examples/demos/Classification/BloodMnist/ClassDemo.py - singa - Git at Google

 #
 # Licensed to the Apache Software Foundation (ASF) under one
 # or more contributor license agreements.  See the NOTICE file
 # distributed with this work for additional information
 # regarding copyright ownership.  The ASF licenses this file
 # to you under the Apache License, Version 2.0 (the
 # "License"); you may not use this file except in compliance
 # with the License.  You may obtain a copy of the License at
 #
 #   http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing,
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
 #

 import json
 import os
 import time
 from glob import glob

 import numpy as np
 from PIL import Image
 from singa import device, layer, model, opt, tensor
 from tqdm import tqdm

 from transforms import Compose, Normalize, ToTensor

 np_dtype = {"float16": np.float16, "float32": np.float32}
 singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}


 class ClassDataset(object):
     """Fetch data from file and generate batches.

     Load data from folder as PIL.Images and convert them into batch array.

     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__()

         self.img_list = list()
         self.transforms = transforms

         classes = os.listdir(img_folder)
         for i in classes:
             images = glob(os.path.join(img_folder, i, "*"))
             for img in images:
                 self.img_list.append((img, i))

     def __len__(self) -> int:
         return len(self.img_list)

     def __getitem__(self, index: int):
         img_path, label_str = self.img_list[index]
         img = Image.open(img_path)
         img = self.transforms.forward(img)
         label = np.array(label_str, dtype=np.int32)

         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):
             sample_x, sample_y = self.__getitem__(i)
             batch_x[idx, :, :, :] = sample_x
             batch_y[idx, :] = sample_y

         return batch_x, batch_y


 class CNNModel(model.Model):
     def __init__(self, num_classes):
         super(CNNModel, self).__init__()
         self.input_size = 28
         self.dimension = 4
         self.num_classes = num_classes

         self.layer1 = layer.Conv2d(16, kernel_size=3, activation="RELU")
         self.bn1 = layer.BatchNorm2d()
         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 = layer.Conv2d(64, kernel_size=3, activation="RELU")
         self.bn3 = layer.BatchNorm2d()
         self.layer4 = layer.Conv2d(64, kernel_size=3, activation="RELU")
         self.bn4 = layer.BatchNorm2d()
         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.linear1 = layer.Linear(128)
         self.linear2 = layer.Linear(128)
         self.linear3 = layer.Linear(self.num_classes)

         self.relu = layer.ReLU()

         self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
         self.dropout = layer.Dropout(ratio=0.3)

     def forward(self, x):
         x = self.layer1(x)
         x = self.bn1(x)
         x = self.layer2(x)
         x = self.bn2(x)
         x = self.pooling2(x)

         x = self.layer3(x)
         x = self.bn3(x)
         x = self.layer4(x)
         x = self.bn4(x)
         x = self.layer5(x)
         x = self.bn5(x)
         x = self.pooling5(x)
         x = self.flatten(x)
         x = self.linear1(x)
         x = self.relu(x)
         x = self.linear2(x)
         x = self.relu(x)
         x = self.linear3(x)
         return x

     def set_optimizer(self, optimizer):
         self.optimizer = optimizer

     def train_one_batch(self, x, y, dist_option, spars):
         out = self.forward(x)
         loss = self.softmax_cross_entropy(out, y)

         if dist_option == 'plain':
             self.optimizer(loss)
         elif dist_option == 'half':
             self.optimizer.backward_and_update_half(loss)
         elif dist_option == 'partialUpdate':
             self.optimizer.backward_and_partial_update(loss)
         elif dist_option == 'sparseTopK':
             self.optimizer.backward_and_sparse_update(loss,
                                                       topK=True,
                                                       spars=spars)
         elif dist_option == 'sparseThreshold':
             self.optimizer.backward_and_sparse_update(loss,
                                                       topK=False,
                                                       spars=spars)
         return out, loss


 def accuracy(pred, target):
     """Compute recall accuracy.

