Fix and simplify semantic-segmentation example (#30145)

* Remove unused augmentation * Fix pad_if_smaller() and remove unused augmentation * Add indentation * Fix requirements * Update dataset use instructions * Replace transforms with albumentations * Replace identity transform with None * Fixing formatting * Fixed comment place
huggingface · Apr 22, 2024 · 9e3aff0 · 9e3aff0
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diff --git a/examples/pytorch/_tests_requirements.txt b/examples/pytorch/_tests_requirements.txt
@@ -25,3 +25,4 @@ torchaudio
 jiwer
 librosa
 evaluate >= 0.2.0
+albumentations
diff --git a/examples/pytorch/semantic-segmentation/README.md b/examples/pytorch/semantic-segmentation/README.md
@@ -97,6 +97,10 @@ The script leverages the [🤗 Trainer API](https://huggingface.co/docs/transfor
 
 Here we show how to fine-tune a [SegFormer](https://huggingface.co/nvidia/mit-b0) model on the [segments/sidewalk-semantic](https://huggingface.co/datasets/segments/sidewalk-semantic) dataset:
 
+In order to use `segments/sidewalk-semantic`: 
+ - Log in to Hugging Face with `huggingface-cli login` (token can be accessed [here](https://huggingface.co/settings/tokens)).
+ - Accept terms of use for `sidewalk-semantic` on [dataset page](https://huggingface.co/datasets/segments/sidewalk-semantic).
+
 ```bash
 python run_semantic_segmentation.py \
     --model_name_or_path nvidia/mit-b0 \
@@ -105,7 +109,6 @@ python run_semantic_segmentation.py \
     --remove_unused_columns False \
     --do_train \
     --do_eval \
-    --evaluation_strategy steps \
     --push_to_hub \
     --push_to_hub_model_id segformer-finetuned-sidewalk-10k-steps \
     --max_steps 10000 \

diff --git a/examples/pytorch/semantic-segmentation/requirements.txt b/examples/pytorch/semantic-segmentation/requirements.txt
@@ -1,4 +1,6 @@
-git://github.com/huggingface/accelerate.git
 datasets >= 2.0.0
 torch >= 1.3
-evaluate
+accelerate
+evaluate
+Pillow
+albumentations
diff --git a/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py b/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py
@@ -16,21 +16,20 @@
 import json
 import logging
 import os
-import random
 import sys
 import warnings
 from dataclasses import dataclass, field
+from functools import partial
 from typing import Optional
 
+import albumentations as A
 import evaluate
 import numpy as np
 import torch
+from albumentations.pytorch import ToTensorV2
 from datasets import load_dataset
 from huggingface_hub import hf_hub_download
-from PIL import Image
 from torch import nn
-from torchvision import transforms
-from torchvision.transforms import functional
 
 import transformers
 from transformers import (
@@ -57,118 +56,19 @@
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt")
 
 
-def pad_if_smaller(img, size, fill=0):
-    size = (size, size) if isinstance(size, int) else size
-    original_width, original_height = img.size
-    pad_height = size[1] - original_height if original_height < size[1] else 0
-    pad_width = size[0] - original_width if original_width < size[0] else 0
-    img = functional.pad(img, (0, 0, pad_width, pad_height), fill=fill)
-    return img
+def reduce_labels_transform(labels: np.ndarray, **kwargs) -> np.ndarray:
+    """Set `0` label as with value 255 and then reduce all other labels by 1.
