EveryDream2trainer/data/ed_dl_wrap.py

57 lines
1.9 KiB
Python

"""
Copyright [2022] Victor C Hall
Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at
https://www.gnu.org/licenses/agpl-3.0.en.html
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 torch.utils.data import DataLoader
from data.every_dream import EveryDreamBatch
class EveryDreamDataLoaderWrapper(DataLoader):
"""
Collates image:caption pairs into batches
"""
def __init__(self, batch_size: int, tokenizer, dataset: EveryDreamBatch):
self.dataset = dataset
self.tokenizer = tokenizer
super().__init__(dataset, batch_size, shuffle=False, pin_memory=True)
#super().__init__(dataset, batch_size, shuffle=False, collate_fn=self.collate_fn, pin_memory=True)
def collate_fn(self, batch):
"""
Collates batches of data
based on https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py
"""
print("collate_fn")
print(len(batch))
captions = [example["caption"] for example in batch]
images = [example["image"] for example in batch]
print("collate_fn2")
images = torch.stack(images)
images = images.to(memory_format=torch.contiguous_format).float()
print("collate_fn3")
captions = self.tokenizer.pad(
{"captions": captions},
padding=True,
return_tensors="pt",
).input_ids
batch = {
"captions": captions,
"images": images,
}
print(f"{batch['captions']} {batch['images'].shape}")
return batch