使用 PyTorch 进行迁移学习 [resnet18]。数据集：狗品种识别
transfer learning [resnet18] using PyTorch. Dataset: Dog-Breed-Identification
我正在尝试在 PyTorch 中实现迁移学习方法。这是我正在使用的数据集：Dog-Breed
这是我要执行的步骤。
1. Load the data and read csv using pandas.
2. Resize (60, 60) the train images and store them as numpy array.
3. Apply stratification and split the train data into 7:1:2 (train:validation:test)
4. use the resnet18 model and train.
数据集的位置
LABELS_LOCATION = './dataset/labels.csv'
TRAIN_LOCATION = './dataset/train/'
TEST_LOCATION = './dataset/test/'
ROOT_PATH = './dataset/'
正在读取 CSV (labels.csv)
def read_csv(csvf):
# print(pandas.read_csv(csvf).values)
data=pandas.read_csv(csvf).values
labels_dict = dict(data)
idz=list(labels_dict.keys())
clazz=list(labels_dict.values())
return labels_dict,idz,clazz
我这样做是因为一个限制，我将在接下来使用 DataLoader 加载数据时提到这个限制。
def class_hashmap(class_arr):
uniq_clazz = Counter(class_arr)
class_dict = {}
for i, j in enumerate(uniq_clazz):
class_dict[j] = i
return class_dict
labels, ids, class_names = read_csv(LABELS_LOCATION)
train_images = os.listdir(TRAIN_LOCATION)
class_numbers = class_hashmap(class_names)
接下来，我使用 opencv 将图像大小调整为 60,60，并将结果存储为 numpy 数组。
resize = []
indexed_labels = []
for t_i in train_images:
# resize.append(transform.resize(io.imread(TRAIN_LOCATION+t_i), (60, 60, 3))) # (60,60) is the height and widht; 3 is the number of channels
resize.append(cv2.resize(cv2.imread(TRAIN_LOCATION+t_i), (60, 60)).reshape(3, 60, 60))
indexed_labels.append(class_numbers[labels[t_i.split('.')[0]]])
resize = np.asarray(resize)
print(resize.shape)
在indexed_labels这里，我给每个标签一个编号。
接下来，我将数据拆分为7:1:2部分
X = resize # numpy array of images [training data]
y = np.array(indexed_labels) # indexed labels for images [training labels]
sss = StratifiedShuffleSplit(n_splits=3, test_size=0.2, random_state=0)
sss.get_n_splits(X, y)
for train_index, test_index in sss.split(X, y):
X_temp, X_test = X[train_index], X[test_index] # split train into train and test [data]
y_temp, y_test = y[train_index], y[test_index] # labels
sss = StratifiedShuffleSplit(n_splits=3, test_size=0.123, random_state=0)
sss.get_n_splits(X_temp, y_temp)
for train_index, test_index in sss.split(X_temp, y_temp):
print("TRAIN:", train_index, "VAL:", test_index)
X_train, X_val = X[train_index], X[test_index] # training and validation data
y_train, y_val = y[train_index], y[test_index] # training and validation labels
接下来，我将上一步的数据加载到 torch DataLoaders
batch_size = 500
learning_rate = 0.001
train = torch.utils.data.TensorDataset(torch.from_numpy(X_train), torch.from_numpy(y_train))
train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=False)
val = torch.utils.data.TensorDataset(torch.from_numpy(X_val), torch.from_numpy(y_val))
val_loader = torch.utils.data.DataLoader(val, batch_size=batch_size, shuffle=False)
test = torch.utils.data.TensorDataset(torch.from_numpy(X_test), torch.from_numpy(y_test))
test_loader = torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=False)
# print(train_loader.size)
dataloaders = {
'train': train_loader,
'val': val_loader
}
接下来，我加载预训练的 rensnet 模型。
model_ft = models.resnet18(pretrained=True)
# freeze all model parameters
# for param in model_ft.parameters():
# param.requires_grad = False
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, len(class_numbers))
if use_gpu:
model_ft = model_ft.cuda()
model_ft.fc = model_ft.fc.cuda()
