334 KiB
334 KiB
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)
def getFile_from_drive( file_id, name ):
downloaded = drive.CreateFile({'id': file_id})
downloaded.GetContentFile(name)
getFile_from_drive('1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86', 'fairface-img-margin025-trainval.zip')
getFile_from_drive('1k5vvyREmHDW5TSM9QgB04Bvc8C8_7dl-', 'fairface-label-train.csv')
getFile_from_drive('1_rtz1M1zhvS0d5vVoXUamnohB6cJ02iJ', 'fairface-label-val.csv')
!unzip -qq fairface-img-margin025-trainval.zip!pip install torch_snippets
from torch_snippets import *
trn_df = pd.read_csv('fairface-label-train.csv')
val_df = pd.read_csv('fairface-label-val.csv')
trn_df.head()Collecting torch_snippets Downloading https://files.pythonhosted.org/packages/e5/57/7d513a66ffc00d1495c8a8eeac8754b42233d8a68aa565077db8939b0452/torch_snippets-0.235-py3-none-any.whl Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (3.2.2) Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (1.1.4) Collecting loguru [?25l Downloading https://files.pythonhosted.org/packages/6d/48/0a7d5847e3de329f1d0134baf707b689700b53bd3066a5a8cfd94b3c9fc8/loguru-0.5.3-py3-none-any.whl (57kB) [K |████████████████████████████████| 61kB 4.3MB/s [?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (1.18.5) Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (4.41.1) Collecting opencv-python-headless [?25l Downloading https://files.pythonhosted.org/packages/08/e9/57d869561389884136be65a2d1bc038fe50171e2ba348fda269a4aab8032/opencv_python_headless-4.4.0.46-cp36-cp36m-manylinux2014_x86_64.whl (36.7MB) [K |████████████████████████████████| 36.7MB 84kB/s [?25hRequirement already satisfied: dill in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (0.3.3) Requirement already satisfied: Pillow in /usr/local/lib/python3.6/dist-packages (from torch_snippets) (7.0.0) Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch_snippets) (2.8.1) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch_snippets) (0.10.0) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch_snippets) (1.3.1) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch_snippets) (2.4.7) Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->torch_snippets) (2018.9) Collecting aiocontextvars>=0.2.0; python_version < "3.7" Downloading https://files.pythonhosted.org/packages/db/c1/7a723e8d988de0a2e623927396e54b6831b68cb80dce468c945b849a9385/aiocontextvars-0.2.2-py2.py3-none-any.whl Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->torch_snippets) (1.15.0) Collecting contextvars==2.4; python_version < "3.7" Downloading https://files.pythonhosted.org/packages/83/96/55b82d9f13763be9d672622e1b8106c85acb83edd7cc2fa5bc67cd9877e9/contextvars-2.4.tar.gz Collecting immutables>=0.9 [?25l Downloading https://files.pythonhosted.org/packages/99/e0/ea6fd4697120327d26773b5a84853f897a68e33d3f9376b00a8ff96e4f63/immutables-0.14-cp36-cp36m-manylinux1_x86_64.whl (98kB) [K |████████████████████████████████| 102kB 13.5MB/s [?25hBuilding wheels for collected packages: contextvars Building wheel for contextvars (setup.py) ... [?25l[?25hdone Created wheel for contextvars: filename=contextvars-2.4-cp36-none-any.whl size=7666 sha256=8739abbc29c7cccf1228c26f5f6c1f8f85fd05588ecd8af21af7ef90e86b8876 Stored in directory: /root/.cache/pip/wheels/a5/7d/68/1ebae2668bda2228686e3c1cf16f2c2384cea6e9334ad5f6de Successfully built contextvars Installing collected packages: immutables, contextvars, aiocontextvars, loguru, opencv-python-headless, torch-snippets Successfully installed aiocontextvars-0.2.2 contextvars-2.4 immutables-0.14 loguru-0.5.3 opencv-python-headless-4.4.0.46 torch-snippets-0.235
| file | age | gender | race | service_test | |
|---|---|---|---|---|---|
| 0 | train/1.jpg | 59 | Male | East Asian | True |
| 1 | train/2.jpg | 39 | Female | Indian | False |
| 2 | train/3.jpg | 11 | Female | Black | False |
| 3 | train/4.jpg | 26 | Female | Indian | True |
| 4 | train/5.jpg | 26 | Female | Indian | True |
IMAGE_SIZE = 224
class GenderAgeClass(Dataset):
def __init__(self, df, tfms=None):
self.df = df
self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
def __len__(self): return len(self.df)
def __getitem__(self, ix):
f = self.df.iloc[ix].squeeze()
file = f.file
gen = f.gender == 'Female'
age = f.age
im = read(file, 1)
return im, age, gen
def preprocess_image(self, im):
im = resize(im, IMAGE_SIZE)
im = torch.tensor(im).permute(2,0,1)
im = self.normalize(im/255.)
