def test_resnet50_export(batch_size=1, num_classes=5):
context.set_context(enable_ir_fusion=False)
input_np = np.random.uniform(0.0, 1.0, size=[batch_size, 3, 224, 224]).astype(np.float32)
net = resnet50(batch_size, num_classes)
#param_dict = load_checkpoint("./resnet50-1_103.ckpt")
#load_param_into_net(net, param_dict)
export(net, Tensor(input_np), file_name="./me_resnet50.pb", file_format="GEIR")
def test_resnet50_export(batch_size=1, num_classes=5):
input_np = np.random.uniform(0.0, 1.0, size=[batch_size, 3, 224,
224]).astype(np.float32)
net = resnet50(batch_size, num_classes)
export(net,
Tensor(input_np),
file_name="./me_resnet50.pb",
file_format="GEIR")
def get_model(dataset, framework='pytorch'):
if dataset == 'mnist_fashion':
return model_mnistfashion(framework)
elif dataset == 'cifar10':
return model_cifar10(framework)
elif dataset == 'cifar100':
return resnet34(framework,
num_classes=100,
input_shape=(32, 32, 3),
dataset=dataset)
elif dataset == 'food101N':
return resnet50(framework,
num_classes=101,
input_shape=(224, 224, 3),
dataset=dataset)
elif dataset == 'clothing1M' or dataset == 'clothing1M50k' or dataset == 'clothing1Mbalanced':
return resnet50(framework,
num_classes=14,
input_shape=(224, 224, 3),
dataset=dataset)
def resnet50(framework, num_classes, input_shape, dataset):
import os
if framework == 'pytorch':
import torch
from torch import nn
from resnet_torch import resnet50
try:
net = resnet50(pretrained=True)
except:
net = resnet50(pretrained=False)
net.load_state_dict(torch.load('resnet50.pt'))
net.fc = nn.Linear(2048, num_classes)
return net
elif framework == 'tensorflow':
import tensorflow as tf
#from resnet50 import ResNet50
model = tf.keras.applications.resnet.ResNet50(include_top=False,
weights='imagenet',
input_shape=(224, 224,
3),
pooling='avg')
output = tf.keras.layers.Dense(num_classes)(model.layers[-1].output)
return tf.keras.models.Model(model.input, outputs=output)