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This PR is part of a series attempting to re-submit https://github.com/pytorch/pytorch/pull/134592 as smaller PRs. Add missing `if __name__ == "__main__":` guards to some tests. Pull Request resolved: https://github.com/pytorch/pytorch/pull/154716 Approved by: https://github.com/Skylion007
102 lines
3.2 KiB
Python
102 lines
3.2 KiB
Python
#!/usr/bin/env python3
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# Owner(s): ["oncall: mobile"]
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# mypy: allow-untyped-defs
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import io
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import cv2 # @manual
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import torch
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import torch.utils.bundled_inputs
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from torch.testing._internal.common_utils import TestCase
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torch.ops.load_library("//caffe2/torch/fb/operators:decode_bundled_image")
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def model_size(sm):
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buffer = io.BytesIO()
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torch.jit.save(sm, buffer)
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return len(buffer.getvalue())
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def save_and_load(sm):
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buffer = io.BytesIO()
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torch.jit.save(sm, buffer)
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buffer.seek(0)
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return torch.jit.load(buffer)
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"""Return an InflatableArg that contains a tensor of the compressed image and the way to decode it
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keyword arguments:
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img_tensor -- the raw image tensor in HWC or NCHW with pixel value of type unsigned int
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if in NCHW format, N should be 1
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quality -- the quality needed to compress the image
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"""
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def bundle_jpeg_image(img_tensor, quality):
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# turn NCHW to HWC
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if img_tensor.dim() == 4:
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assert img_tensor.size(0) == 1
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img_tensor = img_tensor[0].permute(1, 2, 0)
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pixels = img_tensor.numpy()
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encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality]
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_, enc_img = cv2.imencode(".JPEG", pixels, encode_param)
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enc_img_tensor = torch.from_numpy(enc_img)
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enc_img_tensor = torch.flatten(enc_img_tensor).byte()
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obj = torch.utils.bundled_inputs.InflatableArg(
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enc_img_tensor, "torch.ops.fb.decode_bundled_image({})"
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)
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return obj
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def get_tensor_from_raw_BGR(im) -> torch.Tensor:
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raw_data = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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raw_data = torch.from_numpy(raw_data).float()
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raw_data = raw_data.permute(2, 0, 1)
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raw_data = torch.div(raw_data, 255).unsqueeze(0)
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return raw_data
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class TestBundledImages(TestCase):
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def test_single_tensors(self):
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class SingleTensorModel(torch.nn.Module):
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def forward(self, arg):
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return arg
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im = cv2.imread("caffe2/test/test_img/p1.jpg")
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tensor = torch.from_numpy(im)
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inflatable_arg = bundle_jpeg_image(tensor, 90)
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input = [(inflatable_arg,)]
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sm = torch.jit.script(SingleTensorModel())
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torch.utils.bundled_inputs.augment_model_with_bundled_inputs(sm, input)
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loaded = save_and_load(sm)
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inflated = loaded.get_all_bundled_inputs()
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decoded_data = inflated[0][0]
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# raw image
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raw_data = get_tensor_from_raw_BGR(im)
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self.assertEqual(len(inflated), 1)
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self.assertEqual(len(inflated[0]), 1)
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self.assertEqual(raw_data.shape, decoded_data.shape)
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self.assertEqual(raw_data, decoded_data, atol=0.1, rtol=1e-01)
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# Check if fb::image_decode_to_NCHW works as expected
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with open("caffe2/test/test_img/p1.jpg", "rb") as fp:
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weight = torch.full((3,), 1.0 / 255.0).diag()
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bias = torch.zeros(3)
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byte_tensor = torch.tensor(list(fp.read())).byte()
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im2_tensor = torch.ops.fb.image_decode_to_NCHW(byte_tensor, weight, bias)
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self.assertEqual(raw_data.shape, im2_tensor.shape)
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self.assertEqual(raw_data, im2_tensor, atol=0.1, rtol=1e-01)
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if __name__ == "__main__":
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raise RuntimeError(
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"This test is not currently used and should be "
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"enabled in discover_tests.py if required."
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)
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