def test_ssd300_infer():
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
model = Model(ssd300_infer())
model.compile()
loc, score = model.predict(ts.ones((1, 3, 300, 300)))
print(loc.asnumpy(), score.asnumpy())
def test_mobilenetv2_infer():
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
model = Model(mobilenetv2_infer())
model.compile()
z = model.predict(ts.ones((1, 3, 224, 224)))
print(z.asnumpy())
def test_densenetBC_100():
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
model = Model(densenetBC_100())
model.compile()
z = model.predict(ts.ones((1, 3, 32, 32)))
print(z.asnumpy())
def test_sequential():
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
net = layers.SequentialLayer([
layers.Conv2d(1, 6, 5, pad_mode='valid', weight_init="ones"),
layers.ReLU(),
layers.MaxPool2d(kernel_size=2, stride=2)
])
model = Model(net)
model.compile()
z = model.predict(ts.ones((1, 1, 32, 32)))
print(z.asnumpy())
def do_eval(dataset=None,
network=None,
num_class=2,
assessment_method="accuracy",
load_checkpoint_path=""):
""" do eval """
if load_checkpoint_path == "":
raise ValueError(
"Finetune model missed, evaluation task must load finetune model!")
net_for_pretraining = network(bert_net_cfg, False, num_class)
net_for_pretraining.set_train(False)
model = Model(net_for_pretraining)
model.load_checkpoint((load_checkpoint_path))
if assessment_method == "accuracy":
callback = Accuracy()
elif assessment_method == "f1":
callback = F1(False, num_class)
elif assessment_method == "mcc":
callback = MCC()
elif assessment_method == "spearman_correlation":
callback = SpearmanCorrelation()
else:
raise ValueError(
"Assessment method not supported, support: [accuracy, f1, mcc, spearman_correlation]"
)
columns_list = ["input_ids", "input_mask", "segment_ids", "label_ids"]
for data in dataset.create_dict_iterator():
input_data = []
for i in columns_list:
input_data.append(data[i])
input_ids, input_mask, token_type_id, label_ids = input_data
logits = model.predict(input_ids, input_mask, token_type_id, label_ids)
callback.update(logits, label_ids)
print("==============================================================")
eval_result_print(assessment_method, callback)
print("==============================================================")
eval_net = ssd300_infer(class_num=args_opt.num_classes)
model = Model(eval_net)
if args_opt.checkpoint_path:
model.load_checkpoint(args_opt.checkpoint_path)
# perform the model predict operation
print("\n========================================\n")
print("total images num: ", total)
print("Processing, please wait a moment...")
start = time.time()
pred_data = []
id_iter = 0
for data in ds_eval.create_dict_iterator(output_numpy=True):
image_np = data['image']
image_shape = data['image_shape']
output = model.predict(Tensor(image_np))
for batch_idx in range(image_np.shape[0]):
pred_data.append({
"boxes": output[0].asnumpy()[batch_idx],
"box_scores": output[1].asnumpy()[batch_idx],
"img_id": id_iter,
"image_shape": image_shape[batch_idx]
})
id_iter += 1
cost_time = int((time.time() - start) * 1000)
print(f' 100% [{total}/{total}] cost {cost_time} ms')
# calculate mAP for the predict data
voc_cls = [
'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',