a = tlx.nn.LayerNorm(128)

# compare
b = paddle.nn.LayerNorm(128)

Traceback (most recent call last):
  File "D:/ProjectByPython/code/reference/DL_Platform/TensorLayer/TensorLayerX-0.5.6/examples/basic_tutorials/nested_usage_of_layer.py", line 159, in <module>
    grad = tape.gradient(_loss_ce, train_weights)
  File "D:\Software\miniconda3\envs\py37env_tlx\lib\site-packages\tensorflow_core\python\eager\backprop.py", line 984, in gradient
    if not t.dtype.is_floating:
AttributeError: 'paddle.fluid.core_avx.VarType' object has no attribute 'is_floating'

    os.environ['TL_BACKEND'] = 'torch'
    tlx.set_device(device='GPU', id=3)
    # ...
    net = models[backend]()
    net.load_weights('test.npz', format='npz_dict', skip=True)
    test_dataset = MyDataset(root_dir="test/")
    test_loader = DataLoader(test_dataset, batch_size=4, shuffle=True)

    train_weights = net.trainable_weights
    scheduler = tlx.optimizers.lr.StepDecay(learning_rate=0, step_size=30, gamma=0.5, last_epoch=-1)
    optimizer = tlx.optimizers.Adam(lr=scheduler)

    hist = np.zeros((num_classes, num_classes))
    net.set_eval()
    # with torch.no_grad():
    for x, y, y_cls in test_loader:
        _out, _out_cls = net(x)
        seg_loss = tlx.losses.softmax_cross_entropy_with_logits(_out, y)
        cls_loss = tlx.losses.sigmoid_cross_entropy(_out_cls, y_cls)
        _loss = seg_loss + 1 * cls_loss
        # grads = optimizer.gradient(_loss, train_weights)
        # optimizer.apply_gradients(zip(grads, train_weights))
        '''
            compute miou matrix
        '''
        out = tlx.convert_to_numpy(_out)
        y = tlx.convert_to_numpy(y)
        out = np.argmax(out, axis=1)
        for i in range(0, out.shape[0]):
            pred = out[i]
            gt = y[i]
            hist += fast_hist(gt.flatten(), pred.flatten(), num_classes)
            
    # compute miou then print
    mIoUs = per_class_iu(hist)
    for ind_class in range(num_classes):
        print('===>' + name_classes[ind_class] + ':\t' + str(round(mIoUs[ind_class] * 100, 2)))
    print('===> mIoU: ' + str(round(np.nanmean(mIoUs) * 100, 2)))
    print("test loss: {}".format(train_loss))

# ======================================================== #
###### THIS CODE IS AN EXAMPLE, REPLACE WITH YOUR OWN ######
# ======================================================== #
import tensorlayerx as tlx

# ======================================================== #
###### THIS CODE IS AN EXAMPLE, REPLACE WITH YOUR OWN ######
# ======================================================== #
import os
import paddle
os.environ['TL_BACKEND'] = 'paddle'
import tensorlayerx as tlx

tlx_ni = tlx.nn.Input([4, 32, 50, 50], name='input')
tlx_out = tlx.nn.UpSampling2d(scale=(2, 2), data_format="channels_first")(tlx_ni)
print(f"tlx_out.shape={tlx_out.shape}")

pd_ni = paddle.rand([4, 32, 50, 50], dtype="float32")
pd_out = paddle.nn.Upsample(scale_factor=2, data_format="NCHW")(pd_ni)
print(f"pd_out.shape={pd_out.shape}")

# ======================================================== #
###### THIS CODE IS AN EXAMPLE, REPLACE WITH YOUR OWN ######
# ======================================================== #

# ======================================================== #
###### THIS CODE IS AN EXAMPLE, REPLACE WITH YOUR OWN ######
# ======================================================== #
# paddle
import paddle
x = 13
print(isinstance(x, paddle.optimizers.Optimizer))

# tensorlayer
import os
os.environ['TL_BACKEND'] = 'paddle'
import tensorlayer as tlx
x = 13
print(isinstance(x, tlx.optimizers.paddle_optimizers.Optimizer))
# ======================================================== #
###### THIS CODE IS AN EXAMPLE, REPLACE WITH YOUR OWN ######
# ======================================================== #

import os
os.environ['TL_BACKEND'] = 'paddle'
# os.environ['TL_BACKEND'] = 'tensorflow'
import tensorlayerx.nn as nn
from tensorlayerx import logging
from tensorlayerx.files import assign_weights
from ch1_clas.pd_download import get_weights_path_from_url
import numpy as np
import paddle
from paddle import to_tensor
from PIL import Image
import copy

__all__ = []

model_urls = {
    'tlxvgg16': ('https://paddle-hapi.bj.bcebos.com/models/vgg16.pdparams',
              '89bbffc0f87d260be9b8cdc169c991c4'),
    'tlxvgg19': ('https://paddle-hapi.bj.bcebos.com/models/vgg19.pdparams',
              '23b18bb13d8894f60f54e642be79a0dd')
}


class VGG(nn.Module):
    """VGG model from
    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
    Args:
        features (nn.Layer): Vgg features create by function make_layers.
        num_classes (int): Output dim of last fc layer. If num_classes <=0, last fc layer 
                            will not be defined. Default: 1000.
        with_pool (bool): Use pool before the last three fc layer or not. Default: True.
    Examples:
        .. code-block:: python
            from paddle.vision.models import VGG
            from paddle.vision.models.vgg import make_layers
            vgg11_cfg = [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']
            features = make_layers(vgg11_cfg)
            vgg11 = VGG(features)
    """

