Fix model equivalence tests (#15670)
* Fix model equivalence tests * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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@ -625,15 +625,15 @@ class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
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if type(tensor) == bool:
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tf_inputs_dict[key] = tensor
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elif key == "input_values":
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
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elif key == "pixel_values":
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
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else:
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.int32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32)
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# Check we can load pt model in tf and vice-versa with model => model functions
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tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict)
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pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
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pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model).to(torch_device)
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# need to rename encoder-decoder "inputs" for PyTorch
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# if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
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@ -650,7 +650,7 @@ class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
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continue
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tf_out = tf_output.numpy()
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pt_out = pt_output.numpy()
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pt_out = pt_output.cpu().numpy()
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self.assertEqual(tf_out.shape, pt_out.shape, "Output component shapes differ between TF and PyTorch")
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@ -676,6 +676,7 @@ class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
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tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
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tf_model.save_weights(tf_checkpoint_path)
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pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
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pt_model = pt_model.to(torch_device)
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# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
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pt_model.eval()
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@ -686,11 +687,11 @@ class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
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tensor = np.array(tensor, dtype=bool)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor, dtype=tf.int32)
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elif key == "input_values":
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
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elif key == "pixel_values":
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
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else:
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.int32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32)
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# need to rename encoder-decoder "inputs" for PyTorch
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# if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
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@ -708,7 +709,7 @@ class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
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continue
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tf_out = tf_output.numpy()
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pt_out = pt_output.numpy()
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pt_out = pt_output.cpu().numpy()
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self.assertEqual(tf_out.shape, pt_out.shape, "Output component shapes differ between TF and PyTorch")
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@ -1475,17 +1475,17 @@ class ModelTesterMixin:
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if type(tensor) == bool:
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tf_inputs_dict[key] = tensor
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elif key == "input_values":
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
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elif key == "pixel_values":
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
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elif key == "input_features":
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
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else:
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.int32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32)
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# Check we can load pt model in tf and vice-versa with model => model functions
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tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict)
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pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
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pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model).to(torch_device)
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# need to rename encoder-decoder "inputs" for PyTorch
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# if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
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@ -1496,7 +1496,7 @@ class ModelTesterMixin:
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tfo = tf_model(tf_inputs_dict, training=False)
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tf_hidden_states = tfo[0].numpy()
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pt_hidden_states = pto[0].numpy()
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pt_hidden_states = pto[0].cpu().numpy()
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tf_nans = np.copy(np.isnan(tf_hidden_states))
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pt_nans = np.copy(np.isnan(pt_hidden_states))
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@ -1518,6 +1518,7 @@ class ModelTesterMixin:
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tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
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tf_model.save_weights(tf_checkpoint_path)
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pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
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pt_model = pt_model.to(torch_device)
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# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
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pt_model.eval()
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@ -1528,13 +1529,13 @@ class ModelTesterMixin:
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tensor = np.array(tensor, dtype=bool)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor, dtype=tf.int32)
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elif key == "input_values":
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
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elif key == "pixel_values":
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
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elif key == "input_features":
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
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else:
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.int32)
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tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32)
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# need to rename encoder-decoder "inputs" for PyTorch
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# if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
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@ -1545,7 +1546,7 @@ class ModelTesterMixin:
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tfo = tf_model(tf_inputs_dict)
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tfo = tfo[0].numpy()
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pto = pto[0].numpy()
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pto = pto[0].cpu().numpy()
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tf_nans = np.copy(np.isnan(tfo))
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pt_nans = np.copy(np.isnan(pto))
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@ -776,16 +776,16 @@ class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
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else:
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if isinstance(value, (list, tuple)):
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return_dict[key] = (
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tf.convert_to_tensor(iter_value.numpy(), dtype=tf.int32) for iter_value in value
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tf.convert_to_tensor(iter_value.cpu().numpy(), dtype=tf.int32) for iter_value in value
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)
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else:
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return_dict[key] = tf.convert_to_tensor(value.numpy(), dtype=tf.int32)
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return_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.int32)
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return return_dict
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tf_inputs_dict = recursive_numpy_convert(pt_inputs)
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tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict)
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pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
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pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model).to(torch_device)
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# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
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pt_model.eval()
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@ -795,12 +795,6 @@ class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
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if "obj_labels" in inputs_dict:
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del inputs_dict["obj_labels"]
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def torch_type(key):
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if key in ("visual_feats", "visual_pos"):
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return torch.float32
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else:
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return torch.long
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pt_inputs = self._prepare_for_class(inputs_dict, model_class)
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tf_inputs_dict = recursive_numpy_convert(pt_inputs)
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@ -808,7 +802,7 @@ class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
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pto = pt_model(**pt_inputs)
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tfo = tf_model(tf_inputs_dict, training=False)
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tf_hidden_states = tfo[0].numpy()
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pt_hidden_states = pto[0].numpy()
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pt_hidden_states = pto[0].cpu().numpy()
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tf_nans = np.copy(np.isnan(tf_hidden_states))
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pt_nans = np.copy(np.isnan(pt_hidden_states))
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@ -852,7 +846,7 @@ class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
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tfo = tf_model(tf_inputs_dict)
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tfo = tfo[0].numpy()
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pto = pto[0].numpy()
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pto = pto[0].cpu().numpy()
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tf_nans = np.copy(np.isnan(tfo))
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pt_nans = np.copy(np.isnan(pto))
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