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[to #42322933] Update tasks of face_image_generation and image_super_resolution

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Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9465050
This commit is contained in:
baiguan.yt
2022-07-21 18:17:25 +08:00
parent fd4a027a07
commit 52725fa3e9
8 changed files with 32 additions and 15 deletions
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3
modelscope/models/cv/face_generation/op/conv2d_gradfix.py
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@@ -88,9 +88,6 @@ def could_use_op(input):
if input.device.type != 'cuda':
return False
if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.']):
return True
warnings.warn(
f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().'
)

3
modelscope/outputs.py
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@@ -70,7 +70,8 @@ TASK_OUTPUTS = {
Tasks.image_editing: [OutputKeys.OUTPUT_IMG],
Tasks.image_matting: [OutputKeys.OUTPUT_IMG],
Tasks.image_generation: [OutputKeys.OUTPUT_IMG],
Tasks.image_restoration: [OutputKeys.OUTPUT_IMG],
Tasks.face_image_generation: [OutputKeys.OUTPUT_IMG],
Tasks.image_super_resolution: [OutputKeys.OUTPUT_IMG],
# action recognition result for single video
# {

6
modelscope/pipelines/builder.py
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@@ -69,7 +69,11 @@ DEFAULT_MODEL_FOR_PIPELINE = {
(Pipelines.text_to_image_synthesis,
'damo/cv_imagen_text-to-image-synthesis_tiny'),
Tasks.style_transfer: (Pipelines.style_transfer,
'damo/cv_aams_style-transfer_damo')
'damo/cv_aams_style-transfer_damo'),
Tasks.face_image_generation: (Pipelines.face_image_generation,
'damo/cv_gan_face-image-generation'),
Tasks.image_super_resolution: (Pipelines.image_super_resolution,
'damo/cv_rrdb_image-super-resolution'),
}

9
modelscope/pipelines/cv/face_image_generation_pipeline.py
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@@ -20,16 +20,17 @@ logger = get_logger()
@PIPELINES.register_module(
Tasks.image_generation, module_name=Pipelines.face_image_generation)
Tasks.face_image_generation, module_name=Pipelines.face_image_generation)
class FaceImageGenerationPipeline(Pipeline):
def __init__(self, model: str):
"""
use `model` and `preprocessor` to create a kws pipeline for prediction
use `model` to create a kws pipeline for prediction
Args:
model: model id on modelscope hub.
"""
super().__init__(model=model)
self.device = 'cpu'
self.size = 1024
self.latent = 512
self.n_mlp = 8
@@ -40,7 +41,7 @@ class FaceImageGenerationPipeline(Pipeline):
self.size,
self.latent,
self.n_mlp,
channel_multiplier=self.channel_multiplier)
channel_multiplier=self.channel_multiplier).to(self.device)
self.model_file = f'{model}/{ModelFile.TORCH_MODEL_FILE}'
@@ -63,7 +64,7 @@ class FaceImageGenerationPipeline(Pipeline):
torch.cuda.manual_seed_all(input)
self.generator.eval()
with torch.no_grad():
sample_z = torch.randn(1, self.latent)
sample_z = torch.randn(1, self.latent).to(self.device)
sample, _ = self.generator([sample_z],
truncation=self.truncation,

8
modelscope/pipelines/cv/image_super_resolution_pipeline.py
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@@ -19,7 +19,7 @@ logger = get_logger()
@PIPELINES.register_module(
Tasks.image_restoration, module_name=Pipelines.image_super_resolution)
Tasks.image_super_resolution, module_name=Pipelines.image_super_resolution)
class ImageSuperResolutionPipeline(Pipeline):
def __init__(self, model: str):
@@ -29,6 +29,7 @@ class ImageSuperResolutionPipeline(Pipeline):
model: model id on modelscope hub.
"""
super().__init__(model=model)
self.device = 'cpu'
self.num_feat = 64
self.num_block = 23
self.scale = 4
@@ -38,7 +39,7 @@ class ImageSuperResolutionPipeline(Pipeline):
num_feat=self.num_feat,
num_block=self.num_block,
num_grow_ch=32,
scale=self.scale)
scale=self.scale).to(self.device)
model_path = f'{self.model}/{ModelFile.TORCH_MODEL_FILE}'
self.sr_model.load_state_dict(torch.load(model_path), strict=True)
@@ -58,7 +59,8 @@ class ImageSuperResolutionPipeline(Pipeline):
raise TypeError(f'input should be either str, PIL.Image,'
f' np.array, but got {type(input)}')
img = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0) / 255.
img = torch.from_numpy(img).to(self.device).permute(
2, 0, 1).unsqueeze(0) / 255.
result = {'img': img}
return result

3
modelscope/utils/constant.py
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@@ -27,7 +27,8 @@ class CVTasks(object):
ocr_detection = 'ocr-detection'
action_recognition = 'action-recognition'
video_embedding = 'video-embedding'
image_restoration = 'image-restoration'
face_image_generation = 'face-image-generation'
image_super_resolution = 'image-super-resolution'
style_transfer = 'style-transfer'

8
tests/pipelines/test_face_image_generation.py
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@@ -27,11 +27,17 @@ class FaceGenerationTest(unittest.TestCase):
def test_run_modelhub(self):
seed = 10
face_generation = pipeline(
Tasks.image_generation,
Tasks.face_image_generation,
model=self.model_id,
)
self.pipeline_inference(face_generation, seed)
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
def test_run_modelhub_default_model(self):
seed = 10
face_generation = pipeline(Tasks.face_image_generation)
self.pipeline_inference(face_generation, seed)
if __name__ == '__main__':
unittest.main()

7
tests/pipelines/test_image_super_resolution.py
View File

@@ -28,10 +28,15 @@ class ImageSuperResolutionTest(unittest.TestCase):
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
def test_run_modelhub(self):
super_resolution = pipeline(
Tasks.image_restoration, model=self.model_id)
Tasks.image_super_resolution, model=self.model_id)
self.pipeline_inference(super_resolution, self.img)
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
def test_run_modelhub_default_model(self):
super_resolution = pipeline(Tasks.image_super_resolution)
self.pipeline_inference(super_resolution, self.img)
if __name__ == '__main__':
unittest.main()