NicolasHug
commented
1 year ago I think that's because of the batch norm @hamoudyounesyounes . By definition, batch norm expects a batch.
If you're not training the model, set model.eval() and you'll be able to use single images as input.
š Describe the bug
the model only runs if the batch size is > 1 e.g. image_input = torch.randn(2, 3, 224, 224), is this intended?
Using (1, 3, 224, 224) results in a ShapeProp error. Here's the code to replicate it
def deeplabv3_resnet50(pretrained=False): model = models.segmentation.deeplabv3_resnet50(weights=None) return model
image_input = torch.randn(1, 3, 224, 224) model = deeplabv3_resnet50() with torch.no_grad(): out = model(image_input)
Versions
PyTorch version: 2.0.1+cu117 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.2 LTS (x86_64) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0 Clang version: 14.0.0-1ubuntu1 CMake version: version 3.26.3 Libc version: glibc-2.35
Python version: 3.10.6 (main, Mar 10 2023, 10:55:28) [GCC 11.3.0] (64-bit runtime) Python platform: Linux-5.10.16.3-microsoft-standard-WSL2-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: 39 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 12 On-line CPU(s) list: 0-11 Vendor ID: GenuineIntel Model name: 12th Gen Intel(R) Core(TM) i7-1255U CPU family: 6 Model: 154 Thread(s) per core: 2 Core(s) per socket: 6 Socket(s): 1 Stepping: 4 BogoMIPS: 5222.39 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves umip waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm serialize flush_l1d arch_capabilities Virtualization: VT-x Hypervisor vendor: Microsoft Virtualization type: full L1d cache: 288 KiB (6 instances) L1i cache: 192 KiB (6 instances) L2 cache: 7.5 MiB (6 instances) L3 cache: 12 MiB (1 instance) 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: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected
Versions of relevant libraries: [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.24.4 [pip3] onnx2pytorch==0.4.1 [pip3] torch==2.0.1 [pip3] torch-scatter==2.1.1 [pip3] torchinfo==1.8.0 [pip3] torchlop==0.2 [pip3] torchsummary==1.5.1 [pip3] torchvision==0.15.2 [pip3] triton==2.0.0 [conda] Could not collect