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.fc.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25)
然后我使用 train_model，一种在 PyTorch 文档中描述的方法 here。
但是，当我 运行 这样做时，我得到了一个错误。
Traceback (most recent call last):
File "/Users/nirvair/Sites/pyTorch/TL.py",
line 244, in <module>
num_epochs=25)
File "/Users/nirvair/Sites/pyTorch/TL.py", line 176, in train_model
outputs = model(inputs)
File "/Library/Python/2.7/site-packages/torch/nn/modules/module.py", line 224, in __call__
result = self.forward(*input, **kwargs)
File "/Library/Python/2.7/site-packages/torchvision/models/resnet.py", line 149, in forward
x = self.avgpool(x)
File "/Library/Python/2.7/site-packages/torch/nn/modules/module.py", line 224, in __call__
result = self.forward(*input, **kwargs)
File "/Library/Python/2.7/site-packages/torch/nn/modules/pooling.py", line 505, in forward
self.padding, self.ceil_mode, self.count_include_pad)
File "/Library/Python/2.7/site-packages/torch/nn/functional.py", line 264, in avg_pool2d
ceil_mode, count_include_pad)
File "/Library/Python/2.7/site-packages/torch/nn/_functions/thnn/pooling.py", line 360, in forward
ctx.ceil_mode, ctx.count_include_pad)
RuntimeError: Given input size: (512x2x2). Calculated output size: (512x0x0). Output size is too small at /Users/soumith/code/builder/wheel/pytorch-src/torch/lib/THNN/generic/SpatialAveragePooling.c:64
我似乎无法弄清楚这里出了什么问题。
对于您正在使用的图像大小 (60x60)，您的网络太深了。如您所知，随着输入图像在层中传播，CNN 层确实会产生越来越小的特征图。这是因为您没有使用填充。
您的错误只是说下一层需要 512 个大小为 2 x 2 像素的特征图。前向传递产生的实际特征图是 512 个大小为 0x0 的图。这种不匹配是触发错误的原因。
通常，所有股票网络，例如 RESNET-18、Inception 等，都要求输入图像的大小为 224x224（至少）。您可以使用 torchvision transforms[1] 更轻松地完成此操作。您还可以使用更大的图像尺寸，但 AlexNet 有一个例外，它具有硬编码的特征向量大小，如我在 [2] 中的回答所述。
额外提示：如果您在预维护模式下使用网络，则需要使用 [3] 处的 pytorch 文档中的参数对数据进行白化。
链接
我正在尝试在 PyTorch 中实现迁移学习方法。这是我正在使用的数据集：Dog-Breed
这是我要执行的步骤。
1. Load the data and read csv using pandas.
2. Resize (60, 60) the train images and store them as numpy array.
3. Apply stratification and split the train data into 7:1:2 (train:validation:test)
4. use the resnet18 model and train.
数据集的位置
LABELS_LOCATION = './dataset/labels.csv'
TRAIN_LOCATION = './dataset/train/'
TEST_LOCATION = './dataset/test/'
ROOT_PATH = './dataset/'
正在读取 CSV (labels.csv)
def read_csv(csvf):
# print(pandas.read_csv(csvf).values)
data=pandas.read_csv(csvf).values
labels_dict = dict(data)
idz=list(labels_dict.keys())
clazz=list(labels_dict.values())
return labels_dict,idz,clazz
我这样做是因为一个限制，我将在接下来使用 DataLoader 加载数据时提到这个限制。
def class_hashmap(class_arr):
uniq_clazz = Counter(class_arr)
class_dict = {}
for i, j in enumerate(uniq_clazz):
class_dict[j] = i
return class_dict
labels, ids, class_names = read_csv(LABELS_LOCATION)
train_images = os.listdir(TRAIN_LOCATION)
class_numbers = class_hashmap(class_names)
接下来，我使用 opencv 将图像大小调整为 60,60，并将结果存储为 numpy 数组。
resize = []
indexed_labels = []
for t_i in train_images:
# resize.append(transform.resize(io.imread(TRAIN_LOCATION+t_i), (60, 60, 3))) # (60,60) is the height and widht; 3 is the number of channels
resize.append(cv2.resize(cv2.imread(TRAIN_LOCATION+t_i), (60, 60)).reshape(3, 60, 60))
indexed_labels.append(class_numbers[labels[t_i.split('.')[0]]])
resize = np.asarray(resize)
print(resize.shape)
在indexed_labels这里，我给每个标签一个编号。
接下来，我将数据拆分为7:1:2部分
X = resize # numpy array of images [training data]
y = np.array(indexed_labels) # indexed labels for images [training labels]
