Add new 50ts attn models to benchmark/meta csv files

benchmark.py

@@ -279,18 +279,8 @@ class InferenceBenchmarkRunner(BenchmarkRunner):
             img_size=self.input_size[-1],
             param_count=round(self.param_count / 1e6, 2),
         )
-<<<<<<< Updated upstream
-        if not self.scripted:
-            if has_deepspeed_profiling:
-                macs, _ = profile_deepspeed(self.model, self.input_size)
-                results['gmacs'] = round(macs / 1e9, 2)
-            elif has_fvcore_profiling:
-                macs, activations = profile_fvcore(self.model, self.input_size)
-                results['gmacs'] = round(macs / 1e9, 2)
-                results['macts'] = round(activations / 1e6, 2)
-=======
 
-        retries = 2
+        retries = 0 if self.scripted else 2  # skip profiling if model is scripted
         while retries:
             retries -= 1
             try:
@@ -303,7 +293,6 @@ class InferenceBenchmarkRunner(BenchmarkRunner):
                     results['macts'] = round(activations / 1e6, 2)
             except RuntimeError as e:
                 pass
->>>>>>> Stashed changes
 
         _logger.info(
             f"Inference benchmark of {self.model_name} done. "

results/model_benchmark_amp_nchw_rtx3090.csv

@@ -241,6 +241,7 @@ repvgg_b1,1595.1,160.478,256,224,13.16,10.64,57.42
 xcit_tiny_24_p16_224_dist,1586.68,161.331,256,224,2.34,11.82,12.12
 xcit_tiny_24_p16_224,1585.43,161.458,256,224,2.34,11.82,12.12
 xcit_small_12_p16_224_dist,1555.24,164.59,256,224,4.82,12.58,26.25
+lambda_resnet50ts,1554.98,164.616,256,256,5.07,17.48,21.54
 xcit_small_12_p16_224,1551.41,164.997,256,224,4.82,12.58,26.25
 gluon_resnet101_v1c,1549.68,165.185,256,224,8.08,17.04,44.57
 resnest50d,1546.83,165.485,256,224,5.4,14.36,27.48
@@ -262,6 +263,7 @@ densenet201,1416.48,180.718,256,224,4.34,7.85,20.01
 resmlp_36_224,1415.51,180.84,256,224,8.91,16.33,44.69
 resmlp_36_distilled_224,1415.35,180.86,256,224,8.91,16.33,44.69
 gluon_resnet101_v1s,1409.69,181.589,256,224,9.19,18.64,44.67
+lamhalobotnet50ts_256,1390.79,184.054,256,256,5.02,18.44,22.57
 nf_resnet50,1387.87,184.443,256,288,6.88,18.37,25.56
 ecaresnet101d,1376.05,186.03,256,224,8.08,17.07,44.57
 vgg16,1375.43,186.112,256,224,15.47,13.56,138.36
@@ -289,6 +291,7 @@ gluon_resnext101_32x4d,1262.61,202.744,256,224,8.01,21.23,44.18
 swsl_resnext101_32x4d,1261.41,202.934,256,224,8.01,21.23,44.18
 vgg16_bn,1259.82,203.192,256,224,15.5,13.56,138.37
 repvgg_b2g4,1250.78,204.659,256,224,12.63,12.9,61.76
+halo2botnet50ts_256,1247.07,205.268,256,256,5.02,21.78,22.64
 swin_tiny_patch4_window7_224,1247.03,205.273,256,224,4.51,17.06,28.29
 twins_pcpvt_small,1237.18,206.911,256,224,3.83,18.08,24.11
 regnety_080,1231.05,207.941,256,224,8.0,17.97,39.18

results/model_metadata_in1k.csv

@@ -150,6 +150,7 @@ gluon_seresnext50_32x4d,in1k
 gluon_xception65,in1k
 gmixer_24_224,in1k
 gmlp_s16_224,in1k
+halo2botnet50ts_256,in1k
 halonet26t,in1k
 halonet50ts,in1k
 haloregnetz_b,in1k
@@ -180,6 +181,8 @@ jx_nest_small,in1k
 jx_nest_tiny,in1k
 lambda_resnet26rpt_256,in1k
 lambda_resnet26t,in1k
+lambda_resnet50ts,in1k
+lamhalobotnet50ts_256,in1k
 legacy_senet154,in1k
 legacy_seresnet101,in1k
 legacy_seresnet152,in1k