/tensor_names, save/RestoreV2_69/shape_and_slices)]]
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Please can you kindly explain me how to calculate this weight loss ?
def get_loss_weights(self):
loss_weights = tf.ones(shape=[self.TASK_ITER]) * (1.0/self.TASK_ITER)
decay_rate = 1.0 / self.TASK_ITER / (10000 / 3)
min_value= 0.03 / self.TASK_ITER
loss_weights_pre = tf.maximum(loss_weights[:-1] - (tf.multiply(tf.to_float(self.global_step), decay_rate)), min_value)
loss_weight_cur= tf.minimum(loss_weights[-1] + (tf.multiply(tf.to_float(self.global_step),(self.TASK_ITER- 1) * decay_rate)), 1.0 - ((self.TASK_ITER - 1) * min_value))
loss_weights = tf.concat([[loss_weights_pre], [[loss_weight_cur]]], axis=1)
return loss_weights
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Related Issues (20)
- Variable dimensions are incompatible while calculating l1_loss(during Large-Scale_Training) HOT 1
- about the high_resolution image HOT 3
- How to obtain X3 experimental results HOT 2
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- Why is the downsampling operator implemented by a model rather than a algorithm in the meta-test step HOT 3
- When I ran large-scale training code, I have some problems. Could you help me? HOT 4
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- Reproduction with the given model HOT 5
- During reproducing “bicubic” downsampling scenario... HOT 2
- Where is the path I should insert of checkpoint the trained large scale training model ? HOT 1
- Error during Large Scale Training HOT 1
- Problem when i load the pretrained model , specially when it reads the checkpoint HOT 1
- I do not understand how to calculate the weight loss ?
- Error during large scale training
- AlreadyExistsError during Meta-training
- Use MZSR without CUDA?
- Sir,I have a problem when training
- train the model
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