pytorch-deep-learning
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Published
20 hours ago •
mrdbourke
mrdbourke
How to load pretrain model manually in correct way?
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?
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
============================================================================================================================================
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)) '''
