From f5a6420da31cfceef87d84ab998e585b5c35dbbc Mon Sep 17 00:00:00 2001 From: Vik Paruchuri Date: Fri, 17 May 2024 10:28:44 -0700 Subject: [PATCH 1/4] Bump transformers version --- poetry.lock | 669 ++++++++++++++++++++++++------------------------- pyproject.toml | 4 +- 2 files changed, 331 insertions(+), 342 deletions(-) diff --git a/poetry.lock b/poetry.lock index 456098c..f67cb45 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1579,13 +1579,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (> [[package]] name = "jupyterlab" -version = "4.1.8" +version = "4.2.0" description = "JupyterLab computational environment" optional = false python-versions = ">=3.8" files = [ - {file = "jupyterlab-4.1.8-py3-none-any.whl", hash = "sha256:c3baf3a2f91f89d110ed5786cd18672b9a357129d4e389d2a0dead15e11a4d2c"}, - {file = "jupyterlab-4.1.8.tar.gz", hash = "sha256:3384aded8680e7ce504fd63b8bb89a39df21c9c7694d9e7dc4a68742cdb30f9b"}, + {file = 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["datasets (!=2.5.0)", "faiss-cpu"] sagemaker = ["sagemaker (>=2.31.0)"] sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"] -serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"] +serving = ["fastapi", "pydantic", "starlette", "uvicorn"] sigopt = ["sigopt"] sklearn = ["scikit-learn"] speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] -testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic (<2)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"] -tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] -tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] +testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] +tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] +tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] timm = ["timm"] -tokenizers = ["tokenizers (>=0.14,<0.19)"] -torch = ["accelerate (>=0.21.0)", "torch (>=1.10,!=1.12.0)"] +tokenizers = ["tokenizers (>=0.19,<0.20)"] +torch = ["accelerate (>=0.21.0)", "torch"] torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] -torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"] +torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] video = ["av (==9.2.0)", "decord (==0.6.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"] @@ -4776,20 +4765,20 @@ multidict = ">=4.0" [[package]] name = "zipp" -version = "3.18.1" +version = "3.18.2" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.8" files = [ - {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"}, - {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"}, + {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"}, + {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"}, ] [package.extras] docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13,!=3.9.7" -content-hash = "7635bc0bc8168a54234a6f3f8a0269bfe8945df5f7bf9939114ffc8fbaf7cb27" +content-hash = "d250e5223075069c0561f95e970624731feb7ddc20f1bc7b8ef6dd826a8f3085" diff --git a/pyproject.toml b/pyproject.toml index fd93329..f04a101 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,8 +21,8 @@ include = [ [tool.poetry.dependencies] python = ">=3.9,<3.13,!