What are the Training Arguments for ImageNet Pre-Trained Model?

[LeighDavis]

Getting following error message when running the trained ImageNet model for image classification on my machine, which I downloaded from author's Dropbox link posted in this repo's readme link:

model.load_state_dict(torch.load(PATH, map_location=torch.device("cpu"))[\'state_dict\'])\n', ' File "C:\\Program Files\\Python36\\lib\\site-packages\\torch\\nn\\modules\\module.py", line 1052, in load_state_dict\n self.__class__.__name__, "\\n\\t".join(error_msgs)))\n', 'RuntimeError: Error(s) in loading state_dict for DataParallel:\n\tMissing key(s) in state_dict: "module.features.denseblock_1.denselayer_1.conv_1._count", "module.features.denseblock_1.denselayer_1.conv_1._stage", "module.features.denseblock_1.denselayer_1.conv_1._mask", "module.features.denseblock_1.denselayer_2.conv_1._count", "module.features.denseblock_1.denselayer_2.conv_1._stage", "module.features.denseblock_1.denselayer_2.conv_1._mask", "module.features.denseblock_1.denselayer_3.conv_1._count", "module.features.denseblock_1.denselayer_3.conv_1._stage", "module.features.denseblock_1.denselayer_3.conv_1._mask", "module.features.denseblock_1.denselayer_4.conv_1._count", "module.features.denseblock_1.denselayer_4.conv_1._stage", "module.features.denseblock_1.denselayer_4.conv_1._mask", "module.features.denseblock_2.denselayer_1.conv_1._count", "module.features.denseblock_2.denselayer_1.conv_1._stage", "module.features.denseblock_2.denselayer_1.conv_1._mask", "module.features.denseblock_2.denselayer_2.conv_1._count", "module.features.denseblock_2.denselayer_2.conv_1._stage", "module.features.denseblock_2.denselayer_2.conv_1._mask", "module.features.denseblock_2.denselayer_3.conv_1._count", "module.features.denseblock_2.denselayer_3.conv_1._stage", "module.features.denseblock_2.denselayer_3.conv_1._mask", "module.features.denseblock_2.denselayer_4.conv_1._count", "module.features.denseblock_2.denselayer_4.conv_1._stage", "module.features.denseblock_2.denselayer_4.conv_1._mask", "module.features.denseblock_2.denselayer_5.conv_1._count", "module.features.denseblock_2.denselayer_5.conv_1._stage", "module.features.denseblock_2.denselayer_5.conv_1._mask", "module.features.denseblock_2.denselayer_6.conv_1._count", "module.features.denseblock_2.denselayer_6.conv_1._stage", "module.features.denseblock_2.denselayer_6.conv_1._mask", "module.features.denseblock_3.denselayer_1.conv_1._count", "module.features.denseblock_3.denselayer_1.conv_1._stage", "module.features.denseblock_3.denselayer_1.conv_1._mask", "module.features.denseblock_3.denselayer_2.conv_1._count", "module.features.denseblock_3.denselayer_2.conv_1._stage", "module.features.denseblock_3.denselayer_2.conv_1._mask", "module.features.denseblock_3.denselayer_3.conv_1._count", "module.features.denseblock_3.denselayer_3.conv_1._stage", "module.features.denseblock_3.denselayer_3.conv_1._mask", "module.features.denseblock_3.denselayer_4.conv_1._count", "module.features.denseblock_3.denselayer_4.conv_1._stage", "module.features.denseblock_3.denselayer_4.conv_1._mask", "module.features.denseblock_3.denselayer_5.conv_1._count", "module.features.denseblock_3.denselayer_5.conv_1._stage", "module.features.denseblock_3.denselayer_5.conv_1._mask", "module.features.denseblock_3.denselayer_6.conv_1._count", "module.features.denseblock_3.denselayer_6.conv_1._stage", "module.features.denseblock_3.denselayer_6.conv_1._mask", "module.features.denseblock_3.denselayer_7.conv_1._count", "module.features.denseblock_3.denselayer_7.conv_1._stage", "module.features.denseblock_3.denselayer_7.conv_1._mask", "module.features.denseblock_3.denselayer_8.conv_1._count", "module.features.denseblock_3.denselayer_8.conv_1._stage", "module.features.denseblock_3.denselayer_8.conv_1._mask", "module.features.denseblock_4.denselayer_1.conv_1._count", "module.features.denseblock_4.denselayer_1.conv_1._stage", "module.features.denseblock_4.denselayer_1.conv_1._mask", "module.features.denseblock_4.denselayer_2.conv_1._count", "module.features.denseblock_4.denselayer_2.conv_1._stage", "module.features.denseblock_4.denselayer_2.conv_1._mask", "module.features.denseblock_4.denselayer_3.conv_1._count", "module.features.denseblock_4.denselayer_3.conv_1._stage", "module.features.denseblock_4.denselayer_3.conv_1._mask", "module.features.denseblock_4.denselayer_4.conv_1._count", "module.features.denseblock_4.denselayer_4.conv_1._stage", "module.features.denseblock_4.denselayer_4.conv_1._mask", "