I am working on a project where I need to use census data for a couple of towns in MA. For that, I am using cenpy library ASC data, but I got a key error. The same error happens even when I try the example code described for Chicago. Here is the example code I use and the error I see:
chicago = products.ACS(2017).from_place('Chicago, IL', level='tract',
variables=['B00002*', 'B01002H_001E'])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File ~\anaconda3\envs\oxe\lib\site-packages\cenpy\tiger.py:192, in ESRILayer.query(self, raw, strict, **kwargs)
191 try:
--> 192 features = datadict["features"]
193 except KeyError:
KeyError: 'features'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
Input In [4], in <cell line: 1>()
----> 1 chicago = products.ACS(2017).from_place('Chicago, IL', level='tract',
2 variables=['B00002*', 'B01002H_001E'])
File ~\anaconda3\envs\oxe\lib\site-packages\cenpy\products.py:791, in ACS.from_place(self, place, variables, level, return_geometry, place_type, strict_within, return_bounds, replace_missing)
788 variables = self._preprocess_variables(variables)
789 variables.append("GEO_ID")
--> 791 geoms, variables, *rest = super(ACS, self).from_place(
792 place,
793 variables=variables,
794 level=level,
795 return_geometry=return_geometry,
796 place_type=place_type,
797 strict_within=strict_within,
798 return_bounds=return_bounds,
799 replace_missing=replace_missing,
800 )
801 variables["GEOID"] = variables.GEO_ID.str.split("US").apply(lambda x: x[1])
802 return_table = geoms[["GEOID", "geometry"]].merge(
803 variables.drop("GEO_ID", axis=1), how="left", on="GEOID"
804 )
File ~\anaconda3\envs\oxe\lib\site-packages\cenpy\products.py:200, in _Product.from_place(self, place, variables, place_type, level, return_geometry, geometry_precision, strict_within, return_bounds, replace_missing)
197 else:
199 placer = "STATE={} AND PLACE={}".format(placerow.STATEFP, placerow.TARGETFP)
--> 200 env = env_layer.query(where=placer)
202 print(
203 "Matched: {} to {} "
204 "within layer {}".format(
(...)
208 )
209 )
211 geoms, data = self._from_bbox(
212 env.to_crs(epsg=4326).total_bounds,
213 variables=variables,
(...)
219 replace_missing=replace_missing,
220 )
File ~\anaconda3\envs\oxe\lib\site-packages\cenpy\tiger.py:198, in ESRILayer.query(self, raw, strict, **kwargs)
196 if details is []:
197 details = "Mapserver provided no detailed error"
--> 198 raise KeyError(
199 (
200 r"Response from API is malformed. You may have "
201 r"submitted too many queries, formatted the request incorrectly, "
202 r"or experienced significant network connectivity issues."
203 r" Check to make sure that your inputs, like placenames, are spelled"
204 r" correctly, and that your geographies match the level at which you"
205 r" intend to query. The original error from the Census is:\n"
206 r"(API ERROR {}:{}({}))".format(code, msg, details)
207 )
208 )
209 todf = []
210 for i, feature in enumerate(features):
KeyError: 'Response from API is malformed. You may have submitted too many queries, formatted the request incorrectly, or experienced significant network connectivity issues. Check to make sure that your inputs, like placenames, are spelled correctly, and that your geographies match the level at which you intend to query. The original error from the Census is:\\n(API ERROR 400:Unable to complete operation.([]))'
I've been trying to use the HuggingFace nlp library's GLUE metric to check whether a given sentence is a grammatical English sentence. But I'm getting an error and is stuck without being able to proceed.
What I've tried so far;
reference and prediction are 2 text sentences
!pip install transformers
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
reference="Security has been beefed across the country as a 2 day nation wide curfew came into effect."
prediction="Security has been tightened across the country as a 2-day nationwide curfew came into effect."
