models.py

import torch
import torch.nn as nn
import torchvision
import numpy as np
from torch.autograd import Variable
import torchvision.models as models
import transformers
import torchvision.transforms

import torchxrayvision as xrv
from transformers import ViTModel, ViTConfig



class VisualFeatureExtractor(nn.Module):
    def __init__(self, model_name='densenet201', pretrained=False):
        super(VisualFeatureExtractor, self).__init__()
        self.model_name = 'chexnet'
        self.pretrained = pretrained
        self.model, self.out_features, self.avg_func, self.bn, self.linear = self.__get_model()
        self.activation = nn.ReLU()

    def __get_model(self):
        model = None
        out_features = None
        func = None
        
        if self.model_name == 'resnet152':
            resnet = models.resnet152(pretrained=self.pretrained)
            modules = list(resnet.children())[:-2]
            model = nn.Sequential(*modules)
            out_features = resnet.fc.in_features
            func = torch.nn.AvgPool2d(kernel_size=7, stride=1, padding=0)
        
        
        elif self.model_name == 'densenet201':
            densenet = models.densenet201(pretrained=self.pretrained)
            modules = list(densenet.features)
            model = nn.Sequential(*modules)
            func = torch.nn.AvgPool2d(kernel_size=7, stride=1, padding=0)
            out_features = densenet.classifier.in_features


        elif self.model_name == 'chexnet':
            print("vit chest xray pretrained model loading")
            # Load the Vision Transformer (ViT) model configuration
            config = ViTConfig.from_pretrained('nickmuchi/vit-finetuned-chest-xray-pneumonia')

            # Initialize the ViT model with the specific configuration
            vit_model = ViTModel(config)

            # Load the state dict specifically, excluding 'classifier.bias', 'classifier.weight'
            state_dict = torch.load('model/pytorch_model.bin', map_location=torch.device('cpu'))
            state_dict = {k: v for k, v in state_dict.items() if not k.startswith('classifier')}
            vit_model.load_state_dict(state_dict, strict=False)

            model = vit_model
            out_features = config.hidden_size

        linear = nn.Linear(in_features=out_features, out_features=out_features)
        bn = nn.BatchNorm1d(num_features=out_features, momentum=0.1)

        return model, out_features, func, bn, linear

    def forward(self, images):
        """
        :param images: Input images
        :return: visual_features, avg_features
        """
        model_output = self.model(images)

        # Extract the pooler_output

        pooler_output = model_output.pooler_output

        # Apply the linear layer, batch normalization, and activation
        avg_features = self.activation(self.bn(self.linear(pooler_output)))

        return model_output.last_hidden_state, avg_features     

    # def forward(self, images):
    #     """
    #     :param images:
    #     :return:
    #     """
    #     visual_features = self.model(images)
        
    #     avg_features = self.avg_func(visual_features).squeeze()
    #     # avg_features = self.activation(self.bn(self.linear(visual_features)))

    #     return visual_features, avg_features


class MLC(nn.Module):
    def __init__(self,
                 classes=210,
                 sementic_features_dim=512,
                 fc_in_features=2048,
                 k=10,
                 ):
        super(MLC, self).__init__()
        pretrained_model_name="nickmuchi/vit-finetuned-chest-xray-pneumonia"
        vit_config = ViTConfig.from_pretrained(pretrained_model_name)
        self.vit = ViTModel(vit_config)

        # Adjust the classifier to your number of classes
        self.classifier = nn.Linear(in_features=vit_config.hidden_size, out_features=classes)
        self.embed = nn.Embedding(classes, sementic_features_dim)
        self.k = k
        self.sigmoid = nn.Sigmoid()
        self.__init_weight()

    def __init_weight(self):
        nn.init.xavier_uniform_(self.classifier.weight)
        if self.classifier.bias is not None:
            self.classifier.bias.data.fill_(0)

    def forward(self, avg_features):


        tags = self.sigmoid(self.classifier(avg_features))
        semantic_features = self.embed(torch.topk(tags, self.k)[1])
        return tags, semantic_features
    
# class MLC(nn.Module):
#     def __init__(self,
#                  classes=210,
#                  sementic_features_dim=512,
#                  fc_in_features=2048,
#                  k=10):
#         super(MLC, self).__init__()
#         self.classifier = nn.Linear(in_features=fc_in_features, out_features=classes)
#         self.embed = nn.Embedding(classes, sementic_features_dim)
#         self.k = k
#         self.sigmoid = nn.Sigmoid()
#         self.__init_weight()

