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ValueError: Dimension 0 in both shapes must be equal, but are 3 and 64. Shapes are [3,3,18,64] and [64,21,3,3]. for 'Assign_2' (op: 'Assign') with input shapes: [3,3,18,64], [64,21,3,3]. #38
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Reference: github/pose-gan#38
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Originally created by @TwistSun on 10/20/2019
Hi,
when i use your checkpoint file "generator-warp-mask-nn3-cl12.h5" for market test, I get this error:
Using TensorFlow backend.
2019-10-20 16:21:09.954087: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
Generate images...
Number of images: 32668
Number of pairs train: 263632
Number of pairs test: 12000
WARNING:tensorflow:From /data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:1290: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
Traceback (most recent call last):
File "/data00/home/menyifang/code/comparison/pose-gan-fix/pose-gan/test.py", line 152, in
test()
File "/data00/home/menyifang/code/comparison/pose-gan-fix/pose-gan/test.py", line 118, in test
generator.load_weights(args.generator_checkpoint)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/keras/engine/topology.py", line 2619, in load_weights
load_weights_from_hdf5_group(f, self.layers)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/keras/engine/topology.py", line 3095, in load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 2188, in batch_set_value
assign_op = x.assign(assign_placeholder)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 594, in assign
return state_ops.assign(self._variable, value, use_locking=use_locking)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/tensorflow/python/ops/state_ops.py", line 276, in assign
validate_shape=validate_shape)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/tensorflow/python/ops/gen_state_ops.py", line 59, in assign
use_locking=use_locking, name=name)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3162, in create_op
compute_device=compute_device)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3208, in _create_op_helper
set_shapes_for_outputs(op)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2427, in set_shapes_for_outputs
return _set_shapes_for_outputs(op)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2400, in _set_shapes_for_outputs
shapes = shape_func(op)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2330, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 627, in call_cpp_shape_fn
require_shape_fn)
File "/data00/home/menyifang/anaconda3/envs/tfold/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 691, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Dimension 0 in both shapes must be equal, but are 3 and 64. Shapes are [3,3,18,64] and [64,21,3,3]. for 'Assign_2' (op: 'Assign') with input shapes: [3,3,18,64], [64,21,3,3].
Do you have any idea to solve? (environment is as same as yours and it works well for fashion dataset) Thanks!