ResNet

%reload_ext autoreload
%autoreload 2
%matplotlib inline
!/opt/bin/nvidia-smi
!nvcc --version

Libs setup and imports

%pip install fastai==2.5.3
%pip install lapixdl==0.7.17
%pip install -U albumentations
import random
from pathlib import Path

import albumentations as A
import imgaug
import numpy as np
import pandas as pd
from fastai.vision.all import (
    CrossEntropyLossFlat,
    F1Score,
    ImageDataLoaders,
    Normalize,
    PILImage,
    Precision,
    Recall,
    SaveModelCallback,
    ShowGraphCallback,
    Transform,
    accuracy,
    cnn_learner,
    get_image_files,
    imagenet_stats,
)
from lapixdl.evaluation.evaluate import evaluate_classification
from lapixdl.evaluation.model import Classification

# Fix seed
random.seed(81615)
imgaug.seed(81615)

Utils

class AlbumentationsTransform(Transform):
    split_idx = 0

    def __init__(self, aug):
        self.aug = aug

    def encodes(self, img: PILImage):
        aug_img = self.aug(image=np.array(img))["image"]
        return PILImage.create(aug_img)

Paths and metadata configuration

Fill the paths accordingly to the structure of the dataset and the output directories.

from google.colab import drive

drive.mount("/content/gdrive")
path_dataset = Path("[dataset root folder]")
path_img = (
    path_dataset / "nuclei_images"
)  # Images root folder. The images must be inside the [train|val|test]/[class] folders.
path_output = Path("[output root folder]")
path_models = (
    path_output / "models"
)  # Folder where the models/weights should be saved in

path_models.mkdir(parents=True, exist_ok=True)

Train pipeline setup

Architecture Definition

The complete list of supported models is available at https://github.com/fastai/fastai/tree/2.5.3/fastai/vision/models

from fastai.vision.models import resnet34

used_model = resnet34

Pipeline

# Augmentations setup

augmentations = A.Compose(
    [
        A.VerticalFlip(p=0.5),
        A.HorizontalFlip(p=0.5),
        A.Rotate((-10, 10), p=0.75),
        A.RandomBrightnessContrast(0.1, 0.1, p=0.75),
        A.Affine(p=0.75, shear=0.2),
    ]
)

transforms = [AlbumentationsTransform(augmentations)]
metrics = [
    accuracy,
    F1Score(average="macro"),
    Precision(average="macro"),
    Recall(average="macro"),
]
# Organize images as dataframes to better control de dataloader

images_df = pd.DataFrame()
images_df["path"] = get_image_files(path_img)
images_df["is_val"] = images_df["path"].map(lambda e: "/val/" in str(e))
images_df["is_test"] = images_df["path"].map(lambda e: "/test/" in str(e))
images_df["label"] = images_df["path"].map(lambda e: e.parent.stem)
bs = 256  # Adjust the batch size accordingly to the GPU VRAM capacity

# Creates the dataloader
data = ImageDataLoaders.from_df(
    images_df[~images_df["is_test"]],
    fn_col="path",
    label_col="label",
    valid_col="is_val",
    path="/",
    bs=bs,
    item_tfms=transforms,
    batch_tfms=Normalize.from_stats(*imagenet_stats),
)
def get_learner(data, load_model=None, unfreeze: bool = False):
    """Creates and setups the learner for each step of the training approach.

    Args:
        data: Dataloader.
        load_model (str): Path to the model to be loaded.
        unfreeze (bool): Indicates if the model should be unfreezed. Defaults to False.
    """
    learn = cnn_learner(
        data, used_model, metrics=metrics, loss_func=CrossEntropyLossFlat()
    ).to_fp16()

    learn.path = path_models
    if load_model is not None:
        learn.load(load_model)
    if unfreeze:
        learn.unfreeze()
    return learn

Training

data.show_batch()

# Runs the learning rate finder
get_learner(data).lr_find()
Downloading: "https://download.pytorch.org/models/resnet34-333f7ec4.pth" to /root/.cache/torch/hub/checkpoints/resnet34-333f7ec4.pth
SuggestedLRs(lr_min=0.014454397559165954, lr_steep=0.02290867641568184)

lr = slice(1e-3)  # Choose accordingly to the learning rate finder results
wd = 1e-2  # Weight decay
learn = get_learner(data)
callbacks = [
    SaveModelCallback(
        monitor="f1_score", fname="best_model_stg1", with_opt=True
    ),  # Saves the best model as `fname` in `learn.path` considering the metric defined in `monitor`.
    ShowGraphCallback(),  # Shows the train/validation graph
]

