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  1. README.md +201 -0
  2. config.json +26 -0
  3. configuration_resnet.py +40 -0
  4. model.safetensors +3 -0
  5. modeling_resnet.py +70 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ tags: []
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+ This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
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+ - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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+ ### Direct Use
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+
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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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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+
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ <!-- 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. -->
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+ #### Training Hyperparameters
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+
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+ [More Information Needed]
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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
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+ <!-- This should link to a Dataset Card if possible. -->
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+ [More Information Needed]
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+
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+ #### Factors
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+
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
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+ [More Information Needed]
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+ [More Information Needed]
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+ ### Results
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+ [More Information Needed]
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+ #### Summary
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+
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+ ## Model Examination [optional]
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+ <!-- Relevant interpretability work for the model goes here -->
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+ [More Information Needed]
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+ ## Environmental Impact
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+ 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).
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+
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+
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+ ## Technical Specifications [optional]
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+ ### Model Architecture and Objective
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+ [More Information Needed]
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+ ### Compute Infrastructure
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+ [More Information Needed]
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+ #### Hardware
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+ [More Information Needed]
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+ #### Software
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+ [More Information Needed]
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+ ## Citation [optional]
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+ <!-- 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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+ [More Information Needed]
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+ ## Glossary [optional]
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+ <!-- 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]
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+
config.json ADDED
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+ {
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+ "architectures": [
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+ "ResnetModelForImageClassification"
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+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_resnet.ResnetConfig",
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+ "AutoModelForImageClassification": "modeling_resnet.ResnetModelForImageClassification"
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+ },
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+ "avg_down": true,
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+ "base_width": 64,
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+ "block_type": "bottleneck",
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+ "cardinality": 1,
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+ "input_channels": 3,
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+ "layers": [
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+ 3,
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+ 4,
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+ 6,
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+ 3
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+ ],
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+ "model_type": "resnet-t",
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+ "num_classes": 1000,
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+ "stem_type": "deep",
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+ "stem_width": 32,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.39.3"
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+ }
configuration_resnet.py ADDED
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+ from transformers import PretrainedConfig
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+ from typing import List
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+
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+
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+ class ResnetConfig(PretrainedConfig): # 继承PretrainedConfig
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+ model_type = "resnet-t"
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+
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+ def __init__(
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+ self,
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+ block_type="bottleneck",
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+ layers: List[int] = [3, 4, 6, 3],
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+ num_classes: int = 1000,
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+ input_channels: int = 3,
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+ cardinality: int = 1,
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+ base_width: int = 64,
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+ stem_width: int = 64,
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+ stem_type: str = "",
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+ avg_down: bool = False,
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+ **kwargs,
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+ ):
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+ if block_type not in ["basic", "bottleneck"]:
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+ raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
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+ if stem_type not in ["", "deep", "deep-tiered"]:
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+ raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")
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+
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+ self.block_type = block_type
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+ self.layers = layers
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+ self.num_classes = num_classes
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+ self.input_channels = input_channels
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+ self.cardinality = cardinality
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+ self.base_width = base_width
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+ self.stem_width = stem_width
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+ self.stem_type = stem_type
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+ self.avg_down = avg_down
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+ super().__init__(**kwargs)
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+
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+
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+
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+ # resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
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+ # resnet50d_config.save_pretrained("custom-resnet", cache_dir="/") # 在本地保存
model.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:78472643c5bb8b9614a3a08d815c1520408058f47e07b26a0e99918c1f7e3176
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+ size 102550264
modeling_resnet.py ADDED
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+ from transformers import PreTrainedModel
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+ from timm.models.resnet import BasicBlock, Bottleneck, ResNet
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+ from resnet_model.configuration_resnet import ResnetConfig
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+ import torch
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+ import timm
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+
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+ BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck}
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+
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+
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+ class ResnetModel(PreTrainedModel): # 继承基类
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+ config_class = ResnetConfig
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+
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+ def __init__(self, config):
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+ super().__init__(config)
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+ block_layer = BLOCK_MAPPING[config.block_type]
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+ self.model = ResNet(
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+ block_layer,
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+ config.layers,
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+ num_classes=config.num_classes,
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+ in_chans=config.input_channels,
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+ cardinality=config.cardinality,
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+ base_width=config.base_width,
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+ stem_width=config.stem_width,
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+ stem_type=config.stem_type,
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+ avg_down=config.avg_down,
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+ )
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+
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+ def forward(self, tensor):
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+ return self.model.forward_features(tensor)
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+
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+
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+ class ResnetModelForImageClassification(PreTrainedModel): # 继承基类
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+ config_class = ResnetConfig
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+
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+ def __init__(self, config):
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+ super().__init__(config)
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+ block_layer = BLOCK_MAPPING[config.block_type]
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+ self.model = ResNet(
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+ block_layer,
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+ config.layers,
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+ num_classes=config.num_classes,
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+ in_chans=config.input_channels,
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+ cardinality=config.cardinality,
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+ base_width=config.base_width,
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+ stem_width=config.stem_width,
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+ stem_type=config.stem_type,
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+ avg_down=config.avg_down,
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+ )
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+
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+ def forward(self, tensor, labels=None): # 前向方法
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+ logits = self.model(tensor)
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+ if labels is not None:
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+ loss = torch.nn.functional.cross_entropy(logits, labels)
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+ return {"loss": loss, "logits": logits}
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+ return {"logits": logits}
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+
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+
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+ from transformers import AutoConfig, AutoModel, AutoModelForImageClassification
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+
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+
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+ # resnet50d_config = ResnetConfig.from_pretrained("../custom-resnet")
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+ # resnet50d = ResnetModelForImageClassification(resnet50d_config)
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+ # pretrained_model = timm.create_model("resnet50d", pretrained=True)
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+ # resnet50d.model.load_state_dict(pretrained_model.state_dict())
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+
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+
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+
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+ AutoConfig.register("resnet-t", ResnetConfig) # 注册配置
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+ AutoModel.register(ResnetConfig, ResnetModel) # 注册普适模型
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+ AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification) # 注册图像分类模型