Ambiguous dimension while trying to load weights.

[federicoAntosiano]

Hi everyone,
I'm trying to load the SSD300 for inferencing but I'm facing this ValueError:

---

ValueError Traceback (most recent call last)
in
25 iou_threshold=0.45,
26 top_k=200,
---> 27 nms_max_output_size=400)
28
29 # 2: Load the trained weights into the model.

~/.../keras_ssd300.py in ssd_300(image_size, n_classes, mode, l2_regularization, min_scale, max_scale, scales, aspect_ratios_global, aspect_ratios_per_layer, two_boxes_for_ar1, steps, offsets, clip_boxes, variances, coords, normalize_coords, subtract_mean, divide_by_stddev, swap_channels, confidence_thresh, iou_threshold, top_k, nms_max_output_size, return_predictor_sizes)
340 conv4_3_norm_mbox_priorbox = AnchorBoxes(img_height, img_width, this_scale=scales[0], next_scale=scales[1], aspect_ratios=aspect_ratios[0],
341 two_boxes_for_ar1=two_boxes_for_ar1, this_steps=steps[0], this_offsets=offsets[0], clip_boxes=clip_boxes,
--> 342 variances=variances, coords=coords, normalize_coords=normalize_coords, name='conv4_3_norm_mbox_priorbox')(conv4_3_norm_mbox_loc)
343 fc7_mbox_priorbox = AnchorBoxes(img_height, img_width, this_scale=scales[1], next_scale=scales[2], aspect_ratios=aspect_ratios[1],
344 two_boxes_for_ar1=two_boxes_for_ar1, this_steps=steps[1], this_offsets=offsets[1], clip_boxes=clip_boxes,

/opt/DL/tensorflow/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in call(self, inputs, *args, **kwargs)
755 if not in_deferred_mode:
756 self._in_call = True
--> 757 outputs = self.call(inputs, *args, **kwargs)
758 self._in_call = False
759 if outputs is None:

~/I.../keras_object_detection/ssd_keras/keras_layers/keras_layer_AnchorBoxes.py in call(self, x, mask)
203 offset_width = self.this_offsets
204 # Now that we have the offsets and step sizes, compute the grid of anchor box center points.
--> 205 cy = np.linspace(offset_height * step_height, (offset_height + feature_map_height - 1) * step_height, feature_map_height)
206 cx = np.linspace(offset_width * step_width, (offset_width + feature_map_width - 1) * step_width, feature_map_width)
207 cx_grid, cy_grid = np.meshgrid(cx, cy)

/opt/DL/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py in radd(self, other)
180 A Dimension whose value is the sum of self and other.
181 """
--> 182 return self + other
183
184 def sub(self, other):

/opt/DL/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py in add(self, other)
165 A Dimension whose value is the sum of self and other.
166 """
--> 167 other = as_dimension(other)
168 if self._value is None or other.value is None:
169 return Dimension(None)

/opt/DL/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py in as_dimension(value)
480 return value
481 else:
--> 482 return Dimension(value)
483
484

/opt/DL/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py in init(self, value)
38 if (not isinstance(value, compat.bytes_or_text_types) and
39 self._value != value):
---> 40 raise ValueError("Ambiguous dimension: %s" % value)
41 if self._value < 0:
42 raise ValueError("Dimension %d must be >= 0" % self._value)

ValueError: Ambiguous dimension: 0.5

Do you know why this error appears? How can I fix it?