Upload ConditionalUNet

README.md

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+---
+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]
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+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
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+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
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+- **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. -->
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+[More Information Needed]
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+### Downstream Use [optional]
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+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+[More Information Needed]
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+### Out-of-Scope Use
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+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+[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. -->
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+[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. -->
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+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+[More Information Needed]
+
+#### Metrics
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+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
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+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
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+[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
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+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
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+[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. -->
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+**BibTeX:**
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+[More Information Needed]
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+**APA:**
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+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+[More Information Needed]
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+## More Information [optional]
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+[More Information Needed]
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+## Model Card Authors [optional]
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+[More Information Needed]
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+## Model Card Contact
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+[More Information Needed]

config.json

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+{
+  "architectures": [
+    "ConditionalUNet"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_conditional_unet.ConditionalUNetConfig",
+    "AutoModel": "modeling_conditional_unet.ConditionalUNet"
+  },
+  "encoder_rep": "evanrsl/resnet-Alzheimer",
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3"
+  },
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3
+  },
+  "model_type": "conditional-unet",
+  "num_channels": 3,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.2"
+}

configuration_conditional_unet.py

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+from transformers import PretrainedConfig
+
+class ConditionalUNetConfig(PretrainedConfig):
+    model_type = "conditional-unet"
+
+    def __init__(
+        self,
+        encoder_rep="evanrsl/resnet-Alzheimer",
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.encoder_rep = encoder_rep

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:612ef064ea689c6c9610c92f23af10d76320f288bfe26c238bff05aa9e3c6bb2
+size 293858844

modeling_conditional_unet.py

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+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, ResNetBackbone
+from .configuration_conditional_unet import ConditionalUNetConfig
+
+class UpSampleBlock(nn.Module):
+    def __init__(self, in_channels, skip_channels, out_channels, condition_size):
+        super(UpSampleBlock, self).__init__()
+        self.up = nn.Upsample(scale_factor=2, mode='nearest')
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channels + skip_channels + condition_size, out_channels, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        )
+
+    def forward(self, x, skip, condition, upsample=True):
+        if upsample:
+            x = self.up(x)
+        b, _, h, w = x.size()
+        # Expand condition to match spatial dimensions
+        condition = condition.view(b, -1, 1, 1).expand(-1, -1, h, w)
+        x = torch.cat([x, skip, condition], dim=1)
+        x = self.conv(x)
+        return x
+
+class ConditionalUNet(PreTrainedModel):
+    config_class = ConditionalUNetConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        # self.config_class = 'configuration_conditional_unet.ConditionalUNetConfig'
+        self.config = config
+
+        self.encoder_rep = config.encoder_rep
+        self.encoder = ResNetBackbone.from_pretrained(
+            self.encoder_rep,
+            return_dict=False,
+            output_hidden_states=True
+        )
+        self.encoder.eval()
+        self.encoder.requires_grad_(False)
+
+        self.num_labels = self.encoder.config.num_labels
+        self.num_channels = self.encoder.config.num_channels
+
+        self.config.num_labels = self.num_labels
+        self.config.num_channels = self.num_channels
+
+        hidden_sizes = self.encoder.config.hidden_sizes
+        embedding_size = self.encoder.config.embedding_size
+
+        self.up_blocks = nn.ModuleList()
+        num_stages = len(hidden_sizes)
+
+        in_channels = hidden_sizes[-1]
+        for i in range(num_stages - 1, -1, -1):
+            skip_channels = hidden_sizes[i - 1] if i > 0 else embedding_size
+            out_channels = skip_channels
+            self.up_blocks.append(
+                UpSampleBlock(
+                    in_channels=in_channels,
+                    skip_channels=skip_channels,
+                    out_channels=out_channels,
+                    condition_size=self.num_labels
+                )
+            )
+            in_channels = out_channels
+
+        self.final_conv = nn.Sequential(
+            nn.Conv2d(in_channels + self.num_labels, in_channels, kernel_size=3, padding=1),
+            nn.BatchNorm2d(in_channels),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels, self.num_channels, kernel_size=1)
+        )
+
+    def forward(self, x, condition):
+        outputs = self.encoder(x)[-1]
+        x_stages = outputs[::-1]
+        x = x_stages[0]
+
+        for i, up_block in enumerate(self.up_blocks):
+            skip = x_stages[i + 1] if i + 1 < len(x_stages) else None
+            upsample = i < len(self.up_blocks) - 1
+            if skip is not None:
+                x = up_block(x, skip, condition, upsample=upsample)
+            else:
+                x = up_block(x, torch.zeros_like(x), condition, upsample=upsample)
+
+        x_upsampled = nn.functional.interpolate(x, scale_factor=4, mode='bilinear', align_corners=False)
+        b, _, h, w = x_upsampled.size()
+        condition_expanded = condition.view(b, -1, 1, 1).expand(-1, -1, h, w)
+        final_input = torch.cat([x_upsampled, condition_expanded], dim=1)
+        output = self.final_conv(final_input)
+
+        return output