module.features.denseblock_4.denselayer_5.conv_1._count", "module.features.denseblock_4.denselayer_5.conv_1._stage", "module.features.denseblock_4.denselayer_5.conv_1._mask", "module.features.denseblock_4.denselayer_6.conv_1._count", "module.features.denseblock_4.denselayer_6.conv_1._stage", "module.features.denseblock_4.denselayer_6.conv_1._mask", "module.features.denseblock_4.denselayer_7.conv_1._count", "module.features.denseblock_4.denselayer_7.conv_1._stage", "module.features.denseblock_4.denselayer_7.conv_1._mask", "module.features.denseblock_4.denselayer_8.conv_1._count", "module.features.denseblock_4.denselayer_8.conv_1._stage", "module.features.denseblock_4.denselayer_8.conv_1._mask", "module.features.denseblock_4.denselayer_9.conv_1._count", "module.features.denseblock_4.denselayer_9.conv_1._stage", "module.features.denseblock_4.denselayer_9.conv_1._mask", "module.features.denseblock_4.denselayer_10.conv_1._count", "module.features.denseblock_4.denselayer_10.conv_1._stage", "module.features.denseblock_4.denselayer_10.conv_1._mask", "module.features.denseblock_5.denselayer_1.conv_1._count", "module.features.denseblock_5.denselayer_1.conv_1._stage", "module.features.denseblock_5.denselayer_1.conv_1._mask", "module.features.denseblock_5.denselayer_2.conv_1._count", "module.features.denseblock_5.denselayer_2.conv_1._stage", "module.features.denseblock_5.denselayer_2.conv_1._mask", "module.features.denseblock_5.denselayer_3.conv_1._count", "module.features.denseblock_5.denselayer_3.conv_1._stage", "module.features.denseblock_5.denselayer_3.conv_1._mask", "module.features.denseblock_5.denselayer_4.conv_1._count", "module.features.denseblock_5.denselayer_4.conv_1._stage", "module.features.denseblock_5.denselayer_4.conv_1._mask", "module.features.denseblock_5.denselayer_5.conv_1._count", "module.features.denseblock_5.denselayer_5.conv_1._stage", "module.features.denseblock_5.denselayer_5.conv_1._mask", "module.features.denseblock_5.denselayer_6.conv_1._count", "module.features.denseblock_5.denselayer_6.conv_1._stage", "module.features.denseblock_5.denselayer_6.conv_1._mask", "module.features.denseblock_5.denselayer_7.conv_1._count", "module.features.denseblock_5.denselayer_7.conv_1._stage", "module.features.denseblock_5.denselayer_7.conv_1._mask", "module.features.denseblock_5.denselayer_8.conv_1._count", "module.features.denseblock_5.denselayer_8.conv_1._stage", "module.features.denseblock_5.denselayer_8.conv_1._mask", "module.classifier.weight", "module.classifier.bias". \n\tUnexpected key(s) in state_dict: "module.features.denseblock_1.denselayer_1.conv_1.index", "module.features.denseblock_1.denselayer_2.conv_1.index", "module.features.denseblock_1.denselayer_3.conv_1.index", "module.features.denseblock_1.denselayer_4.conv_1.index", "module.features.denseblock_2.denselayer_1.conv_1.index", "module.features.denseblock_2.denselayer_2.conv_1.index", "module.features.denseblock_2.denselayer_3.conv_1.index", "module.features.denseblock_2.denselayer_4.conv_1.index", "module.features.denseblock_2.denselayer_5.conv_1.index", "module.features.denseblock_2.denselayer_6.conv_1.index", "module.features.denseblock_3.denselayer_1.conv_1.index", "module.features.denseblock_3.denselayer_2.conv_1.index", "module.features.denseblock_3.denselayer_3.conv_1.index", "module.features.denseblock_3.denselayer_4.conv_1.index", "module.features.denseblock_3.denselayer_5.conv_1.index", "module.features.denseblock_3.denselayer_6.conv_1.index", "module.features.denseblock_3.denselayer_7.conv_1.index", "module.features.denseblock_3.denselayer_8.conv_1.index", "module.features.denseblock_4.denselayer_1.conv_1.index", "module.features.denseblock_4.denselayer_2.conv_1.index", "module.features.denseblock_4.denselayer_3.conv_1.index", "module.features.denseblock_4.denselayer_4.conv_1.index", "module.features.denseblock_4.denselayer_5.conv_1.index", "module.features.denseblock_4.denselayer_6.conv_1.index", "module.features.denseblock_4.denselayer_7.conv_1.index", "module.features.denseblock_4.denselayer_8.conv_1.index", "module.features.denseblock_4.denselayer_9.conv_1.index", "module.features.denseblock_4.denselayer_10.conv_1.index", "module.features.denseblock_5.denselayer_1.conv_1.index", "module.features.denseblock_5.denselayer_2.conv_1.index", "module.features.denseblock_5.denselayer_3.conv_1.index", "module.features.denseblock_5.denselayer_4.conv_1.index", "module.features.denseblock_5.denselayer_5.conv_1.index", "module.features.denseblock_5.denselayer_6.conv_1.index", "module.features.denseblock_5.denselayer_7.conv_1.index", "module.features.denseblock_5.denselayer_8.conv_1.index", "module.classifier.index", "module.classifier.linear.weight", "module.classifier.linear.bias". \n\tsize mismatch for module.features.denseblock_1.denselayer_1.conv_1.conv.weight: copying a param with shape torch.Size([32, 2, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 16, 1, 1]).