# Train
learn.fit_one_cycle(15, lr_max=lr, wd=wd, cbs=callbacks)
epoch train_loss valid_loss accuracy f1_score precision_score recall_score time
0 2.474386 1.744588 0.435616 0.330540 0.338294 0.382289 01:15
1 1.939551 1.328607 0.522023 0.391875 0.393798 0.419766 01:16
2 1.512487 1.162524 0.547102 0.379171 0.400188 0.406273 01:16
3 1.236423 1.042282 0.545627 0.387111 0.426875 0.406076 01:15
4 1.082852 1.016989 0.565016 0.385589 0.383973 0.409967 01:16
5 1.017346 0.975917 0.569441 0.397004 0.476073 0.412609 01:15
6 0.979478 0.968788 0.573656 0.403047 0.518769 0.420999 01:16
7 0.960073 0.943925 0.576396 0.412637 0.492309 0.420384 01:16
8 0.937811 0.941390 0.583351 0.409222 0.460899 0.428149 01:15
9 0.926209 0.924635 0.588830 0.428235 0.502808 0.433062 01:16
10 0.916739 0.917519 0.591359 0.429460 0.520100 0.437520 01:16
11 0.902705 0.916644 0.589252 0.424372 0.509431 0.434567 01:16
12 0.899566 0.916142 0.592413 0.425591 0.506765 0.436742 01:16
13 0.894124 0.917667 0.593888 0.429418 0.511939 0.438527 01:15
14 0.893615 0.917900 0.591149 0.424207 0.510138 0.433839 01:16 
Better model found at epoch 0 with f1_score value: 0.3305402774069637.

Better model found at epoch 1 with f1_score value: 0.39187494212699825.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 5 with f1_score value: 0.3970043729268677.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 6 with f1_score value: 0.4030465741989606.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 7 with f1_score value: 0.41263749451261983.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 9 with f1_score value: 0.4282349083720349.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 10 with f1_score value: 0.429459646011961.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
get_learner(data, "best_model_stg1").show_results(
    max_n=6, figsize=(20, 30)
)  # Shows example results of the trained model

# Load, unfreeze and lr finder for finetunning
get_learner(data, "best_model_stg1", True).lr_find()
SuggestedLRs(lr_min=0.00831763744354248, lr_steep=0.0691830962896347)

lr = slice(1e-5, 1e-4)
wd = 1e-2
learn = get_learner(data, "best_model_stg1", True)
callbacks = [
    SaveModelCallback(monitor="f1_score", fname="best_model_stg2", with_opt=True),
    ShowGraphCallback(),
]

learn.fit_one_cycle(30, lr_max=lr, wd=wd, cbs=callbacks)
epoch train_loss valid_loss accuracy f1_score precision_score recall_score time
0 0.891220 0.928622 0.590516 0.416079 0.496447 0.430724 01:25
1 0.879627 0.893358 0.606112 0.446536 0.532237 0.452765 01:23
2 0.869689 0.883405 0.609062 0.443328 0.517824 0.453327 01:23
3 0.847449 0.862027 0.627397 0.457980 0.518314 0.467735 01:22
4 0.826111 0.843040 0.628873 0.472242 0.525411 0.470772 01:23
5 0.816850 0.831325 0.634984 0.481027 0.524491 0.478903 01:23
6 0.788784 0.826324 0.649737 0.480913 0.539220 0.479585 01:23
7 0.763790 0.816625 0.645943 0.499776 0.522893 0.499219 01:23
8 0.751624 0.811684 0.648894 0.503248 0.548577 0.494386 01:23
9 0.728228 0.815923 0.647418 0.509602 0.533249 0.509280 01:23
10 0.706144 0.825386 0.652266 0.504786 0.562088 0.488558 01:23
11 0.684217 0.818088 0.658377 0.509231 0.555848 0.499345 01:23
12 0.662023 0.834348 0.646575 0.499641 0.528043 0.498193 01:23
13 0.646017 0.849344 0.641307 0.495630 0.544914 0.484367 01:23
14 0.628290 0.836154 0.648261 0.509061 0.542555 0.500663 01:23
15 0.608221 0.850099 0.651844 0.520209 0.545026 0.512392 01:23
16 0.587121 0.861526 0.647840 0.510108 0.528720 0.510171 01:23
17 0.570424 0.859354 0.652687 0.515892 0.542851 0.506111 01:23
18 0.547513 0.887205 0.641307 0.498192 0.530149 0.490864 01:23
19 0.539223 0.884411 0.642571 0.507634 0.528852 0.500226 01:23
20 0.517264 0.897426 0.641307 0.506625 0.525483 0.503887 01:23
21 0.507369 0.912260 0.642993 0.507037 0.526488 0.502113 01:23
22 0.491067 0.914238 0.643414 0.509914 0.528081 0.504601 01:23
23 0.490030 0.920629 0.638567 0.507085 0.527399 0.500257 01:23
24 0.476422 0.922263 0.640042 0.509492 0.526331 0.503663 01:23
25 0.467436 0.935722 0.639410 0.508062 0.527273 0.503797 01:23
26 0.455804 0.929143 0.638988 0.508131 0.524269 0.503195 01:23
27 0.457106 0.929034 0.638145 0.507482 0.522151 0.502724 01:23
28 0.458451 0.938713 0.636459 0.504235 0.520592 0.500712 01:23
29 0.457969 0.932986 0.637935 0.507792 0.524726 0.501861 01:23 
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 0 with f1_score value: 0.4160788976810143.

