Fix output table column names and float numbers format

[spital]

Small fix / improvement of the output table markdown file.
(1) added PSNR and MSE columns
(2) changed format to %.5g to see numbers in a bit nicer format
Tested with NVDA Torch containers on RTX3080ti, seems fine:

Name	Data Type	Input Shapes	torch2trt kwargs	Max Error	Peak Signal to Noise Ratio	Mean Squared Error	Throughput (PyTorch)	Throughput (TensorRT)	Latency (PyTorch)	Latency (TensorRT)
torch2trt.tests.torchvision.classification.alexnet	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	9.82E-05	nan	0.00E+00	1706.2	3989.4	0.66819	0.3019
torch2trt.tests.torchvision.classification.squeezenet1_0	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	1.95E-03	83.18	7.37E-08	443.78	5379.2	2.2734	0.23403
torch2trt.tests.torchvision.classification.squeezenet1_1	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	1.95E-03	78.75	8.20E-08	467.02	6372.6	2.1582	0.20253
torch2trt.tests.torchvision.classification.resnet18	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	9.77E-03	68.15	7.10E-06	437.28	4456.4	2.3051	0.27306
torch2trt.tests.torchvision.classification.resnet34	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	2.50E-01	67.56	4.39E-03	246.57	2500.8	4.0779	0.45222
torch2trt.tests.torchvision.classification.resnet50	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	1.41E-01	69.97	2.16E-03	167.71	1978.3	5.9556	0.55828
torch2trt.tests.torchvision.classification.resnet101	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	0.00E+00	nan	NAN	86.54	1054.1	11.533	0.92494
torch2trt.tests.torchvision.classification.resnet152	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	0.00E+00	nan	NAN	58.125	768.77	17.16	1.3138
torch2trt.tests.torchvision.classification.densenet121	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	1.17E-02	65.21	3.00E-06	70.819	421.54	14.134	2.2273
torch2trt.tests.torchvision.classification.densenet169	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	1.95E-03	77.21	2.89E-07	50.035	238.08	19.78	3.9731
torch2trt.tests.torchvision.classification.densenet201	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	1.95E-03	75.21	2.52E-07	41.468	175.19	23.495	5.6947
torch2trt.tests.torchvision.classification.densenet161	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	3.91E-03	74.17	6.70E-07	52.874	236.35	18.864	3.9961
torch2trt.tests.torchvision.classification.vgg11	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	2.29E-03	52.95	4.43E-07	1004.5	1771.8	1.1096	0.61939
torch2trt.tests.torchvision.classification.vgg13	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	2.08E-03	51.72	4.25E-07	802.06	1513.3	1.329	0.71259
torch2trt.tests.torchvision.classification.vgg16	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	2.10E-03	50.12	3.53E-07	650.73	1298.2	1.6187	0.82363
torch2trt.tests.torchvision.classification.vgg19	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	2.05E-03	49.17	4.50E-07	546.91	1130.8	1.9119	0.93664
torch2trt.tests.torchvision.classification.vgg11_bn	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	3.99E-03	49.57	1.19E-06	853.21	1756.4	1.3895	0.62139
torch2trt.tests.torchvision.classification.vgg13_bn	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	2.38E-03	49.36	4.63E-07	703.61	1503.9	1.5963	0.716
torch2trt.tests.torchvision.classification.vgg16_bn	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	2.00E-03	49.34	4.13E-07	566.44	1289.8	1.9695	0.82836
torch2trt.tests.torchvision.classification.vgg19_bn	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	2.44E-03	48.31	6.53E-07	471.05	1132.4	2.3146	0.93495
torch2trt.tests.torchvision.classification.mobilenet_v2	float16	[(1, 3, 224, 224)]	{'fp16_mode': True}	0.00E+00	nan	0.00E+00	201.1	3408.5	4.96	0.34366