Add a quantized conv2 unit test for the tflite front-end (#5558)

Signed-off-by: Giuseppe Rossini <giuseppe.rossini@arm.com>

diff --git a/tests/python/frontend/tflite/test_forward.py b/tests/python/frontend/tflite/test_forward.py
index da89a13..9963479 100644 (file)
--- a/tests/python/frontend/tflite/test_forward.py
+++ b/tests/python/frontend/tflite/test_forward.py
@@ -635,7 +635,7 @@ def test_forward_l2_pool2d():
 
 def _test_convolution(tensor_in_sizes, filter_in_sizes,
                       dilations, strides, padding, data_format,
-                      is_depthwise=False):
+                      is_depthwise=False, quantized=False):
     """ One iteration of convolution with given shapes and attributes """
 
     total_size_1 = 1
@@ -646,12 +646,16 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
         total_size_2 *= s
     # Initializes the input tensor with array containing incrementing
     # numbers from 1.
-    data_array = [f * 1.0 for f in range(1, total_size_1 + 1)]
-    filter_array = [f * 1.0 for f in range(1, total_size_2 + 1)]
+    if quantized:
+        data_array = np.random.uniform(0, 255, tensor_in_sizes).astype('uint8')
+        filter_array = np.random.uniform(0, 255, filter_in_sizes).astype('uint8')
+    else:
+        data_array = [f * 1.0 for f in range(1, total_size_1 + 1)]
+        filter_array = [f * 1.0 for f in range(1, total_size_2 + 1)]
 
     with tf.Graph().as_default():
-        in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
-        in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype='float32')
+        in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32', name='in_data')
+        in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype='float32', name='in_filter')
         strides = [1] + strides + [1]
         dilations = [1] + dilations + [1]
 
@@ -667,15 +671,37 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
                                 strides=strides,
                                 padding=padding,
                                 data_format=data_format)
-        data_array = np.reshape(data_array, tensor_in_sizes).astype('float32')
-        compare_tflite_with_tvm(data_array, 'Placeholder:0', [in_data], [out])
+
+        if quantized:
+            # For now only quantized conv2d is supported
+            assert not is_depthwise
+
+            # Quantized the inputs and feed them to the convolution
+            inq_data = tf.quantization.fake_quant_with_min_max_args(in_data, min=-100, max=100, name='inq_data')
+            inq_filter = tf.quantization.fake_quant_with_min_max_args(in_filter, min=-100, max=100, name='inq_filter')
+            out = nn_ops.conv2d(inq_data,
+                                inq_filter,
+                                strides=strides,
+                                padding=padding,
+                                data_format=data_format)
+            out = tf.quantization.fake_quant_with_min_max_args(out, min=-200, max=200, name="out")
+
+            # Set the input quantization range
+            input_range = {'in_data': (-100, 100)} if quantized else None
+
+            # Compare
+            compare_tflite_with_tvm(data_array, 'in_data', [in_data], [out], quantized=quantized, input_range=input_range)
+        else:
+            data_array = np.reshape(data_array, tensor_in_sizes).astype('float32')
+            compare_tflite_with_tvm(data_array, 'in_data', [in_data], [out])
 
 
 def test_forward_convolution():
-    _test_convolution([4, 8, 8, 176], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NHWC')
-    _test_convolution([4, 17, 17, 19], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NHWC')
-    _test_convolution([4, 17, 17, 124], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME', 'NHWC')
-    _test_convolution([4, 17, 17, 12], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID', 'NHWC')
+    for quantized in [False, True]:
+        _test_convolution([4, 8, 8, 176], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NHWC', quantized=quantized)
+        _test_convolution([4, 17, 17, 19], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NHWC', quantized=quantized)
+        _test_convolution([4, 17, 17, 124], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME', 'NHWC', quantized=quantized)
+        _test_convolution([4, 17, 17, 12], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID', 'NHWC', quantized=quantized)
 
     # depthwise convolution
     _test_convolution([4, 8, 8, 176], [1, 1, 176, 1], [1, 1], [1, 1], 'SAME', 'NHWC', True)