Add support for Segformer

README.md

@@ -326,6 +326,7 @@ You can refine your search by selecting the task you're interested in (e.g., [te
 1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
 1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
 1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
 1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.

docs/snippets/6_supported-models.snippet

@@ -61,6 +61,7 @@
 1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
 1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
 1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
 1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.

scripts/supported_models.py

@@ -362,7 +362,7 @@
             'distilbert-base-cased',
         ],
     },
-    'dit': { # NOTE: DiT has the same architecture as BEiT.
+    'dit': {  # NOTE: DiT has the same architecture as BEiT.
         # Feature extraction
         # NOTE: requires --task feature-extraction
         'feature-extraction': [
@@ -680,8 +680,8 @@
             'hf-tiny-model-private/tiny-random-RoFormerForTokenClassification',
         ],
 
-        # TODO 
-        # # Text generation 
+        # TODO
+        # # Text generation
         # 'text-generation': [
         #     'hf-tiny-model-private/tiny-random-RoFormerForCausalLM',
         # ],
@@ -736,6 +736,40 @@
     #     'facebook/sam-vit-large',
     #     'facebook/sam-vit-huge',
     # ],
+    'segformer': {
+        # Image segmentation
+        'image-segmentation': [
+            'mattmdjaga/segformer_b0_clothes',
+            'mattmdjaga/segformer_b2_clothes',
+            'jonathandinu/face-parsing',
+
+            'nvidia/segformer-b0-finetuned-cityscapes-768-768',
+            'nvidia/segformer-b0-finetuned-cityscapes-512-1024',
+            'nvidia/segformer-b0-finetuned-cityscapes-640-1280',
+            'nvidia/segformer-b0-finetuned-cityscapes-1024-1024',
+            'nvidia/segformer-b1-finetuned-cityscapes-1024-1024',
+            'nvidia/segformer-b2-finetuned-cityscapes-1024-1024',
+            'nvidia/segformer-b3-finetuned-cityscapes-1024-1024',
+            'nvidia/segformer-b4-finetuned-cityscapes-1024-1024',
+            'nvidia/segformer-b5-finetuned-cityscapes-1024-1024',
+            'nvidia/segformer-b0-finetuned-ade-512-512',
+            'nvidia/segformer-b1-finetuned-ade-512-512',
+            'nvidia/segformer-b2-finetuned-ade-512-512',
+            'nvidia/segformer-b3-finetuned-ade-512-512',
+            'nvidia/segformer-b4-finetuned-ade-512-512',
+            'nvidia/segformer-b5-finetuned-ade-640-640',
+        ],
+
+        # Image classification
+        'image-classification': [
+            'nvidia/mit-b0',
+            'nvidia/mit-b1',
+            'nvidia/mit-b2',
+            'nvidia/mit-b3',
+            'nvidia/mit-b4',
+            'nvidia/mit-b5',
+        ],
+    },
 
     'speecht5': {
         # Text-to-audio/Text-to-speech

src/models.js

@@ -4736,6 +4736,27 @@ export class VitsModel extends VitsPreTrainedModel {
 }
 //////////////////////////////////////////////////
 
+//////////////////////////////////////////////////
+// Segformer models
+export class SegformerPreTrainedModel extends PreTrainedModel { }
+
+/**
+ * The bare SegFormer encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top.
+ */
+export class SegformerModel extends SegformerPreTrainedModel { }
+
+/**
+ * SegFormer Model transformer with an image classification head on top (a linear layer on top of the final hidden states) e.g. for ImageNet.
+ */
+export class SegformerForImageClassification extends SegformerPreTrainedModel { }
+
+/**
+ * SegFormer Model transformer with an all-MLP decode head on top e.g. for ADE20k, CityScapes.
+ */
+export class SegformerForSemanticSegmentation extends SegformerPreTrainedModel { }
+
+//////////////////////////////////////////////////
+
 
 //////////////////////////////////////////////////
 // AutoModels, used to simplify construction of PreTrainedModels
@@ -5020,6 +5041,7 @@ const MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = new Map([
     ['dinov2', ['Dinov2ForImageClassification', Dinov2ForImageClassification]],
     ['resnet', ['ResNetForImageClassification', ResNetForImageClassification]],
     ['swin', ['SwinForImageClassification', SwinForImageClassification]],
+    ['segformer', ['SegformerForImageClassification', SegformerForImageClassification]],
 ]);
 
 const MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = new Map([
@@ -5036,6 +5058,10 @@ const MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES = new Map([
     ['detr', ['DetrForSegmentation', DetrForSegmentation]],
 ]);
 
+const MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES = new Map([
+    ['segformer', ['SegformerForSemanticSegmentation', SegformerForSemanticSegmentation]],
+]);
+
 const MODEL_FOR_MASK_GENERATION_MAPPING_NAMES = new Map([
     ['sam', ['SamModel', SamModel]],
 ]);
@@ -5081,6 +5107,7 @@ const MODEL_CLASS_TYPE_MAPPING = [
     [MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES, MODEL_TYPES.Vision2Seq],
     [MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
     [MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
+    [MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
     [MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
     [MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
     [MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
@@ -5260,6 +5287,17 @@ export class AutoModelForImageSegmentation extends PretrainedMixin {
     static MODEL_CLASS_MAPPINGS = [MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES];
 }
 
