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from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
from torch.utils.data import TensorDataset, DataLoader
from PIL import Image
import matplotlib.pyplot as plt
from dataloader import imgDataset
import time
import os
import copy
from transformers import BlipProcessor, BlipForConditionalGeneration
from transformers import AutoImageProcessor, ResNetModel
from translate import Translator
PATH = './images/'
class CUPredictor_v2(nn.Module):
def __init__(self, num_class=2):
super(CUPredictor_v2, self).__init__()
self.base = ResNetModel.from_pretrained("microsoft/resnet-50")
num_ftrs = 2048
#self.base.fc = nn.Linear(num_ftrs, num_ftrs//2)
self.classifier = nn.Linear(num_ftrs, num_class)
self.height_regressor = nn.Linear(num_ftrs, 1)
self.relu = nn.ReLU()
def forward(self, input_img):
output = self.base(input_img['pixel_values'].squeeze(1)).pooler_output.squeeze()
predict_cls = self.classifier(output)
predict_height = self.relu(self.height_regressor(output))
return predict_cls, predict_height
class CUPredictor(nn.Module):
def __init__(self, num_class=2):
super(CUPredictor, self).__init__()
self.base = torchvision.models.resnet50(pretrained=True)
for param in self.base.parameters():
param.requires_grad = False
num_ftrs = self.base.fc.in_features
self.base.fc = nn.Sequential(
nn.Linear(num_ftrs, num_ftrs//4),
nn.ReLU(),
nn.Linear(num_ftrs//4, num_ftrs//8),
nn.ReLU()
)
self.classifier = nn.Linear(num_ftrs//8, num_class)
self.regressor_h = nn.Linear(num_ftrs//8, 1)
self.regressor_b = nn.Linear(num_ftrs//8, 1)
self.regressor_w = nn.Linear(num_ftrs//8, 1)
self.regressor_hi = nn.Linear(num_ftrs//8, 1)
self.relu = nn.ReLU()
def forward(self, input_img):
output = self.base(input_img)
predict_cls = self.classifier(output)
predict_h = self.relu(self.regressor_h(output))
predict_b = self.relu(self.regressor_b(output))
predict_w = self.relu(self.regressor_w(output))
predict_hi = self.relu(self.regressor_hi(output))
return predict_cls, predict_h, predict_b, predict_w, predict_hi
def imshow(inp, title=None):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
plt.savefig(f'images/preds/prediction.png')
def train_model(model, device, dataloaders, dataset_sizes, num_epochs=25):
since = time.time()
ce = nn.CrossEntropyLoss()
mse = nn.MSELoss()
optimizer = optim.AdamW(model.parameters(), lr=0.0008)
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print(f'Epoch {epoch+1}/{num_epochs}')
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_ce_loss = 0.0
running_rmse_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels, heights, bust, waist, hips in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
heights = heights.to(device)
bust = bust.to(device)
waist, hips = waist.to(device), hips.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs_c, outputs_h, outputs_b, outputs_w, outputs_hi = model(inputs)
_, preds = torch.max(outputs_c, 1)
ce_loss = ce(outputs_c, labels)
rmse_loss_h = torch.sqrt(mse(outputs_h, heights.unsqueeze(-1)))
rmse_loss_b = torch.sqrt(mse(outputs_b, bust.unsqueeze(-1)))
rmse_loss_w = torch.sqrt(mse(outputs_w, waist.unsqueeze(-1)))
rmse_loss_hi = torch.sqrt(mse(outputs_hi, hips.unsqueeze(-1)))
rmse_loss = rmse_loss_h*4 + rmse_loss_b*2 + rmse_loss_w + rmse_loss_hi
loss = ce_loss + (rmse_loss)*1
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
# statistics
running_ce_loss += ce_loss.item() * inputs.size(0)
running_rmse_loss += rmse_loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_ce_loss = running_ce_loss / dataset_sizes[phase]
epoch_rmse_loss = running_rmse_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print(f'{phase} CE_Loss: {epoch_ce_loss:.4f} RMSE_Loss: {epoch_rmse_loss:.4f} Acc: {epoch_acc:.4f}')
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
#if epoch %2 == 0 and phase == 'val':print(outputs_c, outputs_h)
print()
time_elapsed = time.time() - since
