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Borda committed Mar 10, 2021
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[![Stable API](https://img.shields.io/static/v1.svg?label=API&message=stable&color=green)](https://img.shields.io/static/v1.svg?label=API&message=stable&color=green)
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## Installation

<details>
<summary>View install</summary>

Pip / conda

```bash
Expand All @@ -70,6 +75,7 @@ cd lightning-flash
# install in editable mode
pip install -e .
```
</details>

---

Expand Down Expand Up @@ -162,25 +168,26 @@ Flash has an Image embedding task to encodes an image into a vector of image fea
<details>
<summary>View example</summary>

```python
# import our libraries
import torch
```python
# import our libraries
import torch

from flash.core.data import download_data
from flash.vision import ImageEmbedder

from flash.core.data import download_data
from flash.vision import ImageEmbedder
# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'data/')

# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'data/')
# 2. Create an ImageEmbedder with resnet50 trained on imagenet.
embedder = ImageEmbedder(backbone="resnet50", embedding_dim=128)

# 2. Create an ImageEmbedder with resnet50 trained on imagenet.
embedder = ImageEmbedder(backbone="resnet50", embedding_dim=128)
# 3. Generate an embedding from an image path.
embeddings = embedder.predict('data/hymenoptera_data/predict/153783656_85f9c3ac70.jpg')

# 3. Generate an embedding from an image path.
embeddings = embedder.predict('data/hymenoptera_data/predict/153783656_85f9c3ac70.jpg')
# 4. Print embeddings shape
print(embeddings.shape)
```

# 4. Print embeddings shape
print(embeddings.shape)
```
</details>

### Example 2: Text Summarization
Expand All @@ -189,40 +196,41 @@ Flash has a Summarization task to sum up text from a larger article into a short
<details>
<summary>View example</summary>

```python
# import our libraries
import flash
from flash import download_data
from flash.text import SummarizationData, SummarizationTask

# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", 'data/')

# 2. Load the data
datamodule = SummarizationData.from_files(
train_file="data/xsum/train.csv",
valid_file="data/xsum/valid.csv",
test_file="data/xsum/test.csv",
input="input",
target="target"
)

# 3. Build the model
model = SummarizationTask()

# 4. Create the trainer. Run once on data
trainer = flash.Trainer(max_epochs=1, gpus=1, precision=16)

# 5. Fine-tune the model
trainer.finetune(model, datamodule=datamodule)

# 6. Test model
trainer.test()
```
To run the example:
```bash
python flash_examples/finetuning/summarization.py
```
```python
# import our libraries
import flash
from flash import download_data
from flash.text import SummarizationData, SummarizationTask

# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", 'data/')

# 2. Load the data
datamodule = SummarizationData.from_files(
train_file="data/xsum/train.csv",
valid_file="data/xsum/valid.csv",
test_file="data/xsum/test.csv",
input="input",
target="target"
)

# 3. Build the model
model = SummarizationTask()

# 4. Create the trainer. Run once on data
trainer = flash.Trainer(max_epochs=1, gpus=1, precision=16)

# 5. Fine-tune the model
trainer.finetune(model, datamodule=datamodule)

# 6. Test model
trainer.test()
```
To run the example:
```bash
python flash_examples/finetuning/summarization.py
```

</details>

### Example 3: Tabular Classification
Expand All @@ -232,48 +240,49 @@ Flash has a TabularClassification task to tackle any tabular classification prob
<details>
<summary>View example</summary>

