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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]
+- **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]

SwinCXRConfig.py

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+from transformers import PretrainedConfig
+
+class SwinCXRConfig(PretrainedConfig):
+    model_type = "swin_cxr"
+
+    def __init__(self, num_classes=3, embed_dim=128, num_heads=4, num_layers=4, dropout=0.1, **kwargs):
+        self.num_classes = num_classes
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.num_layers = num_layers
+        self.dropout = dropout
+        super().__init__(**kwargs)

SwinModelForCXRClassification.py

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+import torch
+import torch.nn as nn
+from torch.nn import LayerNorm, Linear, Dropout
+from torch.nn.functional import gelu
+from transformers import PretrainedConfig, PreTrainedModel
+from SwinCXRConfig import SwinCXRConfig
+
+class SwinSelfAttention(nn.Module):
+    def __init__(self, embed_dim, num_heads, dropout):
+        super(SwinSelfAttention, self).__init__()
+        self.query = Linear(embed_dim, embed_dim)
+        self.key = Linear(embed_dim, embed_dim)
+        self.value = Linear(embed_dim, embed_dim)
+        self.dropout = Dropout(p=dropout)
+
+    def forward(self, x):
+        query = self.query(x)
+        key = self.key(x)
+        value = self.value(x)
+        attention_weights = torch.matmul(query, key.transpose(-2, -1)) / query.size(-1)**0.5
+        attention_weights = torch.nn.functional.softmax(attention_weights, dim=-1)
+        attention_output = torch.matmul(attention_weights, value)
+        return self.dropout(attention_output)
+
+class SwinLayer(nn.Module):
+    def __init__(self, embed_dim, num_heads, dropout=0.1):
+        super(SwinLayer, self).__init__()
+        self.layernorm_before = LayerNorm(embed_dim)
+        self.attention = SwinSelfAttention(embed_dim, num_heads, dropout)
+        self.drop_path = Dropout(p=dropout)
+        self.layernorm_after = LayerNorm(embed_dim)
+
+        self.fc1 = Linear(embed_dim, 4 * embed_dim)
+        self.fc2 = Linear(4 * embed_dim, embed_dim)
+        self.intermediate_act_fn = gelu
+
+    def forward(self, x):
+        normed = self.layernorm_before(x)
+        attention_output = self.attention(normed)
+        attention_output = self.drop_path(attention_output)
+        x = x + attention_output
+        
+        normed = self.layernorm_after(x)
+        intermediate = self.fc1(normed)
+        intermediate = self.intermediate_act_fn(intermediate)
+        output = self.fc2(intermediate)
+        
+        return x + output
+
+
+class SwinPatchEmbedding(nn.Module):
+    def __init__(self, in_channels=3, patch_size=4, embed_dim=128):
+        super(SwinPatchEmbedding, self).__init__()
+        self.projection = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
+        self.norm = LayerNorm(embed_dim)
+
+    def forward(self, x):
+        x = self.projection(x)
+        x = x.flatten(2).transpose(1, 2)
+        x = self.norm(x)
+        return x
+
+class SwinEncoder(nn.Module):
+    def __init__(self, num_layers, embed_dim, num_heads, dropout=0.1):
+        super(SwinEncoder, self).__init__()
+        self.layers = nn.ModuleList([ 
+            SwinLayer(embed_dim=embed_dim, num_heads=num_heads, dropout=dropout)
+            for _ in range(num_layers)
+        ])
+
+    def forward(self, x):
+        for layer in self.layers:
+            x = layer(x)
+        return x
+
+class SwinModelForCXRClassification(PreTrainedModel):
+    config_class = SwinCXRConfig
+    def __init__(self, config):
+        super(SwinModelForCXRClassification, self).__init__(config)
+        
+        self.embeddings = nn.Module()
+        self.embeddings.patch_embeddings = SwinPatchEmbedding(
+            in_channels=3, 
+            patch_size=4,
+            embed_dim=128
+        )
+        self.embeddings.norm = LayerNorm(128)
+        self.embeddings.dropout = Dropout(p=0.0)
+
+        self.encoder = SwinEncoder(
+            num_layers=4, 
+            embed_dim=128, 
+            num_heads=4,  
+            dropout=0.1
+        )
+
+        self.layernorm = LayerNorm(128)
+        self.pooler = nn.AdaptiveAvgPool1d(output_size=1)
+
+        self.classifier = Linear(in_features=128, out_features=3, bias=True)
+
+    def forward(self, pixel_values, labels=None):
+        x = self.embeddings.patch_embeddings(pixel_values) 
+        x = self.encoder(x) 
+
+        x = self.layernorm(x)
+        x = self.pooler(x.transpose(1, 2)).squeeze(-1)  
+
+        logits = self.classifier(x)
+
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.config.num_classes), labels.view(-1))
+            return loss, logits
+        
+        return logits

config.json

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+{
+  "architectures": [
+    "SwinModelForCXRClassification"
+  ],
+  "auto_map": {
+    "AutoConfig": "SwinCXRConfig.SwinCXRConfig",
+    "AutoModelForImageClassification": "SwinModelForCXRClassification.SwinModelForCXRClassification"
+  },
+  "dropout": 0.1,
+  "embed_dim": 128,
+  "model_type": "swin_cxr",
+  "num_classes": 3,
+  "num_heads": 4,
+  "num_layers": 4,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ca289ef5e0d605768c0623ff419814ecead12653f36dc127657e64f020d91427
+size 2944444