Add simple transpose_conv tests with post-training quantization.
PiperOrigin-RevId: 281864531 Change-Id: I57795cd589387783602abcdcf393e781888991bc
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@ -509,6 +509,7 @@ edgetpu_ops = [
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"sub",
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"sum", # high error
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"tanh",
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"transpose_conv",
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]
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[gen_zipped_test_file(
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@ -34,22 +34,50 @@ def make_transpose_conv_tests(options):
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"""Make a set of tests to do transpose_conv."""
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# Tensorflow only supports equal strides
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test_parameters = [{
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"input_shape": [[1, 3, 4, 1], [1, 10, 10, 3], [3, 20, 20, 1]],
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"filter_size": [[1, 1], [1, 2], [3, 3]],
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"strides": [[1, 1, 1, 1], [1, 3, 3, 1]],
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"padding": ["SAME", "VALID"],
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"data_format": ["NHWC"],
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"channel_multiplier": [1, 2],
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}]
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test_parameters = [
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{
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"input_shape": [[1, 3, 4, 1], [1, 10, 10, 3], [3, 20, 20, 1]],
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"filter_size": [[1, 1], [1, 2], [3, 3]],
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"strides": [[1, 1, 1, 1], [1, 3, 3, 1]],
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"padding": ["SAME", "VALID"],
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"data_format": ["NHWC"],
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"channel_multiplier": [1, 2],
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"output_shape": [[]],
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"fully_quantize": [False]
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},
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# TODO(yunluli): Adding simple tests for now to unblock edgetpu debugging.
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# Need to add more test cases.
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{
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"input_shape": [[1, 3, 3, 1]],
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"filter_size": [[3, 3, 2, 1]],
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"strides": [[1, 1, 1, 1]],
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"padding": ["SAME"],
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"data_format": ["NHWC"],
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"channel_multiplier": [1],
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"output_shape": [[1, 3, 3, 2]],
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"fully_quantize": [True]
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},
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{
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"input_shape": [[1, 3, 3, 1]],
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"filter_size": [[3, 3, 2, 1]],
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"strides": [[1, 2, 2, 1]],
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"padding": ["SAME"],
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"data_format": ["NHWC"],
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"channel_multiplier": [1],
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"output_shape": [[1, 6, 6, 2]],
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"fully_quantize": [True]
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}
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]
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def get_tensor_shapes(parameters):
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input_shape = parameters["input_shape"]
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filter_size = parameters["filter_size"]
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filter_shape = filter_size + [
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input_shape[3], parameters["channel_multiplier"]
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]
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return [input_shape, filter_shape]
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if not parameters["fully_quantize"]:
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filter_shape = filter_size + [
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input_shape[3], parameters["channel_multiplier"]
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]
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return [input_shape, filter_shape]
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return [input_shape, filter_size]
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def build_graph(parameters):
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"""Build a transpose_conv graph given `parameters`."""
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@ -60,28 +88,48 @@ def make_transpose_conv_tests(options):
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filter_input = tf.compat.v1.placeholder(
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dtype=tf.float32, name="filter", shape=filter_shape)
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conv_outputs = tf.nn.conv2d(
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input_tensor,
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filter_input,
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strides=parameters["strides"],
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padding=parameters["padding"],
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data_format=parameters["data_format"])
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out = tf.compat.v1.nn.conv2d_backprop_input(
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input_shape,
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filter_input,
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conv_outputs,
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strides=parameters["strides"],
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padding=parameters["padding"],
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data_format=parameters["data_format"])
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input_tensors = [input_tensor, filter_input]
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if not parameters["fully_quantize"]:
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input_tensors = [input_tensor, filter_input]
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conv_outputs = tf.nn.conv2d(
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input_tensor,
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filter_input,
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strides=parameters["strides"],
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padding=parameters["padding"],
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data_format=parameters["data_format"])
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out = tf.compat.v1.nn.conv2d_backprop_input(
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input_shape,
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filter_input,
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conv_outputs,
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strides=parameters["strides"],
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padding=parameters["padding"],
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data_format=parameters["data_format"])
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else:
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input_tensors = [input_tensor]
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filter_input = create_tensor_data(
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np.float32, filter_shape, min_value=-1, max_value=1)
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out = tf.nn.conv2d_transpose(
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input_tensor,
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filter_input,
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parameters["output_shape"],
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strides=parameters["strides"],
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padding=parameters["padding"],
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data_format=parameters["data_format"])
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return input_tensors, [out]
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def build_inputs(parameters, sess, inputs, outputs):
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input_shape, filter_shape = get_tensor_shapes(parameters)
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values = [
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create_tensor_data(np.float32, input_shape),
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create_tensor_data(np.float32, filter_shape)
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]
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if not parameters["fully_quantize"]:
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values = [
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create_tensor_data(np.float32, input_shape),
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create_tensor_data(np.float32, filter_shape)
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]
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else:
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values = [
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create_tensor_data(
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np.float32, input_shape, min_value=-1, max_value=1),
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]
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return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
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make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
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