+/**
+ * Helper class which is used to instantiate pretrained image segmentation models with the `from_pretrained` function.
+ * The chosen model class is determined by the type specified in the model config.
+ * 
+ * @example
+ * let model = await AutoModelForSemanticSegmentation.from_pretrained('nvidia/segformer-b3-finetuned-cityscapes-1024-1024');
+ */
+export class AutoModelForSemanticSegmentation extends PretrainedMixin {
+    static MODEL_CLASS_MAPPINGS = [MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES];
+}
+
 /**
  * Helper class which is used to instantiate pretrained object detection models with the `from_pretrained` function.
  * The chosen model class is determined by the type specified in the model config.

src/pipelines.js

@@ -33,6 +33,7 @@ import {
     AutoModelForVision2Seq,
     AutoModelForImageClassification,
     AutoModelForImageSegmentation,
+    AutoModelForSemanticSegmentation,
     AutoModelForObjectDetection,
     AutoModelForZeroShotObjectDetection,
     AutoModelForDocumentQuestionAnswering,
@@ -1710,8 +1711,26 @@ export class ImageSegmentationPipeline extends Pipeline {
             }
 
         } else if (subtask === 'semantic') {
-            throw Error(`semantic segmentation not yet supported.`);
+            const { segmentation, labels } = fn(output, target_sizes ?? imageSizes)[0];
 
+            const id2label = this.model.config.id2label;
+
+            for (let label of labels) {
+                const maskData = new Uint8ClampedArray(segmentation.data.length);
+                for (let i = 0; i < segmentation.data.length; ++i) {
+                    if (segmentation.data[i] === label) {
+                        maskData[i] = 255;
+                    }
+                }
+
+                const mask = new RawImage(maskData, segmentation.dims[1], segmentation.dims[0], 1);
+
+                annotation.push({
+                    score: null,
+                    label: id2label[label],
+                    mask: mask
+                });
+            }
         } else {
             throw Error(`Subtask ${subtask} not supported.`);
         }
@@ -2488,7 +2507,7 @@ const SUPPORTED_TASKS = {
     "image-segmentation": {
         // no tokenizer
         "pipeline": ImageSegmentationPipeline,
-        "model": AutoModelForImageSegmentation,
+        "model": [AutoModelForImageSegmentation, AutoModelForSemanticSegmentation],
         "processor": AutoProcessor,
         "default": {
             // TODO: replace with original

src/processors.js

@@ -609,6 +609,71 @@ export class ImageFeatureExtractor extends FeatureExtractor {
 
 }
 
+export class SegformerFeatureExtractor extends ImageFeatureExtractor {
+
+    /**
+     * Converts the output of `SegformerForSemanticSegmentation` into semantic segmentation maps.
+     * @param {*} outputs Raw outputs of the model.
+     * @param {number[][]} [target_sizes=null] List of tuples corresponding to the requested final size
+     * (height, width) of each prediction. If unset, predictions will not be resized.
+     * @returns {{segmentation: Tensor; labels: number[]}[]} The semantic segmentation maps.
+     */
+    post_process_semantic_segmentation(outputs, target_sizes = null) {
+
+        const logits = outputs.logits;
+        const batch_size = logits.dims[0];
+
+        if (target_sizes !== null && target_sizes.length !== batch_size) {
+            throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits")
+        }
+
+        const toReturn = [];
+        for (let i = 0; i < batch_size; ++i) {
+            const target_size = target_sizes !== null ? target_sizes[i] : null;
+
+            let data = logits[i];
+
+            // 1. If target_size is not null, we need to resize the masks to the target size
+            if (target_size !== null) {
+                // resize the masks to the target size
+                data = interpolate(data, target_size, 'bilinear', false);
+            }
+            const [height, width] = target_size ?? data.dims.slice(-2);
+
+            const segmentation = new Tensor(
+                'int32',
+                new Int32Array(height * width),
+                [height, width]
+            );
+
+            // Buffer to store current largest value
+            const buffer = data[0].data;
+            for (let j = 1; j < data.dims[0]; ++j) {
+                const row = data[j].data;
+                for (let k = 0; k < row.length; ++k) {
+                    if (row[k] > buffer[k]) {
+                        buffer[k] = row[k];
+                        segmentation.data[k] = j;
+                    }
+                }
+            }
+
+            // Store which objects have labels
+            // This is much more efficient that creating a set of the final values
+            const hasLabel = new Array(data.dims[0]);
+            const out = segmentation.data;
+            for (let j = 0; j < out.length; ++j) {
+                const index = out[j];
+                hasLabel[index] = index;
+            }
+            /** @type {number[]} The unique list of labels that were detected */
+            const labels = hasLabel.filter(x => x !== undefined);
+
+            toReturn.push({ segmentation, labels });
+        }
+        return toReturn;
+    }
+}
 export class BitImageProcessor extends ImageFeatureExtractor { }
 export class DPTFeatureExtractor extends ImageFeatureExtractor { }
 export class GLPNFeatureExtractor extends ImageFeatureExtractor { }
@@ -1699,6 +1764,7 @@ export class AutoProcessor {
         ChineseCLIPFeatureExtractor,
         ConvNextFeatureExtractor,
         ConvNextImageProcessor,
+        SegformerFeatureExtractor,
         BitImageProcessor,
         DPTFeatureExtractor,
         GLPNFeatureExtractor,