torchvision Caltech101 collate_fn error
Labels: triaged

### 🐛 Describe the bug

The Caltech101 dataset loads grayscale images as a single channel, resulting in errors when collating images into a batch.

Code to reproduce:
````python
from torchvision.datasets import Caltech101
from torchvision import transforms as T
from torch.utils.data.dataloader import DataLoader

dataset = Caltech101(
    root='/path/to/data',
    transform=T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor()]),
)
loader = DataLoader(
    dataset, batch_size=128, shuffle=False, num_workers=2, pin_memory=False
)

for i, (x, y) in enumerate(loader):
    print(x.shape, y.shape)
````

Output:
````
(torch)bash-4.2$ python experiments/classification/caltechbug.py 
Files already downloaded and verified
torch.Size([128, 3, 224, 224]) torch.Size([128])
torch.Size([128, 3, 224, 224]) torch.Size([128])
torch.Size([128, 3, 224, 224]) torch.Size([128])
torch.Size([128, 3, 224, 224]) torch.Size([128])
torch.Size([128, 3, 224, 224]) torch.Size([128])
torch.Size([128, 3, 224, 224]) torch.Size([128])
torch.Size([128, 3, 224, 224]) torch.Size([128])
torch.Size([128, 3, 224, 224]) torch.Size([128])
torch.Size([128, 3, 224, 224]) torch.Size([128])
torch.Size([128, 3, 224, 224]) torch.Size([128])
torch.Size([128, 3, 224, 224]) torch.Size([128])
torch.Size([128, 3, 224, 224]) torch.Size([128])
Traceback (most recent call last):
  File "/tudelft.net/staff-bulk/ewi/insy/VisionLab/attilalengyel/phd-repos/color-equivariance/experiments/classification/caltechbug.py", line 15, in <module>
    for i, (x, y) in enumerate(loader):
  File "/home/nfs/attilalengyel/envs/torch/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 681, in __next__
    data = self._next_data()
  File "/home/nfs/attilalengyel/envs/torch/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1376, in _next_data
    return self._process_data(data)
  File "/home/nfs/attilalengyel/envs/torch/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1402, in _process_data
    data.reraise()
  File "/home/nfs/attilalengyel/envs/torch/lib/python3.9/site-packages/torch/_utils.py", line 461, in reraise
    raise exception
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/nfs/attilalengyel/envs/torch/lib/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 302, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/nfs/attilalengyel/envs/torch/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
    return self.collate_fn(data)
  File "/home/nfs/attilalengyel/envs/torch/lib/python3.9/site-packages/torch/utils/data/_utils/collate.py", line 175, in default_collate
    return [default_collate(samples) for samples in transposed]  # Backwards compatibility.
  File "/home/nfs/attilalengyel/envs/torch/lib/python3.9/site-packages/torch/utils/data/_utils/collate.py", line 175, in <listcomp>
    return [default_collate(samples) for samples in transposed]  # Backwards compatibility.
  File "/home/nfs/attilalengyel/envs/torch/lib/python3.9/site-packages/torch/utils/data/_utils/collate.py", line 140, in default_collate
    out = elem.new(storage).resize_(len(batch), *list(elem.size()))
RuntimeError: Trying to resize storage that is not resizable
````

Possible fix:
Add `.convert('RGB')` to `torchvision.datasets.caltech` line 106.

### Versions

(torch)bash-4.2$ python collect_env.py
Collecting environment information...
PyTorch version: 1.12.1
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: CentOS Linux release 7.9.2009 (Core) (x86_64)
GCC version: (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)
Clang version: Could not collect
CMake version: version 2.8.12.2
Libc version: glibc-2.17

Python version: 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:56:21)  [GCC 10.3.0] (64-bit runtime)
Python platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: 
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1080 Ti
Nvidia driver version: 525.60.13
cuDNN version: Could not collect
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
Byte Order:            Little Endian
CPU(s):                64
On-line CPU(s) list:   0-63
Thread(s) per core:    2
Core(s) per socket:    16
Socket(s):             2
NUMA node(s):          2
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 79
Model name:            Intel(R) Xeon(R) CPU E5-2683 v4 @ 2.10GHz
Stepping:              1
CPU MHz:               2600.134
CPU max MHz:           3000.0000
CPU min MHz:           1200.0000
BogoMIPS:              4200.17
Virtualization:        VT-x
L1d cache:             32K
L1i cache:             32K
L2 cache:              256K
L3 cache:              40960K
NUMA node0 CPU(s):     0-15,32-47
NUMA node1 CPU(s):     16-31,48-63
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 arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu 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 epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd rsb_ctxsw ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear spec_ctrl intel_stibp flush_l1d

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.23.3
[pip3] pytorch-lightning==1.7.7
[pip3] torch==1.12.1
[pip3] torchinfo==1.7.1
[pip3] torchmetrics==0.10.0
[pip3] torchvision==0.13.1
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.6.0              hecad31d_10    conda-forge
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py39h7e14d7c_0    conda-forge
[conda] mkl_fft                   1.3.1            py39h0c7bc48_1    conda-forge
[conda] mkl_random                1.2.2            py39h51133e4_0  
[conda] numpy                     1.23.3           py39h14f4228_1  
[conda] numpy-base                1.23.3           py39h31eccc5_1  
[conda] pytorch                   1.12.1          py3.9_cuda11.6_cudnn8.3.2_0    pytorch
[conda] pytorch-lightning         1.7.7              pyhd8ed1ab_0    conda-forge
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchmetrics              0.10.0             pyhd8ed1ab_0    conda-forge
[conda] torchvision               0.13.1               py39_cu116    pytorch