Backbones for models are not loading

[sumaira-hussain]

Hi
I have tried to load different backbones for same model but except vgg16 and resnet34, none of the backbone are loading.
Program and error are attached for reference.

import segmentation_models as sm
import os
import matplotlib.pyplot as plt
import skimage.io as io
from keras.callbacks import ModelCheckpoint, EarlyStopping
import tensorflow as tf
import cv2
import numpy as np
from sklearn.model_selection import train_test_split

os.environ["CUDA_VISIBLE_DEVICES"] = "0"

from segmentation_models.losses import bce_jaccard_loss
from segmentation_models.metrics import iou_score

BACKBONE = 'efficientnetb7'
preprocess_input = sm.get_preprocessing(BACKBONE)

train_dir='.....................'
train_label_dir='.....................'
test_dir='.....................'
train_imgs= ['.....................{}'.format(i) for i in os.listdir(train_dir)]
train_label_imgs= ['.....................{}'.format(i) for i in os.listdir(train_label_dir)]
test_imgs= ['.....................{}'.format(i) for i in os.listdir(test_dir)]
nrows=256
ncolumns=256
channels =3
print(len(train_imgs))
def read_process_img(train_imgs):
X=[]
for image in train_imgs:
X.append(cv2.resize(cv2.imread(image, cv2.IMREAD_COLOR), (nrows, ncolumns),interpolation=cv2.INTER_CUBIC))
print(len(X))
return X
X=read_process_img(train_imgs)
y=read_process_img(train_label_imgs)
x_test=read_process_img(test_imgs)
X=np.array(X)
y=np.array(y)
x_test=np.array(x_test)
print(X.shape[0])
print(y.shape)

x_train, x_val, y_train, y_val = train_test_split(X,y,test_size=0.20, train_size=0.8, random_state=2)

print(x_train.shape)
print(x_val.shape)
print(y_train.shape)
print(y_val.shape)

x_train = preprocess_input(x_train)
x_val = preprocess_input(x_val)
x_test = preprocess_input(x_test)

model = sm.Unet(BACKBONE, input_shape=(None, None,3 ),encoder_weights='imagenet')
model.compile('Adam', loss=bce_jaccard_loss, metrics=[iou_score])

callbacks = [
EarlyStopping(patience=10, verbose=1),
ModelCheckpoint('unet_membrane.h5', verbose=1, save_best_only=True, save_weights_only=True)
]

results = model.fit(
x=x_train,
y=y_train,
batch_size=4,
epochs=100,
callbacks = callbacks,
validation_data=(x_val, y_val),
)

Error:

100
100
100
86
100
(100, 256, 256, 3)
(80, 256, 256, 3)
(20, 256, 256, 3)
(80, 256, 256, 3)
(20, 256, 256, 3)

W0912 21:36:02.976926 140192420181760 nn_ops.py:4224] Large dropout rate: 0.5125 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.
W0912 21:36:03.423848 140192420181760 nn_ops.py:4224] Large dropout rate: 0.525 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.

Downloading data from https://github.com/Callidior/keras-applications/releases/download/efficientnet/efficientnet-b7_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5

---

SSLCertVerificationError Traceback (most recent call last)
~/anaconda3/envs/tensorflow/lib/python3.7/urllib/request.py in do_open(self, http_class, req, **http_conn_args)
1316 h.request(req.get_method(), req.selector, req.data, headers,
-> 1317 encode_chunked=req.has_header('Transfer-encoding'))
1318 except OSError as err: # timeout error

~/anaconda3/envs/tensorflow/lib/python3.7/http/client.py in request(self, method, url, body, headers, encode_chunked)
1228 """Send a complete request to the server."""
-> 1229 self._send_request(method, url, body, headers, encode_chunked)
1230

~/anaconda3/envs/tensorflow/lib/python3.7/http/client.py in _send_request(self, method, url, body, headers, encode_chunked)
1274 body = _encode(body, 'body')
-> 1275 self.endheaders(body, encode_chunked=encode_chunked)
1276

~/anaconda3/envs/tensorflow/lib/python3.7/http/client.py in endheaders(self, message_body, encode_chunked)
1223 raise CannotSendHeader()
-> 1224 self._send_output(message_body, encode_chunked=encode_chunked)
1225

~/anaconda3/envs/tensorflow/lib/python3.7/http/client.py in _send_output(self, message_body, encode_chunked)
1015 del self._buffer[:]
-> 1016 self.send(msg)
1017

~/anaconda3/envs/tensorflow/lib/python3.7/http/client.py in send(self, data)
955 if self.auto_open:
--> 956 self.connect()
957 else:

~/anaconda3/envs/tensorflow/lib/python3.7/http/client.py in connect(self)
1391 self.sock = self._context.wrap_socket(self.sock,
-> 1392 server_hostname=server_hostname)
1393

~/anaconda3/envs/tensorflow/lib/python3.7/ssl.py in wrap_socket(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)
411 context=self,
--> 412 session=session
413 )

~/anaconda3/envs/tensorflow/lib/python3.7/ssl.py in _create(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)
852 raise ValueError("do_handshake_on_connect should not be specified for non-blocking sockets")
--> 853 self.do_handshake()
854 except (OSError, ValueError):

~/anaconda3/envs/tensorflow/lib/python3.7/ssl.py in do_handshake(self, block)
1116 self.settimeout(None)
-> 1117 self._sslobj.do_handshake()
1118 finally:

SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1056)

During handling of the above exception, another exception occurred:

URLError Traceback (most recent call last)
~/anaconda3/envs/tensorflow/lib/python3.7/site-packages/keras/utils/data_utils.py in get_file(fname, origin, untar, md5_hash, file_hash, cache_subdir, hash_algorithm, extract, archive_format, cache_dir)
221 try:
--> 222 urlretrieve(origin, fpath, dl_progress)
223 except HTTPError as e:

~/anaconda3/envs/tensorflow/lib/python3.7/urllib/request.py in urlretrieve(url, filename, reporthook, data)
246
--> 247 with contextlib.closing(urlopen(url, data)) as fp:
248 headers = fp.info()

~/anaconda3/envs/tensorflow/lib/python3.7/urllib/request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
221 opener = _opener
--> 222 return opener.open(url, data, timeout)
223

~/anaconda3/envs/tensorflow/lib/python3.7/urllib/request.py in open(self, fullurl, data, timeout)
524
--> 525 response = self._open(req, data)
526

~/anaconda3/envs/tensorflow/lib/python3.7/urllib/request.py in _open(self, req, data)
542 result = self._call_chain(self.handle_open, protocol, protocol +
--> 543 '_open', req)
544 if result:

~/anaconda3/envs/tensorflow/lib/python3.7/urllib/request.py in _call_chain(self, chain, kind, meth_name, *args)
502 func = getattr(handler, meth_name)
--> 503 result = func(*args)
504 if result is not None:

~/anaconda3/envs/tensorflow/lib/python3.7/urllib/request.py in https_open(self, req)
1359 return self.do_open(http.client.HTTPSConnection, req,
-> 1360 context=self._context, check_hostname=self._check_hostname)
1361

~/anaconda3/envs/tensorflow/lib/python3.7/urllib/request.py in do_open(self, http_class, req, **http_conn_args)
1318 except OSError as err: # timeout error
-> 1319 raise URLError(err)
1320 r = h.getresponse()

URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1056)>

During handling of the above exception, another exception occurred:

Exception Traceback (most recent call last)
in
72
73 # define model
---> 74 model = sm.Unet(BACKBONE, input_shape=(None, None,3 ),encoder_weights='imagenet')
75 model.compile('Adam', loss=bce_jaccard_loss, metrics=[iou_score])
76

~/anaconda3/envs/tensorflow/lib/python3.7/site-packages/segmentation_models/init.py in wrapper(*args, **kwargs)
32 kwargs['models'] = _KERAS_MODELS
33 kwargs['utils'] = _KERAS_UTILS
---> 34 return func(*args, **kwargs)
35
36 return wrapper

~/anaconda3/envs/tensorflow/lib/python3.7/site-packages/segmentation_models/models/unet.py in Unet(backbone_name, input_shape, classes, activation, weights, encoder_weights, encoder_freeze, encoder_features, decoder_block_type, decoder_filters, decoder_use_batchnorm, **kwargs)
224 weights=encoder_weights,
225 include_top=False,
--> 226 **kwargs,
227 )
228

~/anaconda3/envs/tensorflow/lib/python3.7/site-packages/segmentation_models/backbones/backbones_factory.py in get_backbone(self, name, *args, **kwargs)
101 def get_backbone(self, name, *args, **kwargs):
102 model_fn, _ = self.get(name)
--> 103 model = model_fn(*args, **kwargs)
104 return model
105

~/anaconda3/envs/tensorflow/lib/python3.7/site-packages/classification_models/models_factory.py in wrapper(*args, **kwargs)
76 modules_kwargs = self.get_kwargs()
77 new_kwargs = dict(list(kwargs.items()) + list(modules_kwargs.items()))
---> 78 return func(*args, **new_kwargs)
79
80 return wrapper

~/anaconda3/envs/tensorflow/lib/python3.7/site-packages/efficientnet/model.py in EfficientNetB7(include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs)
591 input_tensor=input_tensor, input_shape=input_shape,
592 pooling=pooling, classes=classes,
--> 593 **kwargs)
594
595

~/anaconda3/envs/tensorflow/lib/python3.7/site-packages/efficientnet/model.py in EfficientNet(width_coefficient, depth_coefficient, default_resolution, dropout_rate, drop_connect_rate, depth_divisor, blocks_args, model_name, include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs)
466 BASE_WEIGHTS_PATH + file_name,
467 cache_subdir='models',
--> 468 file_hash=file_hash)
469 model.load_weights(weights_path)
470 elif weights is not None:

~/anaconda3/envs/tensorflow/lib/python3.7/site-packages/keras/utils/data_utils.py in get_file(fname, origin, untar, md5_hash, file_hash, cache_subdir, hash_algorithm, extract, archive_format, cache_dir)
224 raise Exception(error_msg.format(origin, e.code, e.msg))
225 except URLError as e:
--> 226 raise Exception(error_msg.format(origin, e.errno, e.reason))
227 except (Exception, KeyboardInterrupt):
228 if os.path.exists(fpath):

Exception: URL fetch failure on https://github.com/Callidior/keras-applications/releases/download/efficientnet/efficientnet-b7_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5: None -- [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1056)

[sumaira-hussain]

@qubvel

[JordanMakesMaps]

Did you already look at #198?

[sumaira-hussain]

Did you already look at #198?

Yes I did but it didn't work in my case. I am only able to run vgg16 and resnet34. rest are not available and trying to check version also returns error "module efficientnet has no attribute name version"

[maremoto]

Did you already look at #198?

Hi, I have the same issue than in #198, the efficientnet backbones are not loding in Windows O.S.

The versions of the packages are:

conda 4.3.21 (I use a  conda environment)
python 3.6.7
jupyter 4.4.0
tensorflow-gpu 1.14.0
keras 2.3.0
segmentation_models 0.2.1
image-classifiers 0.2.0
albumentations 0.3.3
efficientnet 0.0.4

If I try to execute efficientnet.__version__ in the notebook I get
AttributeError: module 'efficientnet' has no attribute '__version__'

Thanks.