=3.9.7" -transformers = "4.36.2" -torch = "^2.2.2" +transformers = "^4.41.0" +torch = "^2.3.0" pydantic = "^2.5.3" pydantic-settings = "^2.1.0" python-dotenv = "^1.0.0" From be082fe92ba2b2bba2ea49d541f32771ce614694 Mon Sep 17 00:00:00 2001 From: Vik Paruchuri Date: Fri, 17 May 2024 13:39:41 -0700 Subject: [PATCH 2/4] Significantly speedup cpu postprocessing for line detection and layout --- README.md | 2 - run_ocr_app.py | 5 +- surya/detection.py | 6 +-- surya/layout.py | 2 +- surya/model/detection/segformer.py | 2 +- surya/model/ordering/model.py | 2 +- surya/model/recognition/model.py | 2 +- surya/ocr.py | 36 ++++++++++--- surya/ordering.py | 2 +- surya/postprocessing/affinity.py | 9 +--- surya/postprocessing/heatmap.py | 9 ++-- surya/recognition.py | 82 ++++++++++++++++++------------ surya/schema.py | 1 - surya/settings.py | 1 + 14 files changed, 95 insertions(+), 66 deletions(-) diff --git a/README.md b/README.md index 7c9413b..2872377 100644 --- a/README.md +++ b/README.md @@ -143,8 +143,6 @@ The `results.json` file will contain a json dictionary where the keys are the in - `confidence` - the confidence of the model in the detected text (0-1) - `vertical_lines` - vertical lines detected in the document - `bbox` - the axis-aligned line coordinates. -- `horizontal_lines` - horizontal lines detected in the document - - `bbox` - the axis-aligned line coordinates. - `page` - the page number in the file - `image_bbox` - the bbox for the image in (x1, y1, x2, y2) format. (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner. All line bboxes will be contained within this bbox. diff --git a/run_ocr_app.py b/run_ocr_app.py index 51c13b1..a1f4a14 100644 --- a/run_ocr_app.py +++ b/run_ocr_app.py @@ -14,4 +14,7 @@ def run_app(): if args.math: cmd.append("--") cmd.append("--math") - subprocess.run(cmd) \ No newline at end of file + subprocess.run(cmd, env={**os.environ, "IN_STREAMLIT": "true"}) + +if __name__ == "__main__": + run_app() \ No newline at end of file diff --git a/surya/detection.py b/surya/detection.py index 3bb397d..152c1e6 100644 --- a/surya/detection.py +++ b/surya/detection.py @@ -6,7 +6,7 @@ from surya.model.detection.segformer import SegformerForRegressionMask from surya.postprocessing.heatmap import get_and_clean_boxes -from surya.postprocessing.affinity import get_vertical_lines, get_horizontal_lines +from surya.postprocessing.affinity import get_vertical_lines from surya.input.processing import prepare_image, split_image, get_total_splits from surya.schema import TextDetectionResult from surya.settings import settings @@ -109,12 +109,10 @@ def parallel_get_lines(preds, orig_sizes): heatmap_size = list(reversed(heatmap.shape)) bboxes = get_and_clean_boxes(heatmap, heatmap_size, orig_sizes) vertical_lines = get_vertical_lines(affinity_map, affinity_size, orig_sizes) - horizontal_lines = get_horizontal_lines(affinity_map, affinity_size, orig_sizes) result = TextDetectionResult( bboxes=bboxes, vertical_lines=vertical_lines, - horizontal_lines=horizontal_lines, heatmap=heat_img, affinity_map=aff_img, image_bbox=[0, 0, orig_sizes[0], orig_sizes[1]] @@ -125,7 +123,7 @@ def parallel_get_lines(preds, orig_sizes): def batch_text_detection(images: List, model, processor, batch_size=None) -> List[TextDetectionResult]: preds, orig_sizes = batch_detection(images, model, processor, batch_size=batch_size) results = [] - if len(images) == 1: # Ensures we don't parallelize with streamlit + if settings.IN_STREAMLIT: # Ensures we don't parallelize with streamlit for i in range(len(images)): result = parallel_get_lines(preds[i], orig_sizes[i]) results.append(result) diff --git a/surya/layout.py b/surya/layout.py index 104a860..fdb6ef4 100644 --- a/surya/layout.py +++ b/surya/layout.py @@ -186,7 +186,7 @@ def batch_layout_detection(images: List, model, processor, detection_results: Op id2label = model.config.id2label results = [] - if len(images) == 1: # Ensures we don't parallelize with streamlit + if settings.IN_STREAMLIT: # Ensures we don't