[Bugfix][Relay][keras] Fix the bug about the attribute 'output_padding' in Deconv

[jikechao]

This PR fixed two bugs in Conv2DTranspose and Conv3DTranspose. Both of them are caused by lacking the parsing of the output_padding attribute.
In addition, corresponding test cases were added to capture the bug.

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 = (2, 33,33,128)
input_data = np.random.random(size=input_shape)
x = layers.Input(shape=input_shape[1:], dtype='float32')

kwargs={'filters':2,'kernel_size':[3, 3],'strides':[2, 2],'output_padding':[1, 1],}# 'data_format':"channels_last"} 
# output_padding lead to this bug
layer = keras.layers.Conv2DTranspose(**kwargs)
layer.set_weights(layer.get_weights())

y = layer(x)
model = models.Model(x, y)
model.summary()
res_keras = model(input_data)

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)

BTW, The Conv3DTranspose has a similar bug. The following keras layer can trigger it:

keras_mod.layers.Conv3DTranspose(
                filters=2, kernel_size=(3, 3, 3), strides=(2, 2, 2), output_padding=(1, 1, 1)
            ),

cc @echuraev @Hzfengsy @masahi

[tvm-bot]

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• cc @shingjan _{See #10317 for details}

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