RuntimeError: Error(s) in loading state_dict for ResNet50_nFC

[cainiaoPCW]

run python3 inference.py test_sample/test_market.jpg --dataset market --model resnet50

RuntimeError: Error(s) in loading state_dict for ResNet50_nFC: Missing key(s) in state_dict: "class_0.classifier.1.weight", "class_0.classifier.1.bias", "class_0.classifier.1.running_mean", "class_0.classifier.1.running_var", "class_0.classifier.4.weight", "class_0.classifier.4.bias", "class_1.classifier.1.weight", "class_1.classifier.1.bias", "class_1.classifier.1.running_mean", "class_1.classifier.1.running_var", "class_1.classifier.4.weight", "class_1.classifier.4.bias", "class_2.classifier.1.weight", "class_2.classifier.1.bias", "class_2.classifier.1.running_mean", "class_2.classifier.1.running_var", "class_2.classifier.4.weight", "class_2.classifier.4.bias", "class_3.classifier.1.weight", "class_3.classifier.1.bias", "class_3.classifier.1.running_mean", "class_3.classifier.1.running_var", "class_3.classifier.4.weight", "class_3.classifier.4.bias", "class_4.classifier.1.weight", "class_4.classifier.1.bias", "class_4.classifier.1.running_mean", "class_4.classifier.1.running_var", "class_4.classifier.4.weight", "class_4.classifier.4.bias", "class_5.classifier.1.weight", "class_5.classifier.1.bias", "class_5.classifier.1.running_mean", "class_5.classifier.1.running_var", "class_5.classifier.4.weight", "class_5.classifier.4.bias", "class_6.classifier.1.weight", "class_6.classifier.1.bias", "class_6.classifier.1.running_mean", "class_6.classifier.1.running_var", "class_6.classifier.4.weight", "class_6.classifier.4.bias", "class_7.classifier.1.weight", "class_7.classifier.1.bias", "class_7.classifier.1.running_mean", "class_7.classifier.1.running_var", "class_7.classifier.4.weight", "class_7.classifier.4.bias", "class_8.classifier.1.weight", "class_8.classifier.1.bias", "class_8.classifier.1.running_mean", "class_8.classifier.1.running_var", "class_8.classifier.4.weight", "class_8.classifier.4.bias", "class_9.classifier.1.weight", "class_9.classifier.1.bias", "class_9.classifier.1.running_mean", "class_9.classifier.1.running_var", "class_9.classifier.4.weight", "class_9.classifier.4.bias", "class_10.classifier.1.weight", "class_10.classifier.1.bias", "class_10.classifier.1.running_mean", "class_10.classifier.1.running_var", "class_10.classifier.4.weight", "class_10.classifier.4.bias", "class_11.classifier.1.weight", "class_11.classifier.1.bias", "class_11.classifier.1.running_mean", "class_11.classifier.1.running_var", "class_11.classifier.4.weight", "class_11.classifier.4.bias", "class_12.classifier.1.weight", "class_12.classifier.1.bias", "class_12.classifier.1.running_mean", "class_12.classifier.1.running_var", "class_12.classifier.4.weight", "class_12.classifier.4.bias", "class_13.classifier.1.weight", "class_13.classifier.1.bias", "class_13.classifier.1.running_mean", "class_13.classifier.1.running_var", "class_13.classifier.4.weight", "class_13.classifier.4.bias", "class_14.classifier.1.weight", "class_14.classifier.1.bias", "class_14.classifier.1.running_mean", "class_14.classifier.1.running_var", "class_14.classifier.4.weight", "class_14.classifier.4.bias", "class_15.classifier.1.weight", "class_15.classifier.1.bias", "class_15.classifier.1.running_mean", "class_15.classifier.1.running_var", "class_15.classifier.4.weight", "class_15.classifier.4.bias", "class_16.classifier.1.weight", "class_16.classifier.1.bias", "class_16.classifier.1.running_mean", "class_16.classifier.1.running_var", "class_16.classifier.4.weight", "class_16.classifier.4.bias", "class_17.classifier.1.weight", "class_17.classifier.1.bias", "class_17.classifier.1.running_mean", "class_17.classifier.1.running_var", "class_17.classifier.4.weight", "class_17.classifier.4.bias", "class_18.classifier.1.weight", "class_18.classifier.1.bias", "class_18.classifier.1.running_mean", "class_18.classifier.1.running_var", "class_18.classifier.4.weight", "class_18.classifier.4.bias", "class_19.classifier.1.weight", "class_19.classifier.1.bias", "class_19.classifier.1.running_mean", "class_19.classifier.1.running_var", "class_19.classifier.4.weight", "class_19.classifier.4.bias", "class_20.classifier.1.weight", "class_20.classifier.1.bias", "class_20.classifier.1.running_mean", "class_20.classifier.1.running_var", "class_20.classifier.4.weight", "class_20.classifier.4.bias", "class_21.classifier.1.weight", "class_21.classifier.1.bias", "class_21.classifier.1.running_mean", "class_21.classifier.1.running_var", "class_21.classifier.4.weight", "class_21.classifier.4.bias", "class_22.classifier.1.weight", "class_22.classifier.1.bias", "class_22.classifier.1.running_mean", "class_22.classifier.1.running_var", "class_22.classifier.4.weight", "class_22.classifier.4.bias", "class_23.classifier.0.weight", "class_23.classifier.0.bias", "class_23.classifier.1.weight", "class_23.classifier.1.bias", "class_23.classifier.1.running_mean", "class_23.classifier.1.running_var", "class_23.classifier.4.weight", "class_23.classifier.4.bias", "class_24.classifier.0.weight", "class_24.classifier.0.bias", "class_24.classifier.1.weight", "class_24.classifier.1.bias", "class_24.classifier.1.running_mean", "class_24.classifier.1.running_var", "class_24.classifier.4.weight", "class_24.classifier.4.bias", "class_25.classifier.0.weight", "class_25.classifier.0.bias", "class_25.classifier.1.weight", "class_25.classifier.1.bias", "class_25.classifier.1.running_mean", "class_25.classifier.1.running_var", "class_25.classifier.4.weight", "class_25.classifier.4.bias", "class_26.classifier.0.weight", "class_26.classifier.0.bias", "class_26.classifier.1.weight", "class_26.classifier.1.bias", "class_26.classifier.1.running_mean", "class_26.classifier.1.running_var", "class_26.classifier.4.weight", "class_26.classifier.4.bias", "class_27.classifier.0.weight", "class_27.classifier.0.bias", "class_27.classifier.1.weight", "class_27.classifier.1.bias", "class_27.classifier.1.running_mean", "class_27.classifier.1.running_var", "class_27.classifier.4.weight", "class_27.classifier.4.bias", "class_28.classifier.0.weight", "class_28.classifier.0.bias", "class_28.classifier.1.weight", "class_28.classifier.1.bias", "class_28.classifier.1.running_mean", "class_28.classifier.1.running_var", "class_28.classifier.4.weight", "class_28.classifier.4.bias", "class_29.classifier.0.weight", "class_29.classifier.0.bias", "class_29.classifier.1.weight", "class_29.classifier.1.bias", "class_29.classifier.1.running_mean", "class_29.classifier.1.running_var", "class_29.classifier.4.weight", "class_29.classifier.4.bias". Unexpected key(s) in state_dict: "class_0.add_block.0.weight", "class_0.add_block.0.bias", "class_0.add_block.1.weight", "class_0.add_block.1.bias", "class_0.add_block.1.running_mean", "class_0.add_block.1.running_var", "class_0.add_block.1.num_batches_tracked", "class_1.add_block.0.weight", "class_1.add_block.0.bias", "class_1.add_block.1.weight", "class_1.add_block.1.bias", "class_1.add_block.1.running_mean", "class_1.add_block.1.running_var", "class_1.add_block.1.num_batches_tracked", "class_2.add_block.0.weight", "class_2.add_block.0.bias", "class_2.add_block.1.weight", "class_2.add_block.1.bias", "class_2.add_block.1.running_mean", "class_2.add_block.1.running_var", "class_2.add_block.1.num_batches_tracked", "class_3.add_block.0.weight", "class_3.add_block.0.bias", "class_3.add_block.1.weight", "class_3.add_block.1.bias", "class_3.add_block.1.running_mean", "class_3.add_block.1.running_var", "class_3.add_block.1.num_batches_tracked", "class_4.add_block.0.weight", "class_4.add_block.0.bias", "class_4.add_block.1.weight", "class_4.add_block.1.bias", "class_4.add_block.1.running_mean", "class_4.add_block.1.running_var", "class_4.add_block.1.num_batches_tracked", "class_5.add_block.0.weight", "class_5.add_block.0.bias", "class_5.add_block.1.weight", "class_5.add_block.1.bias", "class_5.add_block.1.running_mean", "class_5.add_block.1.running_var", "class_5.add_block.1.num_batches_tracked", "class_6.add_block.0.weight", "class_6.add_block.0.bias", "class_6.add_block.1.weight", "class_6.add_block.1.bias", "class_6.add_block.1.running_mean", "class_6.add_block.1.running_var", "class_6.add_block.1.num_batches_tracked", "class_7.add_block.0.weight", "class_7.add_block.0.bias", "class_7.add_block.1.weight", "class_7.add_block.1.bias", "class_7.add_block.1.running_mean", "class_7.add_block.1.running_var", "class_7.add_block.1.num_batches_tracked", "class_8.add_block.0.weight", "class_8.add_block.0.bias", "class_8.add_block.1.weight", "class_8.add_block.1.bias", "class_8.add_block.1.running_mean", "class_8.add_block.1.running_var", "class_8.add_block.1.num_batches_tracked", "class_9.add_block.0.weight", "class_9.add_block.0.bias", "class_9.add_block.1.weight", "class_9.add_block.1.bias", "class_9.add_block.1.running_mean", "class_9.add_block.1.running_var", "class_9.add_block.1.num_batches_tracked", "class_10.add_block.0.weight", "class_10.add_block.0.bias", "class_10.add_block.1.weight", "class_10.add_block.1.bias", "class_10.add_block.1.running_mean", "class_10.add_block.1.running_var", "class_10.add_block.1.num_batches_tracked", "class_11.add_block.0.weight", "class_11.add_block.0.bias", "class_11.add_block.1.weight", "class_11.add_block.1.bias", "class_11.add_block.1.running_mean", "class_11.add_block.1.running_var", "class_11.add_block.1.num_batches_tracked", "class_12.add_block.0.weight", "class_12.add_block.0.bias", "class_12.add_block.1.weight", "class_12.add_block.1.bias", "class_12.add_block.1.running_mean", "class_12.add_block.1.running_var", "class_12.add_block.1.num_batches_tracked", "class_13.add_block.0.weight", "class_13.add_block.0.bias", "class_13.add_block.1.weight", "class_13.add_block.1.bias", "class_13.add_block.1.running_mean", "class_13.add_block.1.running