 
+    Example:
+        Initial class labels:         0 - background; 1 - road; 2 - car;
+        Transformed class labels:   255 - background; 0 - road; 1 - car;
 
-class Compose:
-    def __init__(self, transforms):
-        self.transforms = transforms
-
-    def __call__(self, image, target):
-        for t in self.transforms:
-            image, target = t(image, target)
-        return image, target
-
-
-class Identity:
-    def __init__(self):
-        pass
-
-    def __call__(self, image, target):
-        return image, target
-
-
-class Resize:
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, image, target):
-        image = functional.resize(image, self.size)
-        target = functional.resize(target, self.size, interpolation=transforms.InterpolationMode.NEAREST)
-        return image, target
-
-
-class RandomResize:
-    def __init__(self, min_size, max_size=None):
-        self.min_size = min_size
-        if max_size is None:
-            max_size = min_size
-        self.max_size = max_size
-
-    def __call__(self, image, target):
-        size = random.randint(self.min_size, self.max_size)
-        image = functional.resize(image, size)
-        target = functional.resize(target, size, interpolation=transforms.InterpolationMode.NEAREST)
-        return image, target
-
-
-class RandomCrop:
-    def __init__(self, size):
-        self.size = size if isinstance(size, tuple) else (size, size)
-
-    def __call__(self, image, target):
-        image = pad_if_smaller(image, self.size)
-        target = pad_if_smaller(target, self.size, fill=255)
-        crop_params = transforms.RandomCrop.get_params(image, self.size)
-        image = functional.crop(image, *crop_params)
-        target = functional.crop(target, *crop_params)
-        return image, target
-
-
-class RandomHorizontalFlip:
-    def __init__(self, flip_prob):
-        self.flip_prob = flip_prob
-
-    def __call__(self, image, target):
-        if random.random() < self.flip_prob:
-            image = functional.hflip(image)
-            target = functional.hflip(target)
-        return image, target
-
-
-class PILToTensor:
-    def __call__(self, image, target):
-        image = functional.pil_to_tensor(image)
-        target = torch.as_tensor(np.array(target), dtype=torch.int64)
-        return image, target
-
-
-class ConvertImageDtype:
-    def __init__(self, dtype):
-        self.dtype = dtype
-
-    def __call__(self, image, target):
-        image = functional.convert_image_dtype(image, self.dtype)
-        return image, target
-
-
-class Normalize:
-    def __init__(self, mean, std):
-        self.mean = mean
-        self.std = std
-
-    def __call__(self, image, target):
-        image = functional.normalize(image, mean=self.mean, std=self.std)
-        return image, target
-
-
-class ReduceLabels:
-    def __call__(self, image, target):
-        if not isinstance(target, np.ndarray):
-            target = np.array(target).astype(np.uint8)
-        # avoid using underflow conversion
-        target[target == 0] = 255
-        target = target - 1
-        target[target == 254] = 255
-
-        target = Image.fromarray(target)
-        return image, target
+    **kwargs are required to use this function with albumentations.
+    """
+    labels[labels == 0] = 255
+    labels = labels - 1
+    labels[labels == 254] = 255
+    return labels
 
 
 @dataclass
@@ -365,7 +265,7 @@ def main():
     id2label = {int(k): v for k, v in id2label.items()}
     label2id = {v: str(k) for k, v in id2label.items()}
 
-    # Load the mean IoU metric from the datasets package
+    # Load the mean IoU metric from the evaluate package
     metric = evaluate.load("mean_iou", cache_dir=model_args.cache_dir)
 
     # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
@@ -424,64 +324,62 @@ def compute_metrics(eval_pred):
         token=model_args.token,
         trust_remote_code=model_args.trust_remote_code,
     )
+    # `reduce_labels` is a property of dataset labels, in case we use image_processor
+    # pretrained on another dataset we should override the default setting
+    image_processor.do_reduce_labels = data_args.reduce_labels
 
-    # Define torchvision transforms to be applied to each image + target.
