I was using the SAM-LSTM-RESNET package and i had a this shape problem.

It is also happenning with the examples pics that come with the package, so it’s very wierd that it does not work as it should. Someone knoes how to fix this without modifying the package’s code?

from sam_lstm import SalMap
import os

if not os.path.exists("\samples"):
    os.makedirs("\samples")

SalMap.auto()

and this was the traceback

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [5], in <cell line: 9>()
      6 if not os.path.exists("\samples"):
      7     os.makedirs("\samples")
----> 9 SalMap.auto()

File ~anaconda3libsite-packagessam_lstm__init__.py:48, in SalMap.auto(cls)
     45 @classmethod
     46 def auto(cls):
     47     salmap = cls()
---> 48     salmap.compile()
     49     salmap.load_weights()
     50     salmap.predict_maps()

File ~anaconda3libsite-packagessam_lstm__init__.py:59, in SalMap.compile(self)
     57 def compile(self):
     58     self.model = Model(
---> 59         inputs=[self.x, self.x_maps], outputs=sam_resnet([self.x, self.x_maps])
     60     )
     61     self.model.compile(
     62         RMSprop(learning_rate=1e-4),
     63         loss=[kl_divergence, correlation_coefficient, nss],
     64         loss_weights=[10, -2, -1],
     65     )

File ~anaconda3libsite-packagessam_lstmmodels.py:192, in sam_resnet(x)
    190 def sam_resnet(x):
    191     # Dilated Convolutional Network
--> 192     dcn = dcn_resnet(input_tensor=x[0])
    193     conv_feat = Conv2D(512, (3, 3), padding="same", activation="relu")(dcn.output)
    194     # Attentive Convolutional LSTM

File ~anaconda3libsite-packagessam_lstmdcn_resnet.py:259, in dcn_resnet(input_tensor)
    252 # Load weights
    253 weights_path = get_file(
    254     "resnet50_weights_th_dim_ordering_th_kernels_notop.h5",
    255     TH_WEIGHTS_PATH_NO_TOP,
    256     cache_subdir="weights",
    257     file_hash="f64f049c92468c9affcd44b0976cdafe",
    258 )
--> 259 model.load_weights(weights_path)
    261 return model

File ~anaconda3libsite-packageskerassrcutilstraceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    119     filtered_tb = _process_traceback_frames(e.__traceback__)
    120     # To get the full stack trace, call:
    121     # `keras.config.disable_traceback_filtering()`
--> 122     raise e.with_traceback(filtered_tb) from None
    123 finally:
    124     del filtered_tb

File ~anaconda3libsite-packageskerassrcbackendcommonvariables.py:226, in KerasVariable.assign(self, value)
    224 value = self._convert_to_tensor(value, dtype=self.dtype)
    225 if not shape_equal(value.shape, self.shape):
--> 226     raise ValueError(
    227         "The shape of the target variable and "
    228         "the shape of the target value in "
    229         "`variable.assign(value)` must match. "
    230         f"variable.shape={self.value.shape}, "
    231         f"Received: value.shape={value.shape}. "
    232         f"Target variable: {self}"
    233     )
    234 if in_stateless_scope():
    235     scope = get_stateless_scope()

ValueError: The shape of the target variable and the shape of the target value in `variable.assign(value)` must match. variable.shape=(7, 7, 3, 64), Received: value.shape=(64, 3, 7, 7). Target variable: <KerasVariable shape=(7, 7, 3, 64), dtype=float32, path=conv1/kernel>

ass you can see the error is basicaly the shape that is transposed somehow but i don’t know how to fix it?