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fixing conversion of chanels, config update

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s444415 2023-01-05 13:42:46 +00:00
parent fe0b8c0397
commit b3617532f8
2 changed files with 4 additions and 5 deletions
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4
config-train.yaml
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@ -5,10 +5,10 @@ output_model_path: "Zombely/plwiki-proto-fine-tuned-v3"
wandb_test_name: "fiszki-ocr-fine-tune" wandb_test_name: "fiszki-ocr-fine-tune"
checkpoint_path: "./checkpoint" checkpoint_path: "./checkpoint"
max_length: 768 max_length: 768
image_size: [1920, 2560] image_size: [2560, 1920]
train_config: train_config:
max_epochs: 1 max_epochs: 1
val_check_interval: 0.5 val_check_interval: 0.5
check_val_every_n_epoch: 1 check_val_every_n_epoch: 1
gradient_clip_val: 1.0 gradient_clip_val: 1.0
num_training_samples_per_epoch: 800 num_training_samples_per_epoch: 800

5
utils/donut_dataset.py
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@ -7,7 +7,6 @@ import torch
from transformers import DonutProcessor, VisionEncoderDecoderModel from transformers import DonutProcessor, VisionEncoderDecoderModel
class DonutDataset(Dataset): class DonutDataset(Dataset):
""" """
DonutDataset which is saved in huggingface datasets format. (see details in https://huggingface.co/docs/datasets) DonutDataset which is saved in huggingface datasets format. (see details in https://huggingface.co/docs/datasets)
@ -132,7 +131,7 @@ class DonutDataset(Dataset):
sample = self.dataset[idx] sample = self.dataset[idx]
# inputs # inputs
pixel_values = self.processor(sample["image"], random_padding=self.split == "train", return_tensors="pt").pixel_values pixel_values = self.processor(sample["image"].convert("RGB"), random_padding=self.split == "train", return_tensors="pt").pixel_values
pixel_values = pixel_values.squeeze() pixel_values = pixel_values.squeeze()
# targets # targets
@ -149,4 +148,4 @@ class DonutDataset(Dataset):
labels = input_ids.clone() labels = input_ids.clone()
labels[labels == self.processor.tokenizer.pad_token_id] = self.ignore_id # model doesn't need to predict pad token labels[labels == self.processor.tokenizer.pad_token_id] = self.ignore_id # model doesn't need to predict pad token
# labels[: torch.nonzero(labels == self.prompt_end_token_id).sum() + 1] = self.ignore_id # model doesn't need to predict prompt (for VQA) # labels[: torch.nonzero(labels == self.prompt_end_token_id).sum() + 1] = self.ignore_id # model doesn't need to predict prompt (for VQA)
return pixel_values, labels, target_sequence return pixel_values, labels, target_sequence