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fasterrcnn_demo.py
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fasterrcnn_demo.py
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# -*- coding: utf-8 -*-
"""
训练faster rcnn
"""
import os
import time
import torch.nn as nn
import torch
import random
import numpy as np
import torchvision.transforms as transforms
import torchvision
from PIL import Image
import torch.nn.functional as F
from my_dataset import PennFudanDataset
from common_tools import set_seed
from torch.utils.data import DataLoader
from matplotlib import pyplot as plt
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.transforms import functional as F
import enviroments
set_seed(1) # 设置随机种子
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# classes_coco
COCO_INSTANCE_CATEGORY_NAMES = [
'__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',
'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',
'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table',
'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book',
'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
def vis_bbox(img, output, classes, max_vis=40, prob_thres=0.4):
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(img, aspect='equal')
out_boxes = output_dict["boxes"].cpu()
out_scores = output_dict["scores"].cpu()
out_labels = output_dict["labels"].cpu()
num_boxes = out_boxes.shape[0]
for idx in range(0, min(num_boxes, max_vis)):
score = out_scores[idx].numpy()
bbox = out_boxes[idx].numpy()
class_name = classes[out_labels[idx]]
if score < prob_thres:
continue
ax.add_patch(plt.Rectangle((bbox[0], bbox[1]), bbox[2] - bbox[0], bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=3.5))
ax.text(bbox[0], bbox[1] - 2, '{:s} {:.3f}'.format(class_name, score), bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
plt.show()
plt.close()
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, image, target):
for t in self.transforms:
image, target = t(image, target)
return image, target
class RandomHorizontalFlip(object):
def __init__(self, prob):
self.prob = prob
def __call__(self, image, target):
if random.random() < self.prob:
height, width = image.shape[-2:]
image = image.flip(-1)
bbox = target["boxes"]
bbox[:, [0, 2]] = width - bbox[:, [2, 0]]
target["boxes"] = bbox
return image, target
class ToTensor(object):
def __call__(self, image, target):
image = F.to_tensor(image)
return image, target
if __name__ == "__main__":
# config
LR = 0.001
num_classes = 2
batch_size = 1
start_epoch, max_epoch = 0, 5
train_dir = enviroments.pennFudanPed_data_dir
train_transform = Compose([ToTensor(), RandomHorizontalFlip(0.5)])
# step 1: data
train_set = PennFudanDataset(data_dir=train_dir, transforms=train_transform)
# 收集batch data的函数
def collate_fn(batch):
return tuple(zip(*batch))
train_loader = DataLoader(train_set, batch_size=batch_size, collate_fn=collate_fn)
# step 2: model
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) # replace the pre-trained head with a new one
model.to(device)
# step 3: loss
# in lib/python3.6/site-packages/torchvision/models/detection/roi_heads.py
# def fastrcnn_loss(class_logits, box_regression, labels, regression_targets)
# step 4: optimizer scheduler
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=LR, momentum=0.9, weight_decay=0.0005)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
# step 5: Iteration
for epoch in range(start_epoch, max_epoch):
model.train()
for iter, (images, targets) in enumerate(train_loader):
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
# if torch.cuda.is_available():
# images, targets = images.to(device), targets.to(device)
loss_dict = model(images, targets) # images is list; targets is [ dict["boxes":**, "labels":**], dict[] ]
losses = sum(loss for loss in loss_dict.values())
print("Training:Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} ".format(
epoch, max_epoch, iter + 1, len(train_loader), losses.item()))
optimizer.zero_grad()
losses.backward()
optimizer.step()
lr_scheduler.step()
# test
model.eval()
# config
vis_num = 5
vis_dir = os.path.join(BASE_DIR, "..", "..", "data", "PennFudanPed", "PNGImages")
img_names = list(filter(lambda x: x.endswith(".png"), os.listdir(vis_dir)))
random.shuffle(img_names)
preprocess = transforms.Compose([transforms.ToTensor(), ])
for i in range(0, vis_num):
path_img = os.path.join(vis_dir, img_names[i])
# preprocess
input_image = Image.open(path_img).convert("RGB")
img_chw = preprocess(input_image)
# to device
if torch.cuda.is_available():
img_chw = img_chw.to('cuda')
model.to('cuda')
# forward
input_list = [img_chw]
with torch.no_grad():
tic = time.time()
print("input img tensor shape:{}".format(input_list[0].shape))
output_list = model(input_list)
output_dict = output_list[0]
print("pass: {:.3f}s".format(time.time() - tic))
# visualization
vis_bbox(input_image, output_dict, COCO_INSTANCE_CATEGORY_NAMES, max_vis=20, prob_thres=0.5) # for 2 epoch for nms