Source code for cerebras.modelzoo.data.multimodal.clip.ImageNet1KClipProcessor

# Copyright 2022 Cerebras Systems.
#
# 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.

from typing import Union

import torch
import torchvision
from transformers import CLIPTokenizerFast

import cerebras.pytorch as cstorch
from cerebras.modelzoo.data.common.restartable_dataloader import (
    RestartableDataLoader,
)
from cerebras.modelzoo.data.vision.classification.data.imagenet import (
    ImageNet1KProcessor,
)
from cerebras.modelzoo.data.vision.classification.dataset_factory import (
    VisionSubset,
)


def _verify_dataset(dataset):
    """
    Verify the dataset type is compatible with ImageNet.
    """
    assert (
        isinstance(dataset, torchvision.datasets.VisionDataset)
        or isinstance(dataset, VisionSubset)
        or isinstance(dataset, torch.utils.data.Subset)
    ), f"Got {type(dataset)} but dataset must be type VisionDataset, VisionSubset, or torch.utils.data.Subset"


[docs]class ImageNet1KClipProcessor(ImageNet1KProcessor): def __init__(self, params): super().__init__(params) self.image_size = params.get("image_size") self.patch_size = params.get("patch_size") self.image_channels = params.get("image_channels") self.template = "this is a photo of <>." self.tokenizer = CLIPTokenizerFast.from_pretrained( "openai/clip-vit-base-patch16" ) # the maximum length of tokens for label text after tokenization. # ref: https://huggingface.co/openai/clip-vit-base-patch16/blob/main/config.json#L45 self.text_max_length = 77 def clip_collate_fn(self, data): assert self.classes is not None, "Need class names to construct samples" input_images = torch.stack([d[0] for d in data]) # [bs, c, h, w] # labels = torch.stack([d[1] for d in data]) # [bs, c, h, w] labels = [] for d in data: label = d[1] class_name = self.classes[label][0] # always choose the first label labels.append(self.template.replace("<>", class_name)) # tokenize labels = self.tokenizer( labels, max_length=self.text_max_length, padding="max_length", return_tensors="pt", ) results = {} results["input_images"] = input_images results["input_ids_text"] = labels["input_ids"] results["attention_mask_text"] = labels["attention_mask"] return results def create_dataloader( self, dataset: Union[ torchvision.datasets.VisionDataset, VisionSubset, torch.utils.data.Subset, ], is_training=False, ): _verify_dataset(dataset) self.classes = dataset.classes shuffle = self.shuffle and is_training if self.shuffle_seed is None: self.shuffle_seed = 0 data_sampler = cstorch.utils.data.DistributedSampler( data_source=dataset, shuffle=shuffle, seed=self.shuffle_seed, shard=True, batch_size=self.global_batch_size, drop_last=self.drop_last, ) dataloader = RestartableDataLoader( dataset, batch_sampler=data_sampler, num_workers=self.num_workers, pin_memory=self.distributed, prefetch_factor=self.prefetch_factor, persistent_workers=self.persistent_workers, worker_init_fn=self._worker_init_fn, ) dataloader.collate_fn = self.clip_collate_fn return dataloader