mirror of
https://github.com/zebrajr/pytorch.git
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6d725e7d66
Enables two ruff rules derived from pylint: * PLR1722 replaces any exit() calls with sys.exit(). exit() is only designed to be used in repl contexts as may not always be imported by default. This always use the version in the sys module which is better * PLW3301 replaces nested min / max calls with simplified versions (ie. `min(a, min(b, c))` => `min(a, b. c)`). The new version is more idiomatic and more efficient. Pull Request resolved: https://github.com/pytorch/pytorch/pull/109461 Approved by: https://github.com/ezyang
216 lines
7.8 KiB
Python
216 lines
7.8 KiB
Python
## @package download
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# Module caffe2.python.models.download
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import argparse
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import os
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import sys
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import signal
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import re
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import json
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from caffe2.proto import caffe2_pb2
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# Import urllib
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from urllib.error import HTTPError, URLError
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import urllib.request as urllib
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# urllib requires more work to deal with a redirect, so not using vanity url
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DOWNLOAD_BASE_URL = "https://s3.amazonaws.com/download.caffe2.ai/models/"
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DOWNLOAD_COLUMNS = 70
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# Don't let urllib hang up on big downloads
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def signalHandler(signal, frame):
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print("Killing download...")
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sys.exit(0)
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signal.signal(signal.SIGINT, signalHandler)
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def deleteDirectory(top_dir):
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for root, dirs, files in os.walk(top_dir, topdown=False):
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for name in files:
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os.remove(os.path.join(root, name))
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for name in dirs:
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os.rmdir(os.path.join(root, name))
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os.rmdir(top_dir)
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def progressBar(percentage):
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full = int(DOWNLOAD_COLUMNS * percentage / 100)
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bar = full * "#" + (DOWNLOAD_COLUMNS - full) * " "
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sys.stdout.write(u"\u001b[1000D[" + bar + "] " + str(percentage) + "%")
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sys.stdout.flush()
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def downloadFromURLToFile(url, filename, show_progress=True):
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try:
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print("Downloading from {url}".format(url=url))
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response = urllib.urlopen(url)
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size = int(response.info().get('Content-Length').strip())
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chunk = min(size, 8192)
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print("Writing to {filename}".format(filename=filename))
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if show_progress:
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downloaded_size = 0
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progressBar(0)
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with open(filename, "wb") as local_file:
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while True:
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data_chunk = response.read(chunk)
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if not data_chunk:
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break
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local_file.write(data_chunk)
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if show_progress:
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downloaded_size += len(data_chunk)
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progressBar(int(100 * downloaded_size / size))
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print("") # New line to fix for progress bar
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except HTTPError as e:
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raise Exception("Could not download model. [HTTP Error] {code}: {reason}."
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.format(code=e.code, reason=e.reason)) from e
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except URLError as e:
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raise Exception("Could not download model. [URL Error] {reason}."
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.format(reason=e.reason)) from e
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def getURLFromName(name, filename):
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return "{base_url}{name}/{filename}".format(base_url=DOWNLOAD_BASE_URL,
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name=name, filename=filename)
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def downloadModel(model, args):
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# Figure out where to store the model
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model_folder = '{folder}'.format(folder=model)
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dir_path = os.path.dirname(os.path.realpath(__file__))
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if args.install:
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model_folder = '{dir_path}/{folder}'.format(dir_path=dir_path,
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folder=model)
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# Check if that folder is already there
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if os.path.exists(model_folder) and not os.path.isdir(model_folder):
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if not args.force:
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raise Exception("Cannot create folder for storing the model,\
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there exists a file of the same name.")
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else:
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print("Overwriting existing file! ({filename})"
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.format(filename=model_folder))
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os.remove(model_folder)
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if os.path.isdir(model_folder):
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if not args.force:
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response = ""
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query = "Model already exists, continue? [y/N] "
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try:
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response = raw_input(query)
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except NameError:
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response = input(query)
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if response.upper() == 'N' or not response:
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print("Cancelling download...")
