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# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# 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.
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader, Dataset
from transformers import AutoModelForVision2Seq, AutoProcessor
from peft import LoraConfig, get_peft_model
# Let's define the LoraConfig
config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
)
# We load our model and processor using `transformers`
model = AutoModelForVision2Seq.from_pretrained("Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0})
processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
# Get our peft model and print the number of trainable parameters
model = get_peft_model(model, config)
model.print_trainable_parameters()
# Let's load the dataset here!
dataset = load_dataset("ybelkada/football-dataset", split="train")
class ImageCaptioningDataset(Dataset):
def __init__(self, dataset, processor):
self.dataset = dataset
self.processor = processor
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
item = self.dataset[idx]
encoding = self.processor(images=item["image"], padding="max_length", return_tensors="pt")
# remove batch dimension
encoding = {k: v.squeeze() for k, v in encoding.items()}
encoding["text"] = item["text"]
return encoding
def collator(batch):
# pad the input_ids and attention_mask
processed_batch = {}
for key in batch[0].keys():
if key != "text":
processed_batch[key] = torch.stack([example[key] for example in batch])
else:
text_inputs = processor.tokenizer(
[example["text"] for example in batch], padding=True, return_tensors="pt"
)
processed_batch["input_ids"] = text_inputs["input_ids"]
processed_batch["attention_mask"] = text_inputs["attention_mask"]
return processed_batch
train_dataset = ImageCaptioningDataset(dataset, processor)
train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=2, collate_fn=collator)
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.train()
for epoch in range(50):
print("Epoch:", epoch)
for idx, batch in enumerate(train_dataloader):
input_ids = batch.pop("input_ids").to(device)
pixel_values = batch.pop("pixel_values").to(device, torch.float16)
outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=input_ids)
loss = outputs.loss
print("Loss:", loss.item())
loss.backward()
optimizer.step()
optimizer.zero_grad()
if idx % 10 == 0:
generated_output = model.generate(pixel_values=pixel_values)
print(processor.batch_decode(generated_output, skip_special_tokens=True))
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