import nlp
glue_metric = nlp.load_metric('glue',name="cola")
#Using BertTokenizer
encoded_reference=tokenizer.encode(reference, add_special_tokens=False)
encoded_prediction=tokenizer.encode(prediction, add_special_tokens=False)
glue_score = glue_metric.compute(encoded_prediction, encoded_reference)
Error I'm getting;
ValueError Traceback (most recent call last)
<ipython-input-9-4c3a3ce7b583> in <module>()
----> 1 glue_score = glue_metric.compute(encoded_prediction, encoded_reference)
6 frames
/usr/local/lib/python3.6/dist-packages/nlp/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)
198 predictions = self.data["predictions"]
199 references = self.data["references"]
--> 200 output = self._compute(predictions=predictions, references=references, **metrics_kwargs)
201 return output
202
/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in _compute(self, predictions, references)
101 return pearson_and_spearman(predictions, references)
102 elif self.config_name in ["mrpc", "qqp"]:
--> 103 return acc_and_f1(predictions, references)
104 elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
105 return {"accuracy": simple_accuracy(predictions, references)}
/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in acc_and_f1(preds, labels)
60 def acc_and_f1(preds, labels):
61 acc = simple_accuracy(preds, labels)
---> 62 f1 = f1_score(y_true=labels, y_pred=preds)
63 return {
64 "accuracy": acc,
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)
1097 pos_label=pos_label, average=average,
1098 sample_weight=sample_weight,
-> 1099 zero_division=zero_division)
1100
1101
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)
1224 warn_for=('f-score',),
1225 sample_weight=sample_weight,
-> 1226 zero_division=zero_division)
1227 return f
1228
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)
1482 raise ValueError("beta should be >=0 in the F-beta score")
1483 labels = _check_set_wise_labels(y_true, y_pred, average, labels,
-> 1484 pos_label)
1485
1486 # Calculate tp_sum, pred_sum, true_sum ###
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
1314 raise ValueError("Target is %s but average='binary'. Please "
1315 "choose another average setting, one of %r."
-> 1316 % (y_type, average_options))
1317 elif pos_label not in (None, 1):
1318 warnings.warn("Note that pos_label (set to %r) is ignored when "
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
However, I'm able to get results (pearson and spearmanr) for 'stsb' with the same workaround as given above.
Some help and a workaround for(cola) this is really appreciated. Thank you.
In general, if you are seeing this error with HuggingFace, you are trying to use the f-score as a metric on a text classification problem with more than 2 classes. Pick a different metric, like "accuracy".
For this specific question:
Despite what you entered, it is trying to compute the f-score. From the example notebook, you should set the metric name as:
metric_name = "pearson" if task == "stsb" else "matthews_correlation" if task == "cola" else "accuracy"
When I convert a '.pth' model from PyTorch to ONNX, an error like Unexpected keys and missing keys occur.
This is my model:
1 import torch
2 import torch.onnx
3 from mmcv import runner
4 import torch.`enter code here`nn as nn
5 from mobilenet import MobileNet
6 # A model class instance (class not shown)
7 md=MobileNet(1,2)
8 model = md
9 device_ids = [0,2,6,7,8]
10 model = nn.DataParallel(model,device_ids)
11 #torch.backends.cudnn.benchmark = True
12 # Load the weights from a file (.pth usually)
