Spaces:
Runtime error
Runtime error
File size: 10,899 Bytes
44504f7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 |
import re
from typing import Tuple, Any
def extract_code_in_one_cell(text: str, lang) -> tuple[bool, Any]: #
pattern = r'```{lang}([^\n]*)(.*?)```'.format(lang=lang)
matches = re.findall(pattern, text, re.DOTALL)
if len(matches)>1:
code_blocks = ''
for match in matches:
code_block = match[1]
code_blocks += code_block
return True, code_blocks
elif len(matches):
code_block = matches[-1]
#if 'python' in code_block[0]:
return True, code_block[1]
else:
return False, ''
def extract_code(text: str) -> tuple[bool, Any]:
pattern = r'```python([^\n]*)(.*?)```'
matches = re.findall(pattern, text, re.DOTALL)
if len(matches)>1:
code_blocks = ''
for match in matches:
code_block = match[1]
code_blocks += code_block
return True, code_blocks
elif len(matches):
code_block = matches[-1]
#if 'python' in code_block[0]:
return True, code_block[1]
else:
return False, ''
def remove_code_blocks(text):
pattern = r'(```.*?```)'
text = re.sub(pattern, '', text, flags=re.DOTALL)
text = text.replace("Execution result:", '')
return text
def check_msg_ua(msg):
corrected_lst = []
for item in msg:
if corrected_lst and corrected_lst[-1] == item:
continue
corrected_lst.append(item)
if corrected_lst and corrected_lst[-1] != 'assistant':
corrected_lst.append('assistant')
return corrected_lst
if __name__ == '__main__':
pmt = """
Retrieval: The retriever found the following pieces of code cloud address the problem. All functions are well-defined and have been executed in the back-end. So, you should directly refer to the core code and modify it as appropriate instead of re-implement any function in runnable function.
Core code (All functions have been well-defined in the runnable function):
```core_function
args = argparse.ArgumentParser()
args.net = 'nn_sigmoid'
args.lr = 5e-3
args.epochs = 30
args.wd = 0
args.b = 64
train_nn_network(args)
```
Runnable function (Have been executed in back-end):
```runnable
import numpy as np
import scipy.io as sio
import scipy
import sys
import time
import argparse
import torch
import math
from torch import nn
from torch.nn.utils.parametrizations import spectral_norm
from pathlib import Path
from torch import optim
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision import datasets
from tqdm import tqdm
def initialize_weights(tensor):
return tensor.uniform_() * math.sqrt(0.25 / (tensor.shape[0] + tensor.shape[1]))
class _RRAutoencoder(nn.Module):
def __init__(self):
super().__init__()
self.linear_1 = nn.Linear(784, 200)
self.linear_2 = nn.Linear(200, 784)
self.encoder = self.linear_1
self.decoder = self.linear_2
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
return x
def clamp(self):
pass
class _NNAutoencoder(_RRAutoencoder):
def __init__(self):
super().__init__()
self.linear_1.bias.data.zero_()
self.linear_2.bias.data.zero_()
self.linear_1.weight = nn.Parameter(
initialize_weights(self.linear_1.weight.data)
)
self.linear_2.weight = nn.Parameter(
initialize_weights(self.linear_2.weight.data)
)
def clamp(self):
self.linear_1.weight.data.clamp_(min=0)
self.linear_2.weight.data.clamp_(min=0)
self.linear_1.bias.data.clamp_(min=0)
self.linear_2.bias.data.clamp_(min=0)
class _PNAutoencoder(_NNAutoencoder):
def clamp(self):
self.linear_1.weight.data.clamp_(min=1e-3)
self.linear_2.weight.data.clamp_(min=1e-3)
self.linear_1.bias.data.clamp_(min=0)
self.linear_2.bias.data.clamp_(min=0)
class _NRAutoencoder(_NNAutoencoder):
def clamp(self):
self.linear_1.weight.data.clamp_(min=0)
self.linear_2.weight.data.clamp_(min=0)
class SigmoidNNAutoencoder(_NNAutoencoder):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(self.linear_1, nn.Sigmoid())
self.decoder = nn.Sequential(self.linear_2, nn.Sigmoid())
class TanhNNAutoencoder(_NNAutoencoder):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(self.linear_1, nn.Tanh())
self.decoder = nn.Sequential(self.linear_2, nn.Tanh())
class TanhPNAutoencoder(_PNAutoencoder):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(self.linear_1, nn.Tanh())
