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#!/usr/bin/env python3 | |
from pathlib import Path | |
from safetensors.torch import load_file | |
def summarize_tensor(x): | |
if x is None: | |
return "None" | |
x = x.float() | |
return f"({x.min().item():.3f}, {x.mean().item():.3f}, {x.max().item():.3f})" |
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def summarize_tensor(x): | |
return f"\033[34m{str(tuple(x.shape)).ljust(24)}\033[0m (\033[31mmin {x.min().item():+.4f}\033[0m / \033[32mmean {x.mean().item():+.4f}\033[0m / \033[33mmax {x.max().item():+.4f}\033[0m)" | |
class ModelActivationPrinter: | |
def __init__(self, module, submodules_to_log): | |
self.id_to_name = { | |
id(module): str(name) for name, module in module.named_modules() | |
} | |
self.submodules = submodules_to_log |
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# PyTorch <=2.0 doesn't support bfloat16 F.interpolate natively. | |
# so, we have to do things the old fashioned way. | |
import torch | |
import torch.nn as nn | |
# functional implementation | |
def nearest_neighbor_upsample(x: torch.Tensor, scale_factor: int): | |
"""Upsample {x} (NCHW) by scale factor {scale_factor} using nearest neighbor interpolation.""" | |
s = scale_factor |
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function addCompressor(el) { | |
var AudioContext = window.AudioContext || window.webkitAudioContext; | |
var context = new AudioContext(); | |
var source = context.createMediaElementSource(el); | |
var compressor = context.createDynamicsCompressor(); | |
var makeupGain = context.createGain(); | |
// Set compressor params here | |
compressor.threshold.value = -40; | |
compressor.knee.value = 40; |
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#!/usr/bin/env python | |
filename = "output.txt" | |
with open(filename, 'w') as fout: | |
x = 0 | |
for x in range(5): | |
fout.write(""" def function_number{}(): \n | |
""".format(x)) |
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