mbaret commented on a change in pull request #8368:
URL: https://github.com/apache/tvm/pull/8368#discussion_r680817420
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File path: tests/python/frontend/tflite/test_forward.py
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@@ -868,74 +869,177 @@ def test_forward_l2_pool2d():
# -----------
-def _test_tflite2_quantized_convolution(
- input_shape, kernel_shape, dilations, strides, padding, data_format
+def _test_tflite2_quantized_conv2d(
+ input_shape,
+ weights_shape,
+ dilations,
+ strides,
+ padding,
+ dtype="int8",
+ quantize_per_channel=False,
):
"""One iteration of TFLite2 quantized convolution with given shapes and
attributes"""
- data_format = "channels_last" if "NHWC" else "channels_first"
- data = np.random.uniform(0, 1, input_shape).astype("float32")
- kernel = np.random.uniform(0, 1, kernel_shape).astype("float32")
- data_in = tf.keras.layers.Input(shape=data.shape[1:])
- conv = tf.keras.layers.Conv2D(
- filters=kernel_shape[3],
- kernel_size=(kernel_shape[0], kernel_shape[1]),
- strides=strides,
- padding=padding,
- data_format=data_format,
- activation="relu",
- use_bias=False,
- )(data_in)
- keras_model = tf.keras.models.Model(data_in, conv)
- keras_model.layers[1].set_weights([kernel])
+ dtype_min, dtype_max = test_tflite.get_range_for_dtype_str(dtype)
+ channels = weights_shape[0]
- # To create quantized values with dynamic range of activations, needs
representative dataset
- def representative_data_gen():
- for i in range(1):
- yield [data]
+ input_scale = np.random.random() * 0.1
+ input_zp = np.random.randint(dtype_min, dtype_max)
+ in_tensor = test_tflite.Tensor(
+ data_type=dtype,
+ shape=input_shape,
+ quantization=test_tflite.Quantization(scale=[input_scale],
zero_point=[input_zp]),
+ )
- tflite_model_quant = _quantize_keras_model(keras_model,
representative_data_gen)
+ # Weights in TFLite 2 are symmetric, i.e the zero point is at 0
+ if quantize_per_channel:
+ weights_scale = [np.random.random() * 0.1 for i in range(channels)]
+ weights_zp = [0 for i in range(channels)]
+ else:
+ weights_scale = [np.random.random() * 0.1]
+ weights_zp = [0]
+ weights_quantization = test_tflite.Quantization(
Review comment:
I like the idea of the operators being able to create their own
weights/biases. That is somewhat analogous to what happens when you create the
operators with Keras/TF. This way if we need to create other Conv2D tests, we
don't need to replicate a lot of boilerplate. I'm also inclined to agree that
we can default the data type to int8.
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