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import math
import jsonlines
from pathlib import Path
from typing import Dict, Tuple, List, Union
from torch import Tensor, nn
__all__ = [
"cosine_schedule_with_warmup",
"load_json_annotations",
"bbox_augmentation_resize",
"count_total_parameters",
"compute_grad_norm",
"printer",
"html_table_template",
]
printer = lambda device, output: f"[GPU {device}] " + output
html_table_template = (
lambda table: f"""<html>
<head> <meta charset="UTF-8">
<style>
table, th, td {{
border: 1px solid black;
font-size: 10px;
}}
</style> </head>
<body>
<table frame="hsides" rules="groups" width="100%%">
{table}
</table> </body> </html>"""
)
# adpated from https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/optimization.py
def cosine_schedule_with_warmup(
step: int,
*,
warmup: int,
min_ratio: float,
total_step: int,
cycle: float = 0.5,
):
if step < warmup:
if step == 0:
step = 1
return float(step) / float(max(1, warmup))
if step >= total_step:
step = total_step
progress = float(step - warmup) / float(max(1, total_step - warmup))
return max(
min_ratio, 0.5 * (1.0 + math.cos(math.pi * float(cycle) * 2.0 * progress))
)
def load_json_annotations(json_file_dir: Path, split: str):
"""Preprocess jsonl in dataset."""
image_label_pair = list()
with jsonlines.open(json_file_dir) as f:
for obj in f:
if obj["split"] == split:
image_label_pair.append((obj["filename"], obj["html"]))
return image_label_pair
def bbox_augmentation_resize(
bbox: List[int], image_size: List[int], target_size: int
) -> List[int]:
"""Modify the bbox coordinates according to the image resizing."""
# Assuming the bbox is [xmin, ymin, xmax, ymax]
assert len(image_size) == 2
ratio = [target_size / i for i in image_size]
ratio = ratio * 2
bbox = [int(round(i * j)) for i, j in zip(bbox, ratio)]
return bbox
def count_total_parameters(model: nn.Module) -> int:
"""Count total parameters that need training."""
total_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
return total_parameters
def compute_grad_norm(model: nn.Module) -> float:
total_norm = 0.0
for p in model.parameters():
if p.grad is not None and p.requires_grad:
param_norm = p.grad.detach().data.norm(2)
total_norm += param_norm.item() ** 2
total_norm = total_norm**0.5
return total_norm
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