82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
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import torch
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import os
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import seaborn as sns
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.decomposition import PCA
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FOLDER_PATH = "/scratch/bortoletto/dev/boss/predictions/2023-05-22_12-00-38_train_None" # no_tom seed 1
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#FOLDER_PATH = "/scratch/bortoletto/dev/boss/predictions/2023-05-17_23-39-41_train_None" # impl mult seed 1
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#FOLDER_PATH = "/scratch/bortoletto/dev/boss/predictions/2023-05-18_12-47-07_train_None" # impl sum seed 1
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#FOLDER_PATH = "/scratch/bortoletto/dev/boss/predictions/2023-05-18_15-58-44_train_None" # impl attn seed 1
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#FOLDER_PATH = "/scratch/bortoletto/dev/boss/predictions/2023-05-18_12-58-04_train_None" # impl concat seed 1
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#FOLDER_PATH = "/scratch/bortoletto/dev/boss/predictions/2023-05-17_23-40-01_train_None" # cm mult seed 1
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#FOLDER_PATH = "/scratch/bortoletto/dev/boss/predictions/2023-05-18_12-45-55_train_None" # cm sum seed 1
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#FOLDER_PATH = "/scratch/bortoletto/dev/boss/predictions/2023-05-18_12-50-42_train_None" # cm attn seed 1
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#FOLDER_PATH = "/scratch/bortoletto/dev/boss/predictions/2023-05-18_12-57-15_train_None" # cm concat seed 1
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#FOLDER_PATH = "/scratch/bortoletto/dev/boss/predictions/2023-05-17_23-37-50_train_None" # db seed 1
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print(FOLDER_PATH)
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MTOM_COLORS = {
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"MN1": (110/255, 117/255, 161/255),
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"MN2": (179/255, 106/255, 98/255),
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"Base": (193/255, 198/255, 208/255),
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"CG": (170/255, 129/255, 42/255),
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"IC": (97/255, 112/255, 83/255),
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"DB": (144/255, 63/255, 110/255)
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}
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sns.set_theme(style='white')
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for i in range(60):
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print(f'Computing analysis for test video {i}...', end='\r')
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emb_file = os.path.join(FOLDER_PATH, f'{i}.pt')
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data = torch.load(emb_file)
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if len(data) == 14: # implicit
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model = 'impl'
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out_left, cell_left, out_right, cell_right, feats = data[0], data[1], data[2], data[3], data[4:]
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out_left = out_left.squeeze(0)
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cell_left = cell_left.squeeze(0)
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out_right = out_right.squeeze(0)
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cell_right = cell_right.squeeze(0)
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elif len(data) == 13: # common mind
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model = 'cm'
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out_left, out_right, common_mind, feats = data[0], data[1], data[2], data[3:]
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out_left = out_left.squeeze(0)
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out_right = out_right.squeeze(0)
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common_mind = common_mind.squeeze(0)
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elif len(data) == 12: # speaker-listener
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model = 'sl'
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out_left, out_right, feats = data[0], data[1], data[2:]
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out_left = out_left.squeeze(0)
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out_right = out_right.squeeze(0)
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else: raise ValueError("Data should have 14 (impl), 13 (cm) or 12 (sl) elements!")
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# ====== PCA for left and right embeddings ====== #
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out_left_and_right = np.concatenate((out_left, out_right), axis=0)
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pca = PCA(n_components=2)
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pca_result = pca.fit_transform(out_left_and_right)
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# Separate the PCA results for each tensor
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pca_result_left = pca_result[:out_left.shape[0]]
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pca_result_right = pca_result[out_right.shape[0]:]
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plt.figure(figsize=(7,6))
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plt.scatter(pca_result_left[:, 0], pca_result_left[:, 1], label='$h_1$', color=MTOM_COLORS['MN1'], s=100)
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plt.scatter(pca_result_right[:, 0], pca_result_right[:, 1], label='$h_2$', color=MTOM_COLORS['MN2'], s=100)
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plt.xlabel('Principal Component 1', fontsize=30)
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plt.ylabel('Principal Component 2', fontsize=30)
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plt.grid(False)
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plt.legend(fontsize=30)
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plt.tight_layout()
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plt.savefig(f'{FOLDER_PATH}/{i}_pca.pdf')
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plt.close()
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