File size: 5,284 Bytes
63fba53 | 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 | import torch
import os
import json
from tqdm import tqdm
import gc
from .utils import save_json
class BaseDataset:
def __init__(self):
self.chunk_idx = int(os.environ.get("chunk_idx",0))
self.num_chunks = int(os.environ.get("num_chunks",1))
def run(self,samples,model,batch_size = 2000):
out_samples = []
with torch.no_grad():
messages_list = []
current_messages = []
current_samples = []
for sample in tqdm(samples):
messages = sample["messages"]
current_messages.append(messages)
current_samples.append(sample)
if len(current_messages) >= batch_size:
messages_list.append([current_messages,current_samples])
current_messages = []
current_samples = []
if current_messages:
messages_list.append([current_messages,current_samples])
for current_messages,current_samples in tqdm(messages_list):
outputs = model.generate_outputs(current_messages)
try:
for sample,response in zip(current_samples,outputs):
del sample["messages"]
sample["response"] = response
out_samples.append(sample)
except Exception as e:
from pdb import set_trace;set_trace()
print(e)
gc.collect()
return out_samples
def cal_matrics(self):
pass
def init_dataset(self):
pass
def construct_messages(self):
pass
def eval(self):
model = self.model
dataset_path = self.dataset_path
output_path = self.output_path
num_chunks = self.num_chunks
chunk_idx = self.chunk_idx
if num_chunks == 1:
results_path = os.path.join(output_path,"results.json")
matric_path = os.path.join(output_path,"metrics.json")
out_samples = self.run(self.samples,model)
save_json(results_path,out_samples)
metrics,out_samples = self.cal_metrics(out_samples)
save_json(matric_path,metrics)
save_json(results_path,out_samples)
return metrics
elif num_chunks > 1:
results_path = os.path.join(output_path,f"results_{chunk_idx}.json")
final_results_path = os.path.join(output_path,"results.json")
out_samples = self.run(self.samples,model)
save_json(results_path,out_samples)
total_results_path = os.listdir(output_path)
total_results_path = [result for result in total_results_path if result.startswith("results_")]
if len(total_results_path) == num_chunks:
total_results = []
for result in total_results_path:
results_path = os.path.join(output_path,result)
with open(results_path,"r") as f:
total_results.extend(json.load(f))
save_json(final_results_path,total_results)
metrics,out_samples = self.cal_metrics(total_results)
matric_path = os.path.join(output_path,"metrics.json")
save_json(matric_path,metrics)
save_json(final_results_path,out_samples)
return metrics
else:
return None
else:
raise ValueError("num_chunks must be greater than 0")
def _download_file_local(self,local_path,url):
# download the specific file to local_path
os.makedirs(local_path,exist_ok=True)
# Extract filename from URL
filename = url.split("/")[-1]
file_path = os.path.join(local_path, filename)
# Check if wget or curl is available
if os.system("which wget > /dev/null 2>&1") == 0:
download_cmd = f"wget {url} -O {file_path}"
elif os.system("which curl > /dev/null 2>&1") == 0:
download_cmd = f"curl -L {url} -o {file_path}"
else:
raise RuntimeError("Neither wget nor curl is available for downloading")
# Download with error handling
if os.system(download_cmd) != 0:
if os.path.exists(file_path):
os.remove(file_path)
raise RuntimeError("Failed to download dataset")
def _unzip_img_zip_local(self, local_path, zip_filename):
# suppose zip_filename is like 'images.zip' or 'data.tgz'
zip_file_path = os.path.join(local_path, zip_filename)
if zip_filename.endswith('.zip'):
if os.system(f"unzip -q {zip_file_path} -d {local_path}") != 0:
if os.path.exists(zip_file_path):
os.remove(zip_file_path)
raise RuntimeError("Failed to extract dataset")
elif zip_filename.endswith('.tgz') or zip_filename.endswith('.tar.gz'):
if os.system(f"tar -xzf {zip_file_path} -C {local_path}") != 0:
if os.path.exists(zip_file_path):
os.remove(zip_file_path)
raise RuntimeError("Failed to extract dataset")
else:
if os.path.exists(zip_file_path):
os.remove(zip_file_path)
raise RuntimeError(f"Unsupported file format: {zip_filename}")
if os.path.exists(zip_file_path):
os.remove(zip_file_path) |