parallelize with streamlit for i in range(len(images)): result = parallel_get_regions(preds[i], orig_sizes[i], id2label, detection_results[i] if detection_results else None) results.append(result) diff --git a/surya/model/detection/segformer.py b/surya/model/detection/segformer.py index 345ee65..87634a4 100644 --- a/surya/model/detection/segformer.py +++ b/surya/model/detection/segformer.py @@ -21,7 +21,7 @@ def load_model(checkpoint=settings.DETECTOR_MODEL_CHECKPOINT, device=settings.TO print("Warning: MPS may have poor results. This is a bug with MPS, see here - https://github.com/pytorch/pytorch/issues/84936") model = model.to(device) model = model.eval() - print(f"Loading detection model {checkpoint} on device {device} with dtype {dtype}") + print(f"Loaded detection model {checkpoint} on device {device} with dtype {dtype}") return model diff --git a/surya/model/ordering/model.py b/surya/model/ordering/model.py index da551cb..8c92fee 100644 --- a/surya/model/ordering/model.py +++ b/surya/model/ordering/model.py @@ -30,5 +30,5 @@ def load_model(checkpoint=settings.ORDER_MODEL_CHECKPOINT, device=settings.TORCH model = model.to(device) model = model.eval() - print(f"Loading reading order model {checkpoint} on device {device} with dtype {dtype}") + print(f"Loaded reading order model {checkpoint} on device {device} with dtype {dtype}") return model \ No newline at end of file diff --git a/surya/model/recognition/model.py b/surya/model/recognition/model.py index de2093e..5e92ed1 100644 --- a/surya/model/recognition/model.py +++ b/surya/model/recognition/model.py @@ -35,7 +35,7 @@ def load_model(checkpoint=settings.RECOGNITION_MODEL_CHECKPOINT, device=settings model = model.to(device) model = model.eval() - print(f"Loading recognition model {checkpoint} on device {device} with dtype {dtype}") + print(f"Loaded recognition model {checkpoint} on device {device} with dtype {dtype}") return model diff --git a/surya/ocr.py b/surya/ocr.py index a5851fd..ee853da 100644 --- a/surya/ocr.py +++ b/surya/ocr.py @@ -1,4 +1,5 @@ from collections import defaultdict +from concurrent.futures import ProcessPoolExecutor from typing import List from tqdm import tqdm @@ -10,6 +11,7 @@ from surya.postprocessing.text import truncate_repetitions, sort_text_lines from surya.recognition import batch_recognition from surya.schema import TextLine, OCRResult +from surya.settings import settings def run_recognition(images: List[Image.Image], langs: List[List[str]], rec_model, rec_processor, bboxes: List[List[List[int]]] = None, polygons: List[List[List[List[int]]]] = None, batch_size=None) -> List[OCRResult]: @@ -60,20 +62,38 @@ def run_recognition(images: List[Image.Image], langs: List[List[str]], rec_model return predictions_by_image +def parallel_slice_polys(det_pred, image): + polygons = [p.polygon for p in det_pred.bboxes] + slices = slice_polys_from_image(image, polygons) + return slices + + def run_ocr(images: List[Image.Image], langs: List[List[str]], det_model, det_processor, rec_model, rec_processor, batch_size=None) -> List[OCRResult]: det_predictions = batch_text_detection(images, det_model, det_processor) - if det_model.device == "cuda": + if det_model.device.type == "cuda": torch.cuda.empty_cache() # Empty cache from first model run - slice_map = [] all_slices = [] + + if settings.IN_STREAMLIT: + all_slices = [parallel_slice_polys(det_pred, image) for det_pred, image in zip(det_predictions, images)] + else: + futures = [] + with ProcessPoolExecutor(max_workers=settings.DETECTOR_POSTPROCESSING_CPU_WORKERS) as