add analyze feature map and weights

pytorch_learning/analyze_weights_featuremap/alexnet_model.py

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+import torch.nn as nn
+import torch
+
+
+class AlexNet(nn.Module):
+    def __init__(self, num_classes=1000, init_weights=False):
+        super(AlexNet, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 48, kernel_size=11, stride=4, padding=2),  # input[3, 224, 224]  output[48, 55, 55]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[48, 27, 27]
+            nn.Conv2d(48, 128, kernel_size=5, padding=2),           # output[128, 27, 27]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[128, 13, 13]
+            nn.Conv2d(128, 192, kernel_size=3, padding=1),          # output[192, 13, 13]
+            nn.ReLU(inplace=True),
+            nn.Conv2d(192, 192, kernel_size=3, padding=1),          # output[192, 13, 13]
+            nn.ReLU(inplace=True),
+            nn.Conv2d(192, 128, kernel_size=3, padding=1),          # output[128, 13, 13]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),                  # output[128, 6, 6]
+        )
+        self.classifier = nn.Sequential(
+            nn.Dropout(p=0.5),
+            nn.Linear(128 * 6 * 6, 2048),
+            nn.ReLU(inplace=True),
+            nn.Dropout(p=0.5),
+            nn.Linear(2048, 2048),
+            nn.ReLU(inplace=True),
+            nn.Linear(2048, num_classes),
+        )
+        if init_weights:
+            self._initialize_weights()
+
+    def forward(self, x):
+        outputs = []
+        for name, module in self.features.named_children():
+            x = module(x)
+            if name in ["0", "3", "6"]:
+                outputs.append(x)
+
+        return outputs
+
+    def _initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                nn.init.constant_(m.bias, 0)

pytorch_learning/analyze_weights_featuremap/analyze_feature_map.py

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+import torch
+from alexnet_model import AlexNet
+from resnet_model import resnet34
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+
+data_transform = transforms.Compose(
+    [transforms.Resize((224, 224)),
+     transforms.ToTensor(),
+     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+
+# data_transform = transforms.Compose(
+#     [transforms.Resize(256),
+#      transforms.CenterCrop(224),
+#      transforms.ToTensor(),
+#      transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
+
+# create model
+model = AlexNet(num_classes=5)
+# model = resnet34(num_classes=5)
+# load model weights
+model_weight_path = "./AlexNet.pth"  # "./resNet34.pth"
+model.load_state_dict(torch.load(model_weight_path))
+print(model)
+
+# load image
+img = Image.open("../tulip.jpg")
+# [N, C, H, W]
+img = data_transform(img)
+# expand batch dimension
+img = torch.unsqueeze(img, dim=0)
+
+# forward
+out_put = model(img)
+for feature_map in out_put:
+    # [N, C, H, W] -> [C, H, W]
+    im = np.squeeze(feature_map.detach().numpy())
+    # [C, H, W] -> [H, W, C]
+    im = np.transpose(im, [1, 2, 0])
+
+    # show top 12 feature maps
+    plt.figure()
+    for i in range(12):
+        ax = plt.subplot(3, 4, i+1)
+        # [H, W, C]
+        plt.imshow(im[:, :, i], cmap='gray')
+    plt.show()
+

pytorch_learning/analyze_weights_featuremap/analyze_kernel_weight.py

@@ -0,0 +1,43 @@
+import torch
+from alexnet_model import AlexNet
+from resnet_model import resnet34
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# create model
+model = AlexNet(num_classes=5)
+# model = resnet34(num_classes=5)
+# load model weights
+model_weight_path = "./AlexNet.pth"  # "resNet34.pth"
+model.load_state_dict(torch.load(model_weight_path))
+print(model)
+
+weights_keys = model.state_dict().keys()
+for key in weights_keys:
+    # remove num_batches_tracked para(in bn)
+    if "num_batches_tracked" in key:
+        continue
+    # [kernel_number, kernel_channel, kernel_height, kernel_width]
+    weight_t = model.state_dict()[key].numpy()
+
+    # read a kernel information
+    # k = weight_t[0, :, :, :]
+
+    # calculate mean, std, min, max
+    weight_mean = weight_t.mean()
+    weight_std = weight_t.std(ddof=1)
+    weight_min = weight_t.min()
+    weight_max = weight_t.max()
+    print("mean is {}, std is {}, min is {}, max is {}".format(weight_mean,
+                                                               weight_std,
+                                                               weight_max,
+                                                               weight_min))
+
+    # plot hist image
+    plt.close()
+    weight_vec = np.reshape(weight_t, [-1])
+    plt.hist(weight_vec, bins=50)
+    plt.title(key)
+    plt.show()
+

pytorch_learning/analyze_weights_featuremap/resnet_model.py

@@ -0,0 +1,145 @@
+import torch.nn as nn
+import torch
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_channel, out_channel, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
+                               kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_channel)
+        self.relu = nn.ReLU()
+        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
+                               kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_channel)
+        self.downsample = downsample
+
+    def forward(self, x):
+        identity = x
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, in_channel, out_channel, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
+                               kernel_size=1, stride=1, bias=False)  # squeeze channels
+        self.bn1 = nn.BatchNorm2d(out_channel)
+        # -----------------------------------------
+        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
+                               kernel_size=3, stride=stride, bias=False, padding=1)
+        self.bn2 = nn.BatchNorm2d(out_channel)
+        # -----------------------------------------
+        self.conv3 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel*self.expansion,
+                               kernel_size=1, stride=1, bias=False)  # unsqueeze channels
+        self.bn3 = nn.BatchNorm2d(out_channel*self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+
+    def forward(self, x):
+        identity = x
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, blocks_num, num_classes=1000, include_top=True):
+        super(ResNet, self).__init__()
+        self.include_top = include_top
+        self.in_channel = 64
+
+        self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
+                               padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(self.in_channel)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, blocks_num[0])
+        self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)
+        if self.include_top:
+            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # output size = (1, 1)
+            self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+
+    def _make_layer(self, block, channel, block_num, stride=1):
+        downsample = None
+        if stride != 1 or self.in_channel != channel * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(channel * block.expansion))
+
+        layers = []
+        layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride))
+        self.in_channel = channel * block.expansion
+
+        for _ in range(1, block_num):
+            layers.append(block(self.in_channel, channel))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        outputs = []
+        x = self.conv1(x)
+        outputs.append(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        outputs.append(x)
+        # x = self.layer2(x)
+        # x = self.layer3(x)
+        # x = self.layer4(x)
+        #
+        # if self.include_top:
+        #     x = self.avgpool(x)
+        #     x = torch.flatten(x, 1)
+        #     x = self.fc(x)
+
+        return outputs
+
+
+def resnet34(num_classes=1000, include_top=True):
+    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)
+
+
+def resnet101(num_classes=1000, include_top=True):
+    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top)