ValueError: Error when checking input: expected input_1 to have shape (3, 224, 224) but got array with shape (3, 3, 224)
Nagaraj4896 opened this issue · 2 comments
_________________________________ test_resnet __________________________________
[gw0] linux -- Python 3.6.7 /home/ironman/anaconda3/envs/Tf_Cv_Ker/bin/python3
def test_resnet():
app = random.choice(RESNET_LIST)
module = keras_applications.resnet
last_dim = 2048
_test_application_basic(app, module=module)
test_check.py:197:
test_check.py:86: in wrapper
output = func(*args, **kwargs)
test_check.py:150: in _test_application_basic
lambda: app(weights='imagenet'), module.preprocess_input)
test_check.py:140: in _get_output_shape
return (model.output_shape, model.predict(x))
/home/ironman/anaconda3/envs/Tf_Cv_Ker/lib/python3.6/site-packages/keras/engine/training.py:1441: in predict
x, _, _ = self._standardize_user_data(x)
/home/ironman/anaconda3/envs/Tf_Cv_Ker/lib/python3.6/site-packages/keras/engine/training.py:579: in _standardize_user_data
exception_prefix='input')
data = [array([[[[ 6.4060997e+01, 9.9060997e+01, 1.2106100e+02, ...,
1.1406100e+02, 1.1406100e+02, 1.1406100e+...6.9680000e+01, -6.6800003e+00, ...,
3.4320000e+01, 2.6320000e+01, 5.3320000e+01]]]],
dtype=float32)]
names = ['input_1'], shapes = [(None, 3, 224, 224)], check_batch_axis = False
exception_prefix = 'input'
def standardize_input_data(data,
names,
shapes=None,
check_batch_axis=True,
exception_prefix=''):
"""Normalizes inputs and targets provided by users.
Users may pass data as a list of arrays, dictionary of arrays,
or as a single array. We normalize this to an ordered list of
arrays (same order as `names`), while checking that the provided
arrays have shapes that match the network's expectations.
# Arguments
data: User-provided input data (polymorphic).
names: List of expected array names.
shapes: Optional list of expected array shapes.
check_batch_axis: Boolean; whether to check that
the batch axis of the arrays matches the expected
value found in `shapes`.
exception_prefix: String prefix used for exception formatting.
# Returns
List of standardized input arrays (one array per model input).
# Raises
ValueError: in case of improperly formatted user-provided data.
"""
if not names:
if data is not None and hasattr(data, '__len__') and len(data):
raise ValueError('Error when checking model ' +
exception_prefix + ': '
'expected no data, but got:', data)
return []
if data is None:
return [None for _ in range(len(names))]
if isinstance(data, dict):
try:
data = [
data[x].values
if data[x].__class__.__name__ == 'DataFrame' else data[x]
for x in names
]
except KeyError as e:
raise ValueError('No data provided for "' + e.args[0] +
'". Need data '
'for each key in: ' + str(names))
elif isinstance(data, list):
if isinstance(data[0], list):
data = [np.asarray(d) for d in data]
elif len(names) == 1 and isinstance(data[0], (float, int)):
data = [np.asarray(data)]
else:
data = [
x.values if x.__class__.__name__ == 'DataFrame'
else x for x in data
]
else:
data = data.values if data.__class__.__name__ == 'DataFrame' else data
data = [data]
data = [standardize_single_array(x) for x in data]
if len(data) != len(names):
if data and hasattr(data[0], 'shape'):
raise ValueError(
'Error when checking model ' + exception_prefix +
': the list of Numpy arrays that you are passing to '
'your model is not the size the model expected. '
'Expected to see ' + str(len(names)) + ' array(s), '
'but instead got the following list of ' +
str(len(data)) + ' arrays: ' + str(data)[:200] + '...')
elif len(names) > 1:
raise ValueError(
'Error when checking model ' + exception_prefix +
': you are passing a list as input to your model, '
'but the model expects a list of ' + str(len(names)) +
' Numpy arrays instead. '
'The list you passed was: ' + str(data)[:200])
elif len(data) == 1 and not hasattr(data[0], 'shape'):
raise TypeError('Error when checking model ' + exception_prefix +
': data should be a Numpy array, or list/dict of '
'Numpy arrays. Found: ' + str(data)[:200] + '...')
elif len(names) == 1:
data = [np.asarray(data)]
# Check shapes compatibility.
if shapes:
for i in range(len(names)):
if shapes[i] is not None and not K.is_tensor(data[i]):
data_shape = data[i].shape
shape = shapes[i]
if data[i].ndim != len(shape):
raise ValueError(
'Error when checking ' + exception_prefix +
': expected ' + names[i] + ' to have ' +
str(len(shape)) + ' dimensions, but got array '
'with shape ' + str(data_shape))
if not check_batch_axis:
data_shape = data_shape[1:]
shape = shape[1:]
for dim, ref_dim in zip(data_shape, shape):
if ref_dim != dim and ref_dim:
raise ValueError(
'Error when checking ' + exception_prefix +
': expected ' + names[i] + ' to have shape ' +
str(shape) + ' but got array with shape ' +
str(data_shape))
E ValueError: Error when checking input: expected input_1 to have shape (3, 224, 224) but got array with shape (3, 3, 224)
/home/ironman/anaconda3/envs/Tf_Cv_Ker/lib/python3.6/site-packages/keras/engine/training_utils.py:145: ValueError
@Nagaraj4896, The error is not reproducible. The test _test_application_basic(app, module=module) has been checked in Travis. Are you using tests/data/elephant.jpg?
@taehoonlee yes i am using tests/data/elephant.jpg only.