executor: + for image_idx in range(len(images)): + future = executor.submit(parallel_slice_polys, det_predictions[image_idx], images[image_idx]) + futures.append(future) + + for future in futures: + all_slices.append(future.result()) + + slice_map = [] all_langs = [] - for idx, (image, det_pred, lang) in enumerate(zip(images, det_predictions, langs)): - polygons = [p.polygon for p in det_pred.bboxes] - slices = slice_polys_from_image(image, polygons) - slice_map.append(len(slices)) - all_slices.extend(slices) - all_langs.extend([lang] * len(slices)) + for idx, (slice, lang) in enumerate(zip(all_slices, langs)): + slice_map.append(len(slice)) + all_langs.extend([lang] * len(slice)) + + all_slices = [slice for sublist in all_slices for slice in sublist] rec_predictions, confidence_scores = batch_recognition(all_slices, all_langs, rec_model, rec_processor, batch_size=batch_size) diff --git a/surya/ordering.py b/surya/ordering.py index 48e8dab..dfc2c69 100644 --- a/surya/ordering.py +++ b/surya/ordering.py @@ -1,5 +1,5 @@ from copy import deepcopy -from typing import List, Optional +from typing import List import torch from PIL import Image diff --git a/surya/postprocessing/affinity.py b/surya/postprocessing/affinity.py index 682d490..4cb538c 100644 --- a/surya/postprocessing/affinity.py +++ b/surya/postprocessing/affinity.py @@ -162,11 +162,4 @@ def get_vertical_lines(image, processor_size, image_size, divisor=20, x_toleranc # Always start with top left of page vertical_lines[0].bbox[1] = 0 - return vertical_lines - - -def get_horizontal_lines(affinity_map, processor_size, image_size) -> List[ColumnLine]: - horizontal_lines = get_detected_lines(affinity_map, horizontal=True) - for line in horizontal_lines: - line.rescale_bbox(processor_size, image_size) - return horizontal_lines \ No newline at end of file + return vertical_lines \ No newline at end of file diff --git a/surya/postprocessing/heatmap.py b/surya/postprocessing/heatmap.py index 9a23bb4..c850d18 100644 --- a/surya/postprocessing/heatmap.py +++ b/surya/postprocessing/heatmap.py @@ -85,8 +85,8 @@ def detect_boxes(linemap, text_threshold, low_text): ret, text_score = cv2.threshold(linemap, low_text, 1, cv2.THRESH_BINARY) - text_score_comb = np.clip(text_score, 0, 1) - label_count, labels, stats, centroids = cv2.connectedComponentsWithStats(text_score_comb.astype(np.uint8), connectivity=4) + text_score_comb = np.clip(text_score, 0, 1).astype(np.uint8) + label_count, labels, stats, centroids = cv2.connectedComponentsWithStats(text_score_comb, connectivity=4) det = [] confidences = [] @@ -140,9 +140,8 @@ def detect_boxes(linemap, text_threshold, low_text): box = np.roll(box, 4-startidx, 0) box = np.array(box) - mask = np.zeros_like(linemap).astype(np.uint8) - cv2.fillPoly(mask, [np.int32(box)], 255) - mask = mask.astype(np.float16) / 255 + mask = np.zeros_like(linemap, dtype=np.uint8) + cv2.fillPoly(mask, [np.int32(box)], 1) roi = np.where(mask == 1, linemap, 0) confidence = np.mean(roi[roi != 0]) diff --git a/surya/recognition.py b/surya/recognition.py index b8239a7..1745e97 100644 --- a/surya/recognition.py +++ b/surya/recognition.py @@ -32,6 +32,7 @@ def batch_recognition(images: List, languages: List[List[str]], model, processor output_text = [] confidences = [] + for i in tqdm(range(0, len(images), batch_size), desc="Recognizing Text"): batch_langs = languages[i:i+batch_size] has_math = ["_math" in lang for lang in batch_langs] @@ -46,39 +47,56 @@ def batch_recognition(images: List, languages: List[List[str]], model, processor batch_pixel_values = torch.tensor(np.array(batch_pixel_values), dtype=model.dtype).to(model.device) batch_decoder_input = torch.from_numpy(np.array(batch_decoder_input, dtype=np.int64)).to(model.device) + token_count = 0 + encoder_outputs = None + batch_predictions = [[] for _ in range(len(batch_images))] + sequence_scores = None + + attention_mask = torch.ones_like(batch_decoder_input, device=model.device) + past_key_values = None + with torch.inference_mode(): - return_dict = model.generate( - pixel_values=batch_pixel_values, - decoder_input_ids=batch_decoder_input, - decoder_langs=batch_langs, - eos_token_id=processor.tokenizer.eos_id, - max_new_tokens=settings.RECOGNITION_MAX_TOKENS, - output_scores=True, - return_dict_in_generate=True - ) - generated_ids = return_dict["sequences"] - - # Find confidence scores - scores = return_dict["scores"] # Scores is a tuple, one per new sequence position. Each tuple element is bs x vocab_size - sequence_scores = torch.zeros(generated_ids.shape[0]) - sequence_lens = torch.where( - generated_ids > processor.tokenizer.eos_id, - torch.ones_like(generated_ids), - torch.zeros_like(generated_ids) - ).sum(axis=-1).cpu() - prefix_len = generated_ids.shape[1] - len(scores) # Length of passed in tokens (bos, langs) - for token_idx, score in enumerate(scores): - probs = F.softmax(score, dim=-1) - max_probs = torch.max(probs, dim=-1).values - max_probs = torch.where( - generated_ids[:, token_idx + prefix_len] <= processor.tokenizer.eos_id, - torch.zeros_like(max_probs), - max_probs - ).cpu() - sequence_scores += max_probs - sequence_scores /= sequence_lens - - detected_text = processor.tokenizer.batch_decode(generated_ids) + while token_count < settings.RECOGNITION_MAX_TOKENS: + cache_position = torch.tensor([token_count+ 1], device=model.device) + + return_dict = model( + decoder_input_ids=batch_decoder_input, + decoder_attention_mask=attention_mask, + decoder_langs=batch_langs, + pixel_values=batch_pixel_values, + encoder_outputs=encoder_outputs, + past_key_values=past_key_values, + return_dict=True, + ) + + logits = return_dict["logits"] + preds = torch.argmax(logits[:, -1], dim=-1) + scores = torch.max(F.softmax(logits, dim=-1), dim=-1).values + done = preds == processor.tokenizer.eos_id + + if sequence_scores is None: + sequence_scores = scores + else: + scores[done == 1] = 0 + sequence_scores = torch.cat([sequence_scores, scores], dim=1) + + encoder_outputs = (return_dict["encoder_last_hidden_state"],) + past_key_values = return_dict["past_key_values"] + + if done.all(): + break + + attention_mask = torch.cat([attention_mask, ~done.unsqueeze(1)], dim=1) + + for j, (pred, status) in enumerate(zip(preds.cpu(), done)): + if not status: + batch_predictions[j].append(int(pred)) + + batch_decoder_input = preds.unsqueeze(1) + token_count += 1 + + sequence_scores = torch.sum(sequence_scores, dim=-1) / torch.sum(sequence_scores != 0, dim=-1) + detected_text = processor.tokenizer.batch_decode(batch_predictions) detected_text = [truncate_repetitions(dt) for dt in detected_text] # Postprocess to fix LaTeX output (add $$ signs, etc) detected_text = [fix_math(text) if math and contains_math(text) else text for text, math in zip(detected_text, has_math)] diff --git a/surya/schema.py b/surya/schema.py index 545e5d1..129f991 100644 --- a/surya/schema.py +++ b/surya/schema.py @@ -147,7 +147,6 @@ class OCRResult(BaseModel): class TextDetectionResult(BaseModel): bboxes: List[PolygonBox] vertical_lines: List[ColumnLine] - horizontal_lines: List[ColumnLine] heatmap: Any affinity_map: Any