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#!/usr/bin/env python3
# Owner(s): ["oncall: mobile"]
# mypy: allow-untyped-defs
import io
import cv2 # @manual
import torch
import torch.utils.bundled_inputs
from torch.testing._internal.common_utils import TestCase
torch.ops.load_library("//caffe2/torch/fb/operators:decode_bundled_image")
def model_size(sm):
buffer = io.BytesIO()
torch.jit.save(sm, buffer)
return len(buffer.getvalue())
def save_and_load(sm):
buffer = io.BytesIO()
torch.jit.save(sm, buffer)
buffer.seek(0)
return torch.jit.load(buffer)
"""Return an InflatableArg that contains a tensor of the compressed image and the way to decode it
keyword arguments:
img_tensor -- the raw image tensor in HWC or NCHW with pixel value of type unsigned int
if in NCHW format, N should be 1
quality -- the quality needed to compress the image
"""
def bundle_jpeg_image(img_tensor, quality):
# turn NCHW to HWC
if img_tensor.dim() == 4:
assert img_tensor.size(0) == 1
img_tensor = img_tensor[0].permute(1, 2, 0)
pixels = img_tensor.numpy()
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality]
_, enc_img = cv2.imencode(".JPEG", pixels, encode_param)
enc_img_tensor = torch.from_numpy(enc_img)
enc_img_tensor = torch.flatten(enc_img_tensor).byte()
obj = torch.utils.bundled_inputs.InflatableArg(
enc_img_tensor, "torch.ops.fb.decode_bundled_image({})"
)
return obj
def get_tensor_from_raw_BGR(im) -> torch.Tensor:
raw_data = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
raw_data = torch.from_numpy(raw_data).float()
raw_data = raw_data.permute(2, 0, 1)
raw_data = torch.div(raw_data, 255).unsqueeze(0)
return raw_data
class TestBundledImages(TestCase):
def test_single_tensors(self):
class SingleTensorModel(torch.nn.Module):
def forward(self, arg):
return arg
im = cv2.imread("caffe2/test/test_img/p1.jpg")
tensor = torch.from_numpy(im)
inflatable_arg = bundle_jpeg_image(tensor, 90)
input = [(inflatable_arg,)]
sm = torch.jit.script(SingleTensorModel())
torch.utils.bundled_inputs.augment_model_with_bundled_inputs(sm, input)
loaded = save_and_load(sm)
inflated = loaded.get_all_bundled_inputs()
decoded_data = inflated[0][0]
# raw image
raw_data = get_tensor_from_raw_BGR(im)
self.assertEqual(len(inflated), 1)
self.assertEqual(len(inflated[0]), 1)
self.assertEqual(raw_data.shape, decoded_data.shape)
self.assertEqual(raw_data, decoded_data, atol=0.1, rtol=1e-01)
# Check if fb::image_decode_to_NCHW works as expected
with open("caffe2/test/test_img/p1.jpg", "rb") as fp:
weight = torch.full((3,), 1.0 / 255.0).diag()
bias = torch.zeros(3)
byte_tensor = torch.tensor(list(fp.read())).byte()
im2_tensor = torch.ops.fb.image_decode_to_NCHW(byte_tensor, weight, bias)
self.assertEqual(raw_data.shape, im2_tensor.shape)
self.assertEqual(raw_data, im2_tensor, atol=0.1, rtol=1e-01)
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