    def __init__(self, features, num_classes=1000, with_pool=True):
        super(VGG, self).__init__()
        self.features = features
        self.num_classes = num_classes
        self.with_pool = with_pool

        if self.with_pool:
            # self.avgpool = nn.AdaptiveAvgPool2D((7, 7))
            self.avgpool = nn.AdaptiveMeanPool2d((7, 7))

        if num_classes > 0:
            self.classifier = nn.Sequential(
                nn.Linear(out_features=4096, act=None, in_features=512 * 7 * 7),
                nn.ReLU(),
                nn.Linear(out_features=4096, act=None, in_features=4096),
                nn.ReLU(),
                nn.Linear(in_features=4096, out_features=num_classes),
            )

    def forward(self, x):
        print(self.features[0](x).shape)
        x = self.features(x)
        if self.with_pool:
            x = self.avgpool(x)
        if self.num_classes > 0:
            x = paddle.flatten(x, 1)
            print('x.numpy =', x.shape)
            x = self.classifier(x)
        return x


def make_layers(cfg, batch_norm=False):
    layers = []
    in_channels = 3
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2, padding=0)]  # padding默认为'SAME'
        else:
            conv2d = nn.Conv2d(out_channels=v, kernel_size=(3, 3), stride=(1, 1), act=None, padding=1, in_channels=in_channels)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(num_features=v), nn.ReLU()]
            else:
                layers += [conv2d, nn.ReLU()]
            in_channels = v
    return nn.Sequential(*layers)


cfgs = {
    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'B':
    [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'D': [
        64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512,
        512, 512, 'M'
    ],
    'E': [
        64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512,
        'M', 512, 512, 512, 512, 'M'
    ],
}
####################新增pd2tlx#################
pd2tlx = {'features.0.weight': 'features.0.W',
         'features.2.weight': 'features.2.W',
         'features.5.weight': 'features.5.W',
         'features.7.weight': 'features.7.W',
         'features.10.weight': 'features.10.W',
         'features.12.weight': 'features.12.W',
         'features.14.weight': 'features.14.W',
         'features.17.weight': 'features.17.W',
         'features.19.weight': 'features.19.W',
         'features.21.weight': 'features.21.W',
         'features.24.weight': 'features.24.W',
         'features.26.weight': 'features.26.W',
         'features.28.weight': 'features.28.W',
         'features.0.bias': 'features.0.b',
         'features.2.bias': 'features.2.b',
         'features.5.bias': 'features.5.b',
         'features.7.bias': 'features.7.b',
         'features.10.bias': 'features.10.b',
         'features.12.bias': 'features.12.b',
         'features.14.bias': 'features.14.b',
         'features.17.bias': 'features.17.b',
         'features.19.bias': 'features.19.b',
         'features.21.bias': 'features.21.b',
         'features.24.bias': 'features.24.b',
         'features.26.bias': 'features.26.b',
         'features.28.bias': 'features.28.b',
         'classifier.0.weight': 'classifier.0.W',
         'classifier.3.weight':'classifier.2.W',
         'classifier.6.weight':'classifier.4.W',
         'classifier.0.bias': 'classifier.0.b',
         'classifier.3.bias':'classifier.2.b',
         'classifier.6.bias':'classifier.4.b'}


def get_new_weight(param):
    '''新增函数，调整参数key'''
    new_param = {}
    for key in param.keys():
        new_param[pd2tlx[key]] = param[key]
        print(key, ":", param[key].shape, "vs", pd2tlx[key],":", new_param[pd2tlx[key]].shape)
    return new_param


def restore_model(param, model, model_type='vgg16'):
    """ 直接restore """
    weights = []
    if model_type == 'vgg16':
        for val in param.items():
        # for val in sorted(param.items()):
            weights.append(val[1])
            if len(model.all_weights) == len(weights):
                break
    elif model_type == 'vgg19':
        pass
    # assign weight values
    assign_weights(weights, model)
    del weights


def _tlxvgg(arch, cfg, batch_norm, pretrained, **kwargs):
    model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
    if pretrained:
        assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
            arch)
        weight_path = get_weights_path_from_url(model_urls[arch][0],
                                                model_urls[arch][1])
        param = paddle.load(weight_path)
        # model.load_dict(param)
        new_param = get_new_weight(param)
        model.load_dict(new_param)
        # restore_model(param, model)
    return model


def tlxvgg16(pretrained=False, batch_norm=False, **kwargs):
    """VGG 16-layer model 
    
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
        batch_norm (bool): If True, returns a model with batch_norm layer. Default: False.
    Examples:
        .. code-block:: python
            from paddle.vision.models import vgg16
            # build model
            model = vgg16()
            # build vgg16 model with batch_norm
            model = vgg16(batch_norm=True)
    """
    model_name = 'tlxvgg16'
    if batch_norm:
        model_name += ('_bn')
    return _tlxvgg(model_name, 'D', batch_norm, pretrained, **kwargs)


if __name__ == "__main__":
    from utils.load_image import load_image_tlx

    model = tlxvgg16(pretrained=True, batch_norm=False)
    # model = tlxvgg16(pretrained=True, batch_norm=True)
    # tlx.paddle:[1, 224, 224, 3], paddle:[1, 3, 224, 224]
    x = load_image_tlx("../images/dog.jpeg")
    out = model(x)
    # np.save("tmp/tlx_output.npy", np.array(out)[0])

    file_path = '../images/imagenet_classes.txt'
    with open(file_path) as f:
        classes = [line.strip() for line in f.readlines()]
    print(classes[np.argmax(out[0])])