Performance issue in Videoreader's pyav backend due to unnecessary data copy

[imagazin-intel]

🐛 Describe the bug

Data copy in the tensor.tensor() constructor in the Videoreader.__next__() method for the pyav backend is leading to a significant CPU load and a decrease in potential performance.
The data copy here is unnecessary because the original frame from the pyav backend is not used again in the Videoreader and is replaced with the tensor (see code snipped below).

frame = next(self._c) 
pts = float(frame.pts * frame.time_base)
if "video" in self.pyav_stream: 
	frame = torch.tensor(frame.to_rgb().to_ndarray()).permute(2, 0, 1)

Link to code above.
Performance Impact Example on Intel® Xeon® Gold 6336Y Processor:

    def decode(container):
        frame = next(container.decode(video=0))
        return torch.as_tensor(frame.to_rgb().to_ndarray()).permute(2, 0, 1)

Processing of 10000 FullHD frames with the method from the above takes 36 seconds and overall CPU usage during processing is about 3.9%.
The same method but with torch.tensor() like in the original Videoreader code takes 97 seconds to process the same amount of frames and overall CPU usage during processing is about 65%.

Versions

Collecting environment information...
PyTorch version: 2.1.2
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: Could not collect
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.11.5 (main, Sep 11 2023, 13:54:46) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.47+prerelease23.9.13-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Address sizes:                   52 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          48
On-line CPU(s) list:             0-47
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Gold 6336Y CPU @ 2.40GHz
CPU family:                      6
Model:                           106
Thread(s) per core:              1
Core(s) per socket:              24
Socket(s):                       2
Stepping:                        6
CPU max MHz:                     3600.0000
CPU min MHz:                     800.0000
BogoMIPS:                        4800.00
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       2.3 MiB (48 instances)
L1i cache:                       1.5 MiB (48 instances)
L2 cache:                        60 MiB (48 instances)
L3 cache:                        72 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-23
NUMA node1 CPU(s):               24-47
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Vulnerable: eIBRS with unprivileged eBPF
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.2
[pip3] torch==2.1.2
[pip3] torchaudio==2.1.2
[pip3] torchvision==0.16.2
[conda] blas                      1.0                         mkl  
[conda] cpuonly                   2.0                           0    pytorch
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-service               2.4.0           py311h5eee18b_1  
[conda] mkl_fft                   1.3.8           py311h5eee18b_0  
[conda] mkl_random                1.2.4           py311hdb19cb5_0  
[conda] numpy                     1.26.2          py311h08b1b3b_0  
[conda] numpy-base                1.26.2          py311hf175353_0  
[conda] pytorch                   2.1.2              py3.11_cpu_0    pytorch
[conda] pytorch-mutex             1.0                         cpu    pytorch
[conda] torchaudio                2.1.2                 py311_cpu    pytorch
[conda] torchvision               0.16.2                py311_cpu    pytorch

[pmeier]

Closed in #8173.