print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
print(f'Best val Acc: {best_acc:4f}')
# load best model weights
model.load_state_dict(best_model_wts)
return model
def visualize_model(model, device, dataloaders, class_names, num_images=6):
was_training = model.training
model.eval()
images_so_far = 0
fig = plt.figure()
with torch.no_grad():
for i, (inputs, labels) in enumerate(dataloaders['val']):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
for j in range(inputs.size()[0]):
images_so_far += 1
ax = plt.subplot(num_images//2, 2, images_so_far)
ax.axis('off')
ax.set_title(f'pred: {class_names[preds[j]]}|tar: {class_names[labels[j]]}')
imshow(inputs.cpu().data[j])
if images_so_far == num_images:
model.train(mode=was_training)
return
model.train(mode=was_training)
def evaluation(model, epoch, device, dataloaders):
model.load_state_dict(torch.load(f'models/model_{epoch}.pt'))
model.eval()
with torch.no_grad():
for i, (inputs, labels) in enumerate(dataloaders['val']):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
print(preds)
def inference(inp_img, classes = ['big', 'small'], epoch = 6):
device = torch.device("cuda")
translator= Translator(to_lang="zh-TW")
model = CUPredictor()
model.load_state_dict(torch.load(f'models/model_{epoch}.pt'))
# load image-to-text model
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model_blip = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
model.eval()
trans = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
image_tensor = trans(inp_img)
image_tensor = image_tensor.unsqueeze(0)
with torch.no_grad():
inputs = image_tensor
outputs_c, outputs_h, outputs_b, outputs_w, outputs_hi = model(inputs)
_, preds = torch.max(outputs_c, 1)
idx = preds.numpy()[0]
# unconditional image captioning
inputs = processor(inp_img, return_tensors="pt")
out = model_blip.generate(**inputs)
description = processor.decode(out[0], skip_special_tokens=True)
description_tw = translator.translate(description)
return outputs_c, classes[idx], f"{outputs_h.numpy()[0][0]:.2f}", f"{outputs_b.numpy()[0][0]:.2f}", f"{outputs_w.numpy()[0][0]:.2f}", f"{outputs_hi.numpy()[0][0]:.2f}", [description, description_tw]
def main(epoch = 15, mode = 'val'):
cudnn.benchmark = True
plt.ion() # interactive mode
model = CUPredictor()
train_dataset = imgDataset('labels.txt', mode='train', use_processor=False)
test_dataset = imgDataset('labels.txt', mode='val', use_processor=False)
dataloaders = {
"train": DataLoader(train_dataset, batch_size=64, shuffle=True),
"val": DataLoader(test_dataset, batch_size=64, shuffle=False)
}
dataset_sizes = {
"train": len(train_dataset),
"val": len(test_dataset)
}
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#device = torch.device("cpu")
model = model.to(device)
model_conv = train_model(model, device, dataloaders, dataset_sizes, num_epochs=epoch)
torch.save(model_conv.state_dict(), f'models/model_{epoch}.pt')
def divide_class_dir(path):
file_list = os.listdir(path)
for img_name in file_list:
dest_path = os.path.join(path, img_name.split('-')[3])
if not os.path.exists(dest_path):
os.mkdir(dest_path) # 建立資料夾
os.replace(os.path.join(path, img_name), os.path.join(dest_path, img_name))
def get_label(types):
with open('labels.txt', 'w', encoding='utf-8') as f:
for f_type in types:
for img_type in CLASS:
path = os.path.join('images', f_type, img_type)
file_list = os.listdir(path)
for file_name in file_list:
file_name_list = file_name.split('-')
f.write(" ".join([f_type, file_name, img_type, file_name_list[4].split('_')[0], '\n']))
if __name__ == "__main__":
CLASS = ['big', 'small']
mode = 'train'
get_label(['train', 'val'])
epoch = 7
#main(epoch, mode = mode)
outputs, preds, heights, bust, waist, hips, description = inference('images/test/lin.png', CLASS, epoch=epoch)
print(outputs, preds, heights, bust, waist, hips)
#print(CUPredictor())
#divide_class_dir('./images/train_all')
#divide_class_dir('./images/val_all')
'''''' |