| 67 | |
| 68 | |
| 69 | class INFER_API: |
| 70 | |
| 71 | _instance = None |
| 72 | |
| 73 | def __new__(cls): |
| 74 | if cls._instance is None: |
| 75 | cls._instance = super(INFER_API, cls).__new__(cls) |
| 76 | cls._instance.initialize() |
| 77 | return cls._instance |
| 78 | |
| 79 | def initialize(self): |
| 80 | self.transformer_ = [create_transforms_inference(h=512, w=512), |
| 81 | create_transforms_inference1(h=512, w=512), |
| 82 | create_transforms_inference2(h=512, w=512), |
| 83 | create_transforms_inference3(h=512, w=512), |
| 84 | create_transforms_inference4(h=512, w=512), |
| 85 | create_transforms_inference5(h=512, w=512)] |
| 86 | self.srm = SRMConv2d_simple() |
| 87 | |
| 88 | # model init |
| 89 | self.model = load_model('all', 2) |
| 90 | model_path = './final_model_csv/final_model.pth' |
| 91 | self.model = extract_model_from_pth(model_path, self.model) |
| 92 | |
| 93 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| 94 | self.model = self.model.to(device) |
| 95 | |
| 96 | self.model.eval() |
| 97 | |
| 98 | def _add_new_channels_worker(self, image): |
| 99 | new_channels = [] |
| 100 | |
| 101 | image = einops.rearrange(image, "h w c -> c h w") |
| 102 | image = (image - torch.as_tensor(timm.data.constants.IMAGENET_DEFAULT_MEAN).view(-1, 1, 1)) / torch.as_tensor( |
| 103 | timm.data.constants.IMAGENET_DEFAULT_STD).view(-1, 1, 1) |
| 104 | srm = self.srm(image.unsqueeze(0)).squeeze(0) |
| 105 | new_channels.append(einops.rearrange(srm, "c h w -> h w c").numpy()) |
| 106 | |
| 107 | new_channels = np.concatenate(new_channels, axis=2) |
| 108 | return torch.from_numpy(new_channels).float() |
| 109 | |
| 110 | def add_new_channels(self, images): |
| 111 | images_copied = einops.rearrange(images, "c h w -> h w c") |
| 112 | new_channels = self._add_new_channels_worker(images_copied) |
| 113 | images_copied = torch.concatenate([images_copied, new_channels], dim=-1) |
| 114 | images_copied = einops.rearrange(images_copied, "h w c -> c h w") |
| 115 | |
| 116 | return images_copied |
| 117 | |
| 118 | def test(self, img_path): |
| 119 | # img load |
| 120 | img_data = Image.open(img_path).convert('RGB') |
| 121 | |
| 122 | # transform |
| 123 | all_data = [] |
| 124 | for transform in self.transformer_: |
| 125 | current_data = transform(img_data) |
| 126 | current_data = self.add_new_channels(current_data) |
no outgoing calls
no test coverage detected