#     def __init_weight(self):
#         # Example: Initialize weights with a different strategy
#         nn.init.xavier_uniform_(self.classifier.weight)
#         if self.classifier.bias is not None:
#             self.classifier.bias.data.fill_(0)

#     def forward(self, avg_features):
#         tags = self.sigmoid(self.classifier(avg_features))
#         semantic_features = self.embed(torch.topk(tags, self.k)[1])
#         return tags, semantic_features


class CoAttention(nn.Module):
    def __init__(self,
                 version='v1',
                 embed_size=512,
                 hidden_size=512,
                 visual_size=2048,
                 k=10,
                 momentum=0.1):
        super(CoAttention, self).__init__()
        self.version = version
        self.W_v = nn.Linear(in_features=visual_size, out_features=visual_size)
        self.bn_v = nn.BatchNorm1d(num_features=visual_size, momentum=momentum)

        self.W_v_h = nn.Linear(in_features=hidden_size, out_features=visual_size)
        self.bn_v_h = nn.BatchNorm1d(num_features=visual_size, momentum=momentum)

        self.W_v_att = nn.Linear(in_features=visual_size, out_features=visual_size)
        self.bn_v_att = nn.BatchNorm1d(num_features=visual_size, momentum=momentum)

        self.W_a = nn.Linear(in_features=hidden_size, out_features=hidden_size)
        self.bn_a = nn.BatchNorm1d(num_features=k, momentum=momentum)

        self.W_a_h = nn.Linear(in_features=hidden_size, out_features=hidden_size)
        self.bn_a_h = nn.BatchNorm1d(num_features=1, momentum=momentum)

        self.W_a_att = nn.Linear(in_features=hidden_size, out_features=hidden_size)
        self.bn_a_att = nn.BatchNorm1d(num_features=k, momentum=momentum)

        # self.W_fc = nn.Linear(in_features=visual_size, out_features=embed_size)  # for v3
        self.W_fc = nn.Linear(in_features=visual_size + hidden_size, out_features=embed_size)
        self.bn_fc = nn.BatchNorm1d(num_features=embed_size, momentum=momentum)

        self.tanh = nn.Tanh()
        self.softmax = nn.Softmax()

        self.__init_weight()

    def __init_weight(self):
        self.W_v.weight.data.uniform_(-0.1, 0.1)
        self.W_v.bias.data.fill_(0)

        self.W_v_h.weight.data.uniform_(-0.1, 0.1)
        self.W_v_h.bias.data.fill_(0)

        self.W_v_att.weight.data.uniform_(-0.1, 0.1)
        self.W_v_att.bias.data.fill_(0)

        self.W_a.weight.data.uniform_(-0.1, 0.1)
        self.W_a.bias.data.fill_(0)

        self.W_a_h.weight.data.uniform_(-0.1, 0.1)
        self.W_a_h.bias.data.fill_(0)

        self.W_a_att.weight.data.uniform_(-0.1, 0.1)
        self.W_a_att.bias.data.fill_(0)

        self.W_fc.weight.data.uniform_(-0.1, 0.1)
        self.W_fc.bias.data.fill_(0)

    def forward(self, avg_features, semantic_features, h_sent):
        if self.version == 'v1':
            return self.v1(avg_features, semantic_features, h_sent)
        elif self.version == 'v2':
            return self.v2(avg_features, semantic_features, h_sent)
        elif self.version == 'v3':
            return self.v3(avg_features, semantic_features, h_sent)
        elif self.version == 'v4':
            return self.v4(avg_features, semantic_features, h_sent)
        elif self.version == 'v5':
            return self.v5(avg_features, semantic_features, h_sent)

    def v1(self, avg_features, semantic_features, h_sent) -> object:
        """
        only training
        :rtype: object
        """
        W_v = self.bn_v(self.W_v(avg_features))
        W_v_h = self.bn_v_h(self.W_v_h(h_sent.squeeze(1)))

        alpha_v = self.softmax(self.bn_v_att(self.W_v_att(self.tanh(W_v + W_v_h))))
        v_att = torch.mul(alpha_v, avg_features)