-    # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
-    # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
+    # Define transforms to be applied to each image and target.
     if "shortest_edge" in image_processor.size:
         # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
-        size = (image_processor.size["shortest_edge"], image_processor.size["shortest_edge"])
+        height, width = image_processor.size["shortest_edge"], image_processor.size["shortest_edge"]
     else:
-        size = (image_processor.size["height"], image_processor.size["width"])
-    train_transforms = Compose(
+        height, width = image_processor.size["height"], image_processor.size["width"]
+    train_transforms = A.Compose(
         [
-            ReduceLabels() if data_args.reduce_labels else Identity(),
-            RandomCrop(size=size),
-            RandomHorizontalFlip(flip_prob=0.5),
-            PILToTensor(),
-            ConvertImageDtype(torch.float),
-            Normalize(mean=image_processor.image_mean, std=image_processor.image_std),
+            A.Lambda(
+                name="reduce_labels",
+                mask=reduce_labels_transform if data_args.reduce_labels else None,
+                p=1.0,
+            ),
+            # pad image with 255, because it is ignored by loss
+            A.PadIfNeeded(min_height=height, min_width=width, border_mode=0, value=255, p=1.0),
+            A.RandomCrop(height=height, width=width, p=1.0),
+            A.HorizontalFlip(p=0.5),
+            A.Normalize(mean=image_processor.image_mean, std=image_processor.image_std, max_pixel_value=255.0, p=1.0),
+            ToTensorV2(),
         ]
     )
-    # Define torchvision transform to be applied to each image.
-    # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
-    val_transforms = Compose(
+    val_transforms = A.Compose(
         [
-            ReduceLabels() if data_args.reduce_labels else Identity(),
-            Resize(size=size),
-            PILToTensor(),
-            ConvertImageDtype(torch.float),
-            Normalize(mean=image_processor.image_mean, std=image_processor.image_std),
+            A.Lambda(
+                name="reduce_labels",
+                mask=reduce_labels_transform if data_args.reduce_labels else None,
+                p=1.0,
+            ),
+            A.Resize(height=height, width=width, p=1.0),
+            A.Normalize(mean=image_processor.image_mean, std=image_processor.image_std, max_pixel_value=255.0, p=1.0),
+            ToTensorV2(),
         ]
     )
 
-    def preprocess_train(example_batch):
+    def preprocess_batch(example_batch, transforms: A.Compose):
         pixel_values = []
         labels = []
         for image, target in zip(example_batch["image"], example_batch["label"]):
-            image, target = train_transforms(image.convert("RGB"), target)
-            pixel_values.append(image)
-            labels.append(target)
+            transformed = transforms(image=np.array(image.convert("RGB")), mask=np.array(target))
+            pixel_values.append(transformed["image"])
+            labels.append(transformed["mask"])
 
         encoding = {}
-        encoding["pixel_values"] = torch.stack(pixel_values)
-        encoding["labels"] = torch.stack(labels)
+        encoding["pixel_values"] = torch.stack(pixel_values).to(torch.float)
+        encoding["labels"] = torch.stack(labels).to(torch.long)
 
         return encoding
 
-    def preprocess_val(example_batch):
-        pixel_values = []
-        labels = []
-        for image, target in zip(example_batch["image"], example_batch["label"]):
-            image, target = val_transforms(image.convert("RGB"), target)
-            pixel_values.append(image)
-            labels.append(target)
-
-        encoding = {}
-        encoding["pixel_values"] = torch.stack(pixel_values)
-        encoding["labels"] = torch.stack(labels)
-
-        return encoding
+    # Preprocess function for dataset should have only one argument,
+    # so we use partial to pass the transforms
+    preprocess_train_batch_fn = partial(preprocess_batch, transforms=train_transforms)
+    preprocess_val_batch_fn = partial(preprocess_batch, transforms=val_transforms)
 
     if training_args.do_train:
         if "train" not in dataset:
@@ -491,7 +389,7 @@ def preprocess_val(example_batch):
                 dataset["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
             )
         # Set the training transforms
-        dataset["train"].set_transform(preprocess_train)
+        dataset["train"].set_transform(preprocess_train_batch_fn)
 
     if training_args.do_eval:
         if "validation" not in dataset:
@@ -501,7 +399,7 @@ def preprocess_val(example_batch):
                 dataset["validation"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
             )
         # Set the validation transforms
-        dataset["validation"].set_transform(preprocess_val)
+        dataset["validation"].set_transform(preprocess_val_batch_fn)
 
     # Initialize our trainer
     trainer = Trainer(