Add pipit

Author: Luka0612

README.md

@@ -7,3 +7,21 @@
 Pre-trained models based on TensorLayerX. 
 TensorLayerX is a multi-backend AI framework, which can run on almost all operation systems and AI hardwares, and support hybrid-framework programming. The currently version supports TensorFlow, MindSpore, PaddlePaddle, PyTorch, OneFlow and Jittor as the backends.
 
+# Quick Start
+## Installation
+### Via pip
+```bash
+# install from pypi
+pip3 install tlxzoo
+```
+
+## train
+```bash
+python demo/vision/image_classification/vgg/train.py
+```
+
+## predict
+
+```bash
+python demo/vision/image_classification/vgg/predict.py
+```

demo/vision/face_recognition/retinaface/train.py

@@ -89,7 +89,7 @@ def tf_train(
                         root_path="./wider/widerface",
                         train_ann_path="label.txt",
                         val_ann_path="label.txt",
-                        num_workers=2,
+                        num_workers=0,
                         )
     transform = RetinaFaceTransform()
     wider.register_transform_hook(transform)

demo/vision/human_pose_estimation/hrnet/predict.py

@@ -12,7 +12,7 @@
     model.load_weights("./demo/vision/human_pose_estimation/hrnet/model.npz")
     model.set_eval()
 
-    path = "./demo/vision/human_pose_estimation/hrnet/hrnet.jpg"
+    path = "./coco2017/0.1/val2017/000000527784.jpg"
     image = Image.open(path).convert('RGB')
     image_height, image_width = image.height, image.width
     image = np.array(image, dtype=np.float32)

demo/vision/human_pose_estimation/hrnet/train.py

@@ -64,7 +64,7 @@ def tf_train(
 
             if (epoch + 1) % 5 == 0:
                 valid(network, test_dataset)
-                model.save_weights("./demo/vision/human_pose_estimation/hrnet/model.npz")
+                model.save_weights("./demo/vision/human_pose_estimation/model.npz")
 
             optimizer.lr.step()
 
@@ -75,10 +75,10 @@ def __init__(self, learning_rate, last_epoch=0, verbose=False):
 
     def get_lr(self):
 
-        if int(self.last_epoch) >= 40:
+        if int(self.last_epoch) >= 65:
             return self.base_lr * 0.01
 
-        if int(self.last_epoch) >= 30:
+        if int(self.last_epoch) >= 40:
             return self.base_lr * 0.1
 
         return self.base_lr
@@ -91,7 +91,7 @@ def get_lr(self):
                            data_name="Coco",
                            train_ann_path="./annotations/person_keypoints_train2017.json",
                            val_ann_path="./annotations/person_keypoints_val2017.json",
-                           num_workers=4)
+                           num_workers=0)
 
     transform = HRNetTransform()
     datasets.register_transform_hook(transform)
@@ -103,7 +103,7 @@ def get_lr(self):
     # optimizer = tlx.optimizers.SGD(lr=scheduler)
 
     trainer = Trainer(network=model, loss_fn=model.loss_fn, optimizer=optimizer, metrics=None)
-    trainer.train(n_epoch=50, train_dataset=datasets.train, test_dataset=datasets.test, print_freq=1,
+    trainer.train(n_epoch=80, train_dataset=datasets.train, test_dataset=datasets.test, print_freq=1,
                   print_train_batch=True)
 
-    model.save_weights("./demo/vision/human_pose_estimation/hrnet/model.npz")
+    model.save_weights("./demo/vision/human_pose_estimation/model.npz")

demo/vision/image_classification/vgg/predict.py

@@ -22,7 +22,7 @@
     model.load_weights("./demo/vision/image_classification/vgg/model.npz")
     model.set_eval()
 
-    image = cv2.imread("dog.png")
+    image = cv2.imread("./demo/vision/image_classification/vgg/dog.png")
     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     image = cv2.resize(image, (32, 32))
 

docker/Dockerfile

@@ -8,6 +8,7 @@ RUN pip install pandas
 RUN pip install jupyterlab
 RUN pip install opencv-python
 RUN pip install SoundFile
+RUN apt-get install libsndfile1
 RUN pip install sentencepiece
 RUN pip install sacrebleu
 RUN pip install rouge_score

setup.cfg

@@ -0,0 +1,2 @@
+[metadata]
+desciption-file = README.md

setup.py

@@ -41,7 +41,8 @@ def req_file(filename, folder="requirements"):
     long_description_content_type="text/markdown",
     author="tensorlayerx",
     author_email="",
-    url="",
+    packages=find_packages(),
+    url="https://github.com/tensorlayer/TLXZoo",
     keywords="tensorlayerx zoo",
     python_requires=">=3.5",
     install_requires=install_requires,

tlxzoo/datasets/data_loader.py

@@ -6,7 +6,7 @@
 
 class DataLoaders(object):
     def __init__(self, data_name, train_limit=None, collate_fn=None, transform_hook=None, transform_hook_index=None,
-                 num_workers=2, per_device_train_batch_size=2, per_device_eval_batch_size=2, **kwargs):
+                 num_workers=0, per_device_train_batch_size=2, per_device_eval_batch_size=2, **kwargs):
         self.dataset_dict = Registers.datasets[data_name].load(train_limit=train_limit, **kwargs)
 
         if "train" in self.dataset_dict:
@@ -68,7 +68,7 @@ def register_transform_hook(self, transform_hook, index=None):
             transform_hook.set_eval()
             self.dataset_dict["test"].register_transform_hook(transform_hook, index=index)
 
-    def dataset_dataloader(self, dataset, dataset_type="train", num_workers=8, collate_fn=None,
+    def dataset_dataloader(self, dataset, dataset_type="train", num_workers=0, collate_fn=None,
                            per_device_train_batch_size=2, per_device_eval_batch_size=2):
 
         if dataset_type == "train":