\n\tsize mismatch for module.features.denseblock_1.denselayer_1.conv_2.conv.weight: copying a param with shape torch.Size([8, 4, 3, 3]) from checkpoint, the shape in current model is torch.Size([8, 8, 3, 3]).\n\tsize mismatch for module.features.denseblock_1.denselayer_2.conv_1.conv.weight: copying a param with shape torch.Size([32, 3, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 24, 1, 1]).\n\tsize mismatch for module.features.denseblock_1.denselayer_2.conv_2.conv.weight: copying a param with shape torch.Size([8, 4, 3, 3]) from checkpoint, the shape in current model is torch.Size([8, 8, 3, 3]).\n\tsize mismatch for module.features.denseblock_1.denselayer_3.conv_1.conv.weight: copying a param with shape torch.Size([32, 4, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 1, 1]).\n\tsize mismatch for module.features.denseblock_1.denselayer_3.conv_2.conv.weight: copying a param with shape torch.Size([8, 4, 3, 3]) from checkpoint, the shape in current model is torch.Size([8, 8, 3, 3]).\n\tsize mismatch for module.features.denseblock_1.denselayer_4.conv_1.conv.weight: copying a param with shape torch.Size([32, 5, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 40, 1, 1]).\n\tsize mismatch for module.features.denseblock_1.denselayer_4.conv_2.conv.weight: copying a param with shape torch.Size([8, 4, 3, 3]) from checkpoint, the shape in current model is torch.Size([8, 8, 3, 3]).\n\tsize mismatch for module.features.denseblock_2.denselayer_1.conv_1.conv.weight: copying a param with shape torch.Size([64, 6, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 48, 1, 1]).\n\tsize mismatch for module.features.denseblock_2.denselayer_1.conv_2.conv.weight: copying a param with shape torch.Size([16, 8, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).\n\tsize mismatch for module.features.denseblock_2.denselayer_2.conv_1.conv.weight: copying a param with shape torch.Size([64, 8, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 1, 1]).\n\tsize mismatch for module.features.denseblock_2.denselayer_2.conv_2.conv.weight: copying a param with shape torch.Size([16, 8, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).\n\tsize mismatch for module.features.denseblock_2.denselayer_3.conv_1.conv.weight: copying a param with shape torch.Size([64, 10, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 80, 1, 1]).\n\tsize mismatch for module.features.denseblock_2.denselayer_3.conv_2.conv.weight: copying a param with shape torch.Size([16, 8, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).\n\tsize mismatch for module.features.denseblock_2.denselayer_4.conv_1.conv.weight: copying a param with shape torch.Size([64, 12, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 96, 1, 1]).\n\tsize mismatch for module.features.denseblock_2.denselayer_4.conv_2.conv.weight: copying a param with shape torch.Size([16, 8, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).\n\tsize mismatch for module.features.denseblock_2.denselayer_5.conv_1.conv.weight: copying a param with shape torch.Size([64, 14, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 112, 1, 1]).\n\tsize mismatch for module.features.denseblock_2.denselayer_5.conv_2.conv.weight: copying a param with shape torch.Size([16, 8, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).\n\tsize mismatch for module.features.denseblock_2.denselayer_6.conv_1.conv.weight: copying a param with shape torch.Size([64, 16, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).\n\tsize mismatch for module.features.denseblock_2.denselayer_6.conv_2.conv.weight: copying a param with shape torch.Size([16, 8, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).\n\tsize mismatch for module.features.denseblock_3.denselayer_1.conv_1.conv.weight: copying a param with shape torch.Size([128, 18, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 144, 1, 1]).\n\tsize mismatch for module.features.denseblock_3.denselayer_1.conv_2.conv.weight: copying a param with shape torch.Size([32, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).