onnx: Optimizer is broken for new PyTorch exports and segfault in onnx.checker

Fist export a model (as I did for this issue) in the latest PyTorch

import torch
import torch.nn as nn

class ToFloat(nn.Module):
    def __init__(self):
        """Convert to .float()
        """
        super(ToFloat, self).__init__()

    def forward(self, x):
        return x.float()

@torch.jit.script
def center_slice_helper(x, h_offset, w_offset, h_end, w_end):
    return x[:, :, h_offset:h_end, w_offset:w_end]


class CenterCrop(nn.Module):
    def __init__(self, crop_size, use_jit=False):
        """Crop from the center of a 4d tensor
        Input shape can be dynamic
        :param crop_size: the center crop size
        :param use_jit: if should use the jit helpter
        """
        super(CenterCrop, self).__init__()
        self.crop_size = crop_size
        self.use_jit = use_jit
        self.register_buffer('crop_size_t', torch.tensor(crop_size))

    def extra_repr(self):
        """Extra information
        """
        return 'crop_size={}'.format(
            self.crop_size
        )

    def forward(self, x):
        """
        :type x: torch.Tensor
        """
        height, width = x.shape[2], x.shape[3]
        if not isinstance(height, torch.Tensor):
            height, width = torch.tensor(height).to(x.device), torch.tensor(width).to(x.device)
        h_offset = (height - self.crop_size_t) / 2
        w_offset = (width - self.crop_size_t) / 2
        h_end = h_offset + self.crop_size_t
        w_end = w_offset + self.crop_size_t
        if self.use_jit:
            return center_slice_helper(x, h_offset, w_offset, h_end, w_end)
        return x[:, :, h_offset:h_end, w_offset:w_end]

model = nn.Sequential(ToFloat(), CenterCrop(224, use_jit=True))
onnxfile = "/mnt/output/gr/crop.onnx"

targets = ["cropped"]
dynamic_axes = {'data': [2, 3]}
dummy_input = torch.randn(1, 3, 300, 256, device='cpu').byte()
torch.onnx.export(model, dummy_input, onnxfile,
                  verbose=True, input_names=['data'],
                  dynamic_axes=dynamic_axes,
                  output_names=targets,
                  opset_version=10)

Now try optimzier

import onnx
from onnx import optimizer
onnx_model = onnx.load(onnxfile)
passes = ["extract_constant_to_initializer", "eliminate_unused_initializer"]
optimized_model = optimizer.optimize(onnx_model, passes)
onnx.save(optimized_model, onnxfile)

And you get this cryptic error message:

~/miniconda3/envs/py3/lib/python3.6/site-packages/onnx/optimizer.py in optimize(model, passes, fixed_point)
     53         optimized_model_str = C.optimize_fixedpoint(model_str, passes)
     54     else:
---> 55         optimized_model_str = C.optimize(model_str, passes)
     56
     57     return onnx.load_from_string(optimized_model_str)

IndexError: _Map_base::at

And checker segfaults!

onnx.checker.check_model(onnx_model)
Segmentation fault

Related to issue #1385 but with repro