        W_a_h = self.bn_a_h(self.W_a_h(h_sent))
        W_a = self.bn_a(self.W_a(semantic_features))
        alpha_a = self.softmax(self.bn_a_att(self.W_a_att(self.tanh(torch.add(W_a_h, W_a)))))
        a_att = torch.mul(alpha_a, semantic_features).sum(1)

        ctx = self.W_fc(torch.cat([v_att, a_att], dim=1))

        return ctx, alpha_v, alpha_a

    def v2(self, avg_features, semantic_features, h_sent) -> object:
        """
        no bn
        :rtype: object
        """
        W_v = self.W_v(avg_features)
        W_v_h = self.W_v_h(h_sent.squeeze(1))

        alpha_v = self.softmax(self.W_v_att(self.tanh(W_v + W_v_h)))
        v_att = torch.mul(alpha_v, avg_features)

        W_a_h = self.W_a_h(h_sent)
        W_a = self.W_a(semantic_features)
        alpha_a = self.softmax(self.W_a_att(self.tanh(torch.add(W_a_h, W_a))))
        a_att = torch.mul(alpha_a, semantic_features).sum(1)

        ctx = self.W_fc(torch.cat([v_att, a_att], dim=1))

        return ctx, alpha_v, alpha_a

    def v3(self, avg_features, semantic_features, h_sent) -> object:
        """

        :rtype: object
        """
        W_v = self.bn_v(self.W_v(avg_features))
        W_v_h = self.bn_v_h(self.W_v_h(h_sent.squeeze(1)))

        alpha_v = self.softmax(self.W_v_att(self.tanh(W_v + W_v_h)))
        v_att = torch.mul(alpha_v, avg_features)

        W_a_h = self.bn_a_h(self.W_a_h(h_sent))
        W_a = self.bn_a(self.W_a(semantic_features))
        alpha_a = self.softmax(self.W_a_att(self.tanh(torch.add(W_a_h, W_a))))
        a_att = torch.mul(alpha_a, semantic_features).sum(1)

        ctx = self.W_fc(torch.cat([v_att, a_att], dim=1))

        return ctx, alpha_v, alpha_a

    def v4(self, avg_features, semantic_features, h_sent):
        W_v = self.W_v(avg_features)
        W_v_h = self.W_v_h(h_sent.squeeze(1))

        alpha_v = self.softmax(self.W_v_att(self.tanh(torch.add(W_v, W_v_h))))
        v_att = torch.mul(alpha_v, avg_features)

        W_a_h = self.W_a_h(h_sent)
        W_a = self.W_a(semantic_features)
        alpha_a = self.softmax(self.W_a_att(self.tanh(torch.add(W_a_h, W_a))))
        a_att = torch.mul(alpha_a, semantic_features).sum(1)

        ctx = self.W_fc(torch.cat([v_att, a_att], dim=1))

        return ctx, alpha_v, alpha_a

    def v5(self, avg_features, semantic_features, h_sent):
        W_v = self.W_v(avg_features)
        W_v_h = self.W_v_h(h_sent.squeeze(1))

        alpha_v = self.softmax(self.W_v_att(self.tanh(self.bn_v(torch.add(W_v, W_v_h)))))
        v_att = torch.mul(alpha_v, avg_features)

        W_a_h = self.W_a_h(h_sent)
        W_a = self.W_a(semantic_features)
        alpha_a = self.softmax(self.W_a_att(self.tanh(self.bn_a(torch.add(W_a_h, W_a)))))
        a_att = torch.mul(alpha_a, semantic_features).sum(1)

        ctx = self.W_fc(torch.cat([v_att, a_att], dim=1))

        return ctx, alpha_v, alpha_a


class SentenceLSTM(nn.Module):
    def __init__(self,
                 version='v1',
                 embed_size=512,
                 hidden_size=512,
                 num_layers=1,
                 dropout=0.3,
                 momentum=0.1):
        super(SentenceLSTM, self).__init__()
        self.version = version

        self.lstm = nn.LSTM(input_size=embed_size,
                            hidden_size=hidden_size,
                            num_layers=num_layers,
                            dropout=dropout)

        self.W_t_h = nn.Linear(in_features=hidden_size,
                               out_features=embed_size,
                               bias=True)
        self.bn_t_h = nn.BatchNorm1d(num_features=1, momentum=momentum)

        self.W_t_ctx = nn.Linear(in_features=embed_size,
                                 out_features=embed_size,
                                 bias=True)
        self.bn_t_ctx = nn.BatchNorm1d(num_features=1, momentum=momentum)