return im[None]
def collate_fn(self, batch):
'preprocess images, ages and genders'
ims, ages, genders = [], [], []
for im, age, gender in batch:
im = self.preprocess_image(im)
ims.append(im)
ages.append(float(int(age)/80))
genders.append(float(gender))
ages, genders = [torch.tensor(x).to(device).float() for x in [ages, genders]]
ims = torch.cat(ims).to(device)
return ims, ages, genderstrn = GenderAgeClass(trn_df)
val = GenderAgeClass(val_df)
im, gen, age = trn[0]
show(im, title=f'Gender: {gen}\nAge: {age}', sz=5)train_loader = DataLoader(trn, batch_size=32, shuffle=True, drop_last=True, collate_fn=trn.collate_fn)
test_loader = DataLoader(val, batch_size=32, collate_fn=val.collate_fn)
ims, gens, ages = next(iter(train_loader))
inspect(ims, gens, ages)================================================================== Tensor Shape: torch.Size([32, 3, 224, 224]) Min: -2.118 Max: 2.640 Mean: -0.403 dtype: torch.float32 ================================================================== Tensor Shape: torch.Size([32]) Min: 0.000 Max: 0.762 Mean: 0.345 dtype: torch.float32 ================================================================== Tensor Shape: torch.Size([32]) Min: 0.000 Max: 1.000 Mean: 0.438 dtype: torch.float32 ==================================================================
from torchvision import models
def get_model():
model = models.vgg16(pretrained = True)
# Freeze parameters so we don't backprop through them
for param in model.parameters():
param.requires_grad = False
model.avgpool = nn.Sequential(
nn.Conv2d(512,512, kernel_size=3),
nn.MaxPool2d(2),
nn.ReLU(),
nn.Flatten()
)
class ageGenderClassifier(nn.Module):
def __init__(self):
super(ageGenderClassifier, self).__init__()
self.intermediate = nn.Sequential(
nn.Linear(2048,512),
nn.ReLU(),
nn.Dropout(0.4),
nn.Linear(512,128),
nn.ReLU(),
nn.Dropout(0.4),
nn.Linear(128,64),
nn.ReLU(),
)
self.age_classifier = nn.Sequential(
nn.Linear(64, 1),
nn.Sigmoid()
)
self.gender_classifier = nn.Sequential(
nn.Linear(64, 1),
nn.Sigmoid()
)
def forward(self, x):
x = self.intermediate(x)
age = self.age_classifier(x)
gender = self.gender_classifier(x)
return gender, age
model.classifier = ageGenderClassifier()
gender_criterion = nn.BCELoss()
age_criterion = nn.L1Loss()
loss_functions = gender_criterion, age_criterion
optimizer = torch.optim.Adam(model.parameters(), lr= 1e-4)
return model.to(device), loss_functions, optimizer
model, loss_functions, optimizer = get_model()Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth
HBox(children=(FloatProgress(value=0.0, max=553433881.0), HTML(value='')))
!pip install torchsummary
from torchsummary import summary
summary(model, input_size=(3,224,224), device=device)Requirement already satisfied: torchsummary in /usr/local/lib/python3.6/dist-packages (1.5.1)
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 224, 224] 1,792
ReLU-2 [-1, 64, 224, 224] 0
Conv2d-3 [-1, 64, 224, 224] 36,928
ReLU-4 [-1, 64, 224, 224] 0
MaxPool2d-5 [-1, 64, 112, 112] 0
Conv2d-6 [-1, 128, 112, 112] 73,856
ReLU-7 [-1, 128, 112, 112] 0
Conv2d-8 [-1, 128, 112, 112] 147,584
ReLU-9 [-1, 128, 112, 112] 0
