Python example_requires Examples

Example #1

File: object_detection.py Project: stjordanis/lightning-flash

#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from itertools import chain

import flash
from flash.core.integrations.fiftyone import visualize
from flash.core.utilities.imports import example_requires
from flash.image import ObjectDetectionData, ObjectDetector
from flash.image.detection.serialization import FiftyOneDetectionLabels

example_requires("image")

import icedata  # noqa: E402

# 1. Create the DataModule
data_dir = icedata.fridge.load_data()

datamodule = ObjectDetectionData.from_folders(
    train_folder=data_dir,
    predict_folder=data_dir,
    val_split=0.1,
    image_size=128,
    parser=icedata.fridge.parser,
)

# 2. Build the task

Example #2

File: tabular_forecasting_interpretable.py Project: aniketmaurya/lightning-flash

#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

import flash
from flash.core.integrations.pytorch_forecasting import convert_predictions
from flash.core.utilities.imports import example_requires
from flash.tabular.forecasting import TabularForecaster, TabularForecastingData

example_requires(["tabular", "matplotlib"])

import matplotlib.pyplot as plt  # noqa: E402
import pandas as pd  # noqa: E402
from pytorch_forecasting.data import NaNLabelEncoder  # noqa: E402
from pytorch_forecasting.data.examples import generate_ar_data  # noqa: E402

# Example based on this tutorial: https://pytorch-forecasting.readthedocs.io/en/latest/tutorials/ar.html
# 1. Create the DataModule
data = generate_ar_data(seasonality=10.0, timesteps=400, n_series=100, seed=42)
data["date"] = pd.Timestamp("2020-01-01") + pd.to_timedelta(data.time_idx, "D")

max_prediction_length = 20

training_cutoff = data["time_idx"].max() - max_prediction_length

Example #3

File: face_detection.py Project: aniketmaurya/lightning-flash

# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

import flash
from flash.core.utilities.imports import example_requires
from flash.image import FaceDetectionData, FaceDetector

example_requires("fastface")
import fastface as ff  # noqa: E402

# # 1. Create the DataModule
train_dataset = ff.dataset.FDDBDataset(source_dir="data/", phase="train")
val_dataset = ff.dataset.FDDBDataset(source_dir="data/", phase="val")

datamodule = FaceDetectionData.from_datasets(train_dataset=train_dataset,
                                             val_dataset=val_dataset,
                                             batch_size=2)

# # 2. Build the task
model = FaceDetector(model="lffd_slim")

# # 3. Create the trainer and finetune the model
trainer = flash.Trainer(max_epochs=3,

Example #4

File: question_answering.py Project: aniketmaurya/lightning-flash

# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from flash import Trainer
from flash.core.data.utils import download_data
from flash.core.utilities.imports import example_requires
from flash.text import QuestionAnsweringData, QuestionAnsweringTask

example_requires("text")

import nltk  # noqa: E402

nltk.download("punkt")

# 1. Create the DataModule
download_data("https://pl-flash-data.s3.amazonaws.com/squad_tiny.zip", "./data/")

datamodule = QuestionAnsweringData.from_squad_v2(
    train_file="./data/squad_tiny/train.json",
    val_file="./data/squad_tiny/val.json",
    batch_size=4,
)

# 2. Build the task

Example #5

File: graph_classification.py Project: stjordanis/lightning-flash

# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

import flash
from flash.core.utilities.imports import example_requires
from flash.graph import GraphClassificationData, GraphClassifier

example_requires("graph")

from torch_geometric.datasets import TUDataset  # noqa: E402

# 1. Create the DataModule
dataset = TUDataset(root="data", name="KKI")

datamodule = GraphClassificationData.from_datasets(
    train_dataset=dataset,
    val_split=0.1,
)

# 2. Build the task
model = GraphClassifier(num_features=datamodule.num_features,
                        num_classes=datamodule.num_classes)

Example #6

# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

import flash
from flash.core.utilities.imports import example_requires
from flash.tabular.forecasting import TabularForecaster, TabularForecastingData

example_requires("tabular")

import pandas as pd  # noqa: E402
from pytorch_forecasting.data import NaNLabelEncoder  # noqa: E402
from pytorch_forecasting.data.examples import generate_ar_data  # noqa: E402

# Example based on this tutorial: https://pytorch-forecasting.readthedocs.io/en/latest/tutorials/ar.html
# 1. Create the DataModule
data = generate_ar_data(seasonality=10.0, timesteps=400, n_series=100, seed=42)
data["date"] = pd.Timestamp("2020-01-01") + pd.to_timedelta(data.time_idx, "D")

max_encoder_length = 60
max_prediction_length = 20

training_cutoff = data["time_idx"].max() - max_prediction_length