     Args:
         pred (Numpy ndarray): Prediction array, should be in shape (B, C)
         target (Numpy ndarray): Ground truth array, should be in shape (B, )

     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


 # 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")
 cfg_path = os.path.join(dataset_path, "param.json")

 with open(cfg_path,'r') as load_f:
     num_class = json.load(load_f)["num_classes"]

 train_dataset = ClassDataset(train_path, transforms)
 val_dataset = ClassDataset(val_path, transforms)

 batch_size = 256

 # Model configuration for CNN
 model = CNNModel(num_classes=num_class)
 criterion = layer.SoftMaxCrossEntropy()
 optimizer_ft = opt.Adam(lr=1e-3)

 # Start training
 dev = device.create_cpu_device()
 dev.SetRandSeed(0)
 np.random.seed(0)

 tx = tensor.Tensor(
         (batch_size, 3, model.input_size, model.input_size), dev,
         singa_dtype['float32'])
 ty = tensor.Tensor((batch_size,), dev, tensor.int32)

 num_train_batch = train_dataset.__len__() // batch_size
 num_val_batch = val_dataset.__len__() // batch_size
 idx = np.arange(train_dataset.__len__(), dtype=np.int32)

 model.set_optimizer(optimizer_ft)
 model.compile([tx], is_train=True, use_graph=False, sequential=False)
 dev.SetVerbosity(0)

 max_epoch = 100
 for epoch in range(max_epoch):
     print(f'Epoch {epoch}:')

     start_time = time.time()

     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)):
         # 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'])

         tx.copy_from_numpy(x)
         ty.copy_from_numpy(y)

         out, loss = model(tx, ty, dist_option="plain", spars=None)
         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)))

     # Validation part
     model.eval()
     for b in tqdm(range(num_val_batch)):
         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'])

         tx.copy_from_numpy(x)
         ty.copy_from_numpy(y)

         out = model(tx)
         test_correct += accuracy(tensor.to_numpy(out), y)

     print('Evaluation accuracy = %f, Elapsed Time = %fs' %
                   (test_correct / (num_val_batch * batch_size),
                    time.time() - start_time))
	#
	# Licensed to the Apache Software Foundation (ASF) under one
	# or more contributor license agreements. See the NOTICE file
	# distributed with this work for additional information
	# regarding copyright ownership. The ASF licenses this file
	# to you under the Apache License, Version 2.0 (the
	# "License"); you may not use this file except in compliance
	# with the License. You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing,
	# software distributed under the License is distributed on an
	# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
	# KIND, either express or implied. See the License for the
	# specific language governing permissions and limitations
	# under the License.
	#

	import json
	import os
	import time
	from glob import glob

	import numpy as np
	from PIL import Image
	from singa import device, layer, model, opt, tensor
	from tqdm import tqdm

	from transforms import Compose, Normalize, ToTensor

	np_dtype = {"float16": np.float16, "float32": np.float32}
	singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}


	class ClassDataset(object):
	"""Fetch data from file and generate batches.

	Load data from folder as PIL.Images and convert them into batch array.

	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__()

	self.img_list = list()
	self.transforms = transforms

	classes = os.listdir(img_folder)
	for i in classes:
	images = glob(os.path.join(img_folder, i, "*"))
	for img in images:
	self.img_list.append((img, i))

	def __len__(self) -> int:
	return len(self.img_list)

	def __getitem__(self, index: int):
	img_path, label_str = self.img_list[index]
	img = Image.open(img_path)
	img = self.transforms.forward(img)
	label = np.array(label_str, dtype=np.int32)