MaxPool2d-10 [-1, 128, 56, 56] 0
Conv2d-11 [-1, 256, 56, 56] 295,168
ReLU-12 [-1, 256, 56, 56] 0
Conv2d-13 [-1, 256, 56, 56] 590,080
ReLU-14 [-1, 256, 56, 56] 0
Conv2d-15 [-1, 256, 56, 56] 590,080
ReLU-16 [-1, 256, 56, 56] 0
MaxPool2d-17 [-1, 256, 28, 28] 0
Conv2d-18 [-1, 512, 28, 28] 1,180,160
ReLU-19 [-1, 512, 28, 28] 0
Conv2d-20 [-1, 512, 28, 28] 2,359,808
ReLU-21 [-1, 512, 28, 28] 0
Conv2d-22 [-1, 512, 28, 28] 2,359,808
ReLU-23 [-1, 512, 28, 28] 0
MaxPool2d-24 [-1, 512, 14, 14] 0
Conv2d-25 [-1, 512, 14, 14] 2,359,808
ReLU-26 [-1, 512, 14, 14] 0
Conv2d-27 [-1, 512, 14, 14] 2,359,808
ReLU-28 [-1, 512, 14, 14] 0
Conv2d-29 [-1, 512, 14, 14] 2,359,808
ReLU-30 [-1, 512, 14, 14] 0
MaxPool2d-31 [-1, 512, 7, 7] 0
Conv2d-32 [-1, 512, 5, 5] 2,359,808
MaxPool2d-33 [-1, 512, 2, 2] 0
ReLU-34 [-1, 512, 2, 2] 0
Flatten-35 [-1, 2048] 0
Linear-36 [-1, 512] 1,049,088
ReLU-37 [-1, 512] 0
Dropout-38 [-1, 512] 0
Linear-39 [-1, 128] 65,664
ReLU-40 [-1, 128] 0
Dropout-41 [-1, 128] 0
Linear-42 [-1, 64] 8,256
ReLU-43 [-1, 64] 0
Linear-44 [-1, 1] 65
Sigmoid-45 [-1, 1] 0
Linear-46 [-1, 1] 65
Sigmoid-47 [-1, 1] 0
ageGenderClassifier-48 [[-1, 1], [-1, 1]] 0
================================================================
Total params: 18,197,634
Trainable params: 3,482,946
Non-trainable params: 14,714,688
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 218.55
Params size (MB): 69.42
Estimated Total Size (MB): 288.55
----------------------------------------------------------------
def train_batch(data, model, optimizer, criteria):
model.train()
ims, age, gender = data
optimizer.zero_grad()
pred_gender, pred_age = model(ims)
gender_criterion, age_criterion = criteria
gender_loss = gender_criterion(pred_gender.squeeze(), gender)
age_loss = age_criterion(pred_age.squeeze(), age)
total_loss = gender_loss + age_loss
total_loss.backward()
optimizer.step()
return total_loss
def validate_batch(data, model, criteria):
model.eval()
ims, age, gender = data
with torch.no_grad():
pred_gender, pred_age = model(ims)
gender_criterion, age_criterion = criteria
gender_loss = gender_criterion(pred_gender.squeeze(), gender)
age_loss = age_criterion(pred_age.squeeze(), age)
total_loss = gender_loss + age_loss
pred_gender = (pred_gender > 0.5).squeeze()
gender_acc = (pred_gender == gender).float().sum()
age_mae = torch.abs(age - pred_age).float().sum()
return total_loss, gender_acc, age_maemodel, criterion, optimizer = get_model()
n_epochs = 5
log = Report(n_epochs)
for epoch in range(n_epochs):
N = len(train_loader)
for ix, data in enumerate(train_loader):
if ix==N: break
total_loss = train_batch(data, model, optimizer, criterion)
log.record(epoch+(ix+1)/N, trn_loss=total_loss, end='\r')
N = len(test_loader)
for ix, data in enumerate(test_loader):
if ix==N: break
total_loss, gender_acc, age_mae = validate_batch(data, model, criterion)
gender_acc /= len(data[0])
age_mae /= len(data[0])
log.record(epoch+(ix+1)/N, val_loss=total_loss, val_gender_acc=gender_acc, val_age_mae=age_mae, end='\r')
log.report_avgs(epoch+1)
