pytorch

d4c1684c - reuse consant from jit (#49916)

reuse consant from jit (#49916) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49916 Test Plan: 1. Build pytorch locally. `MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ USE_CUDA=0 DEBUG=1 MAX_JOBS=16 python setup.py develop` 2. Run `python save_lite.py` ``` import torch # ~/Documents/pytorch/data/dog.jpg model = torch.hub.load('pytorch/vision:v0.6.0', 'shufflenet_v2_x1_0', pretrained=True) model.eval() # sample execution (requires torchvision) from PIL import Image from torchvision import transforms import pathlib import tempfile import torch.utils.mobile_optimizer input_image = Image.open('~/Documents/pytorch/data/dog.jpg') preprocess = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) input_tensor = preprocess(input_image) input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model # move the input and model to GPU for speed if available if torch.cuda.is_available(): input_batch = input_batch.to('cuda') model.to('cuda') with torch.no_grad(): output = model(input_batch) # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes print(output[0]) # The output has unnormalized scores. To get probabilities, you can run a softmax on it. print(torch.nn.functional.softmax(output[0], dim=0)) traced = torch.jit.trace(model, input_batch) sum(p.numel() * p.element_size() for p in traced.parameters()) tf = pathlib.Path('~/Documents/pytorch/data/data/example_debug_map_with_tensorkey.ptl') torch.jit.save(traced, tf.name) print(pathlib.Path(tf.name).stat().st_size) traced._save_for_lite_interpreter(tf.name) print(pathlib.Path(tf.name).stat().st_size) print(tf.name) ``` 3. Run `python test_lite.py` ``` import torch from torch.jit.mobile import _load_for_lite_interpreter # sample execution (requires torchvision) from PIL import Image from torchvision import transforms input_image = Image.open('~/Documents/pytorch/data/dog.jpg') preprocess = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) input_tensor = preprocess(input_image) input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model reload_lite_model = _load_for_lite_interpreter('~/Documents/pytorch/experiment/example_debug_map_with_tensorkey.ptl') with torch.no_grad(): output_lite = reload_lite_model(input_batch) # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes print(output_lite[0]) # The output has unnormalized scores. To get probabilities, you can run a softmax on it. print(torch.nn.functional.softmax(output_lite[0], dim=0)) ``` 4. Compare the result with pytorch in master and pytorch built locally with this change, and see the same output. 5. The model size was 16.1 MB and becomes 12.9 with this change. Imported from OSS Reviewed By: kimishpatel, iseeyuan Differential Revision: D25731596 Pulled By: cccclai fbshipit-source-id: 9731ec1e0c1d5dc76cfa374d2ad3d5bb10990cf0