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model.py
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model.py
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#!/usr/bin/python3.6
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
import copy
import numpy as np
class MultiStageModel(nn.Module):
def __init__(self, num_stages, num_layers, num_f_maps, dim, num_classes):
super(MultiStageModel, self).__init__()
self.tower_stage = TowerModel(num_layers, num_f_maps, dim, num_classes)
self.single_stages = nn.ModuleList([copy.deepcopy(SingleStageModel(num_layers, num_f_maps, num_classes, num_classes, 3))
for s in range(num_stages-1)])
def forward(self, x, mask):
middle_out, out = self.tower_stage(x, mask)
outputs = out.unsqueeze(0)
for s in self.single_stages:
middle_out, out = s(F.softmax(out, dim=1) * mask[:, 0:1, :], mask)
outputs = torch.cat((outputs, out.unsqueeze(0)), dim=0)
return middle_out, outputs
class TowerModel(nn.Module):
def __init__(self, num_layers, num_f_maps, dim, num_classes):
super(TowerModel, self).__init__()
self.stage1 = SingleStageModel(num_layers, num_f_maps, dim, num_classes, 3)
self.stage2 = SingleStageModel(num_layers, num_f_maps, dim, num_classes, 5)
def forward(self, x, mask):
out1, final_out1 = self.stage1(x, mask)
out2, final_out2 = self.stage2(x, mask)
return out1 + out2, final_out1 + final_out2
class SingleStageModel(nn.Module):
def __init__(self, num_layers, num_f_maps, dim, num_classes, kernel_size):
super(SingleStageModel, self).__init__()
self.conv_1x1 = nn.Conv1d(dim, num_f_maps, 1)
self.layers = nn.ModuleList([copy.deepcopy(DilatedResidualLayer(2 ** i, num_f_maps, num_f_maps, kernel_size))
for i in range(num_layers)])
self.conv_out = nn.Conv1d(num_f_maps, num_classes, 1)
def forward(self, x, mask):
out = self.conv_1x1(x)
for layer in self.layers:
out = layer(out, mask)
final_out = self.conv_out(out) * mask[:, 0:1, :]
return out, final_out
class DilatedResidualLayer(nn.Module):
def __init__(self, dilation, in_channels, out_channels, kernel_size):
super(DilatedResidualLayer, self).__init__()
padding = int(dilation + dilation * (kernel_size - 3) / 2)
self.conv_dilated = nn.Conv1d(in_channels, out_channels, kernel_size, padding=padding, dilation=dilation)
self.conv_1x1 = nn.Conv1d(out_channels, out_channels, 1)
self.dropout = nn.Dropout()
def forward(self, x, mask):
out = F.relu(self.conv_dilated(x))
out = self.conv_1x1(out)
out = self.dropout(out)
return (x + out) * mask[:, 0:1, :]
class Trainer:
def __init__(self, num_blocks, num_layers, num_f_maps, dim, num_classes):
self.model = MultiStageModel(num_blocks, num_layers, num_f_maps, dim, num_classes)
self.ce = nn.CrossEntropyLoss(ignore_index=-100)
self.mse = nn.MSELoss(reduction='none')
self.num_classes = num_classes
def confidence_loss(self, pred, confidence_mask, device):
batch_size = pred.size(0)
pred = F.log_softmax(pred, dim=1)
loss = 0
for b in range(batch_size):
num_frame = confidence_mask[b].shape[2]
m_mask = torch.from_numpy(confidence_mask[b]).type(torch.float).to(device)
left = pred[b, :, 1:] - pred[b, :, :-1]
left = torch.clamp(left[:, :num_frame] * m_mask[0], min=0)
left = torch.sum(left) / torch.sum(m_mask[0])
loss += left
right = (pred[b, :, :-1] - pred[b, :, 1:])
right = torch.clamp(right[:, :num_frame] * m_mask[1], min=0)
right = torch.sum(right) / torch.sum(m_mask[1])
loss += right
return loss
def train(self, save_dir, batch_gen, writer, num_epochs, batch_size, learning_rate, device):
self.model.train()
self.model.to(device)
optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)
start_epochs = 30
print('start epoch of single supervision is:', start_epochs)
for epoch in range(num_epochs):
epoch_loss = 0
correct = 0
total = 0
while batch_gen.has_next():
batch_input, batch_target, mask, batch_confidence = batch_gen.next_batch(batch_size)
batch_input, batch_target, mask = batch_input.to(device), batch_target.to(device), mask.to(device)
optimizer.zero_grad()
middle_pred, predictions = self.model(batch_input, mask)
# Generate pseudo labels after training 30 epochs for getting more accurate labels
if epoch < start_epochs:
batch_boundary = batch_gen.get_single_random(batch_size, batch_input.size(-1))
else:
batch_boundary = batch_gen.get_boundary(batch_size, middle_pred.detach())
batch_boundary = batch_boundary.to(device)
loss = 0
for p in predictions:
loss += self.ce(p.transpose(2, 1).contiguous().view(-1, self.num_classes), batch_boundary.view(-1))
loss += 0.15 * torch.mean(torch.clamp(
self.mse(F.log_softmax(p[:, :, 1:], dim=1), F.log_softmax(p.detach()[:, :, :-1], dim=1)), min=0,
max=16) * mask[:, :, 1:])
loss += 0.075 * self.confidence_loss(p, batch_confidence, device)
epoch_loss += loss.item()
loss.backward()
optimizer.step()
_, predicted = torch.max(predictions[-1].data, 1)
correct += ((predicted == batch_target).float()*mask[:, 0, :].squeeze(1)).sum().item()
total += torch.sum(mask[:, 0, :]).item()
batch_gen.reset()
torch.save(self.model.state_dict(), save_dir + "/epoch-" + str(epoch + 1) + ".model")
torch.save(optimizer.state_dict(), save_dir + "/epoch-" + str(epoch + 1) + ".opt")
writer.add_scalar('trainLoss', epoch_loss / len(batch_gen.list_of_examples), epoch + 1)
writer.add_scalar('trainAcc', float(correct)/total, epoch + 1)
print("[epoch %d]: epoch loss = %f, acc = %f" % (epoch + 1, epoch_loss / len(batch_gen.list_of_examples),
float(correct)/total))
def predict(self, model_dir, results_dir, features_path, vid_list_file, epoch, actions_dict, device, sample_rate):
self.model.eval()
with torch.no_grad():
self.model.to(device)
self.model.load_state_dict(torch.load(model_dir + "/epoch-" + str(epoch) + ".model"))
file_ptr = open(vid_list_file, 'r')
list_of_vids = file_ptr.read().split('\n')[:-1]
file_ptr.close()
for vid in list_of_vids:
# print(vid)
features = np.load(features_path + vid.split('.')[0] + '.npy')
features = features[:, ::sample_rate]
input_x = torch.tensor(features, dtype=torch.float)
input_x.unsqueeze_(0)
input_x = input_x.to(device)
_, predictions = self.model(input_x, torch.ones(input_x.size(), device=device))
_, predicted = torch.max(predictions[-1].data, 1)
predicted = predicted.squeeze()
recognition = []
for i in range(len(predicted)):
index = list(actions_dict.values()).index(predicted[i].item())
recognition = np.concatenate((recognition, [list(actions_dict.keys())[index]]*sample_rate))
f_name = vid.split('/')[-1].split('.')[0]
f_ptr = open(results_dir + "/" + f_name, "w")
f_ptr.write("### Frame level recognition: ###\n")
f_ptr.write(' '.join(recognition))
f_ptr.close()