| import humanfriendly |
| import numpy as np |
| import torch |
|
|
|
|
| def get_human_readable_count(number: int) -> str: |
| """Return human_readable_count |
| |
| Originated from: |
| https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/core/memory.py |
| |
| Abbreviates an integer number with K, M, B, T for thousands, millions, |
| billions and trillions, respectively. |
| Examples: |
| >>> get_human_readable_count(123) |
| '123 ' |
| >>> get_human_readable_count(1234) # (one thousand) |
| '1 K' |
| >>> get_human_readable_count(2e6) # (two million) |
| '2 M' |
| >>> get_human_readable_count(3e9) # (three billion) |
| '3 B' |
| >>> get_human_readable_count(4e12) # (four trillion) |
| '4 T' |
| >>> get_human_readable_count(5e15) # (more than trillion) |
| '5,000 T' |
| Args: |
| number: a positive integer number |
| Return: |
| A string formatted according to the pattern described above. |
| """ |
| assert number >= 0 |
| labels = [" ", "K", "M", "B", "T"] |
| num_digits = int(np.floor(np.log10(number)) + 1 if number > 0 else 1) |
| num_groups = int(np.ceil(num_digits / 3)) |
| num_groups = min(num_groups, len(labels)) |
| shift = -3 * (num_groups - 1) |
| number = number * (10 ** shift) |
| index = num_groups - 1 |
| return f"{number:.2f} {labels[index]}" |
|
|
|
|
| def to_bytes(dtype) -> int: |
| |
| return int(str(dtype)[-2:]) // 8 |
|
|
|
|
| def model_summary(model: torch.nn.Module) -> str: |
| message = "Model structure:\n" |
| message += str(model) |
| tot_params = sum(p.numel() for p in model.parameters()) |
| num_params = sum(p.numel() for p in model.parameters() if p.requires_grad) |
| percent_trainable = "{:.1f}".format(num_params * 100.0 / tot_params) |
| tot_params = get_human_readable_count(tot_params) |
| num_params = get_human_readable_count(num_params) |
| message += "\n\nModel summary:\n" |
| message += f" Class Name: {model.__class__.__name__}\n" |
| message += f" Total Number of model parameters: {tot_params}\n" |
| message += ( |
| f" Number of trainable parameters: {num_params} ({percent_trainable}%)\n" |
| ) |
| num_bytes = humanfriendly.format_size( |
| sum( |
| p.numel() * to_bytes(p.dtype) for p in model.parameters() if p.requires_grad |
| ) |
| ) |
| message += f" Size: {num_bytes}\n" |
| dtype = next(iter(model.parameters())).dtype |
| message += f" Type: {dtype}" |
| return message |
|
|