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sys.exit(0)
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print("Overwriting existing folder! ({filename})".format(filename=model_folder))
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deleteDirectory(model_folder)
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# Now we can safely create the folder and download the model
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os.makedirs(model_folder)
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for f in ['predict_net.pb', 'init_net.pb']:
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try:
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downloadFromURLToFile(getURLFromName(model, f),
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'{folder}/{f}'.format(folder=model_folder,
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f=f))
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except Exception as e:
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print("Abort: {reason}".format(reason=str(e)))
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print("Cleaning up...")
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deleteDirectory(model_folder)
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sys.exit(0)
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if args.install:
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os.symlink("{folder}/__sym_init__.py".format(folder=dir_path),
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"{folder}/__init__.py".format(folder=model_folder))
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def validModelName(name):
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invalid_names = ['__init__']
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if name in invalid_names:
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return False
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if not re.match("^[/0-9a-zA-Z_-]+$", name):
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return False
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return True
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class ModelDownloader:
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def __init__(self, model_env_name='CAFFE2_MODELS'):
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self.model_env_name = model_env_name
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def _model_dir(self, model):
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caffe2_home = os.path.expanduser(os.getenv('CAFFE2_HOME', '~/.caffe2'))
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models_dir = os.getenv(self.model_env_name, os.path.join(caffe2_home, 'models'))
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return os.path.join(models_dir, model)
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def _download(self, model):
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model_dir = self._model_dir(model)
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assert not os.path.exists(model_dir)
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os.makedirs(model_dir)
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for f in ['predict_net.pb', 'init_net.pb', 'value_info.json']:
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url = getURLFromName(model, f)
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dest = os.path.join(model_dir, f)
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try:
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downloadFromURLToFile(url, dest, show_progress=False)
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except TypeError:
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# show_progress not supported prior to
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# Caffe2 78c014e752a374d905ecfb465d44fa16e02a28f1
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# (Sep 17, 2017)
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downloadFromURLToFile(url, dest)
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except Exception:
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deleteDirectory(model_dir)
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raise
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# This version returns an extra debug_str argument that helps to understand
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# why our work sometimes fails in sandcastle
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def get_c2_model_dbg(self, model_name):
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debug_str = "get_c2_model debug:\n"
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model_dir = self._model_dir(model_name)
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if not os.path.exists(model_dir):
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self._download(model_name)
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c2_predict_pb = os.path.join(model_dir, 'predict_net.pb')
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debug_str += "c2_predict_pb path: " + c2_predict_pb + "\n"
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c2_predict_net = caffe2_pb2.NetDef()
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with open(c2_predict_pb, 'rb') as f:
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len_read = c2_predict_net.ParseFromString(f.read())
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debug_str += "c2_predict_pb ParseFromString = " + str(len_read) + "\n"
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c2_predict_net.name = model_name
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c2_init_pb = os.path.join(model_dir, 'init_net.pb')
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debug_str += "c2_init_pb path: " + c2_init_pb + "\n"
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c2_init_net = caffe2_pb2.NetDef()
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with open(c2_init_pb, 'rb') as f:
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len_read = c2_init_net.ParseFromString(f.read())
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debug_str += "c2_init_pb ParseFromString = " + str(len_read) + "\n"
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c2_init_net.name = model_name + '_init'
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with open(os.path.join(model_dir, 'value_info.json')) as f:
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value_info = json.load(f)
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return c2_init_net, c2_predict_net, value_info, debug_str
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def get_c2_model(self, model_name):
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init_net, predict_net, value_info, _ = self.get_c2_model_dbg(model_name)
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return init_net, predict_net, value_info
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description='Download or install pretrained models.')
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parser.add_argument('model', nargs='+',
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help='Model to download/install.')
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parser.add_argument('-i', '--install', action='store_true',
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help='Install the model.')
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parser.add_argument('-f', '--force', action='store_true',
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help='Force a download/installation.')
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args = parser.parse_args()
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for model in args.model:
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if validModelName(model):
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downloadModel(model, args)
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else:
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print("'{}' is not a valid model name.".format(model))
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