	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):
	sample_x, sample_y = self.__getitem__(i)
	batch_x[idx, :, :, :] = sample_x
	batch_y[idx, :] = sample_y

	return batch_x, batch_y


	class CNNModel(model.Model):
	def __init__(self, num_classes):
	super(CNNModel, self).__init__()
	self.input_size = 28
	self.dimension = 4
	self.num_classes = num_classes

	self.layer1 = layer.Conv2d(16, kernel_size=3, activation="RELU")
	self.bn1 = layer.BatchNorm2d()
	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 = layer.Conv2d(64, kernel_size=3, activation="RELU")
	self.bn3 = layer.BatchNorm2d()
	self.layer4 = layer.Conv2d(64, kernel_size=3, activation="RELU")
	self.bn4 = layer.BatchNorm2d()
	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.linear1 = layer.Linear(128)
	self.linear2 = layer.Linear(128)
	self.linear3 = layer.Linear(self.num_classes)

	self.relu = layer.ReLU()

	self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
	self.dropout = layer.Dropout(ratio=0.3)

	def forward(self, x):
	x = self.layer1(x)
	x = self.bn1(x)
	x = self.layer2(x)
	x = self.bn2(x)
	x = self.pooling2(x)

	x = self.layer3(x)
	x = self.bn3(x)
	x = self.layer4(x)
	x = self.bn4(x)
	x = self.layer5(x)
	x = self.bn5(x)
	x = self.pooling5(x)
	x = self.flatten(x)
	x = self.linear1(x)
	x = self.relu(x)
	x = self.linear2(x)
	x = self.relu(x)
	x = self.linear3(x)
	return x

	def set_optimizer(self, optimizer):
	self.optimizer = optimizer

	def train_one_batch(self, x, y, dist_option, spars):
	out = self.forward(x)
	loss = self.softmax_cross_entropy(out, y)

	if dist_option == 'plain':
	self.optimizer(loss)
	elif dist_option == 'half':
	self.optimizer.backward_and_update_half(loss)
	elif dist_option == 'partialUpdate':
	self.optimizer.backward_and_partial_update(loss)
	elif dist_option == 'sparseTopK':
	self.optimizer.backward_and_sparse_update(loss,
	topK=True,
	spars=spars)
	elif dist_option == 'sparseThreshold':
	self.optimizer.backward_and_sparse_update(loss,
	topK=False,
	spars=spars)
	return out, loss


	def accuracy(pred, target):
	"""Compute recall accuracy.

	Args:
	pred (Numpy ndarray): Prediction array, should be in shape (B, C)
	target (Numpy ndarray): Ground truth array, should be in shape (B, )

	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


	# 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")
	cfg_path = os.path.join(dataset_path, "param.json")

	with open(cfg_path,'r') as load_f:
	num_class = json.load(load_f)["num_classes"]

	train_dataset = ClassDataset(train_path, transforms)
	val_dataset = ClassDataset(val_path, transforms)

	batch_size = 256

	# Model configuration for CNN
	model = CNNModel(num_classes=num_class)
	criterion = layer.SoftMaxCrossEntropy()
	optimizer_ft = opt.Adam(lr=1e-3)

	# Start training
	dev = device.create_cpu_device()
	dev.SetRandSeed(0)
	np.random.seed(0)

	tx = tensor.Tensor(
	(batch_size, 3, model.input_size, model.input_size), dev,
	singa_dtype['float32'])
	ty = tensor.Tensor((batch_size,), dev, tensor.int32)

	num_train_batch = train_dataset.__len__() // batch_size
	num_val_batch = val_dataset.__len__() // batch_size
	idx = np.arange(train_dataset.__len__(), dtype=np.int32)

	model.set_optimizer(optimizer_ft)
	model.compile([tx], is_train=True, use_graph=False, sequential=False)
	dev.SetVerbosity(0)

	max_epoch = 100
	for epoch in range(max_epoch):
	print(f'Epoch {epoch}:')

	start_time = time.time()

	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)):
	# 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'])

	tx.copy_from_numpy(x)
	ty.copy_from_numpy(y)

	out, loss = model(tx, ty, dist_option="plain", spars=None)
	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)))

	# Validation part
	model.eval()
	for b in tqdm(range(num_val_batch)):
	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'])

	tx.copy_from_numpy(x)
	ty.copy_from_numpy(y)

	out = model(tx)
	test_correct += accuracy(tensor.to_numpy(out), y)

	print('Evaluation accuracy = %f, Elapsed Time = %fs' %
	(test_correct / (num_val_batch * batch_size),
	time.time() - start_time))