self.decoder = nn.Sequential(self.linear_2, nn.Tanh())
class ReLUNNAutoencoder(_NNAutoencoder):
def __init__(self):
super().__init__()
self.linear_1 = spectral_norm(self.linear_1)
self.linear_2 = spectral_norm(self.linear_2)
self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
self.decoder = nn.Sequential(self.linear_2, nn.ReLU())
def clamp(self):
self.linear_1.parametrizations.weight.original.data.clamp_(min=0)
self.linear_2.parametrizations.weight.original.data.clamp_(min=0)
self.linear_1.bias.data.clamp_(min=0)
self.linear_2.bias.data.clamp_(min=0)
class ReLUPNAutoencoder(_PNAutoencoder):
def __init__(self):
super().__init__()
self.linear_1 = spectral_norm(self.linear_1)
self.linear_2 = spectral_norm(self.linear_2)
self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
self.decoder = nn.Sequential(self.linear_2, nn.ReLU())
def clamp(self):
self.linear_1.parametrizations.weight.original.data.clamp_(min=1e-3)
self.linear_2.parametrizations.weight.original.data.clamp_(min=1e-3)
self.linear_1.bias.data.clamp_(min=0)
self.linear_2.bias.data.clamp_(min=0)
class TanhSwishNNAutoencoder(_NNAutoencoder):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(self.linear_1, nn.Tanh())
self.decoder = nn.Sequential(self.linear_2, nn.SiLU())
class ReLUSigmoidNRAutoencoder(_NRAutoencoder):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
self.decoder = nn.Sequential(self.linear_2, nn.Sigmoid())
class ReLUSigmoidRRAutoencoder(_RRAutoencoder):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
self.decoder = nn.Sequential(self.linear_2, nn.Sigmoid())
def get_network(name):
match name:
case "nn_sigmoid":
return SigmoidNNAutoencoder()
case "nn_tanh":
return TanhNNAutoencoder()
case "pn_tanh":
return TanhPNAutoencoder()
case "nn_relu":
return ReLUNNAutoencoder()
case "pn_relu":
return ReLUPNAutoencoder()
case "nn_tanh_swish":
return TanhSwishNNAutoencoder()
case "nr_relu_sigmoid":
return ReLUSigmoidNRAutoencoder()
case "rr_relu_sigmoid":
return ReLUSigmoidRRAutoencoder()
case _:
raise NotImplementedError(
f"Autoencoder of name '{name}' currently is not supported"
)
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def epoch(loader, model, device, criterion, opt=None):
losses = AverageMeter()
if opt is None:
model.eval()
else:
model.train()
for inputs, _ in tqdm(loader, leave=False):
inputs = inputs.view(-1, 28 * 28).to(device)
outputs = model(inputs)
loss = criterion(outputs, inputs)
if opt:
opt.zero_grad(set_to_none=True)
loss.backward()
opt.step()
model.clamp()
losses.update(loss.item(), inputs.size(0))
return losses.avg
def train_nn_network(args):
# p = Path(__file__)
# weights_path = f"{p.parent}/weights"
# Path(weights_path).mkdir(parents=True, exist_ok=True)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = get_network(args.net)
model.to(device)
mnist_train = datasets.MNIST(
".", train=True, download=True, transform=transforms.ToTensor()
)
mnist_test = datasets.MNIST(
".", train=False, download=True, transform=transforms.ToTensor()
)
train_loader = DataLoader(
mnist_train, batch_size=args.b, shuffle=True, num_workers=4, pin_memory=True
)
test_loader = DataLoader(
mnist_test, batch_size=args.b, shuffle=False, num_workers=4, pin_memory=True
)
opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
criterion = nn.MSELoss()
best_loss = None
for i in range(1, args.epochs + 1):
train_loss = epoch(train_loader, model, device, criterion, opt)
test_loss = epoch(test_loader, model, device, criterion)
if best_loss is None or best_loss > test_loss:
best_loss = test_loss
# torch.save(model.state_dict(), f"{weights_path}/{args.net}.pth")
print(f"Epoch: {i} | Train Loss: {train_loss:.4f} | Test Loss: {test_loss:.4f}")
```
Your modified code:
```python
import argparse
args = argparse.ArgumentParser()
args.net = 'nn_sigmoid'
args.lr = 5e-3
args.epochs = 5 # Changed epochs to 5 as per the request
args.wd = 0
args.b = 64
train_nn_network(args)
```
"""
print(extract_code(pmt))
print(extract_code_in_one_cell(pmt,'python')) |