How to load pretrain model manually in correct way?

[ChunJen]

At 06. PyTorch Transfer Learning

It needs to download efficientnetB0 model from https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth I tried manually download, and load with:

efficientnet_b0_path = 'models/efficientnet_b0_rwightman-3dd342df.pth'
model = torch.load(efficientnet_b0_path, map_location='cuda')

But it occured error when running summary

summary(model=model, 
        input_size=(32, 3, 224, 224), # make sure this is "input_size", not "input_shape"
        # col_names=["input_size"], # uncomment for smaller output
        col_names=["input_size", "output_size", "num_params", "trainable"],
        col_width=20,
        row_settings=["var_names"]
)

output error:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/tmp/ipykernel_9840/1646196586.py in <module>
      5         col_names=["input_size", "output_size", "num_params", "trainable"],
      6         col_width=20,
----> 7         row_settings=["var_names"]
      8 )

~/.local/lib/python3.7/site-packages/torchinfo/torchinfo.py in summary(model, input_size, input_data, batch_dim, cache_forward_pass, col_names, col_width, depth, device, dtypes, mode, row_settings, verbose, **kwargs)
    207 
    208     if device is None:
--> 209         device = get_device(model)
    210 
    211     validate_user_params(

~/.local/lib/python3.7/site-packages/torchinfo/torchinfo.py in get_device(model)
    452     """
    453     try:
--> 454         model_parameter = next(model.parameters())
    455     except StopIteration:
    456         model_parameter = None

AttributeError: 'collections.OrderedDict' object has no attribute 'parameters'

How to fix it?

[ChunJen]

Well, I think it works with model.load_state_dict

# weights = torchvision.models.EfficientNet_B0_Weights.DEFAULT

efficientnet_b0_path = 'models/efficientnet_b0_rwightman-3dd342df.pth'
model = torchvision.models.efficientnet_b0()

for param in model.parameters():
    param.required_grad = False

model.load_state_dict(torch.load(efficientnet_b0_path))

summary(model=model, 
        input_size=(32, 3, 224, 224), # make sure this is "input_size", not "input_shape"
        # col_names=["input_size"], # uncomment for smaller output
        col_names=["input_size", "output_size", "num_params", "trainable"],
        col_width=20,
        row_settings=["var_names"]
)

for summary output:

============================================================================================================================================
Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable
============================================================================================================================================
EfficientNet (EfficientNet)                                  [32, 3, 224, 224]    [32, 1000]           --                   True
├─Sequential (features)                                      [32, 3, 224, 224]    [32, 1280, 7, 7]     --                   True
│    └─Conv2dNormActivation (0)                              [32, 3, 224, 224]    [32, 32, 112, 112]   --                   True
│    │    └─Conv2d (0)                                       [32, 3, 224, 224]    [32, 32, 112, 112]   864                  True
│    │    └─BatchNorm2d (1)                                  [32, 32, 112, 112]   [32, 32, 112, 112]   64                   True
│    │    └─SiLU (2)                                         [32, 32, 112, 112]   [32, 32, 112, 112]   --                   --
│    └─Sequential (1)                                        [32, 32, 112, 112]   [32, 16, 112, 112]   --                   True
│    │    └─MBConv (0)                                       [32, 32, 112, 112]   [32, 16, 112, 112]   1,448                True
│    └─Sequential (2)                                        [32, 16, 112, 112]   [32, 24, 56, 56]     --                   True
│    │    └─MBConv (0)                                       [32, 16, 112, 112]   [32, 24, 56, 56]     6,004                True
│    │    └─MBConv (1)                                       [32, 24, 56, 56]     [32, 24, 56, 56]     10,710               True
│    └─Sequential (3)                                        [32, 24, 56, 56]     [32, 40, 28, 28]     --                   True
│    │    └─MBConv (0)                                       [32, 24, 56, 56]     [32, 40, 28, 28]     15,350               True
│    │    └─MBConv (1)                                       [32, 40, 28, 28]     [32, 40, 28, 28]     31,290               True
│    └─Sequential (4)                                        [32, 40, 28, 28]     [32, 80, 14, 14]     --                   True
│    │    └─MBConv (0)                                       [32, 40, 28, 28]     [32, 80, 14, 14]     37,130               True
│    │    └─MBConv (1)                                       [32, 80, 14, 14]     [32, 80, 14, 14]     102,900              True
│    │    └─MBConv (2)                                       [32, 80, 14, 14]     [32, 80, 14, 14]     102,900              True
│    └─Sequential (5)                                        [32, 80, 14, 14]     [32, 112, 14, 14]    --                   True
│    │    └─MBConv (0)                                       [32, 80, 14, 14]     [32, 112, 14, 14]    126,004              True
│    │    └─MBConv (1)                                       [32, 112, 14, 14]    [32, 112, 14, 14]    208,572              True
│    │    └─MBConv (2)                                       [32, 112, 14, 14]    [32, 112, 14, 14]    208,572              True
│    └─Sequential (6)                                        [32, 112, 14, 14]    [32, 192, 7, 7]      --                   True
│    │    └─MBConv (0)                                       [32, 112, 14, 14]    [32, 192, 7, 7]      262,492              True
│    │    └─MBConv (1)                                       [32, 192, 7, 7]      [32, 192, 7, 7]      587,952              True
│    │    └─MBConv (2)                                       [32, 192, 7, 7]      [32, 192, 7, 7]      587,952              True
│    │    └─MBConv (3)                                       [32, 192, 7, 7]      [32, 192, 7, 7]      587,952              True
│    └─Sequential (7)                                        [32, 192, 7, 7]      [32, 320, 7, 7]      --                   True
│    │    └─MBConv (0)                                       [32, 192, 7, 7]      [32, 320, 7, 7]      717,232              True
│    └─Conv2dNormActivation (8)                              [32, 320, 7, 7]      [32, 1280, 7, 7]     --                   True
│    │    └─Conv2d (0)                                       [32, 320, 7, 7]      [32, 1280, 7, 7]     409,600              True
│    │    └─BatchNorm2d (1)                                  [32, 1280, 7, 7]     [32, 1280, 7, 7]     2,560                True
│    │    └─SiLU (2)                                         [32, 1280, 7, 7]     [32, 1280, 7, 7]     --                   --
├─AdaptiveAvgPool2d (avgpool)                                [32, 1280, 7, 7]     [32, 1280, 1, 1]     --                   --
├─Sequential (classifier)                                    [32, 1280]           [32, 1000]           --                   True
│    └─Dropout (0)                                           [32, 1280]           [32, 1280]           --                   --
│    └─Linear (1)                                            [32, 1280]           [32, 1000]           1,281,000            True
============================================================================================================================================
Total params: 5,288,548
Trainable params: 5,288,548
Non-trainable params: 0
Total mult-adds (G): 12.35
============================================================================================================================================
Input size (MB): 19.27
Forward/backward pass size (MB): 3452.35
Params size (MB): 21.15
Estimated Total Size (MB): 3492.77
============================================================================================================================================

[Kodar11]

These error popping "'collections.OrderedDict' object has no attribute 'to' " when I am passing loaded train model for predications ''' loaded_model_1 = torch.load(r'/content/drive/MyDrive/models/Model_0')

pred_and_plot_image(model=loaded_model_1, image_path=r'/content/drive/MyDrive/2 B/Training/destroyedbuilding/8.jpeg', class_names=class_names,

transform=weights.transforms(), # optionally pass in a specified transform from our pretrained model weights

                    image_size=(224, 224))

'''