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Posted to commits@singa.apache.org by zh...@apache.org on 2022/04/23 03:28:59 UTC
[singa] branch dev updated: update the main function for bloodmnist
This is an automated email from the ASF dual-hosted git repository.
zhaojing pushed a commit to branch dev
in repository https://gitbox.apache.org/repos/asf/singa.git
The following commit(s) were added to refs/heads/dev by this push:
new a0847106 update the main function for bloodmnist
new 59e60bab Merge pull request #950 from wannature/singa_v9
a0847106 is described below
commit a08471065c1ce688078c00bc7c50277b62b05215
Author: wenqiao zhang <we...@zju.edu.cn>
AuthorDate: Fri Apr 22 13:17:20 2022 +0800
update the main function for bloodmnist
---
.../demos/Classification/BloodMnist/ClassDemo.py | 144 +++++++--------------
1 file changed, 48 insertions(+), 96 deletions(-)
diff --git a/examples/demos/Classification/BloodMnist/ClassDemo.py b/examples/demos/Classification/BloodMnist/ClassDemo.py
index 23e5acae..faf0036f 100644
--- a/examples/demos/Classification/BloodMnist/ClassDemo.py
+++ b/examples/demos/Classification/BloodMnist/ClassDemo.py
@@ -16,38 +16,32 @@
# specific language governing permissions and limitations
# under the License.
#
-from singa import singa_wrap as singa
-from singa import device
-from singa import tensor
-from singa import opt
-from singa import layer
-from singa.layer import Layer
-from singa import model
-import numpy as np
-import time
-import argparse
-from PIL import Image
+import json
import os
+import time
from glob import glob
-from sklearn.metrics import accuracy_score
-import json
+
+import numpy as np
+from PIL import Image
+from singa import device, layer, model, opt, tensor
from tqdm import tqdm
-from transforms import Compose, ToTensor, Normalize
+from transforms import Compose, Normalize, ToTensor
np_dtype = {"float16": np.float16, "float32": np.float32}
-
singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
-transforms = Compose([
- ToTensor(),
- Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-])
+class ClassDataset(object):
+ """Fetch data from file and generate batches.
+ Load data from folder as PIL.Images and convert them into batch array.
-class ClassDataset:
+ Args:
+ img_folder (Str): Folder path of the training/validation images.
+ transforms (Transform): Preprocess transforms.
+ """
def __init__(self, img_folder, transforms):
super(ClassDataset, self).__init__()
@@ -72,6 +66,17 @@ class ClassDataset:
return img, label
def batchgenerator(self, indexes, batch_size, data_size):
+ """Generate batch arrays from transformed image list.
+
+ Args:
+ indexes (Sequence): current batch indexes list, e.g. [n, n + 1, ..., n + batch_size]
+ batch_size (int):
+ data_size (Tuple): input image size of shape (C, H, W)
+
+ Return:
+ batch_x (Numpy ndarray): batch array of input images (B, C, H, W)
+ batch_y (Numpy ndarray): batch array of ground truth lables (B,)
+ """
batch_x = np.zeros((batch_size,) + data_size)
batch_y = np.zeros((batch_size,) + (1,), dtype=np.int32)
for idx, i in enumerate(indexes):
@@ -82,19 +87,6 @@ class ClassDataset:
return batch_x, batch_y
-
-
-class Sequential:
- def __init__(self, *args):
- # super(Sequential, self).__init__()
- self.mod_list = []
- for i in args:
- self.mod_list.append(i)
- def __call__(self, input):
- for module in self.mod_list:
- input = module(input)
- return input
-
class CNNModel(model.Model):
def __init__(self, num_classes):
super(CNNModel, self).__init__()
@@ -102,52 +94,21 @@ class CNNModel(model.Model):
self.dimension = 4
self.num_classes = num_classes
- # self.layer1 = Sequential(
- # layer.Conv2d(16, kernel_size=3),
- # layer.BatchNorm2d(),
- # layer.ReLU())
self.layer1 = layer.Conv2d(16, kernel_size=3, activation="RELU")
self.bn1 = layer.BatchNorm2d()
- # self.layer2 = Sequential(
- # layer.Conv2d(16, kernel_size=3),
- # layer.BatchNorm2d(),
- # layer.ReLU(),
- # layer.MaxPool2d(kernel_size=2, stride=2))
self.layer2 = layer.Conv2d(16, kernel_size=3, activation="RELU")
self.bn2 = layer.BatchNorm2d()
self.pooling2 = layer.MaxPool2d(kernel_size=2, stride=2)
-
- # self.layer3 = Sequential(
- # layer.Conv2d(64, kernel_size=3),
- # layer.BatchNorm2d(),
- # layer.ReLU())
self.layer3 = layer.Conv2d(64, kernel_size=3, activation="RELU")
self.bn3 = layer.BatchNorm2d()
-
- # self.layer4 = Sequential(
- # layer.Conv2d(64, kernel_size=3),
- # layer.BatchNorm2d(),
- # layer.ReLU())
self.layer4 = layer.Conv2d(64, kernel_size=3, activation="RELU")
self.bn4 = layer.BatchNorm2d()
-
- # self.layer5 = Sequential(
- # layer.Conv2d(64, kernel_size=3, padding=1),
- # layer.BatchNorm2d(),
- # layer.ReLU(),
- # layer.MaxPool2d(kernel_size=2, stride=2))
self.layer5 = layer.Conv2d(64, kernel_size=3, padding=1, activation="RELU")
self.bn5 = layer.BatchNorm2d()
self.pooling5 = layer.MaxPool2d(kernel_size=2, stride=2)
self.flatten = layer.Flatten()
- # self.fc = Sequential(
- # layer.Linear(128),
- # layer.ReLU(),
- # layer.Linear(128),
- # layer.ReLU(),
- # layer.Linear(num_classes))
self.linear1 = layer.Linear(128)
self.linear2 = layer.Linear(128)
self.linear3 = layer.Linear(self.num_classes)
@@ -177,7 +138,6 @@ class CNNModel(model.Model):
x = self.linear2(x)
x = self.relu(x)
x = self.linear3(x)
- # x = self.fc(x)
return x
def set_optimizer(self, optimizer):
@@ -204,29 +164,30 @@ class CNNModel(model.Model):
return out, loss
-def getACC(y_true, y_score):
- '''Accuracy metric.
