From e1e611dc446fe597509b4b777bb474c059a1c0b6 Mon Sep 17 00:00:00 2001
From: Jeremy Johnson <jeremy.johnson@arm.com>
Date: Wed, 13 Dec 2023 14:28:12 +0000
Subject: Remove inferred dimension from RESHAPE

Test generation changed to only produce static reshape tests
Reference model changed to produce ERROR_IF on inferred shapes

Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: I92c92a40e7c0e457961bc654630040dff79a750b
---
 verif/generator/tosa_error_if.py | 50 ----------------------------------------
 1 file changed, 50 deletions(-)

(limited to 'verif/generator/tosa_error_if.py')

diff --git a/verif/generator/tosa_error_if.py b/verif/generator/tosa_error_if.py
index 5dd785f..7f719ee 100644
--- a/verif/generator/tosa_error_if.py
+++ b/verif/generator/tosa_error_if.py
@@ -2518,56 +2518,6 @@ class TosaErrorValidator:
         }
         return info_dict
 
-    @staticmethod
-    def evReshapeOutputSizeMultiInference(check=False, **kwargs):
-        error_name = ErrorIf.ReshapeOutputSizeMultiInference
-        param_reqs = {"rank": None, "dtype": None, "shape": None}
-        error_result = False
-        error_reason = "Reshape output tensor contains more than one inferred dimension"
-
-        if check:
-            output_shape = kwargs["output_shape"]
-            inferences = 0
-            for dim in output_shape:
-                if dim == -1:
-                    inferences += 1
-            if inferences > 1:
-                error_result = True
-
-        info_dict = {
-            "error_name": error_name,
-            "error_result": error_result,
-            "error_reason": error_reason,
-            "param_reqs": param_reqs,
-        }
-        return info_dict
-
-    @staticmethod
-    def evReshapeOutputSizeNonInteger(check=False, **kwargs):
-        error_name = ErrorIf.ReshapeOutputSizeNonInteger
-        param_reqs = {"rank": None, "dtype": None, "shape": None}
-        error_result = False
-        error_reason = "Reshape inferred output tensor dimension is non-integer"
-
-        if check:
-            input_shape = kwargs["input_shape"]
-            output_shape = kwargs["output_shape"]
-            input_size = np.prod(input_shape)
-            output_size = 1
-            for dim in output_shape:
-                if dim != -1:
-                    output_size *= dim
-            if -1 in output_shape and input_size % output_size != 0:
-                error_result = True
-
-        info_dict = {
-            "error_name": error_name,
-            "error_result": error_result,
-            "error_reason": error_reason,
-            "param_reqs": param_reqs,
-        }
-        return info_dict
-
     @staticmethod
     def calculateBroadcastShape(input_shape_a, input_shape_b):
         if input_shape_a is not None and input_shape_b is not None:
-- 
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