#
# 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 import download_data, Trainer
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",
val_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(gpus=int(torch.cuda.is_available()), fast_dev_run=True)
# 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 pytorch_lightning import Trainer
from flash import download_data
from flash.text import TranslationData, TranslationTask
# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/wmt_en_ro.zip", "data/")
# 2. Load the model from a checkpoint
model = TranslationTask.load_from_checkpoint(
"https://flash-weights.s3.amazonaws.com/translation_model_en_ro.pt")
# 2a. Translate a few sentences!
predictions = model.predict([
"BBC News went to meet one of the project's first graduates.",
"A recession has come as quickly as 11 months after the first rate hike and as long as 86 months.",
])
print(predictions)
# 2b. Or generate translations from a sheet file!
datamodule = TranslationData.from_file(
predict_file="data/wmt_en_ro/predict.csv",
from flash import Trainer
from flash import download_data
from flash.vision import ImageClassificationData, ImageClassifier
# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'data/')
# 2. Load the model from a checkpoint
model = ImageClassifier.load_from_checkpoint("image_classification_model.pt")
# 3a. Predict what's on a few images! ants or bees?
predictions = model.predict([
"data/hymenoptera_data/test/ants/8124241_36b290d372.jpg",
"data/hymenoptera_data/test/ants/147542264_79506478c2.jpg",
"data/hymenoptera_data/test/ants/212100470_b485e7b7b9.jpg",
])
print(predictions)
# 3b. Generate predictions with a whole folder
datamodule = ImageClassificationData.from_folder(folder="data/hymenoptera_data/test/ants/")
predictions = Trainer().predict(model, datamodule=datamodule)
print(predictions)