13 runner.load_checkpoint(model,'../mmdetection- master/work_dmobile/faster_rcnn_r50_fpn_1x/epoch_60.pth')
14 #model = MMDataParallel(model, device_ids=[0])
15 #state_dict=torch.load('../mmdetection-master/r.pkl.json')
16 # Load the weights now into a model net architecture defined by our class
17 #model.load_state_dict(state_dict)
18 #model = runner.load_state_dict(state_dict)
19 model=runner.load_state_dict({k.replace('module.',' '):v for k,v in state_dict['state_dict'].items()})
20 # Create the right input shape (e.g. for an image)
21 dummy_input = torch.randn(1, 64, 512, 256)
22
23 torch.onnx.export(model, dummy_input, "onnx_model_name.onnx")
And this is the error:
unexpected key in source state_dict: backbone.stem.0.conv.weight, backbone.stem.0.bn.weight, backbone.stem.0.bn.bias, backbone.stem.0.bn.running_mean, backbone.stem.0.bn.running_var, backbone.stem.0.bn.num_batches_tracked, backbone.stem.1.depthwise.0.weight, backbone.stem.1.depthwise.1.weight, backbone.stem.1.depthwise.1.bias, backbone.stem.1.depthwise.1.running_mean, backbone.stem.1.depthwise.1.running_var, backbone.stem.1.depthwise.1.num_batches_tracked, backbone.stem.1.pointwise.0.weight, backbone.stem.1.pointwise.0.bias, backbone.stem.1.pointwise.1.weight, backbone.stem.1.pointwise.1.bias, backbone.stem.1.pointwise.1.running_mean, backbone.stem.1.pointwise.1.running_var, backbone.stem.1.pointwise.1.num_batches_tracked, backbone.conv1.0.depthwise.0.weight, backbone.conv1.0.depthwise.1.weight, backbone.conv1.0.depthwise.1.bias, backbone.conv1.0.depthwise.1.running_mean, backbone.conv1.0.depthwise.1.running_var, backbone.conv1.0.depthwise.1.num_batches_tracked, backbone.conv1.0.pointwise.0.weight, backbone.conv1.0.pointwise.0.bias, backbone.conv1.0.pointwise.1.weight, backbone.conv1.0.pointwise.1.bias, backbone.conv1.0.pointwise.1.running_mean, backbone.conv1.0.pointwise.1.running_var, backbone.conv1.0.pointwise.1.num_batches_tracked, backbone.conv1.1.depthwise.0.weight, backbone.conv1.1.depthwise.1.weight, backbone.conv1.1.depthwise.1.bias, backbone.conv1.1.depthwise.1.running_mean, backbone.conv1.1.depthwise.1.running_var, backbone.conv1.1.depthwise.1.num_batches_tracked, backbone.conv1.1.pointwise.0.weight, backbone.conv1.1.pointwise.0.bias, backbone.conv1.1.pointwise.1.weight, backbone.conv1.1.pointwise.1.bias, backbone.conv1.1.pointwise.1.running_mean, backbone.conv1.1.pointwise.1.running_var, backbone.conv1.1.pointwise.1.num_batches_tracked, backbone.conv2.0.depthwise.0.weight, backbone.conv2.0.depthwise.1.weight, backbone.conv2.0.depthwise.1.bias, backbone.conv2.0.depthwise.1.running_mean, backbone.conv2.0.depthwise.1.running_var, backbone.conv2.0.depthwise.1.num_batches_tracked, backbone.conv2.0.pointwise.0.weight, backbone.conv2.0.pointwise.0.bias, backbone.conv2.0.pointwise.1.weight, backbone.conv2.0.pointwise.1.bias, backbone.conv2.0.pointwise.1.running_mean, backbone.conv2.0.pointwise.1.running_var, backbone.conv2.0.pointwise.1.num_batches_tracked, backbone.conv2.1.depthwise.0.weight, backbone.conv2.1.depthwise.1.weight, backbone.conv2.1.depthwise.1.bias, backbone.conv2.1.depthwise.1.running_mean, backbone.conv2.1.depthwise.1.running_var, backbone.conv2.1.depthwise.1.num_batches_tracked, backbone.conv2.1.pointwise.0.weight, backbone.conv2.1.pointwise.0.bias, backbone.conv2.1.pointwise.1.weight, backbone.conv2.1.pointwise.1.bias, backbone.conv2.1.pointwise.1.running_mean, backbone.conv2.1.pointwise.1.running_var, backbone.conv2.1.pointwise.1.num_batches_tracked, backbone.conv3.0.depthwise.0.weight, backbone.conv3.0.depthwise.1.weight, backbone.conv3.0.depthwise.1.bias, backbone.conv3.0.depthwise.1.running_mean, backbone.conv3.0.depthwise.1.running_var, backbone.conv3.0.depthwise.1.num_batches_tracked, backbone.conv3.0.pointwise.0.weight, backbone.conv3.0.pointwise.0.bias, backbone.conv3.0.pointwise.1.weight, backbone.conv3.0.pointwise.1.bias, backbone.conv3.0.pointwise.1.running_mean, backbone.conv3.0.pointwise.1.running_var, backbone.conv3.0.pointwise.1.num_batches_tracked, backbone.conv3.1.depthwise.0.weight, backbone.conv3.1.depthwise.1.weight, backbone.conv3.1.depthwise.1.bias, backbone.conv3.1.depthwise.1.running_mean, backbone.conv3.1.depthwise.1.running_var, backbone.conv3.1.depthwise.1.num_batches_tracked, backbone.conv3.1.pointwise.0.weight, backbone.conv3.1.pointwise.0.bias, backbone.conv3.1.pointwise.1.weight, backbone.conv3.1.pointwise.1.bias, backbone.conv3.1.pointwise.1.running_mean, backbone.conv3.1.pointwise.1.running_var, backbone.conv3.1.pointwise.1.num_batches_tracked, backbone.conv3.2.depthwise.0.weight, backbone.conv3.2.depthwise.1.weight, backbone.conv3.2.depthwise.1.bias, backbone.conv3.2.depthwise.1.running_mean, backbone.conv3.2.depthwise.1.running_var, backbone.conv3.2.depthwise.1.num_batches_tracked, backbone.conv3.2.pointwise.0.weight, backbone.conv3.2.pointwise.0.bias, backbone.conv3.2.pointwise.1.weight, backbone.conv3.2.pointwise.1.bias, backbone.conv3.2.pointwise.1.running_mean, backbone.conv3.2.pointwise.1.running_var, backbone.conv3.2.pointwise.1.num_batches_tracked, backbone.conv3.3.depthwise.0.weight, backbone.conv3.3.depthwise.1.weight, backbone.conv3.3.depthwise.1.bias, backbone.conv3.3.depthwise.1.running_mean, backbone.conv3.3.depthwise.1.running_var, backbone.conv3.3.depthwise.1.num_batches_tracked, backbone.conv3.3.pointwise.0.weight, backbone.conv3.3.pointwise.0.bias, backbone.conv3.3.pointwise.1.weight, backbone.conv3.3.pointwise.1.bias, backbone.conv3.3.pointwise.1.running_mean, backbone.conv3.3.pointwise.1.running_var, backbone.conv3.3.pointwise.1.num_batches_tracked, backbone.conv3.4.depthwise.0.weight, backbone.conv3.4.depthwise.1.weight, backbone.conv3.4.depthwise.1.bias, backbone.conv3.4.depthwise.1.running_mean, backbone.conv3.4.depthwise.1.running_var, backbone.conv3.4.depthwise.1.num_batches_tracked, backbone.conv3.4.pointwise.0.weight, backbone.conv3.4.pointwise.0.bias, backbone.conv3.4.pointwise.1.weight, backbone.conv3.4.pointwise.1.bias, backbone.conv3.4.pointwise.1.running_mean, backbone.conv3.4.pointwise.1.running_var, backbone.conv3.4.pointwise.1.num_batches_tracked, backbone.conv3.5.depthwise.0.weight, backbone.conv3.5.depthwise.1.weight, backbone.conv3.5.depthwise.1.bias, backbone.conv3.5.depthwise.1.running_mean, backbone.conv3.5.depthwise.1.running_var, backbone.conv3.5.depthwise.1.num_batches_tracked, backbone.conv3.5.pointwise.0.weight, backbone.conv3.5.pointwise.0.bias, backbone.conv3.5.pointwise.1.weight, backbone.conv3.5.pointwise.1.bias, backbone.conv3.5.pointwise.1.running_mean, backbone.conv3.5.pointwise.1.running_var, backbone.conv3.5.pointwise.1.num_batches_tracked, backbone.conv4.0.depthwise.0.weight, backbone.conv4.0.depthwise.1.weight, backbone.conv4.0.depthwise.1.bias, backbone.conv4.0.depthwise.1.running_mean, backbone.conv4.0.depthwise.1.running_var, backbone.conv4.0.depthwise.1.num_batches_tracked, backbone.conv4.0.pointwise.0.weight, backbone.conv4.0.pointwise.0.bias, backbone.conv4.0.pointwise.1.weight, backbone.conv4.0.pointwise.1.bias, backbone.conv4.0.