\n\tsize mismatch for module.features.denseblock_3.denselayer_2.conv_1.conv.weight: copying a param with shape torch.Size([128, 22, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 176, 1, 1]).\n\tsize mismatch for module.features.denseblock_3.denselayer_2.conv_2.conv.weight: copying a param with shape torch.Size([32, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).\n\tsize mismatch for module.features.denseblock_3.denselayer_3.conv_1.conv.weight: copying a param with shape torch.Size([128, 26, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 208, 1, 1]).\n\tsize mismatch for module.features.denseblock_3.denselayer_3.conv_2.conv.weight: copying a param with shape torch.Size([32, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).\n\tsize mismatch for module.features.denseblock_3.denselayer_4.conv_1.conv.weight: copying a param with shape torch.Size([128, 30, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 240, 1, 1]).\n\tsize mismatch for module.features.denseblock_3.denselayer_4.conv_2.conv.weight: copying a param with shape torch.Size([32, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).\n\tsize mismatch for module.features.denseblock_3.denselayer_5.conv_1.conv.weight: copying a param with shape torch.Size([128, 34, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 272, 1, 1]).\n\tsize mismatch for module.features.denseblock_3.denselayer_5.conv_2.conv.weight: copying a param with shape torch.Size([32, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).\n\tsize mismatch for module.features.denseblock_3.denselayer_6.conv_1.conv.weight: copying a param with shape torch.Size([128, 38, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 304, 1, 1]).\n\tsize mismatch for module.features.denseblock_3.denselayer_6.conv_2.conv.weight: copying a param with shape torch.Size([32, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).\n\tsize mismatch for module.features.denseblock_3.denselayer_7.conv_1.conv.weight: copying a param with shape torch.Size([128, 42, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 336, 1, 1]).\n\tsize mismatch for module.features.denseblock_3.denselayer_7.conv_2.conv.weight: copying a param with shape torch.Size([32, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).\n\tsize mismatch for module.features.denseblock_3.denselayer_8.conv_1.conv.weight: copying a param with shape torch.Size([128, 46, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 368, 1, 1]).\n\tsize mismatch for module.features.denseblock_3.denselayer_8.conv_2.conv.weight: copying a param with shape torch.Size([32, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).\n\tsize mismatch for module.features.denseblock_4.denselayer_1.conv_1.conv.weight: copying a param with shape torch.Size([256, 50, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 400, 1, 1]).\n\tsize mismatch for module.features.denseblock_4.denselayer_1.conv_2.conv.weight: copying a param with shape torch.Size([64, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).\n\tsize mismatch for module.features.denseblock_4.denselayer_2.conv_1.conv.weight: copying a param with shape torch.Size([256, 58, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 464, 1, 1]).\n\tsize mismatch for module.features.denseblock_4.denselayer_2.conv_2.conv.weight: copying a param with shape torch.Size([64, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).\n\tsize mismatch for module.features.denseblock_4.denselayer_3.conv_1.conv.weight: copying a param with shape torch.Size([256, 66, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 528, 1, 1]).\n\tsize mismatch for module.features.denseblock_4.denselayer_3.conv_2.conv.weight: copying a param with shape torch.Size([64, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).\n\tsize mismatch for module.features.denseblock_4.denselayer_4.conv_1.conv.weight: copying a param with shape torch.Size([256, 74, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 592, 1, 1]).\n\tsize mismatch for module.features.denseblock_4.denselayer_4.conv_2.conv.weight: copying a param with shape torch.Size([64, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).\n\tsize mismatch for module.features.denseblock_4.denselayer_5.conv_1.conv.weight: copying a param with shape torch.Size([256, 82, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 656, 1, 1]).\n\tsize mismatch for module.features.denseblock_4.denselayer_5.conv_2.conv.weight: copying a param with shape torch.Size([64, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).