[Bugfix][Relay][Keras] Fix SeparableConv2D conversion in dilation_rate attribute

[jikechao]

This PR fixed a bug about dilation_rate in SeparableConv2D.

The attribute dilation_rate about SeparableConv2D layer has the following constraints in Keras documentation:

dilation_rate: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution.

TVM always considered the dilation_rate as a default value. This will lead to wrong calculation results.

This PR fix this bug and added the bug-triggering test cases.

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, 32,32,3)
input_data = np.random.random(size=input_shape)
x = layers.Input(shape=input_shape[1:], dtype='float32')

kwargs={'filters':2,'kernel_size':[3, 3],'dilation_rate':2,} 
layer = keras.layers.SeparableConv2D(**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)

cc @Hzfengsy @echuraev @leandron

[tvm-bot]

Thanks for contributing to TVM! Please refer to the contributing guidelines https://tvm.apache.org/docs/contribute/ for useful information and tips. Please request code reviews from Reviewers by @-ing them in a comment.

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

_{Generated by tvm-bot}

[echuraev]

Thanks for your PR! LGTM!