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Download model if not exists (New Feature) #29

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Download model if not exists (New Feature) #29

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b89ecf9
Fixed "Error in checking torchvision.__version__ with double digits i…
mfatiho Mar 18, 2022
e47c652
Added download model if there is no
mfatiho Mar 18, 2022
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30 changes: 26 additions & 4 deletions models/vgg_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,12 @@
"""
Mostly copy-paste from torchvision references.
"""
from posixpath import split
import torch
import torch.nn as nn
import os
from tqdm import tqdm
import urllib.request


__all__ = [
Expand All @@ -23,14 +27,13 @@
'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
}

checkpoints_dir = os.path.join(os.path.dirname(__file__), "../checkpoints/")

model_paths = {
'vgg16_bn': '/apdcephfs/private_changanwang/checkpoints/vgg16_bn-6c64b313.pth',
'vgg16': '/apdcephfs/private_changanwang/checkpoints/vgg16-397923af.pth',

'vgg16_bn': os.path.join(checkpoints_dir,'vgg16_bn-6c64b313.pth'),
'vgg16': os.path.join(checkpoints_dir,'vgg16-397923af.pth'),
}


class VGG(nn.Module):

def __init__(self, features, num_classes=1000, init_weights=True):
Expand Down Expand Up @@ -98,11 +101,30 @@ def make_layers(cfg, batch_norm=False, sync=False):
}


class DownloadProgressBar(tqdm):
def update_to(self, b=1, bsize=1, tsize=None):
if tsize is not None:
self.total = tsize
self.update(b * bsize - self.n)

def download_arch_if_not_exists(arch):
if not os.path.exists(model_paths[arch]):
print(f"{arch} model not found.")
chunk_size = 1024
os.makedirs(checkpoints_dir, exist_ok=True)

url = model_urls[arch]
filename = os.path.basename(url)
output_path = os.path.join(checkpoints_dir, filename)
with DownloadProgressBar(unit='B', unit_scale=True, miniters=1, desc=url.split('/')[-1]) as t:
urllib.request.urlretrieve(url, filename=output_path, reporthook=t.update_to)

def _vgg(arch, cfg, batch_norm, pretrained, progress, sync=False, **kwargs):
if pretrained:
kwargs['init_weights'] = False
model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm, sync=sync), **kwargs)
if pretrained:
download_arch_if_not_exists(arch)
state_dict = torch.load(model_paths[arch])
model.load_state_dict(state_dict)
return model
Expand Down