\n\tsize mismatch for module.features.denseblock_4.denselayer_6.conv_1.conv.weight: copying a param with shape torch.Size([256, 90, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 720, 1, 1]).\n\tsize mismatch for module.features.denseblock_4.denselayer_6.conv_2.conv.weight: copying a param with shape torch.Size([64, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).\n\tsize mismatch for module.features.denseblock_4.denselayer_7.conv_1.conv.weight: copying a param with shape torch.Size([256, 98, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 784, 1, 1]).\n\tsize mismatch for module.features.denseblock_4.denselayer_7.conv_2.conv.weight: copying a param with shape torch.Size([64, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).\n\tsize mismatch for module.features.denseblock_4.denselayer_8.conv_1.conv.weight: copying a param with shape torch.Size([256, 106, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 848, 1, 1]).\n\tsize mismatch for module.features.denseblock_4.denselayer_8.conv_2.conv.weight: copying a param with shape torch.Size([64, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).\n\tsize mismatch for module.features.denseblock_4.denselayer_9.conv_1.conv.weight: copying a param with shape torch.Size([256, 114, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 912, 1, 1]).\n\tsize mismatch for module.features.denseblock_4.denselayer_9.conv_2.conv.weight: copying a param with shape torch.Size([64, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).\n\tsize mismatch for module.features.denseblock_4.denselayer_10.conv_1.conv.weight: copying a param with shape torch.Size([256, 122, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 976, 1, 1]).\n\tsize mismatch for module.features.denseblock_4.denselayer_10.conv_2.conv.weight: copying a param with shape torch.Size([64, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).\n\tsize mismatch for module.features.denseblock_5.denselayer_1.conv_1.conv.weight: copying a param with shape torch.Size([512, 130, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 1040, 1, 1]).\n\tsize mismatch for module.features.denseblock_5.denselayer_1.conv_2.conv.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).\n\tsize mismatch for module.features.denseblock_5.denselayer_2.conv_1.conv.weight: copying a param with shape torch.Size([512, 146, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 1168, 1, 1]).\n\tsize mismatch for module.features.denseblock_5.denselayer_2.conv_2.conv.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).\n\tsize mismatch for module.features.denseblock_5.denselayer_3.conv_1.conv.weight: copying a param with shape torch.Size([512, 162, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 1296, 1, 1]).\n\tsize mismatch for module.features.denseblock_5.denselayer_3.conv_2.conv.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).\n\tsize mismatch for module.features.denseblock_5.denselayer_4.conv_1.conv.weight: copying a param with shape torch.Size([512, 178, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 1424, 1, 1]).\n\tsize mismatch for module.features.denseblock_5.denselayer_4.conv_2.conv.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).\n\tsize mismatch for module.features.denseblock_5.denselayer_5.conv_1.conv.weight: copying a param with shape torch.Size([512, 194, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 1552, 1, 1]).\n\tsize mismatch for module.features.denseblock_5.denselayer_5.conv_2.conv.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).\n\tsize mismatch for module.features.denseblock_5.denselayer_6.conv_1.conv.weight: copying a param with shape torch.Size([512, 210, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 1680, 1, 1]).\n\tsize mismatch for module.features.denseblock_5.denselayer_6.conv_2.conv.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).\n\tsize mismatch for module.features.denseblock_5.denselayer_7.conv_1.conv.weight: copying a param with shape torch.Size([512, 226, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 1808, 1, 1]).\n\tsize mismatch for module.features.denseblock_5.denselayer_7.conv_2.conv.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).\n\tsize mismatch for module.features.denseblock_5.denselayer_8.conv_1.conv.weight: copying a param with shape torch.Size([512, 242, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 1936, 1, 1]).\n\tsize mismatch for module.features.denseblock_5.denselayer_8.conv_2.conv.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).\n']

