MobileViT

Tried to run MobileViT_S model with input shape 256, 256, 3 and got the following error

UnimplementedError Traceback (most recent call last) <ipython-input-29-14969872c882> in <module>() 2 3 history = model.fit(get_training_dataset_with_oversample(repeat_dataset=True, oversample=True), steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS, ----> 4 validation_data=get_validation_dataset(), validation_steps=VALIDATION_STEPS) 5

1 frames /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py in _numpy(self) 1189 return self._numpy_internal() 1190 except core._NotOkStatusException as e: # pylint: disable=protected-access -> 1191 raise core._status_to_exception(e) from None # pylint: disable=protected-access 1192 1193 @property

UnimplementedError: 9 root error(s) found. (0) UNIMPLEMENTED: {{function_node __inference_train_function_1032011}} Dynamic input dimension to reshape that is both splitted and combined is not supported %dynamic-reshape.13585 = f32[<=32,16,4,2304]{3,2,1,0} dynamic-reshape(f32[<=1024,2,16,144]{3,1,2,0} %transpose.13551, s32[] %divide.13584, s32[] %reshape.13571, s32[] %reshape.13574, s32[] %reshape.13577), metadata={op_type=“Reshape” op_name=“while/body/_1/while/mobilevit_s/tf.reshape_1/Reshape”} [[{{function_node while_body_1010992}}{{node while/TPUReplicateMetadata}}]] (1) UNIMPLEMENTED: {{function_node __inference_train_function_1032011}} Dynamic input dimension to reshape that is both splitted and combined is not supported %dynamic-reshape.13585 = f32[<=32,16,4,2304]{3,2,1,0} dynamic-reshape(f32[<=1024,2,16,144]{3,1,2,0} %transpose.13551, s32[] %divide.13584, s32[] %reshape.13571, s32[] %reshape.13574, s32[] %reshape.13577), metadata={op_type=“Reshape” op_name=“while/body/_1/while/mobilevit_s/tf.reshape_1/Reshape”} [[{{function_node while_body_1010992}}{{node while/TPUReplicateMetadata}}]] [[while/body/_1/while/strided_slice_35/_445]] (2) UNIMPLEMENTED: {{function_node __inference_train_function_1032011}} Dynamic input dimension to reshape that is both splitted and combined is not supported %dynamic-reshape.13585 = f32[<=32,16,4,2304]{3,2,1,0} dynamic-reshape(f32[<=1024,2,16,144]{3,1,2,0} %transpose.13551, s32[] %divide.13584, s32[] %reshape.13571, s32[] %reshape.13574, s32[] %reshape.13577), metadata={op_type=“Reshape” op_name=“while/body/_1/while/mobilevit_s/tf.reshape_1/Reshape”} [[{{function_node while_body_1010992}}{{node while/TPUReplicateMetadata}}]] [[while/body/_1/while/strided_slice_23/_381]] (3) UNIMPLEMENTED: {{function_node __inference_train_function_1032011}} Dynamic input dimension to reshape that is both splitted and combined is not supported %dynamic-reshape.13585 = f32[<=32,16,4,2304]{3,2,1,0} dynamic-reshape(f32[<=1024,2,16,144]{3,1,2,0} %transpose.13551, s32[] %divide.13584, s32[] %reshape.13571, s32[] %reshape.13574, s32[] %reshape.13577), metadata={op_type=“Reshape” op_name=“while/body/_1/while/mobilevit_s/tf.reshape_1/Reshape”} [[{{function_node while_body_1010992}}{{node while/TPUReplicateMetadata}}]] [[while/body/_1/while/Pad_8/_407]] (4) UNIMPLEMENTED: {{function_node __inference_train_function_1032011}} Dynamic input dimension to reshape that is both splitted and combined is not supported %dynamic-reshape.13585 = f32[<=32,16,4,2304]{3,2,1,0} dynamic-reshape(f32[<=1024,2,16,144]{3,1,2,0} %transpose.13551, s32[] %divide.13584, s32[] %reshape.13571, s32[] %reshape.13574, s32[] %reshape.13577), metadata={op_type=“Reshape” op_name=“while/body/_1/while/mobilevit_s/tf.reshape_1/Reshape”} [[{{function_node while_body_1010992}}{{node while/TPUReplicateMetadata}}]] [[while/body/_1/while/Maximum_2/y/_341]] (5) UNIMPLEMENTED: {{function_node __inference_train_function_1032011}} Dynamic input dimension to reshape that is both splitted and combined is not supported %dynamic-reshape.13585 = f3 … [truncated]

[leondgarse]

I’ve added a part TPU training test in above colab kecam_test.ipynb, that using some fake data reproducing this, without using train_script.py. Is it possible for you making something similar for replicating? Like dataset / loss usage, digging out what actually causing this.

[leondgarse]

Model saving in bfloat16 precission also works now. Testing results updated in above kecam_test.ipynb.