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Fix infer model with non 2d tensors (#8752)

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This commit is contained in:
Dmitry Pigasin
2021-12-02 10:59:53 +03:00
committed by GitHub
parent 84ebe77f62
commit 85f5528cad
1 changed files with 8 additions and 4 deletions
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12
samples/python/speech_sample/speech_sample.py
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@@ -38,9 +38,9 @@ def infer_data(
result = {}
for blob_name in output_blobs:
shape = exec_net.outputs[blob_name].shape
batch_size = shape[0]
result[blob_name] = np.ndarray((matrix_shape[0], shape[-1]))
output_shape = exec_net.outputs[blob_name].shape
batch_size = output_shape[0]
result[blob_name] = np.ndarray((matrix_shape[0], np.prod(output_shape[1:])))
for i in range(-cw_l, matrix_shape[0] + cw_r, batch_size):
if i < 0:
@@ -62,13 +62,17 @@ def infer_data(
vectors = temp
for blob_name in input_blobs:
vectors[blob_name] = vectors[blob_name].reshape(exec_net.input_info[blob_name].input_data.shape)
vector_results = exec_net.infer(vectors)
if i - cw_r < 0:
continue
for blob_name in output_blobs:
result[blob_name][i - cw_r:i - cw_r + batch_size] = vector_results[blob_name][:num_of_vectors]
vector_result = vector_results[blob_name].reshape((batch_size, result[blob_name].shape[1]))
result[blob_name][i - cw_r:i - cw_r + batch_size] = vector_result[:num_of_vectors]
return result