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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]

config.json

@@ -0,0 +1,491 @@
+{
+  "_name_or_path": "ragavsachdeva/magiv2",
+  "architectures": [
+    "Magiv2Model"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_magiv2.Magiv2Config",
+    "AutoModel": "modelling_magiv2.Magiv2Model"
+  },
+  "crop_embedding_image_preprocessing_config": {
+    "_processor_class": null,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "image_processor_type": "ViTImageProcessor",
+    "image_std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "resample": 2,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+      "height": 224,
+      "width": 224
+    }
+  },
+  "crop_embedding_model_config": {
+    "_attn_implementation_autoset": true,
+    "_name_or_path": "facebook/vit-mae-base",
+    "add_cross_attention": false,
+    "architectures": [
+      "ViTMAEForPreTraining"
+    ],
+    "attention_probs_dropout_prob": 0.0,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_hidden_size": 512,
+    "decoder_intermediate_size": 2048,
+    "decoder_num_attention_heads": 16,
+    "decoder_num_hidden_layers": 8,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.0,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 224,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
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+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-12,
+    "length_penalty": 1.0,
+    "mask_ratio": 0.75,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "",
+    "no_repeat_ngram_size": 0,
+    "norm_pix_loss": false,
+    "num_attention_heads": 12,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_channels": 3,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_size": 16,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "qkv_bias": true,
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": "float32",
+    "torchscript": false,
+    "typical_p": 1.0,
+    "use_bfloat16": false
+  },
+  "detection_image_preprocessing_config": {
+    "_processor_class": null,
+    "do_normalize": true,
+    "do_pad": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "format": "coco_detection",
+    "image_mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "image_processor_type": "ConditionalDetrImageProcessor",
+    "image_std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "resample": 2,
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+    "size": {
+      "longest_edge": 1333,
+      "shortest_edge": 800
+    }
+  },
+  "detection_model_config": {
+    "_name_or_path": "microsoft/conditional-detr-resnet-50",
+    "activation_dropout": 0.0,
+    "activation_function": "relu",
+    "add_cross_attention": false,
+    "architectures": [
+      "ConditionalDETRForObjectDetection"
+    ],
+    "attention_dropout": 0.0,
+    "auxiliary_loss": false,
+    "backbone": "resnet50",
+    "backbone_config": null,
+    "bad_words_ids": null,
+    "bbox_cost": 5,
+    "bbox_loss_coefficient": 5,
+    "begin_suppress_tokens": null,
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+    "class_cost": 2,
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+    "id2label": {
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+      "1": "LABEL_1",
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+      "3": "LABEL_3"
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+      "LABEL_3": 3
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+    "num_queries": 305,
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+    "position_embedding_type": "sine",
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+    "pruned_heads": {},
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+    "task_specific_params": null,
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+    "top_p": 1.0,
+    "torch_dtype": "float32",
+    "torchscript": false,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_pretrained_backbone": true,
+    "use_timm_backbone": true
+  },
+  "disable_crop_embeddings": false,
+  "disable_detections": false,
+  "disable_ocr": false,
+  "kwargs": {
+    "_commit_hash": null,
+    "_name_or_path": "ragavsachdeva/magiv2",
+    "architectures": [
+      "Magiv2Model"
+    ],
+    "auto_map": {
+      "AutoConfig": "configuration_magiv2.Magiv2Config",
+      "AutoModel": "modelling_magiv2.Magiv2Model"
+    },
+    "model_type": "magiv2",
+    "torch_dtype": "float32",
+    "transformers_version": "4.34.0.dev0"
+  },
+  "model_type": "magiv2",
+  "ocr_model_config": {
+    "_name_or_path": "microsoft/trocr-base-printed",
+    "add_cross_attention": false,
+    "architectures": [
+      "VisionEncoderDecoderModel"
+    ],
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+      },
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+      "length_penalty": 1.0,
+      "max_length": 20,
+      "max_position_embeddings": 512,
+      "min_length": 0,
+      "model_type": "trocr",
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+      "prefix": null,
+      "problem_type": null,
+      "pruned_heads": {},
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+      "repetition_penalty": 1.0,
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+      "task_specific_params": null,
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+      "tie_word_embeddings": true,
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+      "top_k": 50,
+      "top_p": 1.0,
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+      "torchscript": false,
+      "typical_p": 1.0,
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+      "use_cache": false,
+      "use_learned_position_embeddings": true,
+      "vocab_size": 50265
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+    "diversity_penalty": 0.0,
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+      "id2label": {
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+        "1": "LABEL_1"
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+      "top_p": 1.0,
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+      "torchscript": false,
+      "typical_p": 1.0,
+      "use_bfloat16": false
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+    "exponential_decay_length_penalty": null,
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+    "forced_eos_token_id": null,
+    "id2label": {
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+      "1": "LABEL_1"
+    },
+    "is_decoder": false,
+    "is_encoder_decoder": true,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "vision-encoder-decoder",
+    "no_repeat_ngram_size": 0,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": false,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": "float32",
+    "torchscript": false,
+    "typical_p": 1.0,
+    "use_bfloat16": false
+  },
+  "ocr_pretrained_processor_path": "microsoft/trocr-base-printed",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1"
+}

