From ffd1d3decb820994a6592dd2a4718bc64d88e4d9 Mon Sep 17 00:00:00 2001
From: AshburnLee <1578034415@qq.com>
Date: Tue, 8 Feb 2022 06:23:49 +0000
Subject: [PATCH] compute flops&bytes for bilinear_interp

---
 api/dynamic_tests_v2/interp_bilinear.py | 32 +++++++++++++++++++++++++
 1 file changed, 32 insertions(+)

diff --git a/api/dynamic_tests_v2/interp_bilinear.py b/api/dynamic_tests_v2/interp_bilinear.py
index 492782aea8..19df1b6cc7 100644
--- a/api/dynamic_tests_v2/interp_bilinear.py
+++ b/api/dynamic_tests_v2/interp_bilinear.py
@@ -45,6 +45,38 @@ def build_graph(self, config):
         if config.backward:
             self.append_gradients(out, [x])
 
+    def compute_flop_and_byte(self, config):
+        # at least one of out_shape and scale must be set 
+        x_shape = config.x_shape
+        out_size = config.size
+
+        assert (config.scale_factor is not None or out_size is not None
+                ), "at least one of out_shape and scale must be set"
+        # config.size has higher priority than config.scale_factor
+        if isinstance(out_size, (list, tuple)):
+            out_shape = x_shape[0:-len(out_size)] + out_size
+        # scale_factor shouldn`t to be a float in bilinear mode
+        elif isinstance(config.scale_factor, (list, tuple)):
+            scale_length = len(config.scale_factor)
+            change_out = x_shape[-scale_length:]
+            scale_out = [
+                i * j for i, j in zip(change_out, config.scale_factor)
+            ]
+            out_shape = x_shape[0:-scale_length] + scale_out
+
+        # forward flops, sub*10 + mul*9 + div*1 + add*3
+        forward_flop = numel(out_shape) * 23
+
+        # forward byte, read 4 address to compute 1 address
+        read_byte = 4 * numel(out_shape) * sizeof(config.x_dtype)
+        write_byte = numel(out_shape) * sizeof(config.x_dtype)
+        forward_byte = read_byte + write_byte
+        if not config.backward:
+            return forward_flop, forward_byte
+        else:
+            # to be implemented.
+            return None, None
+
 
 class TorchInterpBilinear(PytorchAPIBenchmarkBase):
     def build_graph(self, config):