log.plot_epochs(['trn_loss','val_loss'])EPOCH: 1.000 trn_loss: 0.551 val_loss: 0.465 val_gender_acc: 0.834 val_age_mae: 6.238 (779.37s - 3117.47s remaining) EPOCH: 2.000 trn_loss: 0.401 val_loss: 0.444 val_gender_acc: 0.847 val_age_mae: 6.229 (1555.70s - 2333.56s remaining) EPOCH: 3.000 trn_loss: 0.284 val_loss: 0.493 val_gender_acc: 0.846 val_age_mae: 6.340 (2335.09s - 1556.73s remaining) EPOCH: 4.000 trn_loss: 0.198 val_loss: 0.655 val_gender_acc: 0.842 val_age_mae: 6.339 (3110.83s - 777.71s remaining) EPOCH: 4.994 val_loss: 0.375 val_gender_acc: 0.969 val_age_mae: 6.541 (3887.91s - 4.54s remaining)
0%| | 0/6 [00:00<?, ?it/s]/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice. out=out, **kwargs) /usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) 100%|██████████| 6/6 [00:00<00:00, 291.40it/s]
EPOCH: 4.997 val_loss: 1.030 val_gender_acc: 0.844 val_age_mae: 7.533 (3888.16s - 2.27s remaining) EPOCH: 5.000 val_loss: 0.171 val_gender_acc: 0.900 val_age_mae: 1.977 (3888.25s - 0.00s remaining) EPOCH: 5.000 trn_loss: 0.157 val_loss: 0.733 val_gender_acc: 0.847 val_age_mae: 6.322 (3888.25s - 0.00s remaining)
!wget https://www.dropbox.com/s/6kzr8l68e9kpjkf/5_9.JPG
im = cv2.imread('/content/5_9.JPG')
im = trn.preprocess_image(im).to(device)
gender, age = model(im)
pred_gender = gender.to('cpu').detach().numpy()
pred_age = age.to('cpu').detach().numpy()
im = cv2.imread('/content/5_9.JPG')
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
plt.imshow(im)
print('predicted gender:',np.where(pred_gender[0][0]<0.5,'Male','Female'), '; Predicted age', int(pred_age[0][0]*80))--2020-11-08 12:56:14-- https://www.dropbox.com/s/6kzr8l68e9kpjkf/5_9.JPG Resolving www.dropbox.com (www.dropbox.com)... 162.125.1.1, 2620:100:6016:1::a27d:101 Connecting to www.dropbox.com (www.dropbox.com)|162.125.1.1|:443... connected. HTTP request sent, awaiting response... 301 Moved Permanently Location: /s/raw/6kzr8l68e9kpjkf/5_9.JPG [following] --2020-11-08 12:56:14-- https://www.dropbox.com/s/raw/6kzr8l68e9kpjkf/5_9.JPG Reusing existing connection to www.dropbox.com:443. HTTP request sent, awaiting response... 302 Found Location: https://uc177111820f4b375b4969ae95e7.dl.dropboxusercontent.com/cd/0/inline/BCx_9g_kyOTpcIFyCAe6R019q6zv2sreGAJzOnco0RygfRp0a253M-JC2VhM9pBdc9xj107QBfquDj3PkN8_exIM5Qnz4eSpipop6_K1f5IM8IC-5z2-zxEmkQUiSQRE-XM/file# [following] --2020-11-08 12:56:14-- https://uc177111820f4b375b4969ae95e7.dl.dropboxusercontent.com/cd/0/inline/BCx_9g_kyOTpcIFyCAe6R019q6zv2sreGAJzOnco0RygfRp0a253M-JC2VhM9pBdc9xj107QBfquDj3PkN8_exIM5Qnz4eSpipop6_K1f5IM8IC-5z2-zxEmkQUiSQRE-XM/file Resolving uc177111820f4b375b4969ae95e7.dl.dropboxusercontent.com (uc177111820f4b375b4969ae95e7.dl.dropboxusercontent.com)... 162.125.1.15, 2620:100:6016:15::a27d:10f Connecting to uc177111820f4b375b4969ae95e7.dl.dropboxusercontent.com (uc177111820f4b375b4969ae95e7.dl.dropboxusercontent.com)|162.125.1.15|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 46983 (46K) [image/jpeg] Saving to: ‘5_9.JPG’ 5_9.JPG 100%[===================>] 45.88K --.-KB/s in 0.01s 2020-11-08 12:56:15 (3.59 MB/s) - ‘5_9.JPG’ saved [46983/46983] predicted gender: Female ; Predicted age 25