configuration_magiv2.py

@@ -0,0 +1,19 @@
+from transformers import PretrainedConfig, VisionEncoderDecoderConfig
+from typing import List
+
+
+class Magiv2Config(PretrainedConfig):
+    model_type = "magiv2"
+
+    def __init__(
+        self,
+        crop_embedding_model_config: dict = None,
+        crop_embedding_image_preprocessing_config: dict = None,
+        **kwargs,
+    ):
+        self.kwargs = kwargs
+        self.crop_embedding_model_config = None
+        if crop_embedding_model_config is not None:
+            self.crop_embedding_model_config = PretrainedConfig.from_dict(crop_embedding_model_config)
+        self.crop_embedding_image_preprocessing_config = crop_embedding_image_preprocessing_config
+        super().__init__(**kwargs)

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d6bd5ae35f4bfe26cb4b66b6ae7f23edf56e2d249c10d1d38ff4a97c1a71645
+size 343221032

modelling_magiv2.py

@@ -0,0 +1,58 @@
+from transformers import PreTrainedModel, ViTMAEModel
+from .configuration_magiv2 import Magiv2Config
+import torch
+import numpy as np
+from transformers import ViTImageProcessor
+import PIL
+
+def move_to_device(inputs, device):
+    if hasattr(inputs, "keys"):
+        return {k: move_to_device(v, device) for k, v in inputs.items()}
+    elif isinstance(inputs, list):
+        return [move_to_device(v, device) for v in inputs]
+    elif isinstance(inputs, tuple):
+        return tuple([move_to_device(v, device) for v in inputs])
+    elif isinstance(inputs, np.ndarray):
+        return torch.from_numpy(inputs).to(device)
+    else:
+        return inputs.to(device)
+
+class Magiv2Model(PreTrainedModel):
+    config_class = Magiv2Config
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        self.processor = ViTImageProcessor.from_dict(config.crop_embedding_image_preprocessing_config)
+        self.crop_embedding_model = ViTMAEModel(config.crop_embedding_model_config)
+
+    def move_to_device(self, input):
+        return move_to_device(input, self.device)
+    
+    def forward(self, images, move_to_device_fn=None, mask_ratio=0.0, batch_size=256):
+        if len(images) == 0:
+            return move_to_device_fn(torch.zeros(len(images), self.config.crop_embedding_model_config.hidden_size))
+
+        assert all(isinstance(image, PIL.Image.Image) for image in images), "please provide a list of PIL images"
+
+        move_to_device_fn = self.move_to_device if move_to_device_fn is None else move_to_device_fn
+        images = [np.array(image.convert("L").convert("RGB")) for image in images]
+        images = self.processor(images, return_tensors="pt").pixel_values
+        images = move_to_device_fn(images)
+
+        # temporarily change the mask ratio from default to the one specified
+        old_mask_ratio = self.crop_embedding_model.embeddings.config.mask_ratio
+        self.crop_embedding_model.embeddings.config.mask_ratio = mask_ratio
+        
+        # process the crops in batches to avoid OOM
+        embeddings = []
+        for i in range(0, len(images), batch_size):
+            crops = images[i:i+batch_size]
+            embeddings_per_batch = self.crop_embedding_model(crops).last_hidden_state[:, 0]
+            embeddings.append(embeddings_per_batch)
+        embeddings = torch.cat(embeddings, dim=0)
+        
+        # restore the mask ratio to the default
+        self.crop_embedding_model.embeddings.config.mask_ratio = old_mask_ratio
+
+        return embeddings