Traceback (most recent call last):

 "/app/medusa/<a href="../blob/918cfe7ce01808499585ffe6c1356fd5ed369f76/medusa/__main__.py#L2122">medusa/__main__.py</a>", line 2122, in load_shows_from_db
cur_show = Series(sql_show['indexer'], sql_show['indexer_id'])

 "/app/medusa/<a href="../blob/918cfe7ce01808499585ffe6c1356fd5ed369f76/medusa/tv/series.py#L245">medusa/tv/series.py</a>", line 245, in __init__
self._load_from_db()

 "/app/medusa/<a href="../blob/918cfe7ce01808499585ffe6c1356fd5ed369f76/medusa/tv/series.py#L1493">medusa/tv/series.py</a>", line 1493, in _load_from_db
self.airdate_offset = int(sql_results[0]['airdate_offset'] or 0)KeyError: u'airdate_offset'
Traceback (most recent call last):


 "main.py", line 152, in <module>

train_logger, train_batch_logger)


 "/home/yashbhambhu_18je0949/PyTorch3DCNNs_UCF101/train.py", line 30, in train_epoch

outputs = model(inputs)


 "/home/yashbhambhu_18je0949/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/yashbhambhu_18je0949/.local/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward

return self.module(*inputs[0], **kwargs[0])


 "/home/yashbhambhu_18je0949/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/yashbhambhu_18je0949/PyTorch3DCNNs_UCF101/models/efficientnet.py", line 186, in forward

x = self.extract_features(inputs)


 "/home/yashbhambhu_18je0949/PyTorch3DCNNs_UCF101/models/efficientnet.py", line 175, in extract_features

x = block(x, drop_connect_rate=drop_connect_rate)


 "/home/yashbhambhu_18je0949/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/yashbhambhu_18je0949/PyTorch3DCNNs_UCF101/models/efficientnet.py", line 75, in forward

x = self._swish(self._bn1(self._depthwise_conv(x)))


 "/home/yashbhambhu_18je0949/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/yashbhambhu_18je0949/PyTorch3DCNNs_UCF101/models/efficient_utils.py", line 142, in forward

x = F.conv3d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
RuntimeError: Calculated padded input size per channel: (2 x 8 x 8). Kernel size: (3 x 3 x 3). Kernel size can't be greater than actual input size