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Fix a TF Vision Encoder Decoder test (#15896)

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* send PyTorch inputs to the correct device

* Fix: TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2022-03-03 13:21:31 +01:00
committed by GitHub
parent 39249c9589
commit 4cd7ed4b3b
1 changed files with 5 additions and 2 deletions
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7
tests/vision_encoder_decoder/test_modeling_tf_vision_encoder_decoder.py
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@@ -311,6 +311,9 @@ class TFVisionEncoderDecoderMixin:
if "labels" in pt_inputs: if "labels" in pt_inputs:
pt_inputs["labels"] = pt_inputs["labels"].type(torch.LongTensor) pt_inputs["labels"] = pt_inputs["labels"].type(torch.LongTensor)
# send pytorch inputs to the correct device
pt_inputs = {k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()}
with torch.no_grad(): with torch.no_grad():
pt_outputs = pt_model(**pt_inputs).to_tuple() pt_outputs = pt_model(**pt_inputs).to_tuple()
@@ -321,7 +324,7 @@ class TFVisionEncoderDecoderMixin:
self.assertEqual(len(tf_outputs), len(pt_outputs), "Output lengths differ between TF and PyTorch") self.assertEqual(len(tf_outputs), len(pt_outputs), "Output lengths differ between TF and PyTorch")
for tf_output, pt_output in zip(tf_outputs, pt_outputs): for tf_output, pt_output in zip(tf_outputs, pt_outputs):
self.assert_almost_equals(tf_output.numpy(), pt_output.numpy(), 1e-3) self.assert_almost_equals(tf_output.numpy(), pt_output.detach().to("cpu").numpy(), 1e-3)
# PT -> TF # PT -> TF
with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname: with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname:
@@ -341,7 +344,7 @@ class TFVisionEncoderDecoderMixin:
self.assertEqual(len(tf_outputs_loaded), len(pt_outputs), "Output lengths differ between TF and PyTorch") self.assertEqual(len(tf_outputs_loaded), len(pt_outputs), "Output lengths differ between TF and PyTorch")
for tf_output_loaded, pt_output in zip(tf_outputs_loaded, pt_outputs): for tf_output_loaded, pt_output in zip(tf_outputs_loaded, pt_outputs):
self.assert_almost_equals(tf_output_loaded.numpy(), pt_output.numpy(), 1e-3) self.assert_almost_equals(tf_output_loaded.numpy(), pt_output.detach().to("cpu").numpy(), 1e-3)
def check_equivalence_pt_to_tf(self, config, decoder_config, inputs_dict): def check_equivalence_pt_to_tf(self, config, decoder_config, inputs_dict):