Fix TFLite 2.9 tests (apache#12130)

This pr fixes the tests that will be broken when we will update TFLite to
the 2.9 version.

We will update TensorFlow and TFLite versions to 2.9 so that we can
benefit from improvements in packaging to support multiple platforms
and Operating Systems.

python/tvm/relay/frontend/keras.py

@@ -635,9 +635,11 @@ def _convert_pooling(
             _op.nn.global_max_pool2d(inexpr, **global_pool_params), keras_layer, etab, data_layout
         )
     if pool_type == "GlobalAveragePooling2D":
-        return _convert_flatten(
-            _op.nn.global_avg_pool2d(inexpr, **global_pool_params), keras_layer, etab, data_layout
-        )
+        global_avg_pool2d = _op.nn.global_avg_pool2d(inexpr, **global_pool_params)
+        keep_dims = len(keras_layer.input.shape) == len(keras_layer.output.shape)
+        if keep_dims:
+            return global_avg_pool2d
+        return _convert_flatten(global_avg_pool2d, keras_layer, etab, data_layout)
     pool_h, pool_w = keras_layer.pool_size
     stride_h, stride_w = keras_layer.strides
     params = {

tests/python/frontend/tflite/test_forward.py

@@ -963,6 +963,10 @@ def representative_data_gen():
     input_node = subgraph.Tensors(model_input).Name().decode("utf-8")
 
     tflite_output = run_tflite_graph(tflite_model_quant, data)
+    if tf.__version__ < LooseVersion("2.9"):
+        input_node = data_in.name.replace(":0", "")
+    else:
+        input_node = "serving_default_" + data_in.name + ":0"
     tvm_output = run_tvm_graph(tflite_model_quant, data, input_node)
     tvm.testing.assert_allclose(
         np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-2, atol=1e-2
@@ -1997,10 +2001,12 @@ def _test_abs(data, quantized, int_quant_dtype=tf.int8):
         # TFLite 2.6.x upgrade support
         if tf.__version__ < LooseVersion("2.6.1"):
             in_node = ["serving_default_input_int8"]
-        else:
+        elif tf.__version__ < LooseVersion("2.9"):
             in_node = (
                 ["serving_default_input_int16"] if int_quant_dtype == tf.int16 else ["tfl.quantize"]
             )
+        else:
+            in_node = "serving_default_input"
 
         tvm_output = run_tvm_graph(tflite_model_quant, data, in_node)
         tvm.testing.assert_allclose(
@@ -2028,8 +2034,10 @@ def _test_rsqrt(data, quantized, int_quant_dtype=tf.int8):
             tf.math.rsqrt, data, int_quant_dtype=int_quant_dtype
         )
         tflite_output = run_tflite_graph(tflite_model_quant, data)
-        in_node = ["tfl.quantize"]
-
+        if tf.__version__ < LooseVersion("2.9"):
+            in_node = ["tfl.quantize"]
+        else:
+            in_node = "serving_default_input"
         tvm_output = run_tvm_graph(tflite_model_quant, data, in_node)
         tvm.testing.assert_allclose(
             np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-2
@@ -2110,7 +2118,10 @@ def _test_cos(data, quantized, int_quant_dtype=tf.int8):
             tf.math.cos, data, int_quant_dtype=int_quant_dtype
         )
         tflite_output = run_tflite_graph(tflite_model_quant, data)
-        in_node = ["tfl.quantize"]
+        if tf.__version__ < LooseVersion("2.9"):
+            in_node = ["tfl.quantize"]
+        else:
+            in_node = "serving_default_input"
         tvm_output = run_tvm_graph(tflite_model_quant, data, in_node)
         tvm.testing.assert_allclose(
             np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-2
@@ -3024,7 +3035,6 @@ def _test_quantize_dequantize(data):
     add = tf.keras.layers.Add()([data_in, relu])
     concat = tf.keras.layers.Concatenate(axis=0)([relu, add])
     keras_model = tf.keras.models.Model(inputs=data_in, outputs=concat)
-    input_name = data_in.name.split(":")[0]
 
     # To create quantized values with dynamic range of activations, needs representative dataset
     def representative_data_gen():
@@ -3034,7 +3044,11 @@ def representative_data_gen():
     tflite_model_quant = _quantize_keras_model(keras_model, representative_data_gen, True, True)
 
     tflite_output = run_tflite_graph(tflite_model_quant, data)
-    tvm_output = run_tvm_graph(tflite_model_quant, data, input_name)
+    if tf.__version__ < LooseVersion("2.9"):
+        in_node = data_in.name.split(":")[0]
+    else:
+        in_node = "serving_default_" + data_in.name + ":0"
+    tvm_output = run_tvm_graph(tflite_model_quant, data, in_node)
     tvm.testing.assert_allclose(
         np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-2
     )
@@ -3051,7 +3065,6 @@ def _test_quantize_dequantize_const(data):
     add = tf.keras.layers.Add()([data, relu])
     concat = tf.keras.layers.Concatenate(axis=0)([relu, add])
     keras_model = tf.keras.models.Model(inputs=data_in, outputs=concat)
-    input_name = data_in.name.split(":")[0]
 
     # To create quantized values with dynamic range of activations, needs representative dataset
     def representative_data_gen():
@@ -3061,7 +3074,11 @@ def representative_data_gen():
     tflite_model_quant = _quantize_keras_model(keras_model, representative_data_gen, True, True)
 
     tflite_output = run_tflite_graph(tflite_model_quant, data)
-    tvm_output = run_tvm_graph(tflite_model_quant, data, input_name)
+    if tf.__version__ < LooseVersion("2.9"):
+        in_node = data_in.name.split(":")[0]
+    else:
+        in_node = "serving_default_" + data_in.name + ":0"
+    tvm_output = run_tvm_graph(tflite_model_quant, data, in_node)
     tvm.testing.assert_allclose(
         np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-2
     )