        self.W_stop_s_1 = nn.Linear(in_features=hidden_size,
                                    out_features=embed_size,
                                    bias=True)
        self.bn_stop_s_1 = nn.BatchNorm1d(num_features=1, momentum=momentum)

        self.W_stop_s = nn.Linear(in_features=hidden_size,
                                  out_features=embed_size,
                                  bias=True)
        self.bn_stop_s = nn.BatchNorm1d(num_features=1, momentum=momentum)

        self.W_stop = nn.Linear(in_features=embed_size,
                                out_features=2,
                                bias=True)
        self.bn_stop = nn.BatchNorm1d(num_features=1, momentum=momentum)

        self.W_topic = nn.Linear(in_features=embed_size,
                                 out_features=embed_size,
                                 bias=True)
        self.bn_topic = nn.BatchNorm1d(num_features=1, momentum=momentum)

        self.sigmoid = nn.Sigmoid()
        self.tanh = nn.Tanh()
        self.__init_weight()

    def __init_weight(self):
        self.W_t_h.weight.data.uniform_(-0.1, 0.1)
        self.W_t_h.bias.data.fill_(0)

        self.W_t_ctx.weight.data.uniform_(-0.1, 0.1)
        self.W_t_ctx.bias.data.fill_(0)

        self.W_stop_s_1.weight.data.uniform_(-0.1, 0.1)
        self.W_stop_s_1.bias.data.fill_(0)

        self.W_stop_s.weight.data.uniform_(-0.1, 0.1)
        self.W_stop_s.bias.data.fill_(0)

        self.W_stop.weight.data.uniform_(-0.1, 0.1)
        self.W_stop.bias.data.fill_(0)

        self.W_topic.weight.data.uniform_(-0.1, 0.1)
        self.W_topic.bias.data.fill_(0)

    def forward(self, ctx, prev_hidden_state, states=None) -> object:
        """
        :rtype: object
        """
        if self.version == 'v1':
            return self.v1(ctx, prev_hidden_state, states)
        elif self.version == 'v2':
            return self.v2(ctx, prev_hidden_state, states)
        elif self.version == 'v3':
            return self.v3(ctx, prev_hidden_state, states)

    def v1(self, ctx, prev_hidden_state, states=None):
        """
        v1 (only training)
        :param ctx:
        :param prev_hidden_state:
        :param states:
        :return:
        """
        ctx = ctx.unsqueeze(1)
        hidden_state, states = self.lstm(ctx, states)
        topic = self.W_topic(self.sigmoid(self.bn_t_h(self.W_t_h(hidden_state))
                                          + self.bn_t_ctx(self.W_t_ctx(ctx))))
        p_stop = self.W_stop(self.sigmoid(self.bn_stop_s_1(self.W_stop_s_1(prev_hidden_state))
                                          + self.bn_stop_s(self.W_stop_s(hidden_state))))
        return topic, p_stop, hidden_state, states

    def v2(self, ctx, prev_hidden_state, states=None):
        """
        v2
        :rtype: object
        """
        ctx = ctx.unsqueeze(1)
        hidden_state, states = self.lstm(ctx, states)
        topic = self.bn_topic(self.W_topic(self.tanh(self.bn_t_h(self.W_t_h(hidden_state)
                                                                 + self.W_t_ctx(ctx)))))
        p_stop = self.bn_stop(self.W_stop(self.tanh(self.bn_stop_s(self.W_stop_s_1(prev_hidden_state)
                                                                   + self.W_stop_s(hidden_state)))))
        return topic, p_stop, hidden_state, states

    def v3(self, ctx, prev_hidden_state, states=None):
        """
        v3
        :rtype: object
        """
        ctx = ctx.unsqueeze(1)
        hidden_state, states = self.lstm(ctx, states)
        topic = self.W_topic(self.tanh(self.W_t_h(hidden_state) + self.W_t_ctx(ctx)))
        p_stop = self.W_stop(self.tanh(self.W_stop_s_1(prev_hidden_state) + self.W_stop_s(hidden_state)))
        return topic, p_stop, hidden_state, states


