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[MO] Remove USRNet until they publish license #261

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Dec 9, 2021
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[MO] Remove USRNet until they publish license #261

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1 change: 0 additions & 1 deletion .ci/azure/linux.yml
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Expand Up @@ -144,7 +144,6 @@ jobs:
python3 -m pip install -U setuptools==51.0.0
python3 -m pip install -r requirements.txt
python3 -m pip install -r $(OPENVINO_REPO_DIR)/model-optimizer/requirements_onnx.txt
git clone https://github.com/cszn/USRNet && cd USRNet && git checkout 9564ad2 && cd ..
git clone https://github.com/CBICA/BrainMaGe && cd BrainMaGe && git checkout f188d94d8a44e23927b40ad11b311c97f26457a0 && python3 -m pip install . && cd ..
workingDirectory: $(REPO_DIR)/modules/mo_pytorch/test
displayName: 'Install PyTorch tests dependencies'
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57 changes: 0 additions & 57 deletions modules/mo_pytorch/test/run_tests.py
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Expand Up @@ -236,63 +236,6 @@ def test_rugpt3(self):
diff = np.max(np.abs(out - ref))
self.assertLessEqual(diff, 1e-4)

@unittest.skipIf(version.parse(torch.__version__) >= version.parse('1.8.0'), 'Deprecated FFT')
def test_usrnet(self):
sys.path.append('USRNet')
from models.network_usrnet import USRNet as net # for pytorch version <= 1.7.1
from utils import utils_deblur
from utils import utils_sisr as sr
from utils import utils_image as util

np.random.seed(324)
torch.manual_seed(32)
inp = np.random.standard_normal([1, 3, 56, 112]).astype(np.float32)
sf = 4

# source: https://github.com/cszn/USRNet/blob/master/main_test_realapplication.py
def get_kernel_sigma():
noise_level_img = 2 # noise level for LR image, 0.5~3 for clean images
kernel_width_default_x1234 = [0.4, 0.7, 1.5, 2.0] # default Gaussian kernel widths of clean/sharp images for x1, x2, x3, x4

noise_level_model = noise_level_img/255. # noise level of model
kernel_width = kernel_width_default_x1234[sf-1]

k = utils_deblur.fspecial('gaussian', 25, kernel_width)
k = sr.shift_pixel(k, sf) # shift the kernel
k /= np.sum(k)
kernel = util.single2tensor4(k[..., np.newaxis])
sigma = torch.tensor(noise_level_model).float().view([1, 1, 1, 1])

return kernel, sigma

kernel, sigma = get_kernel_sigma()

model = net(n_iter=8, h_nc=64, in_nc=4, out_nc=3, nc=[64, 128, 256, 512],
nb=2, act_mode="R", downsample_mode='strideconv', upsample_mode="convtranspose")
model.eval()

ref = model(torch.tensor(inp), kernel, sf, sigma)

# Forward random input through the model to check that nothing got stuck from reference data
dummy_inp = torch.randn(inp.shape)
dummy_kernel = torch.randn(kernel.shape)
dummy_sigma = torch.randn(sigma.shape)
model(dummy_inp, dummy_kernel, sf, dummy_sigma)

# Generate OpenVINO IR
mo_pytorch.convert(model, input_shape='[1, 3, 56, 112],[1, 1, 25, 25],[1, 1, 1, 1],[1]',
input='x{f32},k{f32},sigma{f32},sf{f32}->4',
model_name='model')

# Run model with OpenVINO and compare outputs
net = self.ie.read_network('model.xml')
exec_net = self.ie.load_network(net, 'CPU')
out = exec_net.infer({'x': inp, 'k': kernel, 'sigma': sigma})
out = next(iter(out.values()))

diff = np.max(np.abs(ref.detach().numpy() - out))
self.assertLessEqual(diff, 1e-4)


def test_mask_rcnn(self):
# For better efficiency, you may reduce parameters <box_detections_per_img> (default 100)
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