pointwise.1.running_mean, backbone.conv4.0.pointwise.1.running_var, backbone.conv4.0.pointwise.1.num_batches_tracked, backbone.conv4.1.depthwise.0.weight, backbone.conv4.1.depthwise.1.weight, backbone.conv4.1.depthwise.1.bias, backbone.conv4.1.depthwise.1.running_mean, backbone.conv4.1.depthwise.1.running_var, backbone.conv4.1.depthwise.1.num_batches_tracked, backbone.conv4.1.pointwise.0.weight, backbone.conv4.1.pointwise.0.bias, backbone.conv4.1.pointwise.1.weight, backbone.conv4.1.pointwise.1.bias, backbone.conv4.1.pointwise.1.running_mean, backbone.conv4.1.pointwise.1.running_var, backbone.conv4.1.pointwise.1.num_batches_tracked, neck.lateral_convs.0.conv.weight, neck.lateral_convs.0.conv.bias, neck.lateral_convs.1.conv.weight, neck.lateral_convs.1.conv.bias, neck.lateral_convs.2.conv.weight, neck.lateral_convs.2.conv.bias, neck.fpn_convs.0.conv.weight, neck.fpn_convs.0.conv.bias, neck.fpn_convs.1.conv.weight, neck.fpn_convs.1.conv.bias, neck.fpn_convs.2.conv.weight, neck.fpn_convs.2.conv.bias, rpn_head.rpn_conv.weight, rpn_head.rpn_conv.bias, rpn_head.rpn_cls.weight, rpn_head.rpn_cls.bias, rpn_head.rpn_reg.weight, rpn_head.rpn_reg.bias, bbox_head.fc_cls.weight, bbox_head.fc_cls.bias, bbox_head.fc_reg.weight, bbox_head.fc_reg.bias, bbox_head.shared_fcs.0.weight, bbox_head.shared_fcs.0.bias, bbox_head.shared_fcs.1.weight, bbox_head.shared_fcs.1.bias
missing keys in source state_dict: conv2.1.depthwise.1.weight, conv4.0.depthwise.0.weight, conv4.1.pointwise.1.weight, conv3.2.depthwise.0.weight, conv3.1.pointwise.0.weight, conv3.4.pointwise.1.bias, conv3.5.depthwise.1.bias, conv2.1.pointwise.1.weight, stem.1.pointwise.1.running_mean, conv3.3.pointwise.1.weight, conv3.3.depthwise.1.running_mean, conv3.1.depthwise.1.num_batches_tracked, conv3.0.depthwise.1.num_batches_tracked, conv2.1.depthwise.1.running_var, conv1.0.depthwise.1.weight, conv3.5.depthwise.1.running_var, stem.0.bn.bias, conv3.2.depthwise.1.num_batches_tracked, conv2.0.depthwise.0.weight, conv2.1.pointwise.0.bias, conv3.1.pointwise.1.bias, conv3.2.pointwise.1.bias, conv2.0.pointwise.1.num_batches_tracked, stem.1.pointwise.0.weight, conv2.0.depthwise.1.weight, stem.1.depthwise.0.weight, conv1.1.pointwise.1.weight, conv3.5.pointwise.0.weight, conv3.4.depthwise.1.running_var, conv1.0.pointwise.0.bias, conv3.3.depthwise.1.running_var, conv3.0.pointwise.1.weight, conv4.0.pointwise.1.num_batches_tracked, conv4.1.depthwise.1.running_var, stem.1.depthwise.1.running_var, conv3.0.pointwise.1.running_var, conv3.4.depthwise.0.weight, conv3.4.pointwise.1.num_batches_tracked, conv4.0.depthwise.1.num_batches_tracked, conv3.0.depthwise.1.weight, conv3.3.pointwise.0.bias, conv3.0.depthwise.1.running_mean, conv3.2.pointwise.1.running_mean, conv3.1.pointwise.0.bias, conv3.5.depthwise.1.num_batches_tracked, conv3.5.pointwise.1.running_mean, conv3.1.pointwise.1.running_var, conv1.0.depthwise.1.running_mean, stem.1.pointwise.1.bias, conv1.0.depthwise.0.weight, conv3.2.pointwise.0.weight, conv4.0.pointwise.1.running_mean, conv2.1.pointwise.1.running_mean, stem.1.pointwise.1.weight, conv4.1.depthwise.1.weight, conv4.0.pointwise.0.weight, conv1.1.depthwise.1.bias, conv3.2.pointwise.1.num_batches_tracked, conv4.1.depthwise.0.weight, conv3.4.depthwise.1.running_mean, conv1.0.depthwise.1.bias, conv2.0.pointwise.0.bias, conv3.4.depthwise.1.num_batches_tracked, conv4.1.pointwise.1.running_mean, conv2.1.depthwise.1.bias, conv3.2.depthwise.1.weight, conv2.0.pointwise.1.weight, conv1.0.pointwise.0.weight, conv3.1.depthwise.1.running_var, conv2.0.pointwise.1.bias, conv4.0.depthwise.1.bias, conv3.3.pointwise.1.running_var, conv3.4.pointwise.1.weight, conv4.0.pointwise.0.bias, conv3.4.depthwise.1.bias, conv4.1.depthwise.1.num_batches_tracked, conv2.0.pointwise.1.running_mean, conv1.1.depthwise.1.weight, conv2.0.pointwise.1.running_var, stem.1.depthwise.1.running_mean, conv3.4.pointwise.1.running_var, stem.1.depthwise.1.num_batches_tracked, conv3.3.depthwise.1.weight, stem.1.pointwise.1.running_var, conv4.1.depthwise.1.bias, conv3.0.pointwise.1.bias, conv2.0.depthwise.1.running_mean, conv1.1.pointwise.1.bias, conv4.1.pointwise.0.bias, conv3.2.pointwise.0.bias, conv1.1.pointwise.0.weight, conv1.0.pointwise.1.weight, conv1.0.pointwise.1.running_mean, stem.0.conv.weight, stem.1.depthwise.1.bias, conv3.3.depthwise.0.weight, conv1.1.depthwise.1.num_batches_tracked, conv3.3.pointwise.1.num_batches_tracked, conv3.2.pointwise.1.running_var, conv3.2.depthwise.1.running_mean, conv3.3.depthwise.1.bias, conv4.1.pointwise.1.num_batches_tracked, conv2.0.depthwise.1.num_batches_tracked, conv3.0.pointwise.0.bias, conv3.1.depthwise.1.running_mean, conv3.1.depthwise.1.weight, conv3.0.pointwise.1.num_batches_tracked, conv3.1.pointwise.1.weight, conv4.0.pointwise.1.bias, conv3.3.depthwise.1.num_batches_tracked, conv3.4.pointwise.0.weight, stem.1.pointwise.0.bias, conv3.0.depthwise.1.bias, conv1.1.pointwise.0.bias, conv4.0.pointwise.1.running_var, stem.0.bn.weight, conv1.0.pointwise.1.num_batches_tracked, conv2.1.depthwise.1.running_mean, conv4.1.depthwise.1.running_mean, conv1.1.pointwise.1.running_var, conv2.1.pointwise.1.num_batches_tracked, conv2.0.depthwise.1.running_var, conv3.5.depthwise.1.weight, conv3.0.depthwise.0.weight, conv4.0.depthwise.1.running_mean, stem.0.bn.num_batches_tracked, conv3.3.pointwise.1.running_mean, conv2.1.pointwise.1.running_var, conv3.0.pointwise.1.running_mean, conv1.1.depthwise.1.running_var, conv3.0.depthwise.1.running_var, conv1.0.depthwise.1.running_var, stem.1.pointwise.1.num_batches_tracked, conv4.0.pointwise.1.weight, conv1.1.pointwise.1.running_mean, conv2.1.depthwise.0.weight, conv1.0.depthwise.1.num_batches_tracked, conv1.0.pointwise.1.running_var, conv3.5.pointwise.1.weight, conv3.5.depthwise.1.running_mean, conv3.1.depthwise.1.bias, conv3.1.depthwise.0.weight, conv1.1.depthwise.1.running_mean, conv2.0.pointwise.0.weight, conv4.1.pointwise.1.bias, conv3.2.depthwise.1.running_var, conv3.5.pointwise.0.bias, conv3.4.depthwise.1.weight, conv3.2.depthwise.1.bias, stem.0.bn.running_mean, conv4.0.depthwise.1.running_var, conv1.1.depthwise.0.weight, stem.0.bn.running_var, conv4.1.pointwise.0.weight, conv2.1.pointwise.1.bias, conv3.4.pointwise.0.bias, conv1.0.pointwise.1.bias, conv3.5.pointwise.1.running_var, conv1.1.pointwise.1.num_batches_tracked, conv3.1.pointwise.1.running_mean, conv2.1.depthwise.1.num_batches_tracked, conv2.1.pointwise.0.weight, stem.1.depthwise.1.weight, conv3.5.pointwise.1.bias, conv3.5.pointwise.1.num_batches_tracked, conv3.1.pointwise.1.num_batches_tracked, conv3.2.pointwise.1.weight, conv3.5.depthwise.0.weight, conv3.3.pointwise.0.weight, conv2.0.depthwise.1.bias, conv3.0.pointwise.0.weight, conv3.3.pointwise.1.bias, conv3.4.pointwise.1.running_mean, conv4.0.depthwise.1.weight, conv4.1.pointwise.1.running_var
In line 19, try using model=runner.load_state_dict(..., strict=False).
Using the parameter strict=False tells the load_state_dict function that there might be missing keys in the checkpoint, which usually come from the BatchNorm layer as I see in this case.