sss = StratifiedShuffleSplit(n_splits=3, test_size=0.2, random_state=0)
sss.get_n_splits(X, y)
for train_index, test_index in sss.split(X, y):
X_temp, X_test = X[train_index], X[test_index] # split train into train and test [data]
y_temp, y_test = y[train_index], y[test_index] # labels
sss = StratifiedShuffleSplit(n_splits=3, test_size=0.123, random_state=0)
sss.get_n_splits(X_temp, y_temp)
for train_index, test_index in sss.split(X_temp, y_temp):
print("TRAIN:", train_index, "VAL:", test_index)
X_train, X_val = X[train_index], X[test_index] # training and validation data
y_train, y_val = y[train_index], y[test_index] # training and validation labels
接下来，我将上一步的数据加载到 torch DataLoaders
batch_size = 500
learning_rate = 0.001
train = torch.utils.data.TensorDataset(torch.from_numpy(X_train), torch.from_numpy(y_train))
train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=False)
val = torch.utils.data.TensorDataset(torch.from_numpy(X_val), torch.from_numpy(y_val))
val_loader = torch.utils.data.DataLoader(val, batch_size=batch_size, shuffle=False)
test = torch.utils.data.TensorDataset(torch.from_numpy(X_test), torch.from_numpy(y_test))
test_loader = torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=False)
# print(train_loader.size)
dataloaders = {
'train': train_loader,
'val': val_loader
}
接下来，我加载预训练的 rensnet 模型。
model_ft = models.resnet18(pretrained=True)
# freeze all model parameters
# for param in model_ft.parameters():
# param.requires_grad = False
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, len(class_numbers))
if use_gpu:
model_ft = model_ft.cuda()
model_ft.fc = model_ft.fc.cuda()
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.fc.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25)
然后我使用 train_model，一种在 PyTorch 文档中描述的方法 here。
但是，当我 运行 这样做时，我得到了一个错误。
Traceback (most recent call last):
File "/Users/nirvair/Sites/pyTorch/TL.py",
line 244, in <module>
num_epochs=25)
File "/Users/nirvair/Sites/pyTorch/TL.py", line 176, in train_model
outputs = model(inputs)
File "/Library/Python/2.7/site-packages/torch/nn/modules/module.py", line 224, in __call__
result = self.forward(*input, **kwargs)
File "/Library/Python/2.7/site-packages/torchvision/models/resnet.py", line 149, in forward
x = self.avgpool(x)
File "/Library/Python/2.7/site-packages/torch/nn/modules/module.py", line 224, in __call__
result = self.forward(*input, **kwargs)
File "/Library/Python/2.7/site-packages/torch/nn/modules/pooling.py", line 505, in forward
self.padding, self.ceil_mode, self.count_include_pad)
File "/Library/Python/2.7/site-packages/torch/nn/functional.py", line 264, in avg_pool2d
ceil_mode, count_include_pad)
File "/Library/Python/2.7/site-packages/torch/nn/_functions/thnn/pooling.py", line 360, in forward
ctx.ceil_mode, ctx.count_include_pad)
RuntimeError: Given input size: (512x2x2). Calculated output size: (512x0x0). Output size is too small at /Users/soumith/code/builder/wheel/pytorch-src/torch/lib/THNN/generic/SpatialAveragePooling.c:64
我似乎无法弄清楚这里出了什么问题。
对于您正在使用的图像大小 (60x60)，您的网络太深了。如您所知，随着输入图像在层中传播，CNN 层确实会产生越来越小的特征图。这是因为您没有使用填充。
您的错误只是说下一层需要 512 个大小为 2 x 2 像素的特征图。前向传递产生的实际特征图是 512 个大小为 0x0 的图。这种不匹配是触发错误的原因。
通常，所有股票网络，例如 RESNET-18、Inception 等，都要求输入图像的大小为 224x224（至少）。您可以使用 torchvision transforms[1] 更轻松地完成此操作。您还可以使用更大的图像尺寸，但 AlexNet 有一个例外，它具有硬编码的特征向量大小，如我在 [2] 中的回答所述。
额外提示：如果您在预维护模式下使用网络，则需要使用 [3] 处的 pytorch 文档中的参数对数据进行白化。
链接