push pipeline using my custom method

MyPipe.py

@@ -0,0 +1,76 @@
+
+import torch, os
+import torch.nn.functional as F
+from torchvision.transforms.functional import normalize
+import numpy as np
+from transformers import Pipeline
+from skimage import io
+from PIL import Image
+
+class RMBGPipe(Pipeline):
+  def __init__(self,**kwargs):
+    Pipeline.__init__(self,**kwargs)
+    self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    self.model.to(self.device)
+    self.model.eval()
+
+  def _sanitize_parameters(self, **kwargs):
+    # parse parameters
+    preprocess_kwargs = {}
+    postprocess_kwargs = {}
+    if "model_input_size" in kwargs : 
+      preprocess_kwargs["model_input_size"] = kwargs["model_input_size"]
+    if "out_name" in kwargs: 
+      postprocess_kwargs["out_name"] = kwargs["out_name"]
+    return preprocess_kwargs, {}, postprocess_kwargs
+
+  def preprocess(self,im_path:str,model_input_size: list=[1024,1024]):
+      # preprocess the input 
+      orig_im = io.imread(im_path)
+      orig_im_size = orig_im.shape[0:2]
+      image = self.preprocess_image(orig_im, model_input_size).to(self.device)
+      inputs = {
+          "image":image,
+          "orig_im_size":orig_im_size,
+          "im_path" : im_path
+      }
+      return inputs
+
+  def _forward(self,inputs):
+    result = self.model(inputs.pop("image"))
+    inputs["result"] = result
+    return inputs
+  def postprocess(self,inputs,out_name = ""):
+    result = inputs.pop("result")
+    orig_im_size = inputs.pop("orig_im_size")
+    im_path = inputs.pop("im_path")
+    result_image = self.postprocess_image(result[0][0], orig_im_size)
+    if out_name != "" : 
+      # if out_name is specified we save the image using that name
+      pil_im = Image.fromarray(result_image)
+      no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0))
+      orig_image = Image.open(im_path)
+      no_bg_image.paste(orig_image, mask=pil_im)
+      no_bg_image.save(out_name)
+    else : 
+      return result_image
+
+  # utilities functions
+  def preprocess_image(self,im: np.ndarray, model_input_size: list=[1024,1024]) -> torch.Tensor:
+    # same as utilities.py with minor modification
+    if len(im.shape) < 3:
+        im = im[:, :, np.newaxis]
+    # orig_im_size=im.shape[0:2]
+    im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
+    im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=model_input_size, mode='bilinear').type(torch.uint8)
+    image = torch.divide(im_tensor,255.0)
+    image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
+    return image
+  def postprocess_image(self,result: torch.Tensor, im_size: list)-> np.ndarray:
+      result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear') ,0)
+      ma = torch.max(result)
+      mi = torch.min(result)
+      result = (result-mi)/(ma-mi)
+      im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
+      im_array = np.squeeze(im_array)
+      return im_array

README.md

@@ -0,0 +1,201 @@
+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+

config.json

@@ -1,15 +1,25 @@
 {
-  "_name_or_path": "./out",
+  "_name_or_path": "not-lain/CustomCodeForRMBG",
   "architectures": [
     "BriaRMBG"
   ],
   "auto_map": {
-    "AutoConfig": "MyConfig.RMBGConfig",
-    "AutoModelForImageSegmentation": "briarmbg.BriaRMBG"
+    "AutoConfig": "not-lain/CustomCodeForRMBG--MyConfig.RMBGConfig",
+    "AutoModelForImageSegmentation": "not-lain/CustomCodeForRMBG--briarmbg.BriaRMBG"
+  },
+  "custom_pipelines": {
+    "image-segmentation": {
+      "impl": "MyPipe.RMBGPipe",
+      "pt": [
+        "AutoModelForImageSegmentation"
+      ],
+      "tf": [],
+      "type": "image"
+    }
   },
   "in_ch": 3,
   "model_type": "SegformerForSemanticSegmentation",
   "out_ch": 1,
   "torch_dtype": "float32",
-  "transformers_version": "4.35.2"
+  "transformers_version": "4.38.0.dev0"
 }