[GitHub] [tvm] jikechao opened a new issue, #15084: [Bug] The bilinear or bicubic calculation logical in UpSampling2D is wrong

["jikechao (via GitHub)" <gi...@apache.org>]

jikechao opened a new issue, #15084:
URL: https://github.com/apache/tvm/issues/15084

   For the UpSampling2D operator in keras. If we set the value of attr `interpolation` to **bilinear** or **bicubic**, TVM will give different predicted results with Keras. 
   
   
   ### Expected behavior
   
   TVM and Keras have the same inference results.
   
   ### Actual behavior
   
   TVM gave different inference results with Keras.
   ![image](https://github.com/apache/tvm/assets/29506758/0d09e7b6-eb97-4dba-9adc-cd4094617845)
   
   
   ### Environment
   
   TVM: latest
   Keras: 2.12.0
   
   ### Steps to reproduce
   
   ```
   import tvm
   import tvm.relay as relay
   import numpy as np
   from tensorflow import keras
   from tensorflow.keras import layers, models
   input_shape = (1, 1, 2, 3)
   input_data = np.random.random(size=input_shape)
   x = layers.Input(shape=input_shape[1:], dtype='float32')
   
   kwargs={'size':[2, 2],'data_format':"channels_last",'interpolation':"bilinear"} # bilinear, bicubic lead to the wrong result 
   
   layer = keras.layers.UpSampling2D(**kwargs)
   layer.set_weights(layer.get_weights())
   
   y = layer(x)
   model = models.Model(x, y)
   model.summary()
   res_keras = model(input_data)
   print(res_keras.shape)
   shape_dict = {'input_1': input_shape}
   mod, params = relay.frontend.from_keras(model, shape_dict,layout='NHWC')
   with tvm.transform.PassContext(opt_level=3):
       model = relay.build_module.create_executor("graph", mod, tvm.cpu(0), 'llvm', params).evaluate()
   
   test_x_tvm = input_data
   res_tvm = model(tvm.nd.array(test_x_tvm.astype('float32'))).numpy()
   
   np.testing.assert_allclose(res_keras, res_tvm, atol=1e-3, rtol=1e-3)
   ```
   
   ### Triage
   
   Please refer to the list of label tags [here](https://github.com/apache/tvm/wiki/Issue-Triage-Labels) to find the relevant tags and add them below in a bullet format (example below).
   
   * needs-triage
   * frontend:keras
   * topi
   
   
   ### Analysis
   1. if we use interpolation="nearest", the inference results are the same between keras and tvm.
   2. the bug seems in the file [topi/image/resize](https://github.com/apache/tvm/blob/main/python/tvm/topi/image/resize.py)
   
   I tried to locate/fix this bug but failed. Wish someone could help me review and fix this bug. Thanks!
   
   


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Re: [I] [Bug] The bilinear or bicubic calculation logical in UpSampling2D is wrong [tvm]

["tqchen (via GitHub)" <gi...@apache.org>]

tqchen closed issue #15084: [Bug] The bilinear or bicubic calculation logical in UpSampling2D is wrong
URL: https://github.com/apache/tvm/issues/15084


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