To illustrate, say we want to build a model to predict if a passenger survived on the Titanic.

```python
# import our libraries
from pytorch_lightning.metrics.classification import Accuracy, Precision, Recall
import flash
from flash import download_data
from flash.tabular import TabularClassifier, TabularData

# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", 'data/')

# 2. Load the data
datamodule = TabularData.from_csv(
"./data/titanic/titanic.csv",
test_csv="./data/titanic/test.csv",
categorical_input=["Sex", "Age", "SibSp", "Parch", "Ticket", "Cabin", "Embarked"],
numerical_input=["Fare"],
target="Survived",
val_size=0.25,
)

# 3. Build the model
model = TabularClassifier.from_data(datamodule, metrics=[Accuracy(), Precision(), Recall()])

# 4. Create the trainer. Run 10 times on data
trainer = flash.Trainer(max_epochs=10)

# 5. Train the model
trainer.fit(model, datamodule=datamodule)

# 6. Test model
trainer.test()

# 7. Predict!
predictions = model.predict("data/titanic/titanic.csv")
print(predictions)
```
To run the example:
```bash
python flash_examples/finetuning/tabular_data.py
```
To illustrate, say we want to build a model to predict if a passenger survived on the Titanic.

```python
# import our libraries
from pytorch_lightning.metrics.classification import Accuracy, Precision, Recall
import flash
from flash import download_data
from flash.tabular import TabularClassifier, TabularData

# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", 'data/')

# 2. Load the data
datamodule = TabularData.from_csv(
"./data/titanic/titanic.csv",
test_csv="./data/titanic/test.csv",
categorical_input=["Sex", "Age", "SibSp", "Parch", "Ticket", "Cabin", "Embarked"],
numerical_input=["Fare"],
target="Survived",
val_size=0.25,
)

# 3. Build the model
model = TabularClassifier.from_data(datamodule, metrics=[Accuracy(), Precision(), Recall()])

# 4. Create the trainer. Run 10 times on data
trainer = flash.Trainer(max_epochs=10)

# 5. Train the model
trainer.fit(model, datamodule=datamodule)

# 6. Test model
trainer.test()

# 7. Predict!
predictions = model.predict("data/titanic/titanic.csv")
print(predictions)
```
To run the example:
```bash
python flash_examples/finetuning/tabular_data.py
```

</details>

### Example 4: Object Detection
Expand All @@ -283,41 +292,42 @@ Flash has a ObjectDetection task to identify and locate objects in images.
<details>
<summary>View example</summary>

To illustrate, say we want to build a model on a tiny coco dataset.

```python
# import our libraries
import flash
from flash.core.data import download_data
from flash.vision import ObjectDetectionData, ObjectDetector

# 1. Download the data
# Dataset Credit: https://www.kaggle.com/ultralytics/coco128
download_data("https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip", "data/")

# 2. Load the Data
datamodule = ObjectDetectionData.from_coco(
train_folder="data/coco128/images/train2017/",
train_ann_file="data/coco128/annotations/instances_train2017.json",
batch_size=2
)

# 3. Build the model
model = ObjectDetector(num_classes=datamodule.num_classes)

# 4. Create the trainer. Run twice on data
trainer = flash.Trainer(max_epochs=3)

# 5. Finetune the model
trainer.fit(model, datamodule)

# 6. Save it!
trainer.save_checkpoint("object_detection_model.pt")
```
To run the example:
```bash
python flash_examples/finetuning/object_detection.py
```
To illustrate, say we want to build a model on a tiny coco dataset.

```python
# import our libraries
import flash
from flash.core.data import download_data
from flash.vision import ObjectDetectionData, ObjectDetector

# 1. Download the data
# Dataset Credit: https://www.kaggle.com/ultralytics/coco128
download_data("https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip", "data/")

# 2. Load the Data
datamodule = ObjectDetectionData.from_coco(
train_folder="data/coco128/images/train2017/",
train_ann_file="data/coco128/annotations/instances_train2017.json",
batch_size=2
)

# 3. Build the model
model = ObjectDetector(num_classes=datamodule.num_classes)

# 4. Create the trainer. Run twice on data
trainer = flash.Trainer(max_epochs=3)

# 5. Finetune the model
trainer.fit(model, datamodule)

# 6. Save it!
trainer.save_checkpoint("object_detection_model.pt")
```
To run the example:
```bash
python flash_examples/finetuning/object_detection.py
```

</details>

## A general task
Expand Down

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