#!/usr/bin/env python # coding: utf-8 # Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks. # - Author: Sebastian Raschka # - GitHub Repository: https://github.com/rasbt/deeplearning-models # In[1]: get_ipython().run_line_magic('load_ext', 'watermark') get_ipython().run_line_magic('watermark', "-a 'Sebastian Raschka' -v -p torch") # # ResNet-34 QuickDraw Classifier # ### Network Architecture # The network in this notebook is an implementation of the ResNet-34 [1] architecture on the MNIST digits dataset (http://yann.lecun.com/exdb/mnist/) to train a handwritten digit classifier. # # # References # # - [1] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). ([CVPR Link](https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html)) # # - [2] http://yann.lecun.com/exdb/mnist/ # # ![](../images/resnets/resnet34/resnet34-arch.png) # # The following figure illustrates residual blocks with skip connections such that the input passed via the shortcut matches the dimensions of the main path's output, which allows the network to learn identity functions. # # ![](../images/resnets/resnet-ex-1-1.png) # # # The ResNet-34 architecture actually uses residual blocks with skip connections such that the input passed via the shortcut matches is resized to dimensions of the main path's output. Such a residual block is illustrated below: # # ![](../images/resnets/resnet-ex-1-2.png) # For a more detailed explanation see the other notebook, [resnet-ex-1.ipynb](resnet-ex-1.ipynb). # ## Imports # In[2]: import os import time import numpy as np import pandas as pd import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import DataLoader from torch.utils.data import Dataset from torchvision import transforms import matplotlib.pyplot as plt from PIL import Image if torch.cuda.is_available(): torch.backends.cudnn.deterministic = True # ## Model Settings # In[3]: ########################## ### SETTINGS ########################## # Hyperparameters RANDOM_SEED = 1 LEARNING_RATE = 0.001 BATCH_SIZE = 128 NUM_EPOCHS = 10 # Architecture NUM_FEATURES = 28*28 NUM_CLASSES = 10 # Other DEVICE = "cuda:3" GRAYSCALE = True # ## Dataset # This notebook is based on Google's Quickdraw dataset (https://quickdraw.withgoogle.com). In particular we will be working with an arbitrary subset of 10 categories in png format: # # label_dict = { # "lollipop": 0, # "binoculars": 1, # "mouse": 2, # "basket": 3, # "penguin": 4, # "washing machine": 5, # "canoe": 6, # "eyeglasses": 7, # "beach": 8, # "screwdriver": 9, # } # # (The class labels 0-9 can be ignored in this notebook). # # For more details on obtaining and preparing the dataset, please see the # # - [custom-data-loader-quickdraw.ipynb](custom-data-loader-quickdraw.ipynb) # # notebook. # In[4]: df = pd.read_csv('quickdraw_png_set1_train.csv', index_col=0) df.head() main_dir = 'quickdraw-png_set1/' img = Image.open(os.path.join(main_dir, df.index[99])) img = np.asarray(img, dtype=np.uint8) print(img.shape) plt.imshow(np.array(img), cmap='binary') plt.show() # ### Create a Custom Data Loader # In[5]: class QuickdrawDataset(Dataset): """Custom Dataset for loading Quickdraw images""" def __init__(self, txt_path, img_dir, transform=None): df = pd.read_csv(txt_path, sep=",", index_col=0) self.img_dir = img_dir self.txt_path = txt_path self.img_names = df.index.values self.y = df['Label'].values self.transform = transform def __getitem__(self, index): img = Image.open(os.path.join(self.img_dir, self.img_names[index])) if self.transform is not None: img = self.transform(img) label = self.y[index] return img, label def __len__(self): return self.y.shape[0] # In[6]: # Note that transforms.ToTensor() # already divides pixels by 255. internally BATCH_SIZE = 128 custom_transform = transforms.Compose([#transforms.Lambda(lambda x: x/255.), transforms.ToTensor()]) train_dataset = QuickdrawDataset(txt_path='quickdraw_png_set1_train.csv', img_dir='quickdraw-png_set1/', transform=custom_transform) train_loader = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4) valid_dataset = QuickdrawDataset(txt_path='quickdraw_png_set1_valid.csv', img_dir='quickdraw-png_set1/', transform=custom_transform) valid_loader = DataLoader(dataset=valid_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=4) test_dataset = QuickdrawDataset(txt_path='quickdraw_png_set1_train.csv', img_dir='quickdraw-png_set1/', transform=custom_transform) test_loader = DataLoader(dataset=test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=4) # In[7]: device = torch.device(DEVICE if torch.cuda.is_available() else "cpu") torch.manual_seed(0) num_epochs = 2 for epoch in range(num_epochs): for batch_idx, (x, y) in enumerate(train_loader): print('Epoch:', epoch+1, end='') print(' | Batch index:', batch_idx, end='') print(' | Batch size:', y.size()[0]) x = x.to(device) y = y.to(device) break # ## Model # The following code cell that implements the ResNet-34 architecture is a derivative of the code provided at https://pytorch.org/docs/0.4.0/_modules/torchvision/models/resnet.html. # In[8]: ########################## ### MODEL ########################## def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class ResNet(nn.Module): def __init__(self, block, layers, num_classes, grayscale): self.inplanes = 64 if grayscale: in_dim = 1 else: in_dim = 3 super(ResNet, self).__init__() self.conv1 = nn.Conv2d(in_dim, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) self.avgpool = nn.AvgPool2d(7, stride=1) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, (2. / n)**.5) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) # because MNIST is already 1x1 here: # disable avg pooling #x = self.avgpool(x) x = x.view(x.size(0), -1) logits = self.fc(x) probas = F.softmax(logits, dim=1) return logits, probas def resnet34(num_classes): """Constructs a ResNet-34 model.""" model = ResNet(block=BasicBlock, layers=[3, 4, 6, 3], num_classes=NUM_CLASSES, grayscale=GRAYSCALE) return model # In[9]: torch.manual_seed(RANDOM_SEED) model = resnet34(NUM_CLASSES) model.to(DEVICE) optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE) # ## Training # In[10]: def compute_accuracy(model, data_loader, device): correct_pred, num_examples = 0, 0 for i, (features, targets) in enumerate(data_loader): features = features.to(device) targets = targets.to(device) logits, probas = model(features) _, predicted_labels = torch.max(probas, 1) num_examples += targets.size(0) correct_pred += (predicted_labels == targets).sum() return correct_pred.float()/num_examples * 100 start_time = time.time() for epoch in range(NUM_EPOCHS): model.train() for batch_idx, (features, targets) in enumerate(train_loader): features = features.to(DEVICE) targets = targets.to(DEVICE) ### FORWARD AND BACK PROP logits, probas = model(features) cost = F.cross_entropy(logits, targets) optimizer.zero_grad() cost.backward() ### UPDATE MODEL PARAMETERS optimizer.step() ### LOGGING if not batch_idx % 500: print ('Epoch: %03d/%03d | Batch %04d/%04d | Cost: %.4f' %(epoch+1, NUM_EPOCHS, batch_idx, len(train_loader), cost)) model.eval() with torch.set_grad_enabled(False): # save memory during inference print('Epoch: %03d/%03d | Train: %.3f%% | Validation: %.3f%%' % ( epoch+1, NUM_EPOCHS, compute_accuracy(model, train_loader, device=DEVICE), compute_accuracy(model, valid_loader, device=DEVICE))) print('Time elapsed: %.2f min' % ((time.time() - start_time)/60)) print('Total Training Time: %.2f min' % ((time.time() - start_time)/60)) # ## Evaluation # In[11]: with torch.set_grad_enabled(False): # save memory during inference print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader, device=DEVICE))) # In[12]: for batch_idx, (features, targets) in enumerate(test_loader): features = features targets = targets break nhwc_img = np.transpose(features[5], axes=(1, 2, 0)) nhw_img = np.squeeze(nhwc_img.numpy(), axis=2) plt.imshow(nhw_img, cmap='Greys'); # In[13]: model.eval() logits, probas = model(features.to(device)[0, None]) print('Probability Washing Machine %.2f%%' % (probas[0][4]*100)) # In[14]: get_ipython().run_line_magic('watermark', '-iv')