一个小改动，CNN输入固定尺寸图像改为任意尺寸图像
重磅干货，第一时间送达
# from the torchvision's implementation of ResNet
class ResNet:
# ... self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = norm_layer(self.inplanes) 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, dilate = replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate = replace_stride_with_dilation[1]) self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate = replace_stride_with_dilation[2]) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes)
# ...
def _forward_impl(self, x): # See note [TorchScript super()] 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)
x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x)
return xclass FullyConvolutionalResnet18(models.ResNet): def __init__(self, num_classes=1000, pretrained=False, **kwargs):
# Start with standard resnet18 defined here super().__init__(block = models.resnet.BasicBlock, layers = [2, 2, 2, 2], num_classes = num_classes, **kwargs) if pretrained: state_dict = load_state_dict_from_url( models.resnet.model_urls["resnet18"], progress=True) self.load_state_dict(state_dict)
# Replace AdaptiveAvgPool2d with standard AvgPool2d self.avgpool = nn.AvgPool2d((7, 7))
# Convert the original fc layer to a convolutional layer. self.last_conv = torch.nn.Conv2d( in_channels = self.fc.in_features, out_channels = num_classes, kernel_size = 1) self.last_conv.weight.data.copy_( self.fc.weight.data.view ( *self.fc.weight.data.shape, 1, 1)) self.last_conv.bias.data.copy_ (self.fc.bias.data)
# Reimplementing forward pass. def _forward_impl(self, x): # Standard forward for resnet18 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) x = self.avgpool(x)
# Notice, there is no forward pass # through the original fully connected layer. # Instead, we forward pass through the last conv layer x = self.last_conv(x) return x#1. 导入标准库import torchimport torch.nn as nnfrom torchvision import modelsfrom torch.hub import load_state_dict_from_url
from PIL import Imageimport cv2import numpy as npfrom matplotlib import pyplot as plt
#2. 读取ImageNet类ID到名称的映射if __name__ == "__main__": # Read ImageNet class id to name mapping with open('imagenet_classes.txt') as f: labels = [line.strip() for line in f.readlines()]# Read imageoriginal_image = cv2.imread('camel.jpg')
# Convert original image to RGB formatimage = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
# Transform input image# 1. Convert to Tensor# 2. Subtract mean# 3. Divide by standard deviation
transform = transforms.Compose([ transforms.ToTensor(), #Convert image to tensor. transforms.Normalize( mean=[0.485, 0.456, 0.406], # Subtract mean std=[0.229, 0.224, 0.225] # Divide by standard deviation )])
image = transform(image)image = image.unsqueeze(0)# Load modified resnet18 model with pretrained ImageNet weightsmodel = FullyConvolutionalResnet18(pretrained=True).eval()with torch.no_grad(): # Perform inference. # Instead of a 1x1000 vector, we will get a # 1x1000xnxm output ( i.e. a probabibility map # of size n x m for each 1000 class, # where n and m depend on the size of the image.) preds = model(image) preds = torch.softmax(preds, dim=1) print('Response map shape : ', preds.shape)
# Find the class with the maximum score in the n x m output map pred, class_idx = torch.max(preds, dim=1) print(class_idx)
row_max, row_idx = torch.max(pred, dim=1) col_max, col_idx = torch.max(row_max, dim=1) predicted_class = class_idx[0, row_idx[0, col_idx], col_idx] # Print top predicted class print('Predicted Class : ', labels[predicted_class], predicted_class)Response map shape : torch.Size([1, 1000, 3, 8])tensor([[[977, 977, 977, 977, 977, 978, 354, 437], [978, 977, 980, 977, 858, 970, 354, 461], [977, 978, 977, 977, 977, 977, 354, 354]]])Predicted Class : Arabian camel, dromedary, Camelus dromedarius tensor([354])# Find the n x m score map for the predicted classscore_map = preds[0, predicted_class, :, :].cpu().numpy()score_map = score_map[0]
# Resize score map to the original image sizescore_map = cv2.resize(score_map, (original_image.shape[1], original_image.shape[0]))
# Binarize score map_, score_map_for_contours = cv2.threshold(score_map, 0.25, 1, type=cv2.THRESH_BINARY)score_map_for_contours = score_map_for_contours.astype(np.uint8).copy()
# Find the countour of the binary blobcontours, _ = cv2.findContours(score_map_for_contours, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)
# Find bounding box around the object.rect = cv2.boundingRect(contours[0])# Apply score map as a mask to original imagescore_map = score_map - np.min(score_map[:])score_map = score_map / np.max(score_map[:])
接下来，我们将响应图与原始图像相乘并显示边界框。score_map = cv2.cvtColor(score_map, cv2.COLOR_GRAY2BGR)masked_image = (original_image * score_map).astype(np.uint8)
# Display bounding boxcv2.rectangle(masked_image, rect[:2], (rect[0] + rect[2], rect[1] + rect[3]), (0, 0, 255), 2)
# Display imagescv2.imshow("Original Image", original_image)cv2.imshow("scaled_score_map", score_map)cv2.imshow("activations_and_bbox", masked_image)cv2.waitKey(0) 赞 (0)