image_bbox: List[float] diff --git a/surya/settings.py b/surya/settings.py index 64347f5..813c455 100644 --- a/surya/settings.py +++ b/surya/settings.py @@ -11,6 +11,7 @@ class Settings(BaseSettings): # General TORCH_DEVICE: Optional[str] = None IMAGE_DPI: int = 96 + IN_STREAMLIT: bool = False # Whether we're running in streamlit # Paths DATA_DIR: str = "data" From af5ed6d52a09fd14f5b74a30dd2e8bd54164e2b8 Mon Sep 17 00:00:00 2001 From: Vik Paruchuri Date: Fri, 17 May 2024 14:01:40 -0700 Subject: [PATCH 3/4] Speed up image slicing for ocr --- surya/input/processing.py | 25 ++++++++++++++++--------- surya/settings.py | 2 +- 2 files changed, 17 insertions(+), 10 deletions(-) diff --git a/surya/input/processing.py b/surya/input/processing.py index 32b7a57..d857a61 100644 --- a/surya/input/processing.py +++ b/surya/input/processing.py @@ -80,31 +80,38 @@ def slice_bboxes_from_image(image: Image.Image, bboxes): def slice_polys_from_image(image: Image.Image, polys): + image_array = np.array(image) lines = [] for idx, poly in enumerate(polys): - lines.append(slice_and_pad_poly(image, poly, idx)) + lines.append(slice_and_pad_poly(image, image_array, poly, idx)) return lines -def slice_and_pad_poly(image: Image.Image, coordinates, idx): +def slice_and_pad_poly(image: Image.Image, image_array: np.array, coordinates, idx): # Create a mask for the polygon mask = Image.new('L', image.size, 0) - # coordinates must be in tuple form for PIL + # Draw polygon onto mask coordinates = [(corner[0], corner[1]) for corner in coordinates] ImageDraw.Draw(mask).polygon(coordinates, outline=1, fill=1) bbox = mask.getbbox() + + if bbox is None: + return None + mask = np.array(mask) - # Extract the polygonal area from the image - polygon_image = np.array(image) + # We mask out anything not in the polygon + polygon_image = image_array.copy() polygon_image[mask == 0] = settings.RECOGNITION_PAD_VALUE - polygon_image = Image.fromarray(polygon_image) - rectangle = Image.new('RGB', (bbox[2] - bbox[0], bbox[3] - bbox[1]), 'white') + # Crop out the bbox, and ensure we pad the area outside the polygon with the pad value + cropped_polygon = polygon_image[bbox[1]:bbox[3], bbox[0]:bbox[2]] + rectangle = np.full((bbox[3] - bbox[1], bbox[2] - bbox[0], 3), settings.RECOGNITION_PAD_VALUE, dtype=np.uint8) + rectangle[:, :] = cropped_polygon # Paste the polygon into the rectangle - rectangle.paste(polygon_image.crop(bbox), (0, 0)) + rectangle_image = Image.fromarray(rectangle) - return rectangle + return rectangle_image diff --git a/surya/settings.py b/surya/settings.py index 813c455..3d01f06 100644 --- a/surya/settings.py +++ b/surya/settings.py @@ -70,7 +70,7 @@ def TORCH_DEVICE_DETECTION(self) -> str: } RECOGNITION_FONT_DL_BASE: str = "https://github.com/satbyy/go-noto-universal/releases/download/v7.0" RECOGNITION_BENCH_DATASET_NAME: str = "vikp/rec_bench" - RECOGNITION_PAD_VALUE: int = 0 # Should be 0 or 255 + RECOGNITION_PAD_VALUE: int = 255 # Should be 0 or 255 # Layout LAYOUT_MODEL_CHECKPOINT: str = "vikp/surya_layout2" From 2479472eee5c3f5b8f3810d95309422b754a8350 Mon Sep 17 00:00:00 2001 From: Vik Paruchuri Date: Fri, 17 May 2024 14:43:33 -0700 Subject: [PATCH 4/4] Undo generation changes --- pyproject.toml | 2 +- surya/recognition.py | 81 +++++++++++++++++--------------------------- 2 files changed, 33 insertions(+), 50 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f04a101..d800609 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "surya-ocr" -version = "0.4.5" +version = "0.4.6" description = "OCR, layout, reading order, and