_var", "class_13.add_block.1.num_batches_tracked", "class_14.add_block.0.weight", "class_14.add_block.0.bias", "class_14.add_block.1.weight", "class_14.add_block.1.bias", "class_14.add_block.1.running_mean", "class_14.add_block.1.running_var", "class_14.add_block.1.num_batches_tracked", "class_15.add_block.0.weight", "class_15.add_block.0.bias", "class_15.add_block.1.weight", "class_15.add_block.1.bias", "class_15.add_block.1.running_mean", "class_15.add_block.1.running_var", "class_15.add_block.1.num_batches_tracked", "class_16.add_block.0.weight", "class_16.add_block.0.bias", "class_16.add_block.1.weight", "class_16.add_block.1.bias", "class_16.add_block.1.running_mean", "class_16.add_block.1.running_var", "class_16.add_block.1.num_batches_tracked", "class_17.add_block.0.weight", "class_17.add_block.0.bias", "class_17.add_block.1.weight", "class_17.add_block.1.bias", "class_17.add_block.1.running_mean", "class_17.add_block.1.running_var", "class_17.add_block.1.num_batches_tracked", "class_18.add_block.0.weight", "class_18.add_block.0.bias", "class_18.add_block.1.weight", "class_18.add_block.1.bias", "class_18.add_block.1.running_mean", "class_18.add_block.1.running_var", "class_18.add_block.1.num_batches_tracked", "class_19.add_block.0.weight", "class_19.add_block.0.bias", "class_19.add_block.1.weight", "class_19.add_block.1.bias", "class_19.add_block.1.running_mean", "class_19.add_block.1.running_var", "class_19.add_block.1.num_batches_tracked", "class_20.add_block.0.weight", "class_20.add_block.0.bias", "class_20.add_block.1.weight", "class_20.add_block.1.bias", "class_20.add_block.1.running_mean", "class_20.add_block.1.running_var", "class_20.add_block.1.num_batches_tracked", "class_21.add_block.0.weight", "class_21.add_block.0.bias", "class_21.add_block.1.weight", "class_21.add_block.1.bias", "class_21.add_block.1.running_mean", "class_21.add_block.1.running_var", "class_21.add_block.1.num_batches_tracked", "class_22.add_block.0.weight", "class_22.add_block.0.bias", "class_22.add_block.1.weight", "class_22.add_block.1.bias", "class_22.add_block.1.running_mean", "class_22.add_block.1.running_var", "class_22.add_block.1.num_batches_tracked". size mismatch for class_0.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_0.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_1.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_1.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_2.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_2.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_3.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_3.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_4.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_4.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_5.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_5.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_6.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_6.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_7.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_7.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_8.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_8.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_9.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_9.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_10.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_10.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_11.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_11.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_12.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_12.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_13.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_13.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_14.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_14.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_15.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_15.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_16.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_16.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_17.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_17.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_18.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_18.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_19.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_19.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_20.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_20.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_21.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_21.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for class_22.classifier.0.weight: copying a param with shape torch.Size([1, 512]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for class_22.classifier.0.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([512]).