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Image Classification Prediction

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This tutorial introduces how to call the pretraining model in MindCV to make classification prediction on the test image.

Model Loading

View All Available Models

By calling the registry.list_models function in mindcv.models, the names of all network models can be printed. The models of a network in different parameter configurations will also be printed, such as resnet18 / resnet34 / resnet50 / resnet101 / resnet152.

import sys
sys.path.append("..")
from mindcv.models import registry
registry.list_models()
['BiTresnet50',
 'RepMLPNet_B224',
 'RepMLPNet_B256',
 'RepMLPNet_D256',
 'RepMLPNet_L256',
 'RepMLPNet_T224',
 'RepMLPNet_T256',
 'convit_base',
 'convit_base_plus',
 'convit_small',
 ...
 'visformer_small',
 'visformer_small_v2',
 'visformer_tiny',
 'visformer_tiny_v2',
 'vit_b_16_224',
 'vit_b_16_384',
 'vit_b_32_224',
 'vit_b_32_384',
 'vit_l_16_224',
 'vit_l_16_384',
 'vit_l_32_224',
 'xception']

Load Pretraining Model

Taking the resnet50 model as an example, we introduce two methods to load the model checkpoint using the create_model function in mindcv.models. 1). When the pretrained parameter in the interface is set to True, network weights can be automatically downloaded.

from mindcv.models import create_model
model = create_model(model_name='resnet50', num_classes=1000, pretrained=True)
# Switch the execution logic of the network to the inference scenario
model.set_train(False)
102453248B [00:16, 6092186.31B/s]                                                                                      

ResNet<
  (conv1): Conv2d<input_channels=3, output_channels=64, kernel_size=(7, 7), stride=(2, 2), pad_mode=pad, padding=3, dilation=(1, 1), group=1, has_bias=False, weight_init=normal, bias_init=zeros, format=NCHW>
  (bn1): BatchNorm2d<num_features=64, eps=1e-05, momentum=0.9, gamma=Parameter (name=bn1.gamma, shape=(64,), dtype=Float32, requires_grad=True), beta=Parameter (name=bn1.beta, shape=(64,), dtype=Float32, requires_grad=True), moving_mean=Parameter (name=bn1.moving_mean, shape=(64,), dtype=Float32, requires_grad=False), moving_variance=Parameter (name=bn1.moving_variance, shape=(64,), dtype=Float32, requires_grad=False)>
  (relu): ReLU<>
  (max_pool): MaxPool2d<kernel_size=3, stride=2, pad_mode=SAME>
  ...
  (pool): GlobalAvgPooling<>
  (classifier): Dense<input_channels=2048, output_channels=1000, has_bias=True>
  >

2). When the checkpoint_path parameter in the interface is set to the file path, the model parameter file with the .ckpt can be loaded.

from mindcv.models import create_model
model = create_model(model_name='resnet50', num_classes=1000, checkpoint_path='./resnet50_224.ckpt')
# Switch the execution logic of the network to the inference scenario
model.set_train(False)

Data Preparation

Create Dataset

Here, we download a Wikipedia image as a test image, and use the create_dataset function in mindcv.data to construct a custom dataset for a single image.

from mindcv.data import create_dataset
num_workers = 1
# path of dataset
data_dir = "./data/"
dataset = create_dataset(root=data_dir, split='test', num_parallel_workers=num_workers)
# Image visualization
from PIL import Image
Image.open("./data/test/dog/dog.jpg")

png

Data Preprocessing

Call the create_transforms function to obtain the data processing strategy (transform list) of the ImageNet dataset used by the pre training model.

We pass the obtained transform list into the create_loader function, specify batch_size=1 and other parameters, and then complete the preparation of test data. The Dataset object is returned as the input of the model.

from mindcv.data import create_transforms, create_loader
transforms_list = create_transforms(dataset_name='imagenet', is_training=False)
data_loader = create_loader(
        dataset=dataset,
        batch_size=1,
        is_training=False,
        num_classes=1000,
        transform=transforms_list,
        num_parallel_workers=num_workers
    )

Model Inference

The picture of the user-defined dataset is transferred to the model to obtain the inference result. Here, use the Squeeze function of mindspore.ops to remove the batch dimension.

import mindspore.ops as P
import numpy as np
images, _ = next(data_loader.create_tuple_iterator())
output = P.Squeeze()(model(images))
pred = np.argmax(output.asnumpy())
with open("imagenet1000_clsidx_to_labels.txt") as f:
    idx2label = eval(f.read())
print('predict: {}'.format(idx2label[pred]))
predict: Labrador retriever