line detection in 90+ languages" authors = ["Vik Paruchuri "] readme = "README.md" diff --git a/surya/recognition.py b/surya/recognition.py index 1745e97..8b8be74 100644 --- a/surya/recognition.py +++ b/surya/recognition.py @@ -47,56 +47,39 @@ def batch_recognition(images: List, languages: List[List[str]], model, processor batch_pixel_values = torch.tensor(np.array(batch_pixel_values), dtype=model.dtype).to(model.device) batch_decoder_input = torch.from_numpy(np.array(batch_decoder_input, dtype=np.int64)).to(model.device) - token_count = 0 - encoder_outputs = None - batch_predictions = [[] for _ in range(len(batch_images))] - sequence_scores = None - - attention_mask = torch.ones_like(batch_decoder_input, device=model.device) - past_key_values = None - with torch.inference_mode(): - while token_count < settings.RECOGNITION_MAX_TOKENS: - cache_position = torch.tensor([token_count+ 1], device=model.device) - - return_dict = model( - decoder_input_ids=batch_decoder_input, - decoder_attention_mask=attention_mask, - decoder_langs=batch_langs, - pixel_values=batch_pixel_values, - encoder_outputs=encoder_outputs, - past_key_values=past_key_values, - return_dict=True, - ) - - logits = return_dict["logits"] - preds = torch.argmax(logits[:, -1], dim=-1) - scores = torch.max(F.softmax(logits, dim=-1), dim=-1).values - done = preds == processor.tokenizer.eos_id - - if sequence_scores is None: - sequence_scores = scores - else: - scores[done == 1] = 0 - sequence_scores = torch.cat([sequence_scores, scores], dim=1) - - encoder_outputs = (return_dict["encoder_last_hidden_state"],) - past_key_values = return_dict["past_key_values"] - - if done.all(): - break - - attention_mask = torch.cat([attention_mask, ~done.unsqueeze(1)], dim=1) - - for j, (pred, status) in enumerate(zip(preds.cpu(), done)): - if not status: - batch_predictions[j].append(int(pred)) - - batch_decoder_input = preds.unsqueeze(1) - token_count += 1 - - sequence_scores = torch.sum(sequence_scores, dim=-1) / torch.sum(sequence_scores != 0, dim=-1) - detected_text = processor.tokenizer.batch_decode(batch_predictions) + return_dict = model.generate( + pixel_values=batch_pixel_values, + decoder_input_ids=batch_decoder_input, + decoder_langs=batch_langs, + eos_token_id=processor.tokenizer.eos_id, + max_new_tokens=settings.RECOGNITION_MAX_TOKENS, + output_scores=True, + return_dict_in_generate=True + ) + generated_ids = return_dict["sequences"] + + # Find confidence scores + scores = return_dict["scores"] # Scores is a tuple, one per new sequence position. Each tuple element is bs x vocab_size + sequence_scores = torch.zeros(generated_ids.shape[0]) + sequence_lens = torch.where( + generated_ids > processor.tokenizer.eos_id, + torch.ones_like(generated_ids), + torch.zeros_like(generated_ids) + ).sum(axis=-1).cpu() + prefix_len = generated_ids.shape[1] - len(scores) # Length of passed in tokens (bos, langs) + for token_idx, score in enumerate(scores): + probs = F.softmax(score, dim=-1) + max_probs = torch.max(probs, dim=-1).values + max_probs = torch.where( + generated_ids[:, token_idx + prefix_len] <= processor.tokenizer.eos_id, + torch.zeros_like(max_probs), + max_probs + ).cpu() + sequence_scores += max_probs + sequence_scores /= sequence_lens + + detected_text = processor.tokenizer.batch_decode(generated_ids) detected_text = [truncate_repetitions(dt) for dt in detected_text] # Postprocess to fix LaTeX output (add $$ signs, etc) detected_text = [fix_math(text) if math and contains_math(text) else text for text, math in zip(detected_text, has_math)]