This is the training argument I have used in my image classification prediction script:
args = parser.parse_args(["--model", "condensenet_converted", "-b", "64", "-j", "20", "imagenet", "--stages", "4-6-8-10-8", "--growth", "8-16-32-64-128", "--gpu", "0"]). I have tried both, (C=G=4) and (C=G=8) pre-trained models from this repo. Thank you.

[lizhenstat]

@LeighDavis Hi, same questions here, did you find the answer? Thanks

[lizhenstat]

@LeighDavis, Hi, I find the training setting for imagenet in script.sh, it is

python main.py --model condensenet -b 256 -j 20 /PATH/TO/IMAGENET --epochs 120 --stages 4-6-8-10-8 --growth 8-16-32-64-128 --group-1x1 4 --group-3x3 4 --condense-factor 4 --bottleneck 4 --resume --group-lasso 0.00001 --gpu 0,1,2,3

[LeighDavis]

Hi @lizhenstat, thanks for following up. Yes, I had tried those arguments from that training command from script.sh file but I kept getting similar shape errors. Did these arguments work for you?

[lizhenstat]

@LeighDavis Hi, to evaluate from the pre-trained converted mode, you can use the following code

python main.py --model condensenet_converted -b 64 -j 20 /PATH/TO/IMAGENET \
--stages 4-6-8-10-8 --growth 8-16-32-64-128 --gpu 0 --resume \
--evaluate-from /PATH/TO/CONVERTED/MODEL

Your log shows the mismatch of the parameters in the same layer
Since the converted model is rearranged to a group convolution, and on ImageNet, the condense factor is taking
the value of 2, which is equivalent to pruning 50% of the total filters.

I evaluate condensenet-74 on my one 1080-ti successfully.

[ShichenLiu]

@lizhenstat Thanks for addressing @LeighDavis 's questions. I currently have no ImageNet installed on my machine, so I am really glad you can help!