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 1 with f1_score value: 0.44653574538288704.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 3 with f1_score value: 0.4579797357026596.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 4 with f1_score value: 0.4722418212180717.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 5 with f1_score value: 0.4810266072969061.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 7 with f1_score value: 0.4997764509715627.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 8 with f1_score value: 0.5032483603559176.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 9 with f1_score value: 0.50960233247939.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
Better model found at epoch 15 with f1_score value: 0.520209208295054.
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
get_learner(data, "best_model_stg2").show_results(max_n=6, figsize=(20, 30))

Test

Run the metrics evaluations on the test set

bs = 1
data_test = ImageDataLoaders.from_df(
    images_df[~images_df["is_bbox_only"]],
    fn_col="path",
    label_col="label",
    valid_col="is_test",
    path="/",
    bs=bs,
    batch_tfms=Normalize.from_stats(*imagenet_stats),
)
learn = get_learner(data_test, "best_model_stg2")
ds_test = data_test.valid_ds
# Iterators to convert to the LapixDL input format


def gt_classifications(ds):
    for img, cls in ds:
        yield Classification(int(cls))


def pred_classifications(ds):
    for img, cls in ds:
        (cls_name, pred_cls, scores) = learn.predict(img)
        yield Classification(int(pred_cls), scores[int(pred_cls)])
evaluation_test = evaluate_classification(
    gt_classifications(ds_test), pred_classifications(ds_test), ds_test.vocab
)
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Classification Metrics:
    Sample Count: 4745
    Accuracy: 0.591359325605901
    Avg Recall: 0.43748874674900917
    Avg Precision: 0.5200670146291876
    Avg Specificity: 0.9027242343890162
    Avg FPR: 0.09727576561098387
    Avg F-Score: 0.42946161447001724

By Class:

anormal:
    TP: 1032
    TN: 2379
    FP: 829
    FN: 505
    FPR: 0.25841645885286785
    Accuracy: 0.7188619599578504
    Recall: 0.6714378659726741
    Precision: 0.554540569586244
    Specificity: 0.7415835411471322
    F-Score: 0.607416127133608
em_divisao:
    TP: 0
    TN: 4733
    FP: 0
    FN: 12
    FPR: 0.0
    Accuracy: 0.9974710221285564
    Recall: 0.0
    Precision: nan
    Specificity: 1.0
    F-Score: 0.0
fora_de_foco:
    TP: 1018
    TN: 2350
    FP: 602
    FN: 775
    FPR: 0.20392953929539295
    Accuracy: 0.7097997892518441
    Recall: 0.5677635248187396
    Precision: 0.6283950617283951
    Specificity: 0.7960704607046071
    F-Score: 0.59654263111632
reacional:
    TP: 21
    TN: 4498
    FP: 8
    FN: 218
    FPR: 0.001775410563692854
    Accuracy: 0.9523709167544784
    Recall: 0.08786610878661087
    Precision: 0.7241379310344828
    Specificity: 0.9982245894363071
    F-Score: 0.15671641791044777
sanguinea:
    TP: 303
    TN: 3944
    FP: 161
    FN: 337
    FPR: 0.03922046285018271
    Accuracy: 0.8950474183350896
    Recall: 0.4734375
    Precision: 0.6530172413793104
    Specificity: 0.9607795371498173
    F-Score: 0.5489130434782609
saudavel:
    TP: 432
    TN: 3882
    FP: 339
    FN: 92
    FPR: 0.08031272210376687
    Accuracy: 0.9091675447839831
    Recall: 0.8244274809160306
    Precision: 0.5603112840466926
    Specificity: 0.9196872778962332
    F-Score: 0.6671814671814672

/usr/local/lib/python3.7/dist-packages/lapixdl/evaluation/model.py:217: RuntimeWarning: invalid value encountered in long_scalars
  return self.TP/(self.FP + self.TP)
evaluation_test.show_confusion_matrix()

(<Figure size 432x288 with 2 Axes>,
 <matplotlib.axes._subplots.AxesSubplot>)