message: strided convolutions support in ops backend of plaidbench
  * Fix: Ops backend to run on PlaidML
  Previously, Ops backend was running on Tensorflow
  backend despite the backend being set to `plaid`.
  This patch fixes this issue.
  * Fix: Ops backend changes to support strided convs
diff: @@ -9,7 +9,7 @@ from plaidbench.networks.ops import op
  class Conv2d(op.Op):
  - def __init__(self, params, ci, h, w, co, i, j):
  + def __init__(self, params, ci, h, w, co, i, j, stride_i=1, stride_j=1):
  super(Conv2d, self).__init__(params)
  self.ci = ci
  self.h = h
  @@ -17,10 +17,13 @@ class Conv2d(op.Op):
  self.co = co
  self.i = i
  self.j = j
  + self.stride_i = stride_i
  + self.stride_j = stride_j
  self.bs = params.batch_size
  def flops(self):
  - flops = (self.i * self.j * self.co * 2) * ((self.h - self.i) * (self.w - self.j)) * self.ci
  + flops = (self.i * self.j * self.co * 2) * ((self.h - self.i) / self.stride_i) * (
  + (self.w - self.j) / self.stride_j) * self.ci
  return flops
  def create_dataset_plaid(self):
  @@ -32,7 +35,10 @@ class Conv2d(op.Op):
  from keras.models import Model
  inp = Input(name='data', shape=(self.h, self.w, self.ci))
  # No bias cause TVM doesn't support it
  - op = Conv2D(self.co, (self.i, self.j), data_format='channels_last', use_bias=False)(inp)
  + op = Conv2D(self.co, (self.i, self.j),
  + strides=(self.stride_i, self.stride_j),
  + data_format='channels_last',
  + use_bias=False)(inp)
  return Model(inp, op, name=self.get_key())
  def get_key(self):
  