class WordLSTM(nn.Module):
    def __init__(self,
                 embed_size,
                 hidden_size,
                 vocab_size,
                 num_layers,
                 n_max=50):
        super(WordLSTM, self).__init__()
        self.embed = nn.Embedding(vocab_size, embed_size)
        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
        self.linear = nn.Linear(hidden_size, vocab_size)
        self.__init_weights()
        self.n_max = n_max
        self.vocab_size = vocab_size

    def __init_weights(self):
        self.embed.weight.data.uniform_(-0.1, 0.1)
        self.linear.weight.data.uniform_(-0.1, 0.1)
        self.linear.bias.data.fill_(0)

    def forward(self, topic_vec, captions):
        embeddings = self.embed(captions)
        embeddings = torch.cat((topic_vec, embeddings), 1)
        hidden, _ = self.lstm(embeddings)
        outputs = self.linear(hidden[:, -1, :])
        return outputs

    def sample(self, features, start_tokens):
        sampled_ids = np.zeros((np.shape(features)[0], self.n_max))
        sampled_ids[:, 0] = start_tokens.view(-1, )
        predicted = start_tokens
        embeddings = features
        embeddings = embeddings

        for i in range(1, self.n_max):
            predicted = self.embed(predicted)
            embeddings = torch.cat([embeddings, predicted], dim=1)
            hidden_states, _ = self.lstm(embeddings)
            hidden_states = hidden_states[:, -1, :]
            outputs = self.linear(hidden_states)
            predicted = torch.max(outputs, 1)[1]
            sampled_ids[:, i] = predicted
            predicted = predicted.unsqueeze(1)
        return sampled_ids


if __name__ == '__main__':
    import torchvision.transforms as transforms

    import warnings
    warnings.filterwarnings("ignore")
#
    extractor = VisualFeatureExtractor(model_name='resnet152')
    mlc = MLC(fc_in_features=extractor.out_features)
    co_att = CoAttention(visual_size=extractor.out_features)
    sent_lstm = SentenceLSTM()
    word_lstm = WordLSTM(embed_size=512, hidden_size=512, vocab_size=100, num_layers=1)

    images = torch.randn((4, 3, 224, 224))
    captions = torch.ones((4, 10)).long()
    hidden_state = torch.randn((4, 1, 512))

    # # image_file = '../data/images/CXR2814_IM-1239-1001.png'
#     # # images = Image.open(image_file).convert('RGB')
#     # # captions = torch.ones((1, 10)).long()
#     # # hidden_state = torch.randn((10, 512))
# #
# norm = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
#
# transform = transforms.Compose([
#     transforms.Resize(256),
#     transforms.TenCrop(224),
#     transforms.Lambda(lambda crops: torch.stack([norm(transforms.ToTensor()(crop)) for crop in crops])),
# ])

# images = transform(images)
# images.unsqueeze_(0)
#
# # bs, ncrops, c, h, w = images.size()
# # images = images.view(-1, c, h, w)
#
    print("images:{}".format(images.shape))
    print("captions:{}".format(captions.shape))
    print("hidden_states:{}".format(hidden_state.shape))

    visual_features, avg_features = extractor.forward(images)

    print("visual_features:{}".format(visual_features.shape))
    print("avg features:{}".format(avg_features.shape))

    tags, semantic_features = mlc.forward(avg_features)

    print("tags:{}".format(tags.shape))
    print("semantic_features:{}".format(semantic_features.shape))

    ctx, alpht_v, alpht_a = co_att.forward(avg_features, semantic_features, hidden_state)

    print("ctx:{}".format(ctx.shape))
    print("alpht_v:{}".format(alpht_v.shape))
    print("alpht_a:{}".format(alpht_a.shape))

    topic, p_stop, hidden_state, states = sent_lstm.forward(ctx, hidden_state)
    # p_stop_avg = p_stop.view(bs, ncrops, -1).mean(1)

    print("Topic:{}".format(topic.shape))
    print("P_STOP:{}".format(p_stop.shape))
    # print("P_stop_avg:{}".format(p_stop_avg.shape))

    words = word_lstm.forward(topic, captions)
    print("words:{}".format(words.shape))

    cam = torch.mul(visual_features, alpht_v.view(alpht_v.shape[0], alpht_v.shape[1], 1, 1)).sum(1)
    cam.squeeze_()
    cam = cam.cpu().data.numpy()
    for i in range(cam.shape[0]):
        heatmap = cam[i]
        heatmap = heatmap / np.max(heatmap)
        print(heatmap.shape)