kaggle 无法下载 resnet50 预训练模型

Question

I am trying to use resnet50 pretrained model on Kaggle kernel.

But, when I run the following code, Error occurs and it could not download the pretrained model. How Can I make it work?

from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np

model = ResNet50(weights='imagenet', include_top=False)

Error:

-> 1318 encode_chunked=req.has_header('Transfer-encoding')) 1319
except OSError as err: # timeout error ...

Exception: URL fetch failure on https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5 : None -- [Errno -2] Name or service not known

All logs:

Using TensorFlow backend. /opt/conda/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6 return f(*args, **kwds)

Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5

--------------------------------------------------------------------------- gaierror Traceback (most recent call last) /opt/conda/lib/python3.6/urllib/request.py in do_open(self, http_class, req, **http_conn_args) 1317
h.request(req.get_method(), req.selector, req.data, headers, -> 1318 encode_chunked=req.has_header('Transfer-encoding')) 1319
except OSError as err: # timeout error

/opt/conda/lib/python3.6/http/client.py in request(self, method, url, body, headers, encode_chunked) 1238 """Send a complete request to the server.""" -> 1239 self._send_request(method, url, body, headers, encode_chunked) 1240

/opt/conda/lib/python3.6/http/client.py in _send_request(self, method, url, body, headers, encode_chunked) 1284 body = _encode(body, 'body') -> 1285 self.endheaders(body, encode_chunked=encode_chunked) 1286

/opt/conda/lib/python3.6/http/client.py in endheaders(self, message_body, encode_chunked) 1233 raise CannotSendHeader() -> 1234 self._send_output(message_body, encode_chunked=encode_chunked) 1235

/opt/conda/lib/python3.6/http/client.py in _send_output(self, message_body, encode_chunked) 1025 del self._buffer[:] -> 1026 self.send(msg) 1027

/opt/conda/lib/python3.6/http/client.py in send(self, data) 963 if self.auto_open: --> 964 self.connect() 965 else:

/opt/conda/lib/python3.6/http/client.py in connect(self) 1391 -> 1392 super().connect() 1393

/opt/conda/lib/python3.6/http/client.py in connect(self) 935 self.sock = self._create_connection( --> 936 (self.host,self.port), self.timeout, self.source_address) 937 self.sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)

/opt/conda/lib/python3.6/socket.py in create_connection(address, timeout, source_address) 703 err = None --> 704 for res in getaddrinfo(host, port, 0, SOCK_STREAM): 705 af, socktype, proto, canonname, sa = res

/opt/conda/lib/python3.6/socket.py in getaddrinfo(host, port, family, type, proto, flags) 744 addrlist = [] --> 745 for res in _socket.getaddrinfo(host, port, family, type, proto, flags): 746 af, socktype, proto, canonname, sa = res

gaierror: [Errno -2] Name or service not known

During handling of the above exception, another exception occurred:

URLError Traceback (most recent call last) /opt/conda/lib/python3.6/site-packages/Keras-2.0.6-py3.6.egg/keras/utils/data_utils.py in get_file(fname, origin, untar, md5_hash, file_hash, cache_subdir, hash_algorithm, extract, archive_format, cache_dir) 219 try: --> 220 urlretrieve(origin, fpath, dl_progress) 221 except URLError as e:

/opt/conda/lib/python3.6/urllib/request.py in urlretrieve(url, filename, reporthook, data) 247 --> 248 with contextlib.closing(urlopen(url, data)) as fp: 249 headers = fp.info()

/opt/conda/lib/python3.6/urllib/request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context) 222 opener = _opener --> 223 return opener.open(url, data, timeout) 224

/opt/conda/lib/python3.6/urllib/request.py in open(self, fullurl, data, timeout) 525 --> 526 response = self._open(req, data) 527

/opt/conda/lib/python3.6/urllib/request.py in _open(self, req, data) 543 result = self._call_chain(self.handle_open, protocol, protocol + --> 544 '_open', req) 545 if result:

/opt/conda/lib/python3.6/urllib/request.py in _call_chain(self, chain, kind, meth_name, *args) 503 func = getattr(handler, meth_name) --> 504 result = func(*args) 505 if result is not None:

/opt/conda/lib/python3.6/urllib/request.py in https_open(self, req)
1360 return self.do_open(http.client.HTTPSConnection, req, -> 1361 context=self._context, check_hostname=self._check_hostname) 1362

/opt/conda/lib/python3.6/urllib/request.py in do_open(self, http_class, req, **http_conn_args) 1319 except OSError as err: # timeout error -> 1320 raise URLError(err) 1321 r = h.getresponse()

URLError:

During handling of the above exception, another exception occurred:

Exception Traceback (most recent call last) in () 4 import numpy as np 5 ----> 6 model = ResNet50(weights='imagenet', include_top=False)

/opt/conda/lib/python3.6/site-packages/Keras-2.0.6-py3.6.egg/keras/applications/resnet50.py in ResNet50(include_top, weights, input_tensor, input_shape, pooling, classes) 261 WEIGHTS_PATH_NO_TOP, 262 cache_subdir='models', --> 263 md5_hash='a268eb855778b3df3c7506639542a6af') 264 model.load_weights(weights_path) 265 if K.backend() == 'theano':

/opt/conda/lib/python3.6/site-packages/Keras-2.0.6-py3.6.egg/keras/utils/data_utils.py in get_file(fname, origin, untar, md5_hash, file_hash, cache_subdir, hash_algorithm, extract, archive_format, cache_dir) 220 urlretrieve(origin, fpath, dl_progress) 221 except URLError as e: --> 222 raise Exception(error_msg.format(origin, e.errno, e.reason)) 223 except HTTPError as e: 224 raise Exception(error_msg.format(origin, e.code, e.msg))

Exception: URL fetch failure on https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5 : None -- [Errno -2] Name or service not known

Answer 1

Internet setting comes off as default on kaggle. If you turn on it, you can download the pre-trained models.

On the right side of the kernel, you will see the settings you can enable the Internet there.

Answer 2

As far as I know, Kaggle kernels run in isolated containers without Internet access. All training models must be set up and attached as data sets. You can try searching for what you need within the public dataset library. For example, Resnet50 can be found here: https://www.kaggle.com/keras/resnet50