[Relay][Frontend] Add Crop op converter (apache#3241)

* Add Crop op converter

* lint

* x

nnvm/python/nnvm/frontend/mxnet.py

@@ -269,7 +269,7 @@ def _crop_like(inputs, attrs):
         raise tvm.error.OpAttributeUnimplemented(
             'Center crop is not supported in operator crop_like.')
     if len(inputs) < 2:
-        raise RuntimeError("Only support crop_like pattern.")
+        raise tvm.error.OpAttributeUnimplemented("Only support crop_like pattern.")
     new_attrs["axis"] = [2, 3]
     return get_nnvm_op('slice_like')(inputs[0], inputs[1], **new_attrs)
 

python/tvm/relay/frontend/mxnet.py

@@ -149,7 +149,7 @@ def _mx_conv2d_transpose(inputs, attrs):
     new_attrs["groups"] = attrs.get_int("num_group", 1)
     new_attrs["data_layout"] = data_layout
     new_attrs["kernel_layout"] = kernel_layout
-    use_bias = not attrs.get_bool("no_bias", False)
+    use_bias = not attrs.get_bool("no_bias", True)
     res = _op.nn.conv2d_transpose(inputs[0], inputs[1], **new_attrs)
 
     if use_bias:
@@ -277,6 +277,28 @@ def _mx_slice_axis(inputs, attrs):
     return _op.strided_slice(inputs[0], begin, end)
 
 
+def _mx_crop_like(inputs, attrs):
+    if len(inputs) < 2:
+        raise tvm.error.OpAttributeUnimplemented(
+            "Only support crop_like pattern for operator Crop.")
+    if attrs.get_bool("center_crop", False):
+        raise tvm.error.OpAttributeUnimplemented(
+            "Center crop is not supported in operator Crop.")
+    if attrs.get_int_tuple("h_w", (0, 0)) != (0, 0):
+        raise tvm.error.OpAttributeUnimplemented(
+            "Doesn't support h_w in operator Crop.")
+    offset = attrs.get_int_tuple("offset", (0, 0))
+    new_attrs = {}
+    if offset == (0, 0):
+        new_attrs["axes"] = (2, 3)
+        return _op.slice_like(*inputs, **new_attrs)
+    like_shape = ir_pass.infer_type(inputs[1]).checked_type.shape
+    new_attrs['begin'] = [0, 0, offset[0], offset[1]]
+    new_attrs['end'] = [like_shape[0], like_shape[1], offset[0]+like_shape[2],
+                        offset[1]+like_shape[3]]
+    return _op.strided_slice(inputs[0], **new_attrs)
+
+
 def _mx_split(inputs, attrs):
     axis = attrs.get_int("axis", 1)
     new_attrs = {}
@@ -300,6 +322,10 @@ def _mx_softmax_output(inputs, attrs):
     return _op.nn.softmax(inputs[0])
 
 
+def _mx_linear_regression_output(inputs, _):
+    return inputs[0]
+
+
 def _mx_concat(inputs, attrs):
     axis = attrs.get_int("dim", 1)
     return _op.concatenate(tuple(inputs), axis=axis)
@@ -890,6 +916,7 @@ def _lstm_cell(data, states, i2h_weight, h2h_weight, i2h_bias, h2h_bias):
     "argsort"       : _mx_argsort,
     "SoftmaxOutput" : _mx_softmax_output,
     "SoftmaxActivation" : _mx_softmax_activation,
+    "LinearRegressionOutput" : _mx_linear_regression_output,
     "smooth_l1"     : _mx_smooth_l1,
     # vision
     "_contrib_BilinearResize2D" : _mx_resize,
@@ -905,11 +932,12 @@ def _lstm_cell(data, states, i2h_weight, h2h_weight, i2h_bias, h2h_bias):
     # NLP
     "RNN"               : _mx_rnn_layer,
     "_rnn_param_concat" : _mx_rnn_param_concat,
+    # Depricated:
+    "Crop"              : _mx_crop_like,
     # List of missing operators that are present in NNVMv1
     # TODO(tvm-tvm): support all operators.
     #
     # "broadcast_to",
-    # "Crop"          : _crop_like,
 }
 
 # set identity list

tests/python/frontend/mxnet/test_forward.py

@@ -583,6 +583,31 @@ def verify(mode, input_size, seq_len, hidden_size, num_layers, batch=1):
         verify(mode, 64, 10, 64, 2)
         verify(mode, 64, 10, 32, 2)
 
+def test_forward_Crop():
+    def verify(xshape, yshape, offset=None):
+        x_data = np.random.uniform(size=xshape).astype("float32")
+        y_data = np.random.uniform(size=yshape).astype("float32")
+        if offset is None:
+            mx_sym = mx.sym.Crop(mx.sym.var("x"), mx.sym.var("y"))
+            ref_res = mx.nd.Crop(mx.nd.array(x_data), mx.nd.array(y_data))
+        else:
+            mx_sym = mx.sym.Crop(mx.sym.var("x"), mx.sym.var("y"), offset=offset)
+            ref_res = mx.nd.Crop(mx.nd.array(x_data), mx.nd.array(y_data), offset=offset)
+        new_sym, _ = relay.frontend.from_mxnet(mx_sym, {"x": xshape, "y": yshape})
+        for target, ctx in ctx_list():
+            for kind in ["graph", "debug"]:
+                intrp = relay.create_executor(kind, ctx=ctx, target=target)
+                if offset is None or offset == (0, 0):
+                    op_res = intrp.evaluate(new_sym)(x_data, y_data)
+                else:
+                    op_res = intrp.evaluate(new_sym)(x_data)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy())
+    verify((1, 3, 40, 40), (1, 3, 20, 20))
+    verify((1, 3, 40, 40), (1, 3, 20, 20), (0, 0))
+    verify((1, 3, 40, 40), (1, 3, 20, 20), (10, 10))
+    verify((5, 32, 40, 40), (5, 32, 25, 25))
+    verify((5, 32, 40, 40), (5, 32, 25, 25), (5, 5))
+
 
 if __name__ == '__main__':
     test_forward_mlp()
@@ -624,3 +649,4 @@ def verify(mode, input_size, seq_len, hidden_size, num_layers, batch=1):
     test_forward_gather_nd()
     test_forward_bilinear_resize()
     test_forward_rnn_layer()
+    test_forward_Crop()