数据集UCF101 + CRNN模型 + 模型预测结果

import os import numpy as np from PIL import Image from torch.utils import data import torch import torch.nn as nn import torch.nn.functional as F import torchvision.models as models import torchvision.transforms as transforms from tqdm import tqdm ## ------------------- label conversion tools ------------------ ## def labels2cat(label_encoder, list): return label_encoder.transform(list) def labels2onehot(OneHotEncoder, label_encoder, list): return OneHotEncoder.transform(label_encoder.transform(list).reshape(-1, 1)).toarray() def onehot2labels(label_encoder, y_onehot): return label_encoder.inverse_transform(np.where(y_onehot == 1)[1]).tolist() def cat2labels(label_encoder, y_cat): return label_encoder.inverse_transform(y_cat).tolist() ## ---------------------- Dataloaders ---------------------- ## # for 3DCNN class Dataset_3DCNN(data.Dataset): "Characterizes a dataset for PyTorch" def __init__(self, data_path, folders, labels, frames, transform=None): "Initialization" self.data_path = data_path self.labels = labels self.folders = folders self.transform = transform self.frames = frames def __len__(self): "Denotes the total number of samples" return len(self.folders) def read_images(self, path, selected_folder, use_transform): X = [] for i in self.frames: image = Image.open(os.path.join(path, selected_folder, 'frame{:06d}.jpg'.format(i))).convert('L') if use_transform is not None: image = use_transform(image) X.append(image.squeeze_(0)) X = torch.stack(X, dim=0) return X def __getitem__(self, index): "Generates one sample of data" # Select sample folder = self.folders[index] # Load data X = self.read_images(self.data_path, folder, self.transform).unsqueeze_(0) # (input) spatial images y = torch.LongTensor([self.labels[index]]) # (labels) LongTensor are for int64 instead of FloatTensor # print(X.shape) return X, y # for CRNN class Dataset_CRNN(data.Dataset): "Characterizes a dataset for PyTorch" def __init__(self, data_path, folders, labels, frames, transform=None): "Initialization" self.data_path = data_path self.labels = labels self.folders = folders self.transform = transform self.frames = frames def __len__(self): "Denotes the total number of samples" return len(self.folders) def read_images(self, path, selected_folder, use_transform): X = [] for i in self.frames: image = Image.open(os.path.join(path, selected_folder, 'frame{:06d}.jpg'.format(i))) if use_transform is not None: image = use_transform(image) X.append(image) X = torch.stack(X, dim=0) return X def __getitem__(self, index): "Generates one sample of data" # Select sample folder = self.folders[index] # Load data X = self.read_images(self.data_path, folder, self.transform) # (input) spatial images y = torch.LongTensor([self.labels[index]]) # (labels) LongTensor are for int64 instead of FloatTensor # print(X.shape) return X, y ## ---------------------- end of Dataloaders ---------------------- ## ## -------------------- (reload) model prediction ---------------------- ## def Conv3d_final_prediction(model, device, loader): model.eval() all_y_pred = [] with torch.no_grad(): for batch_idx, (X, y) in enumerate(tqdm(loader)): # distribute data to device X = X.to(device) output = model(X) y_pred = output.max(1, keepdim=True)[1] # location of max log-probability as prediction all_y_pred.extend(y_pred.cpu().data.squeeze().numpy().tolist()) return all_y_pred def CRNN_final_prediction(model, device, loader): cnn_encoder, rnn_decoder = model cnn_encoder.eval() rnn_decoder.eval() all_y_pred = [] with torch.no_grad(): for batch_idx, (X, y) in enumerate(tqdm(loader)): # distribute data to device X = X.to(device) output = rnn_decoder(cnn_encoder(X)) y_pred = output.max(1, keepdim=True)[1] # location of max log-probability as prediction all_y_pred.extend(y_pred.cpu().data.squeeze().numpy().tolist()) return all_y_pred ## -------------------- end of model prediction ---------------------- ## ## ------------------------ 3D CNN module ---------------------- ## def conv3D_output_size(img_size, padding, kernel_size, stride): # compute output shape of conv3D outshape = (np.floor((img_size[0] + 2 * padding[0] - (kernel_size[0] - 1) - 1) / stride[0] + 1).astype(int), np.floor((img_size[1] + 2 * padding[1] - (kernel_size[1] - 1) - 1) / stride[1] + 1).astype(int), np.floor((img_size[2] + 2 * padding[2] - (kernel_size[2] - 1) - 1) / stride[2] + 1).astype(int)) return outshape class CNN3D(nn.Module): def __init__(self, t_dim=120, img_x=90, img_y=120, drop_p=0.2, fc_hidden1=256, fc_hidden2=128, num_classes=50): super(CNN3D, self).__init__() # set video dimension self.t_dim = t_dim self.img_x = img_x self.img_y = img_y # fully connected layer hidden nodes self.fc_hidden1, self.fc_hidden2 = fc_hidden1, fc_hidden2 self.drop_p = drop_p self.num_classes = num_classes self.ch1, self.ch2 = 32, 48 self.k1, self.k2 = (5, 5, 5), (3, 3, 3) # 3d kernel size self.s1, self.s2 = (2, 2, 2), (2, 2, 2) # 3d strides self.pd1, self.pd2 = (0, 0, 0), (0, 0, 0) # 3d padding # compute conv1 & conv2 output shape self.conv1_outshape = conv3D_output_size((self.t_dim, self.img_x, self.img_y), self.pd1, self.k1, self.s1) self.conv2_outshape = conv3D_output_size(self.conv1_outshape, self.pd2, self.k2, self.s2) self.conv1 = nn.Conv3d(in_channels=1, out_channels=self.ch1, kernel_size=self.k1, stride=self.s1, padding=self.pd1) self.bn1 = nn.BatchNorm3d(self.ch1) self.conv2 = nn.Conv3d(in_channels=self.ch1, out_channels=self.ch2, kernel_size=self.k2, stride=self.s2, padding=self.pd2) self.bn2 = nn.BatchNorm3d(self.ch2) self.relu = nn.ReLU(inplace=True) self.drop = nn.Dropout3d(self.drop_p) self.pool = nn.MaxPool3d(2) self.fc1 = nn.Linear(self.ch2 * self.conv2_outshape[0] * self.conv2_outshape[1] * self.conv2_outshape[2], self.fc_hidden1) # fully connected hidden layer self.fc2 = nn.Linear(self.fc_hidden1, self.fc_hidden2) self.fc3 = nn.Linear(self.fc_hidden2, self.num_classes) # fully connected layer, output = multi-classes def forward(self, x_3d): # Conv 1 x = self.conv1(x_3d) x = self.bn1(x) x = self.relu(x) x = self.drop(x) # Conv 2 x = self.conv2(x) x = self.bn2(x) x = self.relu(x) x = self.drop(x) # FC 1 and 2 x = x.view(x.size(0), -1) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.dropout(x, p=self.drop_p, training=self.training) x = self.fc3(x) return x ## --------------------- end of 3D CNN module ---------------- ## ## ------------------------ CRNN module ---------------------- ## def conv2D_output_size(img_size, padding, kernel_size, stride): # compute output shape of conv2D outshape = (np.