- :param y_true: the ground truth labels, shape: (n_samples, n_labels) or (n_samples,) if n_labels==1
- :param y_score: the predicted score of each class,
- shape: (n_samples, n_labels) or (n_samples, n_classes) or (n_samples,) if n_labels==1 or n_classes==1
- :param task: the task of current dataset
- :param threshold: the threshold for multilabel and binary-class tasks
- '''
- y_true = y_true.squeeze()
- y_score = y_score.squeeze()
+def accuracy(pred, target):
+ """Compute recall accuracy.
-
- ret = accuracy_score(y_true, np.argmax(y_score, axis=-1))
+ Args:
+ pred (Numpy ndarray): Prediction array, should be in shape (B, C)
+ target (Numpy ndarray): Ground truth array, should be in shape (B, )
- return ret
-def accuracy(pred, target):
+ Return:
+ correct (Float): Recall accuracy
+ """
# y is network output to be compared with ground truth (int)
y = np.argmax(pred, axis=1)
a = (y[:,None]==target).sum()
correct = np.array(a, "int").sum()
return correct
-# %% dataset configuration
+
+# define pre-processing methods (transforms)
+transforms = Compose([
+ ToTensor(),
+ Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+# dataset loading
dataset_path = "./bloodmnist"
train_path = os.path.join(dataset_path, "train")
val_path = os.path.join(dataset_path, "val")
@@ -240,13 +201,12 @@ val_dataset = ClassDataset(val_path, transforms)
batch_size = 256
-# %% model configuration
+# model configuration
model = CNNModel(num_classes=num_class)
criterion = layer.SoftMaxCrossEntropy()
optimizer_ft = opt.Adam(lr=1e-3)
-# optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=lr)
-# %% start training
+# start training
dev = device.create_cpu_device()
dev.SetRandSeed(0)
np.random.seed(0)
@@ -266,41 +226,35 @@ dev.SetVerbosity(0)
max_epoch = 100
for epoch in range(max_epoch):
- print('start**************************************************')
+ print(f'Epoch {epoch}:')
start_time = time.time()
- # np.random.shuffle(idx)
train_correct = np.zeros(shape=[1], dtype=np.float32)
test_correct = np.zeros(shape=[1], dtype=np.float32)
train_loss = np.zeros(shape=[1], dtype=np.float32)
+ # training part
model.train()
for b in tqdm(range(num_train_batch)):
- # forward + backward + optimize
-
+ # extract batch from image list
x, y = train_dataset.batchgenerator(idx[b * batch_size:(b + 1) * batch_size],
batch_size=batch_size, data_size=(3, model.input_size, model.input_size))
x = x.astype(np_dtype['float32'])
- # print(tx.size())
- # print(y.dtype)
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
out, loss = model(tx, ty, dist_option="plain", spars=None)
- # print(out)
- # print(loss)
- # train_correct += getACC(y, tensor.to_numpy(out))
train_correct += accuracy(tensor.to_numpy(out), y)
train_loss += tensor.to_numpy(loss)[0]
print('Training loss = %f, training accuracy = %f' %
(train_loss, train_correct /
(num_train_batch * batch_size)))
- model.eval()
+ # validation part
+ model.eval()
for b in tqdm(range(num_val_batch)):
- # forward + backward + optimize
x, y = train_dataset.batchgenerator(idx[b * batch_size:(b + 1) * batch_size],
batch_size=batch_size, data_size=(3, model.input_size, model.input_size))
x = x.astype(np_dtype['float32'])
@@ -309,8 +263,6 @@ for epoch in range(max_epoch):
ty.copy_from_numpy(y)
out = model(tx)
-
- # test_correct += getACC(y, tensor.to_numpy(out))
test_correct += accuracy(tensor.to_numpy(out), y)
print('Evaluation accuracy = %f, Elapsed Time = %fs' %